diff --git a/.editorconfig b/.editorconfig index 27ae0e75..b99bec2f 100644 --- a/.editorconfig +++ b/.editorconfig @@ -5,4 +5,12 @@ root = true # Suppress S3776 (Cognitive Complexity) dotnet_diagnostic.S3776.severity = none # Suppress CA1416 (Platform Compatibility) -dotnet_diagnostic.CA1416.severity = none \ No newline at end of file +dotnet_diagnostic.CA1416.severity = none +dotnet_style_parentheses_in_control_flow_statements = always_for_clarity:suggestion +csharp_new_line_before_open_brace = none +csharp_new_line_before_else = false +csharp_new_line_before_catch = false +csharp_new_line_before_finally = false +csharp_new_line_before_members_in_object_initializers = false +csharp_new_line_before_members_in_anonymous_types = false +csharp_new_line_between_query_expression_clauses = false \ No newline at end of file diff --git a/.github/TradingPlatform.BusinessLayer-Miha’s MacBook Pro.dll b/.github/TradingPlatform.BusinessLayer-Miha’s MacBook Pro.dll new file mode 100644 index 00000000..11c3b4e3 Binary files /dev/null and b/.github/TradingPlatform.BusinessLayer-Miha’s MacBook Pro.dll differ diff --git a/.github/workflow/SonarCloud.yml b/.github/workflow/SonarCloud.yml new file mode 100644 index 00000000..93975f32 --- /dev/null +++ b/.github/workflow/SonarCloud.yml @@ -0,0 +1,30 @@ +name: SonarCloud analysis +on: + push: + pull_request: + workflow_dispatch: + +permissions: + pull-requests: read # allows SonarCloud to decorate PRs with analysis results + +jobs: + Analysis: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + with: + fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis + + - name: Analyze with SonarCloud + uses: SonarSource/sonarcloud-github-action@v2.0.2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Needed to get PR information + SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} # Generate a token on Sonarcloud.io, add it to the secrets of this repo with the name SONAR_TOKEN + with: + # Additional arguments for the SonarScanner CLI + args: > + -Dsonar.projectKey=mihakralj_QuanTAlib + -Dsonar.organization=mihakralj + -Dsonar.sources=. + -Dsonar.verbose=false diff --git a/.github/workflows/main_automation.yml b/.github/workflows/main_automation.yml index 2130d485..12fac48c 100644 --- a/.github/workflows/main_automation.yml +++ b/.github/workflows/main_automation.yml @@ -3,10 +3,10 @@ on: workflow_dispatch: push: branches: - - '*' + - main pull_request: branches: - - '*' + - main jobs: build_test: diff --git a/.refactoring/base.cs b/.refactoring/base.cs new file mode 100644 index 00000000..883293e9 --- /dev/null +++ b/.refactoring/base.cs @@ -0,0 +1,237 @@ +using System; + +public readonly record struct TValue(DateTime Time, double Value, bool IsNew = true, bool IsHot = true) +{ + public DateTime Time { get; init; } = Time; + public double Value { get; init; } = Value; + public bool IsNew { get; init; } = IsNew; + public bool IsHot { get; init; } = IsHot; + + public TValue() : this(DateTime.UtcNow, 0) { } + public TValue(double value) : this(DateTime.UtcNow, value) { } + public TValue((DateTime time, double value) tuple) : this(tuple.time, tuple.value) { } + + public static implicit operator double(TValue tv) => tv.Value; + public static implicit operator DateTime(TValue tv) => tv.Time; + public static implicit operator TValue(double value) => new TValue(DateTime.UtcNow, value); + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: {Value:F2}]"; +} + + +public readonly record struct TBar(DateTime Time, double Open, double High, double Low, double Close, double Volume, bool IsNew = true) +{ + public DateTime Time { get; init; } = Time; + public double Open { get; init; } = Open; + public double High { get; init; } = High; + public double Low { get; init; } = Low; + public double Close { get; init; } = Close; + public double Volume { get; init; } = Volume; + public bool IsNew { get; init; } = IsNew; + + public TBar() : this(DateTime.UtcNow, 0, 0, 0, 0, 0) { } + public TBar(double open, double high, double low, double close, double volume) : this(DateTime.UtcNow, open, high, low, close, volume) { } + public TBar((DateTime time, double open, double high, double low, double close, double volume) tuple) : this(tuple.time, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume) { } + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: O={Open:F2}, H={High:F2}, L={Low:F2}, C={Close:F2}, V={Volume:F2}]"; +} + +///////////////////// +/// +///////////////////// + +public class GBM_Feed +{ + private readonly double _mu; + private readonly double _sigma; + private readonly Random _random; + private double _lastClose; + private double _lastHigh; + private double _lastLow; + + public GBM_Feed(double initialPrice, double mu, double sigma) + { + _lastClose = initialPrice; + _lastHigh = initialPrice; + _lastLow = initialPrice; + _mu = mu; + _sigma = sigma; + _random = Random.Shared; + } + + public TBar Generate(bool IsNew = true) + { + DateTime time = DateTime.UtcNow; + double dt = 1.0 / 252; // Assuming daily steps in a trading year of 252 days + double drift = (_mu - 0.5 * _sigma * _sigma) * dt; + double diffusion = _sigma * Math.Sqrt(dt) * NormalRandom(); + double newClose = _lastClose * Math.Exp(drift + diffusion); + + double open = _lastClose; + double high = Math.Max(open, newClose) * (1 + _random.NextDouble() * 0.01); + double low = Math.Min(open, newClose) * (1 - _random.NextDouble() * 0.01); + double volume = 1000 + _random.NextDouble() * 1000; // Random volume between 1000 and 2000 + + if (!IsNew) + { + high = Math.Max(_lastHigh, high); + low = Math.Min(_lastLow, low); + } + else + { + _lastClose = newClose; + } + + _lastHigh = high; + _lastLow = low; + + return new TBar(time, open, high, low, newClose, volume, IsNew); + } + + private double NormalRandom() + { + // Box-Muller transform to generate standard normal random variable + double u1 = 1.0 - _random.NextDouble(); // Uniform(0,1] random doubles + double u2 = 1.0 - _random.NextDouble(); + return Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); + } +} + + +/// +/// //////////////// +/// + +public class EMA +{ + private double lastEma, lastEmaCandidate, k; + private int period, i; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public EMA(int period) { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.k = 2.0 / (period + 1); + this.lastEma = this.lastEmaCandidate = double.NaN; + this.i = 0; + } + public TValue Update(TValue input, bool IsNew = true) { + double ema; + + if (double.IsNaN(lastEma)) { lastEma = input.Value; } + + if (IsNew) { + lastEma = lastEmaCandidate; + i++; + } + + double kk = (i= period; + Value = new TValue(input.Time, ema, IsNew, IsHot); + return Value; + } +} + +///////////////// +/// + +public class SMA +{ + private CircularBuffer buffer; + private int period; + private double sum; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public SMA(int period) + { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.buffer = new CircularBuffer(period); + this.sum = 0; + this.IsHot = false; + this.Value = default; + } + + public TValue Update(TValue input, bool IsNew = true) + { + if (IsNew) + { + if (buffer.Count == period) { + sum -= buffer[0]; + } + buffer.Add(input); + sum += input.Value; + } else { + if (buffer.Count > 0) { + sum -= buffer[buffer.Count - 1]; + sum += input.Value; + buffer[buffer.Count - 1] = input; + } else { + buffer.Add(input); + sum += input.Value; + } + } + + double sma = buffer.Count > 0 ? sum / buffer.Count : double.NaN; + IsHot = buffer.Count >= period; + Value = new TValue(input.Time, sma, IsNew, IsHot); + return Value; + } +} + +///////////////////// +/// +///////////////////// + + +public class CircularBuffer +{ + private double[] _buffer; + private int _start; + private int _size; + + public CircularBuffer(int capacity) { + _buffer = new double[capacity]; + _start = 0; + _size = 0; + } + + public int Capacity => _buffer.Length; + public int Count => _size; + + public void Add(double item) { + if (_size < Capacity) { + _buffer[(_start + _size) % Capacity] = item; + _size++; + } else { + _buffer[_start] = item; + _start = (_start + 1) % Capacity; + } + } + + public double this[int index] { + get { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + return _buffer[(_start + index) % Capacity]; + } + set { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + _buffer[(_start + index) % Capacity] = value; + } + } +} \ No newline at end of file diff --git a/.refactoring/test.dib b/.refactoring/test.dib new file mode 100644 index 00000000..d2277e66 --- /dev/null +++ b/.refactoring/test.dib @@ -0,0 +1,163 @@ +#!meta + +{"kernelInfo":{"defaultKernelName":"csharp","items":[{"aliases":[],"name":"csharp"}]}} + +#!csharp + +#r "\bin\Debug\calculations.dll" +using QuanTAlib; + +#!csharp + +TValue vv = new(10); +display(vv.ToString()); +display(vv.IsHot); + +TBar bb = new(1,1,1,1,10); +display(bb.ToString()); +display(bb.IsNew); + +#!csharp + +int i=10; +SMA sma = new(i); +Console.WriteLine($"{"Close",10} {"SMA(" + i + ")",10}"); +for (int i = 0; i < 20; i++) +{ + TValue c =(double)i+1; + sma.Update(10000,true); + sma.Update(1,false); + sma.Update(-1000,false); + sma.Update(c,false); + + Console.WriteLine($"{i+1} {(double)c,10:F2} {(double)sma.Value,10:F2} {sma.Value.IsHot}"); +} + +#!csharp + +public class Emitter { + private Random random = new Random(); + public event EventHandler> Pub; + public void Emit() { + DateTime now = DateTime.Now; + double randomValue = random.NextDouble() * 100; // Generates a random number between 0 and 100 + TValue value = new TValue(now, randomValue); + + EventArg eventArg = new EventArg(value, true, true); + OnValuePub(eventArg); + } + protected virtual void OnValuePub(EventArg eventArg) { + Pub?.Invoke(this, eventArg); + } +} + +public class BarEmitter +{ + private Random random = new Random(); + public event EventHandler> Pub; + private double lastClose = 100.0; // Starting price + + public void Emit() + { + double open = lastClose; + double close = open * (1 + (random.NextDouble() - 0.5) * 0.02); // +/- 1% change + double high = Math.Max(open, close) * (1 + random.NextDouble() * 0.005); // Up to 0.5% higher + double low = Math.Min(open, close) * (1 - random.NextDouble() * 0.005); // Up to 0.5% lower + double volume = random.NextDouble() * 1000000; // Random volume between 0 and 1,000,000 + + TBar bar = new TBar(DateTime.Now, open, high, low, close, volume); + lastClose = close; + + EventArg eventArg = new EventArg(bar, true, true); + OnBarPub(eventArg); + } + + protected virtual void OnBarPub(EventArg eventArg) + { + Pub?.Invoke(this, eventArg); + } +} + + +public class Listener +{ + public void Sub(object sender, EventArgs e) + { + if (e is EventArg tValueArg) { + Console.WriteLine($"TValue: {tValueArg.Data.Value:F2}"); + } else if (e is EventArg tBarArg) { + Console.WriteLine($"TBar: o={tBarArg.Data.Open:F2}, v={tBarArg.Data.Volume:F2}"); + } else { + Console.WriteLine($"Unknown type: {e.GetType().Name}"); + } + } +} + +#!csharp + +Emitter em1 = new(); +BarEmitter em2 = new(); +Listener list = new(); + +em1.Pub += list.Sub; +em2.Pub += list.Sub; + +// Emit 5 random values +for (int i = 0; i < 3; i++) { + em1.Emit(); + em2.Emit(); +} + +#!csharp + +public abstract class Indicator { + protected Indicator() { + Init(); } + public virtual void Init() {} + public virtual TValue Calc(TValue input, bool isNew=true, bool isHot=true) { + return new TValue(); + } +} + +public class EMA : Indicator +{ + private double lastEma, lastEmaCandidate, k; + private int period, i; + + public EMA(int period) { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.k = 2.0 / (period + 1); + this.lastEma = this.lastEmaCandidate = double.NaN; + this.i = 0; + } + + public override TValue Calc(TValue input, bool isNew = true, bool isHot = true) { + double ema; + + if (double.IsNaN(lastEma)) { lastEma = lastEmaCandidate = input.Value; } + + if (isNew) { + lastEma = lastEmaCandidate; + i++; + } + + double kk = (i>=period)?k:(2.0/(i+1)); + ema = lastEma + kk * (input.Value - lastEma); + lastEmaCandidate = ema; + + return new TValue(input.Timestamp, ema); + } +} + +#!csharp + +EMA ema = new(3); +display(ema.Calc(100)); +display(ema.Calc(0,false)); +display(ema.Calc(100,false)); +display(ema.Calc(0)); diff --git a/.sonarlint/mihakralj_quantalibcsharp.ruleset b/.sonarlint/mihakralj_quantalibcsharp.ruleset index 5ad478ec..a2e953a9 100644 --- a/.sonarlint/mihakralj_quantalibcsharp.ruleset +++ b/.sonarlint/mihakralj_quantalibcsharp.ruleset @@ -1,390 +1,390 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Calculations/Basics/ADD_Series.cs b/Calculations/Basics/ADD_Series.cs index 0588d216..6d0436fe 100644 --- a/Calculations/Basics/ADD_Series.cs +++ b/Calculations/Basics/ADD_Series.cs @@ -9,21 +9,24 @@ Remarks: */ -public class ADD_Series : Pair_TSeries_Indicator +public class ADD_Series : Pair_TSeries_Indicator { - public ADD_Series(TSeries d1, TSeries d2 ) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + public ADD_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } } - public ADD_Series(TSeries d1, double dd2 ) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + public ADD_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } } - public ADD_Series(double dd1, TSeries d2 ) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i=0; i< base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + public ADD_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } } - public override void Add((System.DateTime t, double v)TValue1, (System.DateTime t, double v)TValue2, bool update) + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v+TValue2.v); + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v + TValue2.v); if (update) { base[base.Count - 1] = result; } else { base.Add(result); } } } diff --git a/Calculations/Basics/CORR_Series.cs b/Calculations/Basics/CORR_Series.cs index 246baa79..8052b4e2 100644 --- a/Calculations/Basics/CORR_Series.cs +++ b/Calculations/Basics/CORR_Series.cs @@ -1,52 +1,52 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -CORR: Pearson's Correlation Coefficient - PCC is a measure of linear correlation between two sets of data. - It is the ratio between the covariance of two variables and the product of - their standard deviations; it is essentially a normalized measurement of - the covariance, such that the result always has a value between −1 and 1. - -Sources: - https://en.wikipedia.org/wiki/Pearson_correlation_coefficient - - */ - -public class CORR_Series : Pair_TSeries_Indicator -{ - public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) - { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } - } - - private readonly System.Collections.Generic.List _x = new(); - private readonly System.Collections.Generic.List _xx = new(); - private readonly System.Collections.Generic.List _y = new(); - private readonly System.Collections.Generic.List _yy = new(); - private readonly System.Collections.Generic.List _xy = new(); - - public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) - { - Add_Replace_Trim(_x, TValue1.v, _p, update); - Add_Replace_Trim(_xx, TValue1.v * TValue1.v, _p, update); - Add_Replace_Trim(_y, TValue2.v, _p, update); - Add_Replace_Trim(_yy, TValue2.v * TValue2.v, _p, update); - Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update); - - double _sumx = _x.Sum(); - double _sumxx = _xx.Sum(); - double _sumy = _y.Sum(); - double _sumyy = _yy.Sum(); - double _sumxy = _xy.Sum(); - - double _covar = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p); - double _cor = (_covar != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_covar) : 0.0; - - var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor); - if (update) { base[base.Count - 1] = result; } else { base.Add(result); } - - } -} +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +CORR: Pearson's Correlation Coefficient + PCC is a measure of linear correlation between two sets of data. + It is the ratio between the covariance of two variables and the product of + their standard deviations; it is essentially a normalized measurement of + the covariance, such that the result always has a value between −1 and 1. + +Sources: + https://en.wikipedia.org/wiki/Pearson_correlation_coefficient + + */ + +public class CORR_Series : Pair_TSeries_Indicator +{ + public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + + private readonly System.Collections.Generic.List _x = new(); + private readonly System.Collections.Generic.List _xx = new(); + private readonly System.Collections.Generic.List _y = new(); + private readonly System.Collections.Generic.List _yy = new(); + private readonly System.Collections.Generic.List _xy = new(); + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + Add_Replace_Trim(_x, TValue1.v, _p, update); + Add_Replace_Trim(_xx, TValue1.v * TValue1.v, _p, update); + Add_Replace_Trim(_y, TValue2.v, _p, update); + Add_Replace_Trim(_yy, TValue2.v * TValue2.v, _p, update); + Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update); + + double _sumx = _x.Sum(); + double _sumxx = _xx.Sum(); + double _sumy = _y.Sum(); + double _sumyy = _yy.Sum(); + double _sumxy = _xy.Sum(); + + double _covar = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p); + double _cor = (_covar != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_covar) : 0.0; + + var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + + } +} diff --git a/Calculations/Basics/COVAR_Series.cs b/Calculations/Basics/COVAR_Series.cs index 3826ee9d..7368689c 100644 --- a/Calculations/Basics/COVAR_Series.cs +++ b/Calculations/Basics/COVAR_Series.cs @@ -1,46 +1,48 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -COVAR: Covariance - Covariance is defined as the expected value (or mean) of the product - of their deviations from their individual expected values. - -Sources: - https://en.wikipedia.org/wiki/Covariance - - */ - - -public class COVAR_Series : Pair_TSeries_Indicator -{ - public COVAR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) - { - if (base._d1.Count > 0 && base._d2.Count > 0) { - for (int i = 0; i < base._d1.Count; i++) { - this.Add(base._d1[i], base._d2[i], false); - } - } - } - - private readonly System.Collections.Generic.List _x = new(); - private readonly System.Collections.Generic.List _y = new(); - private readonly System.Collections.Generic.List _xy = new(); - - public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) - { - BufferTrim(_x, TValue1.v, _p, update); - BufferTrim(_y, TValue2.v, _p, update); - BufferTrim(_xy, TValue1.v * TValue2.v, _p, update); - - double _avgx = _x.Average(); - double _avgy = _y.Average(); - double _avgxy = _xy.Average(); - double _covar = _avgxy - (_avgx * _avgy); - - var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _covar); - if (update) { base[base.Count - 1] = result; } else { base.Add(result); } - } -} +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +COVAR: Covariance + Covariance is defined as the expected value (or mean) of the product + of their deviations from their individual expected values. + +Sources: + https://en.wikipedia.org/wiki/Covariance + + */ + + +public class COVAR_Series : Pair_TSeries_Indicator +{ + public COVAR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) + { + if (base._d1.Count > 0 && base._d2.Count > 0) + { + for (int i = 0; i < base._d1.Count; i++) + { + this.Add(base._d1[i], base._d2[i], false); + } + } + } + + private readonly System.Collections.Generic.List _x = new(); + private readonly System.Collections.Generic.List _y = new(); + private readonly System.Collections.Generic.List _xy = new(); + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + BufferTrim(_x, TValue1.v, _p, update); + BufferTrim(_y, TValue2.v, _p, update); + BufferTrim(_xy, TValue1.v * TValue2.v, _p, update); + + double _avgx = _x.Average(); + double _avgy = _y.Average(); + double _avgxy = _xy.Average(); + double _covar = _avgxy - (_avgx * _avgy); + + var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _covar); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} diff --git a/Calculations/Basics/DIV_Series.cs b/Calculations/Basics/DIV_Series.cs index 16069917..962e4f6f 100644 --- a/Calculations/Basics/DIV_Series.cs +++ b/Calculations/Basics/DIV_Series.cs @@ -8,22 +8,25 @@ Remarks: Most of scaffolding is packaged in abstracty class Pair_TSeries_Indicator. */ -public class DIV_Series : Pair_TSeries_Indicator +public class DIV_Series : Pair_TSeries_Indicator { - public DIV_Series(TSeries d1, TSeries d2 ) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + public DIV_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } } - public DIV_Series(TSeries d1, double dd2 ) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + public DIV_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } } - public DIV_Series(double dd1, TSeries d2 ) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i=0; i< base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + public DIV_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } } - public override void Add((System.DateTime t, double v)TValue1, (System.DateTime t, double v)TValue2, bool update) + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, - (TValue2.v is not 0) ? TValue1.v/TValue2.v : Double.PositiveInfinity); + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + (TValue2.v is not 0) ? TValue1.v / TValue2.v : Double.PositiveInfinity); if (update) { base[base.Count - 1] = result; } else { base.Add(result); } } } \ No newline at end of file diff --git a/Calculations/Basics/MUL_Series.cs b/Calculations/Basics/MUL_Series.cs index c1c573bd..b2bae613 100644 --- a/Calculations/Basics/MUL_Series.cs +++ b/Calculations/Basics/MUL_Series.cs @@ -6,22 +6,25 @@ MUL - multiply TSeries*TSeries together, or TSeries*double, or double*TSeries */ -public class MUL_Series : Pair_TSeries_Indicator +public class MUL_Series : Pair_TSeries_Indicator { - public MUL_Series(TSeries d1, TSeries d2 ) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + public MUL_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } } - public MUL_Series(TSeries d1, double dd2 ) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + public MUL_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } } - public MUL_Series(double dd1, TSeries d2 ) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i=0; i< base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + public MUL_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } } - public override void Add((System.DateTime t, double v)TValue1, (System.DateTime t, double v)TValue2, bool update) + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, - TValue1.v*TValue2.v); + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + TValue1.v * TValue2.v); if (update) { base[base.Count - 1] = result; } else { base.Add(result); } } } \ No newline at end of file diff --git a/Calculations/Basics/SUB_Series.cs b/Calculations/Basics/SUB_Series.cs index 88511f81..e4333ec6 100644 --- a/Calculations/Basics/SUB_Series.cs +++ b/Calculations/Basics/SUB_Series.cs @@ -7,22 +7,25 @@ SUB - subtracting TSeries-TSeries, or TSeries-double, or double-TSeries */ -public class SUB_Series : Pair_TSeries_Indicator +public class SUB_Series : Pair_TSeries_Indicator { - public SUB_Series(TSeries d1, TSeries d2 ) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + public SUB_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } } - public SUB_Series(TSeries d1, double dd2 ) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i=0; i< base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + public SUB_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } } - public SUB_Series(double dd1, TSeries d2 ) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i=0; i< base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + public SUB_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } } - public override void Add((System.DateTime t, double v)TValue1, (System.DateTime t, double v)TValue2, bool update) + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, - TValue1.v-TValue2.v); + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + TValue1.v - TValue2.v); if (update) { base[base.Count - 1] = result; } else { base.Add(result); } } } \ No newline at end of file diff --git a/Calculations/Calculations.csproj b/Calculations/Calculations.csproj index 701f67f7..0ed29821 100644 --- a/Calculations/Calculations.csproj +++ b/Calculations/Calculations.csproj @@ -1,80 +1,80 @@ - - - - QuanTAlib - 0.2.30 - 0.2.30 - 0.2.30 - Library of TA Calculations, Charts and Strategies for Quantower - Quantitative Technical Analysis Library in C# for Quantower - git - https://github.com/mihakralj/QuanTAlib - true - Miha Kralj - Miha Kralj - Apache-2.0 - readme.md - net8.0;net7.0 - disable - preview - disable - true - en-US - QuanTAlib - QuanTAlib - True - AnyCPU - False - full - True - True - - Indicators;Stock;Market;Technical;Analysis;Algorithmic;Trading;Trade;Trend;Momentum;Finance;Algorithm;Algo; - AlgoTrading;Financial;Strategy;Chart;Charting;Oscillator;Overlay;Equity;Bitcoin;Crypto;Cryptocurrency;Forex; - Quantitative;Historical;Quotes; - - - - - - - - - - full - True - 7 - True - anycpu - - - full - True - 7 - True - anycpu - - - QuanTAlib2.png - https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png - True - ..\.sonarlint\mihakralj_quantalibcsharp.ruleset - 0.2.1-dev.2 - - - - - - - True - - - - - True - False - - - - + + + + QuanTAlib + 0.2.30 + 0.2.30 + 0.2.30 + Library of TA Calculations, Charts and Strategies for Quantower + Quantitative Technical Analysis Library in C# for Quantower + git + https://github.com/mihakralj/QuanTAlib + true + Miha Kralj + Miha Kralj + Apache-2.0 + readme.md + net8.0;net7.0 + disable + preview + disable + true + en-US + QuanTAlib + QuanTAlib + True + AnyCPU + False + full + True + True + + Indicators;Stock;Market;Technical;Analysis;Algorithmic;Trading;Trade;Trend;Momentum;Finance;Algorithm;Algo; + AlgoTrading;Financial;Strategy;Chart;Charting;Oscillator;Overlay;Equity;Bitcoin;Crypto;Cryptocurrency;Forex; + Quantitative;Historical;Quotes; + + + + + + + + + + full + True + 7 + True + anycpu + + + full + True + 7 + True + anycpu + + + QuanTAlib2.png + https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png + True + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1-dev.2 + + + + + + + True + + + + + True + False + + + + \ No newline at end of file diff --git a/Calculations/ClassStructures/Pair_TSeries_Abstract.cs b/Calculations/ClassStructures/Pair_TSeries_Abstract.cs index 2203d3f1..682b1b87 100644 --- a/Calculations/ClassStructures/Pair_TSeries_Abstract.cs +++ b/Calculations/ClassStructures/Pair_TSeries_Abstract.cs @@ -1,132 +1,157 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -Abstract classes with all scaffolding required to build indicators. - All abstracts support period, NaN, and all permutations of Add() methods. - Indicator classess need to implement: - - Chaining constructor (Abstract's constructor executes first) - - Default Add(value) class - - optional Add(series) bulk insert class (for optimization of historical analysis) - - Single_TSeries_Indicator - one single-value TSeries in, one TSeries out. - Pair_TSeries_Indicator - Two TSeries in, one TSeries out. (includes simple semaphoring) - Single_TBars_Indicator - One OHLCV TBars in, one TSeries out. - - */ - -public abstract class Pair_TSeries_Indicator : TSeries { - protected readonly int _p; - protected readonly bool _NaN; - protected readonly TSeries _d1; - protected readonly TSeries _d2; - protected readonly double _dd1, _dd2; - - // Chainable Constructors - add them at the end of primary constructors if needed - protected Pair_TSeries_Indicator(TSeries source1, TSeries source2, int period, bool useNaN) { - _p = period; - _NaN = useNaN; - _d1 = source1; - _d2 = source2; - _dd1 = double.NaN; - _dd2 = double.NaN; - _d1.Pub += Sub; - _d2.Pub += Sub; - } - - protected Pair_TSeries_Indicator(TSeries source1, TSeries source2) { - _d1 = source1; - _d2 = source2; - _dd1 = double.NaN; - _dd2 = double.NaN; - _d1.Pub += Sub; - _d2.Pub += Sub; - } - - protected Pair_TSeries_Indicator(TSeries source1, double dd2) { - _d1 = source1; - _d2 = new TSeries(); - _dd1 = double.NaN; - _dd2 = dd2; - _d1.Pub += Sub; - } - - protected Pair_TSeries_Indicator(double dd1, TSeries source2) { - _d1 = new TSeries(); - _d2 = source2; - _dd1 = dd1; - _dd2 = double.NaN; - _d2.Pub += Sub; - } - - // overridable Add(Tvalue, Tvalue) method to add/update a single value at the end of the list - public virtual void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2, bool update) { - base.Add((TValue1.t, 0), update); - // default inserts zeros - } - - // potentially overridable Add() bulk variations (could be replaced with faster bulk algos) - public virtual void Add(TSeries d1, TSeries d2) { - for (var i = 0; i < d1.Count; i++) { - Add(d1[i], d2[i], false); - } - } - - public virtual void Add(TSeries d1, double dd2) { - for (var i = 0; i < d1.Count; i++) { - Add(d1[i], (d1[i].t, dd2), false); - } - } - - public virtual void Add(double dd1, TSeries d2) { - for (var i = 0; i < d2.Count; i++) { - Add((d2[i].t, dd1), d2[i], false); - } - } - - public void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2) { - Add(TValue1, TValue2, false); - } - - public void Add(bool update) { - if (_dd1 is double.NaN && _dd2 is double.NaN) { - // (Series, Series) - if (update || (_d1.Count > Count && _d2.Count > Count)) { - Add(_d1[_d1.Count - 1], _d2[_d2.Count - 1], update); - } - } - else if (_dd2 is not double.NaN && _dd1 is double.NaN) { - // (Series, Double) - Add(_d1[_d1.Count - 1], (_d1[_d1.Count - 1].t, _dd2), update); - } - else { - // (Double, Series) - Add((_d2[_d2.Count - 1].t, _dd1), _d2[_d2.Count - 1], update); - } - } - - public void Add() { - Add(false); - } - - public new void Sub(object source, TSeriesEventArgs e) { - Add(e.update); - } - - protected static void Add_Replace(List l, double v, bool update) { - if (update) { - l[l.Count - 1] = v; - } - else { - l.Add(v); - } - } - - protected static void Add_Replace_Trim(List l, double v, int p, bool update) { - Add_Replace(l, v, update); - if (l.Count > p && p != 0) { - l.RemoveAt(0); - } - } -} +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +Abstract classes with all scaffolding required to build indicators. + All abstracts support period, NaN, and all permutations of Add() methods. + Indicator classess need to implement: + - Chaining constructor (Abstract's constructor executes first) + - Default Add(value) class + - optional Add(series) bulk insert class (for optimization of historical analysis) + + Single_TSeries_Indicator - one single-value TSeries in, one TSeries out. + Pair_TSeries_Indicator - Two TSeries in, one TSeries out. (includes simple semaphoring) + Single_TBars_Indicator - One OHLCV TBars in, one TSeries out. + + */ + +public abstract class Pair_TSeries_Indicator : TSeries +{ + protected readonly int _p; + protected readonly bool _NaN; + protected readonly TSeries _d1; + protected readonly TSeries _d2; + protected readonly double _dd1, _dd2; + + // Chainable Constructors - add them at the end of primary constructors if needed + protected Pair_TSeries_Indicator(TSeries source1, TSeries source2, int period, bool useNaN) + { + _p = period; + _NaN = useNaN; + _d1 = source1; + _d2 = source2; + _dd1 = double.NaN; + _dd2 = double.NaN; + _d1.Pub += Sub; + _d2.Pub += Sub; + } + + protected Pair_TSeries_Indicator(TSeries source1, TSeries source2) + { + _d1 = source1; + _d2 = source2; + _dd1 = double.NaN; + _dd2 = double.NaN; + _d1.Pub += Sub; + _d2.Pub += Sub; + } + + protected Pair_TSeries_Indicator(TSeries source1, double dd2) + { + _d1 = source1; + _d2 = new TSeries(); + _dd1 = double.NaN; + _dd2 = dd2; + _d1.Pub += Sub; + } + + protected Pair_TSeries_Indicator(double dd1, TSeries source2) + { + _d1 = new TSeries(); + _d2 = source2; + _dd1 = dd1; + _dd2 = double.NaN; + _d2.Pub += Sub; + } + + // overridable Add(Tvalue, Tvalue) method to add/update a single value at the end of the list + public virtual void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2, bool update) + { + base.Add((TValue1.t, 0), update); + // default inserts zeros + } + + // potentially overridable Add() bulk variations (could be replaced with faster bulk algos) + public virtual void Add(TSeries d1, TSeries d2) + { + for (var i = 0; i < d1.Count; i++) + { + Add(d1[i], d2[i], false); + } + } + + public virtual void Add(TSeries d1, double dd2) + { + for (var i = 0; i < d1.Count; i++) + { + Add(d1[i], (d1[i].t, dd2), false); + } + } + + public virtual void Add(double dd1, TSeries d2) + { + for (var i = 0; i < d2.Count; i++) + { + Add((d2[i].t, dd1), d2[i], false); + } + } + + public void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2) + { + Add(TValue1, TValue2, false); + } + + public void Add(bool update) + { + if (_dd1 is double.NaN && _dd2 is double.NaN) + { + // (Series, Series) + if (update || (_d1.Count > Count && _d2.Count > Count)) + { + Add(_d1[_d1.Count - 1], _d2[_d2.Count - 1], update); + } + } + else if (_dd2 is not double.NaN && _dd1 is double.NaN) + { + // (Series, Double) + Add(_d1[_d1.Count - 1], (_d1[_d1.Count - 1].t, _dd2), update); + } + else + { + // (Double, Series) + Add((_d2[_d2.Count - 1].t, _dd1), _d2[_d2.Count - 1], update); + } + } + + public void Add() + { + Add(false); + } + + public new void Sub(object source, TSeriesEventArgs e) + { + Add(e.update); + } + + protected static void Add_Replace(List l, double v, bool update) + { + if (update) + { + l[l.Count - 1] = v; + } + else + { + l.Add(v); + } + } + + protected static void Add_Replace_Trim(List l, double v, int p, bool update) + { + Add_Replace(l, v, update); + if (l.Count > p && p != 0) + { + l.RemoveAt(0); + } + } +} diff --git a/Calculations/Feeds/Alphavantage_Feed.cs b/Calculations/Feeds/Alphavantage_Feed.cs index 5c9fc35f..f2bcc60e 100644 --- a/Calculations/Feeds/Alphavantage_Feed.cs +++ b/Calculations/Feeds/Alphavantage_Feed.cs @@ -13,7 +13,7 @@ Alphavantage - Free API to collect 100 recent daily quotes. It requires a (free) */ public class Alphavantage_Feed : TBars { - public enum Interval { Month, Week, Day, Hour, Min30, Min15, Min5, Min1} + public enum Interval { Month, Week, Day, Hour, Min30, Min15, Min5, Min1 } public Alphavantage_Feed(string Symbol = "IBM", string APIkey = "demo") { System.Net.Http.HttpClient client = new(); @@ -22,8 +22,8 @@ public class Alphavantage_Feed : TBars var msg = client.GetStringAsync(req).Result; var jres = JsonSerializer.Deserialize(msg).RootElement; jres.TryGetProperty("Time Series (Daily)", out JsonElement json); - - if (json.ValueKind == JsonValueKind.Undefined) {throw new InvalidOperationException("Stock symbol "+Symbol+" not found"); } + + if (json.ValueKind == JsonValueKind.Undefined) { throw new InvalidOperationException("Stock symbol " + Symbol + " not found"); } foreach (var val in json.EnumerateObject()) { base.Add(GetOHLC(val)); } base.Reverse(); } diff --git a/Calculations/Feeds/GBM_Feed.cs b/Calculations/Feeds/GBM_Feed.cs index a08568c1..c99a282f 100644 --- a/Calculations/Feeds/GBM_Feed.cs +++ b/Calculations/Feeds/GBM_Feed.cs @@ -23,41 +23,45 @@ public class GBM_Feed : TBars private double seed; readonly double drift, volatility; readonly int precision; - public GBM_Feed(int Bars = 252, double Volatility = 1.0, double Drift = 0.05, double Seed = 100.0, int Precision = 2) { + public GBM_Feed(int Bars = 252, double Volatility = 1.0, double Drift = 0.05, double Seed = 100.0, int Precision = 2) + { this.seed = Seed; - volatility = Volatility*0.01; - drift = Drift*0.01; + volatility = Volatility * 0.01; + drift = Drift * 0.01; precision = Precision; - for (int i = 0; i OCMin)? (2 * OCMin) - Low : Low; + double OCMin = Math.Min(Open, Close); + double Low = (GBM_value(seed, volatility * 0.5, 0, precision)); + Low = (Low > OCMin) ? (2 * OCMin) - Low : Low; - double Volume = GBM_value(seed*10, volatility*2, Drift:0, precision: 1); + double Volume = GBM_value(seed * 10, volatility * 2, Drift: 0, precision: 1); base.Add((timestamp, Open, High, Low, Close, Volume), update); seed = Close; } - private static double GBM_value(double Seed, double Volatility, double Drift, int precision) { + private static double GBM_value(double Seed, double Volatility, double Drift, int precision) + { Random rnd = new(); - double U1 = 1.0-rnd.NextDouble(); - double U2 = 1.0-rnd.NextDouble(); + double U1 = 1.0 - rnd.NextDouble(); + double U2 = 1.0 - rnd.NextDouble(); double Z = Math.Sqrt(-2.0 * Math.Log(U1)) * Math.Sin(2.0 * Math.PI * U2); - return Math.Round(Seed * Math.Exp( Drift - (Volatility*Volatility*0.5) + (Volatility * Z)), digits: precision); + return Math.Round(Seed * Math.Exp(Drift - (Volatility * Volatility * 0.5) + (Volatility * Z)), digits: precision); } } \ No newline at end of file diff --git a/Calculations/Feeds/Yahoo_Feed.cs b/Calculations/Feeds/Yahoo_Feed.cs index 5b9029a1..1d87e97d 100644 --- a/Calculations/Feeds/Yahoo_Feed.cs +++ b/Calculations/Feeds/Yahoo_Feed.cs @@ -14,34 +14,36 @@ Yahoo Finance - Free API feed to collect daily market quotes */ public class Yahoo_Feed : TBars { - public Yahoo_Feed(string Symbol = "IBM", int Period = 252) { - Period = (int)(Period*1.45); - string requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/"+ - Symbol+"?interval=1d&period1="+ - (int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period+1)).ToUnixTimeSeconds()+"&period2="+ + public Yahoo_Feed(string Symbol = "IBM", int Period = 252) + { + Period = (int)(Period * 1.45); + string requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/" + + Symbol + "?interval=1d&period1=" + + (int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period + 1)).ToUnixTimeSeconds() + "&period2=" + (int)new DateTimeOffset(DateTime.UtcNow).ToUnixTimeSeconds(); System.Net.Http.HttpClient client = new(); var msg = client.GetStringAsync(requestUrl).Result; var jresult = JsonSerializer.Deserialize(msg).RootElement; - jresult.TryGetProperty("chart",out JsonElement json); - json.TryGetProperty("result",out json); - json[0].TryGetProperty("timestamp",out JsonElement datetime); - json[0].TryGetProperty("indicators",out json); - json.TryGetProperty("quote",out json); - json[0].TryGetProperty("open",out JsonElement open); - json[0].TryGetProperty("high",out JsonElement high); - json[0].TryGetProperty("low",out JsonElement low); - json[0].TryGetProperty("close",out JsonElement close); - json[0].TryGetProperty("volume",out JsonElement volume); + jresult.TryGetProperty("chart", out JsonElement json); + json.TryGetProperty("result", out json); + json[0].TryGetProperty("timestamp", out JsonElement datetime); + json[0].TryGetProperty("indicators", out json); + json.TryGetProperty("quote", out json); + json[0].TryGetProperty("open", out JsonElement open); + json[0].TryGetProperty("high", out JsonElement high); + json[0].TryGetProperty("low", out JsonElement low); + json[0].TryGetProperty("close", out JsonElement close); + json[0].TryGetProperty("volume", out JsonElement volume); - for (int i=0; i */ -public class COMPARE_Series : Pair_TSeries_Indicator { +public class COMPARE_Series : Pair_TSeries_Indicator +{ - public COMPARE_Series(TSeries d1, TSeries d2) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } - } - public COMPARE_Series(TSeries d1, double dd2) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } - } - public COMPARE_Series(double dd1, TSeries d2) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } - } + public COMPARE_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public COMPARE_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public COMPARE_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } - public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { - double val = TValue1.v > TValue2.v ? 1 : -1; - val = TValue1.v == TValue2.v ? 0 : val; - (System.DateTime t, double v) over = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v > TValue2.v ? 1 : val); - if (update) { base[^1] = over; } - else { base.Add(over); } + double val = TValue1.v > TValue2.v ? 1 : -1; + val = TValue1.v == TValue2.v ? 0 : val; + (System.DateTime t, double v) over = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v > TValue2.v ? 1 : val); + if (update) { base[^1] = over; } + else { base.Add(over); } - } + } } diff --git a/Calculations/Logic/CROSS_Series.cs b/Calculations/Logic/CROSS_Series.cs index aab93397..6d62a54c 100644 --- a/Calculations/Logic/CROSS_Series.cs +++ b/Calculations/Logic/CROSS_Series.cs @@ -1,44 +1,49 @@ -namespace QuanTAlib; -using System; - -/* -OVER - Generates +1 if A is above B, -1 if A is below B and 0 if A=B - -Remarks: - OVER.Cross generates 1 when A breaks B from below and -1 when A breaks B from above - - */ - -public class CROSS_Series : Pair_TSeries_Indicator { - public TSeries Cross { get; set; } = new(); - - private double _previous = double.NaN; - public CROSS_Series(TSeries d1, TSeries d2) : base(d1, d2) { - if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } - } - public CROSS_Series(TSeries d1, double dd2) : base(d1, dd2) { - if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } - } - public CROSS_Series(double dd1, TSeries d2) : base(dd1, d2) { - if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } - } - - public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { - - double val = TValue1.v > TValue2.v ? 1 : -1; - val = TValue1.v == TValue2.v ? 0 : val; - double over = TValue1.v > TValue2.v ? 1 : val; - - val = (_previous < over) ? 1 : -1; - val = ((_previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val; - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t,val); - - this._previous = over; - - if (update) { base[^1] = result; } - else { base.Add(result); } - - } -} - - +namespace QuanTAlib; +using System; + +/* +OVER - Generates +1 if A is above B, -1 if A is below B and 0 if A=B + +Remarks: + OVER.Cross generates 1 when A breaks B from below and -1 when A breaks B from above + + */ + +public class CROSS_Series : Pair_TSeries_Indicator +{ + public TSeries Cross { get; set; } = new(); + + private double _previous = double.NaN; + public CROSS_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public CROSS_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public CROSS_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + + double val = TValue1.v > TValue2.v ? 1 : -1; + val = TValue1.v == TValue2.v ? 0 : val; + double over = TValue1.v > TValue2.v ? 1 : val; + + val = (_previous < over) ? 1 : -1; + val = ((_previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val; + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, val); + + this._previous = over; + + if (update) { base[^1] = result; } + else { base.Add(result); } + + } +} + + diff --git a/Calculations/Logic/EQUITY_Series.cs b/Calculations/Logic/EQUITY_Series.cs index 8323feb7..36a97667 100644 --- a/Calculations/Logic/EQUITY_Series.cs +++ b/Calculations/Logic/EQUITY_Series.cs @@ -1,91 +1,91 @@ -namespace QuanTAlib; -using System; - -/* -EQUITY - Generates P&L portfolio based on trades signals and equity prices - - */ - - -//base prices: bars.close -//trade signals: trades -//optional: long, short, long&short -//optional: warmup period: warmup - -/* - -public class EQUITY_Series : Single_TSeries_Indicator { - readonly TSeries inmarket; //for every bar - private readonly TSeries _price; - private double _equity; - private readonly double _capital; - - readonly int _warmup; - double _cash; - int _units; - private bool _longbuy, _longsell; - double _long_order, _open_order; - double _investment_value; - short _inmarket; - - public EQUITY_Series(TSeries signal, TSeries price, int warmup = 0, double capital = 1000) : base(signal, period: 0, useNaN: false) { - _capital = capital; - _cash = _capital; - _investment_value = 0; - _warmup = (warmup > 0) ? warmup : 1; - - inmarket = new(); - _longbuy = _longsell = false; - _open_order = 0; - _inmarket = 0; - _units = 0; - _long_order = 0; - - _price = price; //we buy on the Open price of the NEXT bar - _long_order = 0; - - if (base._data.Count > 0) { base.Add(base._data); } - } - - public override void Add((System.DateTime t, double v) TValue, bool update) { - - if (this.Count > _warmup) { - - // harvest the gain-loss from previous day - _investment_value = _units * _price[this.Count - 1].v; - _equity = _cash + _investment_value; - - - //execute orders from previous bar - if (_longbuy && _inmarket == 0) { //time to execute the long buy - _units = (int)(_cash / _price[this.Count - 1].v); - _long_order = _units * _price[this.Count - 1].v; - _cash -= _long_order; - _open_order = _long_order; - _equity = _cash + _open_order; - _inmarket = 1; - _longbuy = false; - } - - if (_longsell && _inmarket == 1) { //time to execute the long sell - _long_order = (_units * _price[this.Count - 1].v); - _cash += _long_order; - _units = 0; - - _open_order = 0; - _equity = _cash + _open_order; - _inmarket = 0; - _longsell = false; - } - - if (_inmarket == 0 && TValue.v == 1) { _longbuy = true; } //out of market, enter long - if (_inmarket == 1 && TValue.v == -1) { _longsell = true; } //long market, exit long - - //Console.WriteLine($"{TValue.v,3}\t {(_inmarket)} : {_cash,10:f2} + {_units*_price[^1].v,7:f2} = {_equity-_capital:f2}"); - } - inmarket.Add((TValue.t, (double)_inmarket)); - base.Add((TValue.t, _equity), update, _NaN); - } -} - +namespace QuanTAlib; +using System; + +/* +EQUITY - Generates P&L portfolio based on trades signals and equity prices + + */ + + +//base prices: bars.close +//trade signals: trades +//optional: long, short, long&short +//optional: warmup period: warmup + +/* + +public class EQUITY_Series : Single_TSeries_Indicator { + readonly TSeries inmarket; //for every bar + private readonly TSeries _price; + private double _equity; + private readonly double _capital; + + readonly int _warmup; + double _cash; + int _units; + private bool _longbuy, _longsell; + double _long_order, _open_order; + double _investment_value; + short _inmarket; + + public EQUITY_Series(TSeries signal, TSeries price, int warmup = 0, double capital = 1000) : base(signal, period: 0, useNaN: false) { + _capital = capital; + _cash = _capital; + _investment_value = 0; + _warmup = (warmup > 0) ? warmup : 1; + + inmarket = new(); + _longbuy = _longsell = false; + _open_order = 0; + _inmarket = 0; + _units = 0; + _long_order = 0; + + _price = price; //we buy on the Open price of the NEXT bar + _long_order = 0; + + if (base._data.Count > 0) { base.Add(base._data); } + } + + public override void Add((System.DateTime t, double v) TValue, bool update) { + + if (this.Count > _warmup) { + + // harvest the gain-loss from previous day + _investment_value = _units * _price[this.Count - 1].v; + _equity = _cash + _investment_value; + + + //execute orders from previous bar + if (_longbuy && _inmarket == 0) { //time to execute the long buy + _units = (int)(_cash / _price[this.Count - 1].v); + _long_order = _units * _price[this.Count - 1].v; + _cash -= _long_order; + _open_order = _long_order; + _equity = _cash + _open_order; + _inmarket = 1; + _longbuy = false; + } + + if (_longsell && _inmarket == 1) { //time to execute the long sell + _long_order = (_units * _price[this.Count - 1].v); + _cash += _long_order; + _units = 0; + + _open_order = 0; + _equity = _cash + _open_order; + _inmarket = 0; + _longsell = false; + } + + if (_inmarket == 0 && TValue.v == 1) { _longbuy = true; } //out of market, enter long + if (_inmarket == 1 && TValue.v == -1) { _longsell = true; } //long market, exit long + + //Console.WriteLine($"{TValue.v,3}\t {(_inmarket)} : {_cash,10:f2} + {_units*_price[^1].v,7:f2} = {_equity-_capital:f2}"); + } + inmarket.Add((TValue.t, (double)_inmarket)); + base.Add((TValue.t, _equity), update, _NaN); + } +} + */ \ No newline at end of file diff --git a/Calculations/Logic/TOrders.cs b/Calculations/Logic/TOrders.cs index bb1a1e71..1e122b2b 100644 --- a/Calculations/Logic/TOrders.cs +++ b/Calculations/Logic/TOrders.cs @@ -1,34 +1,38 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Collections.ObjectModel; -using System.Data; -using System.Linq; - - -public enum OType { - NIL = 0, // No position - BTO = 1, // Buy to Open - STC = 2, // Sell to Close - STO = 3, // Sell to Open - BTC = 4, // Buy to Close - END = 5, // Exit the trade -} - - -public class TOrders : List<(DateTime t, OType o)> { - - public void Add((DateTime t, OType o) TOrder, bool update = false) - { - if (update) { this[^1] = TOrder; } - else { base.Add(TOrder); } - OnEvent(update); - } - - - protected virtual void OnEvent(bool update = false) { - Pub?.Invoke(this, new TSeriesEventArgs { update = update }); } - public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); - public event NewDataEventHandler Pub; - +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Collections.ObjectModel; +using System.Data; +using System.Linq; + + +public enum OType +{ + NIL = 0, // No position + BTO = 1, // Buy to Open + STC = 2, // Sell to Close + STO = 3, // Sell to Open + BTC = 4, // Buy to Close + END = 5, // Exit the trade +} + + +public class TOrders : List<(DateTime t, OType o)> +{ + + public void Add((DateTime t, OType o) TOrder, bool update = false) + { + if (update) { this[^1] = TOrder; } + else { base.Add(TOrder); } + OnEvent(update); + } + + + protected virtual void OnEvent(bool update = false) + { + Pub?.Invoke(this, new TSeriesEventArgs { update = update }); + } + public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); + public event NewDataEventHandler Pub; + } \ No newline at end of file diff --git a/Calculations/_Updated/ADL_Series.cs b/Calculations/_Updated/ADL_Series.cs index aa33c7b2..d84c1cf6 100644 --- a/Calculations/_Updated/ADL_Series.cs +++ b/Calculations/_Updated/ADL_Series.cs @@ -1,69 +1,79 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -ADL: Chaikin Accumulation/Distribution Line - ADL is a volume-based indicator that measures the cumulative Money Flow Volume: - - 1. Money Flow Multiplier = [(Close - Low) - (High - Close)] /(High - Low) - 2. Money Flow Volume = Money Flow Multiplier x Volume for the Period - 3. ADL = Previous ADL + Current Period's Money Flow Volume - -Sources: - https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line - - */ - -public class ADL_Series : TSeries { - protected readonly TBars _data; - private double _lastadl, _lastlastadl; - - //core constructors - public ADL_Series() { - Name = $"ADL()"; - _lastadl = _lastlastadl = 0; - } - public ADL_Series(TBars source) { - _data = source; - Name = $"ADL({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _lastadl = _lastlastadl = 0; - _data.Pub += Sub; - Add(data: _data); - } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - if (update) { this._lastadl = this._lastlastadl; } - else { this._lastlastadl = this._lastadl; } - - double _adl = 0; - double tmp = TBar.h - TBar.l; - if (tmp > 0.0) { - _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); - } - _lastadl = _adl; - - var ret = (TBar.t, _adl); - return base.Add(ret, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _lastadl = _lastlastadl = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ADL: Chaikin Accumulation/Distribution Line + ADL is a volume-based indicator that measures the cumulative Money Flow Volume: + + 1. Money Flow Multiplier = [(Close - Low) - (High - Close)] /(High - Low) + 2. Money Flow Volume = Money Flow Multiplier x Volume for the Period + 3. ADL = Previous ADL + Current Period's Money Flow Volume + +Sources: + https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line + + */ + +public class ADL_Series : TSeries +{ + protected readonly TBars _data; + private double _lastadl, _lastlastadl; + + //core constructors + public ADL_Series() + { + Name = $"ADL()"; + _lastadl = _lastlastadl = 0; + } + public ADL_Series(TBars source) + { + _data = source; + Name = $"ADL({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _lastadl = _lastlastadl = 0; + _data.Pub += Sub; + Add(data: _data); + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { this._lastadl = this._lastlastadl; } + else { this._lastlastadl = this._lastadl; } + + double _adl = 0; + double tmp = TBar.h - TBar.l; + if (tmp > 0.0) + { + _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); + } + _lastadl = _adl; + + var ret = (TBar.t, _adl); + return base.Add(ret, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastadl = _lastlastadl = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/ADOSC_Series.cs b/Calculations/_Updated/ADOSC_Series.cs index 63aa2104..bc5b4305 100644 --- a/Calculations/_Updated/ADOSC_Series.cs +++ b/Calculations/_Updated/ADOSC_Series.cs @@ -1,90 +1,100 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -ADOSC: Chaikin Accumulation/Distribution Oscillator - ADO measures the momentum of ADL using the difference between slow (10-day) EMA(ADL) - and fast (3-day) EMA(ADL): - - Chaikin A/D Oscillator is defined as 3-day EMA of ADL minus 10-day EMA of ADL - -Sources: - https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_oscillator - - */ - -public class ADOSC_Series : TSeries { - protected readonly TBars _data; - private readonly double _k1, _k2; - private double _lastema1, _lastlastema1, _lastema2, _lastlastema2; - private double _lastadl, _lastlastadl; - - //core constructors - public ADOSC_Series(int shortPeriod, int longPeriod, bool useNaN = false) { - Name = $"ADOSC()"; - _k1 = 2.0 / (shortPeriod + 1); - _k2 = 2.0 / (longPeriod + 1); - _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; - } - public ADOSC_Series(TBars source, int shortPeriod, int longPeriod, bool useNaN = false) :this(shortPeriod, longPeriod, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _lastadl = _lastlastadl = 0; - _data.Pub += Sub; - Add(data: _data); - } - - public ADOSC_Series() : this(shortPeriod: 3, longPeriod: 10, useNaN: false) {} - - public ADOSC_Series(TBars source) : this(source, shortPeriod: 3, longPeriod:10, useNaN:false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update= false) { - - if (update) { - _lastadl = _lastlastadl; - _lastema1 = _lastlastema1; - _lastema2 = _lastlastema2; - } - - double _adl = 0; - double tmp = TBar.h - TBar.l; - if (tmp > 0.0) { _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); } - if (this.Count == 0) { _lastema1 = _lastema2 = _adl; } - - double _ema1 = (_adl - _lastema1) * _k1 + _lastema1; - double _ema2 = (_adl - _lastema2) * _k2 + _lastema2; - - _lastlastadl = _lastadl; - _lastadl = _adl; - _lastlastema1 = _lastema1; - _lastema1 = _ema1; - _lastlastema2 = _lastema2; - _lastema2 = _ema2; - - double _adosc = _ema1 - _ema2; - - var ret = (TBar.t, _adosc); - return base.Add(ret, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ADOSC: Chaikin Accumulation/Distribution Oscillator + ADO measures the momentum of ADL using the difference between slow (10-day) EMA(ADL) + and fast (3-day) EMA(ADL): + + Chaikin A/D Oscillator is defined as 3-day EMA of ADL minus 10-day EMA of ADL + +Sources: + https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_oscillator + + */ + +public class ADOSC_Series : TSeries +{ + protected readonly TBars _data; + private readonly double _k1, _k2; + private double _lastema1, _lastlastema1, _lastema2, _lastlastema2; + private double _lastadl, _lastlastadl; + + //core constructors + public ADOSC_Series(int shortPeriod, int longPeriod, bool useNaN = false) + { + Name = $"ADOSC()"; + _k1 = 2.0 / (shortPeriod + 1); + _k2 = 2.0 / (longPeriod + 1); + _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; + } + public ADOSC_Series(TBars source, int shortPeriod, int longPeriod, bool useNaN = false) : this(shortPeriod, longPeriod, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _lastadl = _lastlastadl = 0; + _data.Pub += Sub; + Add(data: _data); + } + + public ADOSC_Series() : this(shortPeriod: 3, longPeriod: 10, useNaN: false) { } + + public ADOSC_Series(TBars source) : this(source, shortPeriod: 3, longPeriod: 10, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + _lastadl = _lastlastadl; + _lastema1 = _lastlastema1; + _lastema2 = _lastlastema2; + } + + double _adl = 0; + double tmp = TBar.h - TBar.l; + if (tmp > 0.0) { _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); } + if (this.Count == 0) { _lastema1 = _lastema2 = _adl; } + + double _ema1 = (_adl - _lastema1) * _k1 + _lastema1; + double _ema2 = (_adl - _lastema2) * _k2 + _lastema2; + + _lastlastadl = _lastadl; + _lastadl = _adl; + _lastlastema1 = _lastema1; + _lastema1 = _ema1; + _lastlastema2 = _lastema2; + _lastema2 = _ema2; + + double _adosc = _ema1 - _ema2; + + var ret = (TBar.t, _adosc); + return base.Add(ret, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/ALMA_Series.cs b/Calculations/_Updated/ALMA_Series.cs index 514e0e34..a25ac7d5 100644 --- a/Calculations/_Updated/ALMA_Series.cs +++ b/Calculations/_Updated/ALMA_Series.cs @@ -1,114 +1,129 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -ALMA: Arnaud Legoux Moving Average - The ALMA moving average uses the curve of the Normal (Gauss) distribution, which - can be shifted from 0 to 1. This allows regulating the smoothness and high - sensitivity of the indicator. Sigma is another parameter that is responsible for - the shape of the curve coefficients. This moving average reduces lag of the data - in conjunction with smoothing to reduce noise. - - -Sources: - https://phemex.com/academy/what-is-arnaud-legoux-moving-averages - https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/ - - Discrepancy with Pandas-TA (but passes the validation with Skender.GetAlma) - */ - -public class ALMA_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - private readonly System.Collections.Generic.List _buffer = new(); - private readonly System.Collections.Generic.List _weight; - private double _norm; - private readonly double _offset, _sigma; - - //core constructors - public ALMA_Series(int period, double offset, double sigma, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"ALMA({period})"; - _offset = offset; - _sigma = sigma; - _weight = new(); - } - public ALMA_Series(TSeries source, int period, double offset, double sigma, bool useNaN) : this(period, offset, sigma, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - - public ALMA_Series() : this(period:0, offset:0.85, sigma:6.0, useNaN: false) { } - public ALMA_Series(int period) : this(period: period, offset:0.85, sigma:6.0, useNaN:false) { } - public ALMA_Series(TBars source) : this(source:source.Close, period:0, offset:0.85, sigma:6.0, useNaN:false) { } - public ALMA_Series(TBars source, int period) : this(source:source.Close, period:period, offset: 0.85, sigma: 6.0, useNaN: false) { } - public ALMA_Series(TBars source, int period, double offset, double sigma, bool useNaN) : this(source.Close, period:period, offset: offset, sigma: sigma, useNaN: false) { } - public ALMA_Series(TSeries source) : this(source, period:0, offset:0.85, sigma:6.0, useNaN:false) { } - public ALMA_Series(TSeries source, int period) : this(source:source, period:period, offset:0.85, sigma:6.0, useNaN:false) { } - public ALMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: useNaN) { } - - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, double.NaN), update); - } - - BufferTrim(_buffer, TValue.v, _period, update); - if (_weight.Count < _buffer.Count) { - for (var i = 0; i < _buffer.Count - _weight.Count; i++) { - _weight.Add(0.0); - } - } - - - if (_buffer.Count <= _period || _period == 0) { - var _len = _buffer.Count; - _norm = 0; - var _m = _offset * (_len - 1); - var _s = _len / _sigma; - for (var i = 0; i < _len; i++) { - var _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s)); - _weight[i] = _wt; - _norm += _wt; - } - } - - double _weightedSum = 0; - for (var i = 0; i < _buffer.Count; i++) { - _weightedSum += _weight[i] * _buffer[i]; - } - - var _alma = _weightedSum / _norm; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _alma); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - //reset calculation - public override void Reset() { - _buffer.Clear(); - _weight.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +ALMA: Arnaud Legoux Moving Average + The ALMA moving average uses the curve of the Normal (Gauss) distribution, which + can be shifted from 0 to 1. This allows regulating the smoothness and high + sensitivity of the indicator. Sigma is another parameter that is responsible for + the shape of the curve coefficients. This moving average reduces lag of the data + in conjunction with smoothing to reduce noise. + + +Sources: + https://phemex.com/academy/what-is-arnaud-legoux-moving-averages + https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/ + + Discrepancy with Pandas-TA (but passes the validation with Skender.GetAlma) + */ + +public class ALMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + private readonly System.Collections.Generic.List _buffer = new(); + private readonly System.Collections.Generic.List _weight; + private double _norm; + private readonly double _offset, _sigma; + + //core constructors + public ALMA_Series(int period, double offset, double sigma, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"ALMA({period})"; + _offset = offset; + _sigma = sigma; + _weight = new(); + } + public ALMA_Series(TSeries source, int period, double offset, double sigma, bool useNaN) : this(period, offset, sigma, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public ALMA_Series() : this(period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(int period) : this(period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source) : this(source: source.Close, period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source, int period) : this(source: source.Close, period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source, int period, double offset, double sigma, bool useNaN) : this(source.Close, period: period, offset: offset, sigma: sigma, useNaN: false) { } + public ALMA_Series(TSeries source) : this(source, period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TSeries source, int period) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: useNaN) { } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + BufferTrim(_buffer, TValue.v, _period, update); + if (_weight.Count < _buffer.Count) + { + for (var i = 0; i < _buffer.Count - _weight.Count; i++) + { + _weight.Add(0.0); + } + } + + + if (_buffer.Count <= _period || _period == 0) + { + var _len = _buffer.Count; + _norm = 0; + var _m = _offset * (_len - 1); + var _s = _len / _sigma; + for (var i = 0; i < _len; i++) + { + var _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s)); + _weight[i] = _wt; + _norm += _wt; + } + } + + double _weightedSum = 0; + for (var i = 0; i < _buffer.Count; i++) + { + _weightedSum += _weight[i] * _buffer[i]; + } + + var _alma = _weightedSum / _norm; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _alma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _weight.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/ATRP_Series.cs b/Calculations/_Updated/ATRP_Series.cs index 3ba2c1dc..ee429ea0 100644 --- a/Calculations/_Updated/ATRP_Series.cs +++ b/Calculations/_Updated/ATRP_Series.cs @@ -1,87 +1,97 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -ATRP: Average True Range Percent - Average True Range Percent is (ATR/Close Price)*100. - This normalizes so it can be compared to other stocks. - -Sources: - https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp - - */ - -public class ATRP_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TBars _data; - private double _k; - private int _len; - private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; - - //core constructors - public ATRP_Series(int period, bool useNaN) { - _period = period; - _k = 1.0 / (double)(_period); - _NaN = useNaN; - _len = 0; - Name = $"ATRP({period})"; - } - public ATRP_Series(TBars source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(data: _data); - } - public ATRP_Series() : this(period: 1, useNaN: false) { } - public ATRP_Series(int period) : this(period: period, useNaN: false) { } - public ATRP_Series(TBars source) : this(source, period: 1, useNaN: false) { } - public ATRP_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } - else { - _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; - _k = (_period == 0) ? 1 / (double)_len : _k; - _len++; - } - - if (_len == 1) { _cm1 = TBar.c; } - double d1 = Math.Abs(TBar.h - TBar.l); - double d2 = Math.Abs(_cm1 - TBar.h); - double d3 = Math.Abs(_cm1 - TBar.l); - (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); - _cm1 = TBar.c; - - double _atr = 0; - if (this.Count == 0) { _atr = d.v; } - else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } - else { _atr = _k * (d.v - _lastatr) + _lastatr; } - _lastatr = _atr; - double _atrp = 100 * (_atr / TBar.c); - - var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atrp); - return base.Add(res, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _len = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ATRP: Average True Range Percent + Average True Range Percent is (ATR/Close Price)*100. + This normalizes so it can be compared to other stocks. + +Sources: + https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp + + */ + +public class ATRP_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _k; + private int _len; + private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; + + //core constructors + public ATRP_Series(int period, bool useNaN) + { + _period = period; + _k = 1.0 / (double)(_period); + _NaN = useNaN; + _len = 0; + Name = $"ATRP({period})"; + } + public ATRP_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public ATRP_Series() : this(period: 1, useNaN: false) { } + public ATRP_Series(int period) : this(period: period, useNaN: false) { } + public ATRP_Series(TBars source) : this(source, period: 1, useNaN: false) { } + public ATRP_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } + else + { + _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; + _k = (_period == 0) ? 1 / (double)_len : _k; + _len++; + } + + if (_len == 1) { _cm1 = TBar.c; } + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + _cm1 = TBar.c; + + double _atr = 0; + if (this.Count == 0) { _atr = d.v; } + else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } + else { _atr = _k * (d.v - _lastatr) + _lastatr; } + _lastatr = _atr; + double _atrp = 100 * (_atr / TBar.c); + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atrp); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/ATR_Series.cs b/Calculations/_Updated/ATR_Series.cs index 9935f5c4..c09ce7b2 100644 --- a/Calculations/_Updated/ATR_Series.cs +++ b/Calculations/_Updated/ATR_Series.cs @@ -1,88 +1,98 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -ATR: wildeR Moving Average - The average true range (ATR) is a price volatility indicator - showing the average price variation of assets within a given time period. - -Sources: - https://en.wikipedia.org/wiki/Average_true_range - https://www.tradingview.com/wiki/Average_True_Range_(ATR) - https://www.investopedia.com/terms/a/atr.asp - - */ - -public class ATR_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TBars _data; - private double _k; - private int _len; - private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; - - //core constructors - public ATR_Series(int period, bool useNaN) { - _period = period; - _k = 1.0 / (double)(_period); - _NaN = useNaN; - _len = 0; - Name = $"ATR({period})"; - } - public ATR_Series(TBars source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(data: _data); - } - public ATR_Series() : this(period: 1, useNaN: false) { } - public ATR_Series(int period) : this(period: period, useNaN: false) { } - public ATR_Series(TBars source) : this(source, period: 1, useNaN: false) { } - public ATR_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } - else { - _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; - _k = (_period == 0) ? 1 / (double)_len : _k; - _len++; - } - - if (_len == 1) { _cm1 = TBar.c; } - double d1 = Math.Abs(TBar.h - TBar.l); - double d2 = Math.Abs(_cm1 - TBar.h); - double d3 = Math.Abs(_cm1 - TBar.l); - (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); - _cm1 = TBar.c; - - double _atr = 0; - if (this.Count == 0) { _atr = d.v; } - else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } - else { _atr = _k * (d.v - _lastatr) + _lastatr; } - _lastatr = _atr; - - var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atr); - return base.Add(res, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _len = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ATR: wildeR Moving Average + The average true range (ATR) is a price volatility indicator + showing the average price variation of assets within a given time period. + +Sources: + https://en.wikipedia.org/wiki/Average_true_range + https://www.tradingview.com/wiki/Average_True_Range_(ATR) + https://www.investopedia.com/terms/a/atr.asp + + */ + +public class ATR_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _k; + private int _len; + private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; + + //core constructors + public ATR_Series(int period, bool useNaN) + { + _period = period; + _k = 1.0 / (double)(_period); + _NaN = useNaN; + _len = 0; + Name = $"ATR({period})"; + } + public ATR_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public ATR_Series() : this(period: 1, useNaN: false) { } + public ATR_Series(int period) : this(period: period, useNaN: false) { } + public ATR_Series(TBars source) : this(source, period: 1, useNaN: false) { } + public ATR_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } + else + { + _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; + _k = (_period == 0) ? 1 / (double)_len : _k; + _len++; + } + + if (_len == 1) { _cm1 = TBar.c; } + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + _cm1 = TBar.c; + + double _atr = 0; + if (this.Count == 0) { _atr = d.v; } + else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } + else { _atr = _k * (d.v - _lastatr) + _lastatr; } + _lastatr = _atr; + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atr); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/BBANDS_Series.cs b/Calculations/_Updated/BBANDS_Series.cs index 198b2a38..e64398ff 100644 --- a/Calculations/_Updated/BBANDS_Series.cs +++ b/Calculations/_Updated/BBANDS_Series.cs @@ -1,112 +1,121 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -BBANDS: Bollinger Bands® - Price channels created by John Bollinger, depict volatility as standard deviation boundary - line range from a moving average of price. The bands automatically widen when volatility - increases and contract when volatility decreases. Their dynamic nature allows them to be - used on different securities with the standard settings. - - Mid Band = simple moving average (SMA) - Upper Band = SMA + (standard deviation of price x multiplier) - Lower Band = SMA - (standard deviation of price x multiplier) - Bandwidth = Width of the channel: (Upper-Lower)/SMA - %B = The location of the data point within the channel: (Price-Lower)/(Upper/Lower) - Z-Score = number of standard deviations of the data point from SMA - -Sources: - https://www.investopedia.com/terms/b/bollingerbands.asp - https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands - -Note: - Bollinger Bands® is a registered trademark of John A. Bollinger. - - */ - -public class BBANDS_Series : TSeries { - protected readonly int _period; - protected readonly double _multiplier; - protected readonly bool _NaN; - protected readonly TSeries _data; - public SMA_Series Mid { get; } - public TSeries Upper { get; } - public TSeries Lower { get; } - public TSeries PercentB { get; } - public TSeries Bandwidth { get; } - public TSeries Zscore { get; } - private readonly SDEV_Series _sdev; - - //core constructors - public BBANDS_Series(int period, double multiplier, bool useNaN) { - _period = period; - _multiplier = multiplier; - _NaN = useNaN; - Name = $"BBANDS({period})"; - } - public BBANDS_Series(TSeries source, int period, double multiplier, bool useNaN) : this(period, multiplier, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - Upper = new("BB_Up"); - Lower = new("BB_Low"); - Bandwidth = new("BBandwidth"); - PercentB = new("%BBandwidth"); - Zscore = new("Zscore"); - - Mid = new(period, false); - _sdev = new(period, false); - - _data.Pub += Sub; - Add(_data); - } - - public BBANDS_Series() : this(period:0, multiplier: 2.0, useNaN: false) { } - public BBANDS_Series(int period) : this(period: period, multiplier: 2.0, useNaN:false) { } - public BBANDS_Series(TBars source) : this(source:source.Close, period:0, multiplier: 2.0, useNaN:false) { } - public BBANDS_Series(TBars source, int period) : this(source:source.Close, period:period, multiplier: 2.0, useNaN: false) { } - public BBANDS_Series(TBars source, int period, double multiplier, bool useNaN) : this(source.Close, period:period, multiplier:multiplier, useNaN: false) { } - public BBANDS_Series(TSeries source) : this(source, period:0, useNaN:false) { } - public BBANDS_Series(TSeries source, int period) : this(source:source, period:period, useNaN:false) { } - public BBANDS_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, multiplier: 2.0, useNaN: useNaN) { } - - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update=false) { - var _mid = Mid.Add(TValue,update); - var _sd = this._sdev.Add(TValue, update); - var _upper = Upper.Add((TValue.t, _mid.v + _sd.v * _multiplier), update); - var _lower = Lower.Add((TValue.t, _mid.v - _sd.v * _multiplier), update); - double _pbdnd = TValue.v - _lower.v; - double _pbdvr = _upper.v - _lower.v; - PercentB.Add((TValue.t, _pbdnd/_pbdvr), update); - Zscore.Add((TValue.t, (TValue.v-_mid.v)/_sd.v), update); - Bandwidth.Add((TValue.t, _pbdvr / _mid.v), update); - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pbdvr / _mid.v); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - //reset calculation - public override void Reset() { - Mid.Clear(); - _sdev.Clear(); - Upper.Clear(); - Lower.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +BBANDS: Bollinger Bands® + Price channels created by John Bollinger, depict volatility as standard deviation boundary + line range from a moving average of price. The bands automatically widen when volatility + increases and contract when volatility decreases. Their dynamic nature allows them to be + used on different securities with the standard settings. + + Mid Band = simple moving average (SMA) + Upper Band = SMA + (standard deviation of price x multiplier) + Lower Band = SMA - (standard deviation of price x multiplier) + Bandwidth = Width of the channel: (Upper-Lower)/SMA + %B = The location of the data point within the channel: (Price-Lower)/(Upper/Lower) + Z-Score = number of standard deviations of the data point from SMA + +Sources: + https://www.investopedia.com/terms/b/bollingerbands.asp + https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands + +Note: + Bollinger Bands® is a registered trademark of John A. Bollinger. + + */ + +public class BBANDS_Series : TSeries +{ + protected readonly int _period; + protected readonly double _multiplier; + protected readonly bool _NaN; + protected readonly TSeries _data; + public SMA_Series Mid { get; } + public TSeries Upper { get; } + public TSeries Lower { get; } + public TSeries PercentB { get; } + public TSeries Bandwidth { get; } + public TSeries Zscore { get; } + private readonly SDEV_Series _sdev; + + //core constructors + public BBANDS_Series(int period, double multiplier, bool useNaN) + { + _period = period; + _multiplier = multiplier; + _NaN = useNaN; + Name = $"BBANDS({period})"; + } + public BBANDS_Series(TSeries source, int period, double multiplier, bool useNaN) : this(period, multiplier, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + Upper = new("BB_Up"); + Lower = new("BB_Low"); + Bandwidth = new("BBandwidth"); + PercentB = new("%BBandwidth"); + Zscore = new("Zscore"); + + Mid = new(period, false); + _sdev = new(period, false); + + _data.Pub += Sub; + Add(_data); + } + + public BBANDS_Series() : this(period: 0, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(int period) : this(period: period, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source) : this(source: source.Close, period: 0, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source, int period) : this(source: source.Close, period: period, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source, int period, double multiplier, bool useNaN) : this(source.Close, period: period, multiplier: multiplier, useNaN: false) { } + public BBANDS_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public BBANDS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + public BBANDS_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, multiplier: 2.0, useNaN: useNaN) { } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + var _mid = Mid.Add(TValue, update); + var _sd = this._sdev.Add(TValue, update); + var _upper = Upper.Add((TValue.t, _mid.v + _sd.v * _multiplier), update); + var _lower = Lower.Add((TValue.t, _mid.v - _sd.v * _multiplier), update); + double _pbdnd = TValue.v - _lower.v; + double _pbdvr = _upper.v - _lower.v; + PercentB.Add((TValue.t, _pbdnd / _pbdvr), update); + Zscore.Add((TValue.t, (TValue.v - _mid.v) / _sd.v), update); + Bandwidth.Add((TValue.t, _pbdvr / _mid.v), update); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pbdvr / _mid.v); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + //reset calculation + public override void Reset() + { + Mid.Clear(); + _sdev.Clear(); + Upper.Clear(); + Lower.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/BIAS_Series.cs b/Calculations/_Updated/BIAS_Series.cs index df9bc5a5..cf724bf3 100644 --- a/Calculations/_Updated/BIAS_Series.cs +++ b/Calculations/_Updated/BIAS_Series.cs @@ -1,72 +1,81 @@ -namespace QuanTAlib; -using System; - -/* -BIAS: Rate of change between the source and a moving average. - Bias is a statistical term which means a systematic deviation from the actual value. - -BIAS = (close - SMA) / SMA - = (close / SMA) - 1 - -Sources: - https://en.wikipedia.org/wiki/Bias_of_an_estimator - - */ - -public class BIAS_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly SMA_Series _sma; - - //core constructors - public BIAS_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"BIAS({period})"; - _sma = new(period, false); - } - public BIAS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public BIAS_Series() : this(period: 0, useNaN: false) { } - public BIAS_Series(int period) : this(period: period, useNaN: false) { } - public BIAS_Series(TBars source) : this(source.Close, 0, false) { } - public BIAS_Series(TBars source, int period) : this(source.Close, period, false) { } - public BIAS_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public BIAS_Series(TSeries source) : this(source, 0, false) { } - public BIAS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - var _s = _sma.Add(TValue,update); - double _bias = (TValue.v / ((_s.v!=0)?_s.v:1)) - 1; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _bias); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _sma.Reset(); - } +namespace QuanTAlib; +using System; + +/* +BIAS: Rate of change between the source and a moving average. + Bias is a statistical term which means a systematic deviation from the actual value. + +BIAS = (close - SMA) / SMA + = (close / SMA) - 1 + +Sources: + https://en.wikipedia.org/wiki/Bias_of_an_estimator + + */ + +public class BIAS_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly SMA_Series _sma; + + //core constructors + public BIAS_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"BIAS({period})"; + _sma = new(period, false); + } + public BIAS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public BIAS_Series() : this(period: 0, useNaN: false) { } + public BIAS_Series(int period) : this(period: period, useNaN: false) { } + public BIAS_Series(TBars source) : this(source.Close, 0, false) { } + public BIAS_Series(TBars source, int period) : this(source.Close, period, false) { } + public BIAS_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public BIAS_Series(TSeries source) : this(source, 0, false) { } + public BIAS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + var _s = _sma.Add(TValue, update); + double _bias = (TValue.v / ((_s.v != 0) ? _s.v : 1)) - 1; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _bias); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sma.Reset(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/CCI_Series.cs b/Calculations/_Updated/CCI_Series.cs index 3bf2e553..4886857b 100644 --- a/Calculations/_Updated/CCI_Series.cs +++ b/Calculations/_Updated/CCI_Series.cs @@ -1,86 +1,97 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -CCI: Commodity Channel Index - Commodity Channel Index is a momentum oscillator used to primarily identify overbought - and oversold levels relative to a mean. CCI measures the current price level relative - to an average price level over a given period of time: - - CCI is relatively high when prices are far above their average. - - CCI is relatively low when prices are far below their average. - Using this method, CCI can be used to identify overbought and oversold levels. - -Sources: - https://www.investopedia.com/terms/c/commoditychannelindex.asp - https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cci - - */ - -public class CCI_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TBars _data; - private readonly System.Collections.Generic.List _tp = new(); - - //core constructors - public CCI_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"CCI({period})"; - } - public CCI_Series(TBars source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(data: _data); - } - public CCI_Series() : this(period: 2, useNaN: false) { } - public CCI_Series(int period) : this(period: period, useNaN: false) { } - public CCI_Series(TBars source) : this(source, period: 2, useNaN: false) { } - public CCI_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - double _tpItem = (TBar.h + TBar.l + TBar.c) / 3.0; - if (update) { - this._tp[this._tp.Count - 1] = _tpItem; - } - else { - this._tp.Add(_tpItem); - } - if (this._tp.Count > this._period) { this._tp.RemoveAt(0); } - - // average TP over _tp buffer - double _avgTp = _tp.Average(); - - // average Deviation over _tp buffer - double _avgDv = 0; - for (int i = 0; i < this._tp.Count; i++) { _avgDv += Math.Abs(_avgTp - this._tp[i]); } - _avgDv /= this._tp.Count; - - double _cci = (_avgDv == 0) ? 0 : (this._tp[this._tp.Count - 1] - _avgTp) / (0.015 * _avgDv); - var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _cci); - return base.Add(res, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _tp.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +CCI: Commodity Channel Index + Commodity Channel Index is a momentum oscillator used to primarily identify overbought + and oversold levels relative to a mean. CCI measures the current price level relative + to an average price level over a given period of time: + - CCI is relatively high when prices are far above their average. + - CCI is relatively low when prices are far below their average. + Using this method, CCI can be used to identify overbought and oversold levels. + +Sources: + https://www.investopedia.com/terms/c/commoditychannelindex.asp + https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cci + + */ + +public class CCI_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private readonly System.Collections.Generic.List _tp = new(); + + //core constructors + public CCI_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CCI({period})"; + } + public CCI_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public CCI_Series() : this(period: 2, useNaN: false) { } + public CCI_Series(int period) : this(period: period, useNaN: false) { } + public CCI_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public CCI_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + double _tpItem = (TBar.h + TBar.l + TBar.c) / 3.0; + if (update) + { + this._tp[this._tp.Count - 1] = _tpItem; + } + else + { + this._tp.Add(_tpItem); + } + if (this._tp.Count > this._period) { this._tp.RemoveAt(0); } + + // average TP over _tp buffer + double _avgTp = _tp.Average(); + + // average Deviation over _tp buffer + double _avgDv = 0; + for (int i = 0; i < this._tp.Count; i++) { _avgDv += Math.Abs(_avgTp - this._tp[i]); } + _avgDv /= this._tp.Count; + + double _cci = (_avgDv == 0) ? 0 : (this._tp[this._tp.Count - 1] - _avgTp) / (0.015 * _avgDv); + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _cci); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _tp.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/CMO_Series.cs b/Calculations/_Updated/CMO_Series.cs index 6906067a..fe793bb0 100644 --- a/Calculations/_Updated/CMO_Series.cs +++ b/Calculations/_Updated/CMO_Series.cs @@ -1,90 +1,100 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -CMO: Chande Momentum Oscillator - Chande Momentum Oscillator (also known as CMO indicator) was developed by Tushar S. Chande - CMO is similar to other momentum oscillators (e.g. RSI or Stochastics). Alike RSI oscillator, - the CMO values move in the range from -100 to +100 points and its aim is to detect the - overbought and oversold market conditions. CMO calculates the price momentum on both the up - days as well as the down days. The CMO calculation is based on non-smoothed price values - meaning that it can reach its extremes more frequently and the short-time swings are more visible. - -Sources: - https://www.technicalindicators.net/indicators-technical-analysis/144-cmo-chande-momentum-oscillator - - */ - -public class CMO_Series : TSeries { - private readonly System.Collections.Generic.List _buff_up = new(); - private readonly System.Collections.Generic.List _buff_dn = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private double _plast_value, _last_value; - - //core constructors - public CMO_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"CMO({period})"; - } - public CMO_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public CMO_Series() : this(period: 0, useNaN: false) { } - public CMO_Series(int period) : this(period: period, useNaN: false) { } - public CMO_Series(TBars source) : this(source.Close, 0, false) { } - public CMO_Series(TBars source, int period) : this(source.Close, period, false) { } - public CMO_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public CMO_Series(TSeries source) : this(source, 0, false) { } - public CMO_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { _last_value = _plast_value; } else { _plast_value = _last_value; } - BufferTrim(buffer:_buff_up, (TValue.v > _last_value) ? TValue.v - _last_value : 0, period:_period, update: update); - BufferTrim(buffer: _buff_dn, (TValue.v < _last_value) ? _last_value - TValue.v : 0, period: _period, update: update); - _last_value = TValue.v; - double _cmo_up = 0; - double _cmo_dn = 0; - for (int i = 0; i < Math.Min(_buff_up.Count, _buff_dn.Count); i++) { - _cmo_up += _buff_up[i]; - _cmo_dn += _buff_dn[i]; - } - double _cmo = 100 * (_cmo_up - _cmo_dn) / (_cmo_up + _cmo_dn); - if (_cmo_up + _cmo_dn == 0) { _cmo = 0; } - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _cmo); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buff_up.Clear(); - _buff_dn.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +CMO: Chande Momentum Oscillator + Chande Momentum Oscillator (also known as CMO indicator) was developed by Tushar S. Chande + CMO is similar to other momentum oscillators (e.g. RSI or Stochastics). Alike RSI oscillator, + the CMO values move in the range from -100 to +100 points and its aim is to detect the + overbought and oversold market conditions. CMO calculates the price momentum on both the up + days as well as the down days. The CMO calculation is based on non-smoothed price values + meaning that it can reach its extremes more frequently and the short-time swings are more visible. + +Sources: + https://www.technicalindicators.net/indicators-technical-analysis/144-cmo-chande-momentum-oscillator + + */ + +public class CMO_Series : TSeries +{ + private readonly System.Collections.Generic.List _buff_up = new(); + private readonly System.Collections.Generic.List _buff_dn = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _plast_value, _last_value; + + //core constructors + public CMO_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CMO({period})"; + } + public CMO_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public CMO_Series() : this(period: 0, useNaN: false) { } + public CMO_Series(int period) : this(period: period, useNaN: false) { } + public CMO_Series(TBars source) : this(source.Close, 0, false) { } + public CMO_Series(TBars source, int period) : this(source.Close, period, false) { } + public CMO_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public CMO_Series(TSeries source) : this(source, 0, false) { } + public CMO_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) { _last_value = _plast_value; } else { _plast_value = _last_value; } + BufferTrim(buffer: _buff_up, (TValue.v > _last_value) ? TValue.v - _last_value : 0, period: _period, update: update); + BufferTrim(buffer: _buff_dn, (TValue.v < _last_value) ? _last_value - TValue.v : 0, period: _period, update: update); + _last_value = TValue.v; + double _cmo_up = 0; + double _cmo_dn = 0; + for (int i = 0; i < Math.Min(_buff_up.Count, _buff_dn.Count); i++) + { + _cmo_up += _buff_up[i]; + _cmo_dn += _buff_dn[i]; + } + double _cmo = 100 * (_cmo_up - _cmo_dn) / (_cmo_up + _cmo_dn); + if (_cmo_up + _cmo_dn == 0) { _cmo = 0; } + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _cmo); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buff_up.Clear(); + _buff_dn.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/CUSUM_Series.cs b/Calculations/_Updated/CUSUM_Series.cs index e61d288d..38036f94 100644 --- a/Calculations/_Updated/CUSUM_Series.cs +++ b/Calculations/_Updated/CUSUM_Series.cs @@ -1,71 +1,80 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -CUSUM: Cumulative Sum (aka Running Total) - SUM across a period provides a rolling sum of all values across the period. - If SUM values would be divided with period, the output would be SMA() - -Sources: - https://en.wikipedia.org/wiki/CUSUM - */ - -public class CUSUM_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public CUSUM_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"CUSUM({period})"; - } - public CUSUM_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public CUSUM_Series() : this(period: 0, useNaN: false) { } - public CUSUM_Series(int period) : this(period: period, useNaN: false) { } - public CUSUM_Series(TBars source) : this(source.Close, 0, false) { } - public CUSUM_Series(TBars source, int period) : this(source.Close, period, false) { } - public CUSUM_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public CUSUM_Series(TSeries source) : this(source, period: 0, useNaN: false) { } - public CUSUM_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sum = 0; - for (int i = 0; i < _buffer.Count; i++) { _sum += _buffer[i]; } - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sum); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +CUSUM: Cumulative Sum (aka Running Total) + SUM across a period provides a rolling sum of all values across the period. + If SUM values would be divided with period, the output would be SMA() + +Sources: + https://en.wikipedia.org/wiki/CUSUM + */ + +public class CUSUM_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public CUSUM_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CUSUM({period})"; + } + public CUSUM_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public CUSUM_Series() : this(period: 0, useNaN: false) { } + public CUSUM_Series(int period) : this(period: period, useNaN: false) { } + public CUSUM_Series(TBars source) : this(source.Close, 0, false) { } + public CUSUM_Series(TBars source, int period) : this(source.Close, period, false) { } + public CUSUM_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public CUSUM_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public CUSUM_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sum = 0; + for (int i = 0; i < _buffer.Count; i++) { _sum += _buffer[i]; } + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sum); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/DECAY_Series.cs b/Calculations/_Updated/DECAY_Series.cs index a0193dc2..8236956e 100644 --- a/Calculations/_Updated/DECAY_Series.cs +++ b/Calculations/_Updated/DECAY_Series.cs @@ -1,83 +1,93 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -DECAY: - Linear decay can be modeled by a straight line with a negative slope of 1/period. - The value decreases in a straight line from the last maximum to 0. - Decay = Last Max - distance/period - - Exponential decay is modeled as an exponential curve with diminishing factor of - 1-1/p - - */ - -public class DECAY_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly bool _exp; - private double _pdecay, _ppdecay; - private readonly double _dfactor; - - //core constructors - public DECAY_Series(int period, bool exponential, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"DECAY({period})"; - _exp = exponential; - _dfactor = (_exp) ? 1.0 - 1.0 / (double)_period : 1 / (double)_period; - _pdecay = _ppdecay = 0; - } - public DECAY_Series(TSeries source, int period, bool exponential, bool useNaN) : this(period, exponential, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public DECAY_Series() : this(period: 0, exponential: false, useNaN: false) { } - public DECAY_Series(int period) : this(period: period, exponential: false, useNaN: false) { } - public DECAY_Series(TBars source) : this(source.Close, period: 0, exponential: false, useNaN: false) { } - public DECAY_Series(TBars source, int period) : this(source.Close, period: period, exponential:false, useNaN:false) { } - public DECAY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, exponential: false, useNaN) { } - public DECAY_Series(TSeries source) : this(source, period: 0, exponential: false, useNaN:false) { } - public DECAY_Series(TSeries source, int period) : this(source: source, period: period, exponential: false, useNaN: false) { } - public DECAY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, exponential: false, useNaN: useNaN) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - if (update) { _pdecay = _ppdecay; } - else { _ppdecay = _pdecay; } - - if (this.Count == 0) { _pdecay = TValue.v; } - double _decay = Math.Max(TValue.v, Math.Max((_exp) ? _pdecay * _dfactor : _pdecay - _dfactor, 0)); - _pdecay = _decay; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _decay); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _pdecay = _ppdecay = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +DECAY: + Linear decay can be modeled by a straight line with a negative slope of 1/period. + The value decreases in a straight line from the last maximum to 0. + Decay = Last Max - distance/period + + Exponential decay is modeled as an exponential curve with diminishing factor of + 1-1/p + + */ + +public class DECAY_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly bool _exp; + private double _pdecay, _ppdecay; + private readonly double _dfactor; + + //core constructors + public DECAY_Series(int period, bool exponential, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"DECAY({period})"; + _exp = exponential; + _dfactor = (_exp) ? 1.0 - 1.0 / (double)_period : 1 / (double)_period; + _pdecay = _ppdecay = 0; + } + public DECAY_Series(TSeries source, int period, bool exponential, bool useNaN) : this(period, exponential, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public DECAY_Series() : this(period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(int period) : this(period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source) : this(source.Close, period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source, int period) : this(source.Close, period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, exponential: false, useNaN) { } + public DECAY_Series(TSeries source) : this(source, period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(TSeries source, int period) : this(source: source, period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, exponential: false, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (update) { _pdecay = _ppdecay; } + else { _ppdecay = _pdecay; } + + if (this.Count == 0) { _pdecay = TValue.v; } + double _decay = Math.Max(TValue.v, Math.Max((_exp) ? _pdecay * _dfactor : _pdecay - _dfactor, 0)); + _pdecay = _decay; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _decay); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _pdecay = _ppdecay = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/DEMA_Series.cs b/Calculations/_Updated/DEMA_Series.cs index 0cdac2b0..8bcf0ed7 100644 --- a/Calculations/_Updated/DEMA_Series.cs +++ b/Calculations/_Updated/DEMA_Series.cs @@ -1,127 +1,144 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -DEMA: Double Exponential Moving Average - DEMA uses EMA(EMA()) to calculate smoother Exponential moving average. - -Sources: - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/ - -Remark: - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - DEMA = 2 * ema1 - ema2 - - */ - -public class DEMA_Series : TSeries { - private double _k; - private double _sum, _oldsum; - private double _lastema1, _oldema1, _lastema2, _oldema2; - private int _len; - private readonly bool _useSMA; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - -//core constructor - public DEMA_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - _useSMA = useSMA; - Name = $"DEMA({period})"; - _k = 2.0 / (_period + 1); - _len = 0; - _sum = _oldsum = _lastema1 = _lastema2 = 0; - } - //generic constructors (source) - - public DEMA_Series() : this(0, false, true) {} - public DEMA_Series(int period) : this(period, false, true) {} - public DEMA_Series(TBars source) : this(source.Close, 0, false) {} - public DEMA_Series(TBars source, int period) : this(source.Close, period, false) {} - public DEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public DEMA_Series(TSeries source, int period) : this(source, period, false, true) {} - public DEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {} - public DEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - -// core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - _lastema1 = _oldema1; - _lastema2 = _oldema2; - _sum = _oldsum; - } - else { - _oldema1 = _lastema1; - _oldema2 = _lastema2; - _oldsum = _sum; - _len++; - } - - if (_period == 0) { - _k = 2.0 / (_len + 1); - } - - double _ema1, _ema2, _dema; - if (Count == 0) { - _ema1 = _ema2 = _sum = TValue.v; - } - else if (_len <= _period && _useSMA && _period != 0) { - _sum += TValue.v; - _ema1 = _sum / Math.Min(_len, _period); - _ema2 = _ema1; - } - else { - _ema1 = (TValue.v - _lastema1) * _k + _lastema1; - _ema2 = (_ema1 - _lastema2) * _k + _lastema2; - } - - _dema = 2 * _ema1 - _ema2; - - _lastema1 = double.IsNaN(_ema1) ? _lastema1 : _ema1; - _lastema2 = double.IsNaN(_ema2) ? _lastema2 : _ema2; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dema); - return base.Add(res, update); - } - -//variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { - return (DateTime.Today, double.NaN); - } - - foreach (var item in data) { - Add(item, false); - } - - return _data.Last; - } - - public (DateTime t, double v) Add(bool update) { - return Add(_data.Last, update); - } - - public (DateTime t, double v) Add() { - return Add(_data.Last, false); - } - - private new void Sub(object source, TSeriesEventArgs e) { - Add(_data.Last, e.update); - } - - //reset calculation - public override void Reset() { - _sum = _oldsum = _lastema1 = _lastema2 = 0; - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +DEMA: Double Exponential Moving Average + DEMA uses EMA(EMA()) to calculate smoother Exponential moving average. + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/ + +Remark: + ema1 = EMA(close, length) + ema2 = EMA(ema1, length) + DEMA = 2 * ema1 - ema2 + + */ + +public class DEMA_Series : TSeries +{ + private double _k; + private double _sum, _oldsum; + private double _lastema1, _oldema1, _lastema2, _oldema2; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public DEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"DEMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema1 = _lastema2 = 0; + } + //generic constructors (source) + + public DEMA_Series() : this(0, false, true) { } + public DEMA_Series(int period) : this(period, false, true) { } + public DEMA_Series(TBars source) : this(source.Close, 0, false) { } + public DEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public DEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public DEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public DEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public DEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _sum = _oldsum; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldsum = _sum; + _len++; + } + + if (_period == 0) + { + _k = 2.0 / (_len + 1); + } + + double _ema1, _ema2, _dema; + if (Count == 0) + { + _ema1 = _ema2 = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + _ema1 = _sum / Math.Min(_len, _period); + _ema2 = _ema1; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + } + + _dema = 2 * _ema1 - _ema2; + + _lastema1 = double.IsNaN(_ema1) ? _lastema1 : _ema1; + _lastema2 = double.IsNaN(_ema2) ? _lastema2 : _ema2; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) + { + return (DateTime.Today, double.NaN); + } + + foreach (var item in data) + { + Add(item, false); + } + + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return Add(_data.Last, update); + } + + public (DateTime t, double v) Add() + { + return Add(_data.Last, false); + } + + private new void Sub(object source, TSeriesEventArgs e) + { + Add(_data.Last, e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema1 = _lastema2 = 0; + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/DWMA_Series.cs b/Calculations/_Updated/DWMA_Series.cs index 3e5f4808..d9d9b847 100644 --- a/Calculations/_Updated/DWMA_Series.cs +++ b/Calculations/_Updated/DWMA_Series.cs @@ -1,122 +1,143 @@ -namespace QuanTAlib; - -using System; -using System.Collections.Generic; -using System.Threading.Tasks; - -/* -DWMA: Double Weighted Moving Average - The weights are decreasing over the period with p^2 decay - and the most recent data has the heaviest weight. - - */ - -public class DWMA_Series : TSeries { - private readonly List _buffer = new(); - private List _weights; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - protected int _len; - -//core constructors - public DWMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"DWMA({period})"; - _len = 0; - _weights = CalculateWeights(_period); - } - - public DWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - - public DWMA_Series() : this(0, false) { - } - - public DWMA_Series(int period) : this(period, false) { - } - - public DWMA_Series(TBars source) : this(source.Close, 0, false) { - } - - public DWMA_Series(TBars source, int period) : this(source.Close, period, false) { - } - - public DWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { - } - - public DWMA_Series(TSeries source, int period) : this(source, period, false) { - } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(_buffer, TValue.v, _period, update); - if (_period == 0) { - _len++; - _weights = CalculateWeights(_len); - } - - double _dwma = 0, _wsum = 0; - var bufferCount = _buffer.Count; - - var lockObj = new object(); - Parallel.For(0, bufferCount, i => - { - var temp = _buffer[i] * _weights[i]; - lock (lockObj) { - _dwma += temp; - _wsum += _weights[i]; - } - }); - _dwma /= _wsum; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dwma); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { - return (DateTime.Today, double.NaN); - } - - foreach (var item in data) { - Add(item, false); - } - - return _data.Last; - } - - public (DateTime t, double v) Add(bool update) { - return Add(_data.Last, update); - } - - public (DateTime t, double v) Add() { - return Add(_data.Last, false); - } - - private new void Sub(object source, TSeriesEventArgs e) { - Add(_data.Last, e.update); - } - - //calculating weights - private static List CalculateWeights(int period) { - var weights = new List(period); - for (var i = 0; i < period; i++) { - weights.Add((i + 1) * (i + 1)); - } - - return weights; - } - - //reset calculation - public override void Reset() { - _len = 0; - _buffer.Clear(); - _weights = CalculateWeights(_period); - } +namespace QuanTAlib; + +using System; +using System.Collections.Generic; +using System.Threading.Tasks; + +/* +DWMA: Double Weighted Moving Average + The weights are decreasing over the period with p^2 decay + and the most recent data has the heaviest weight. + + */ + +public class DWMA_Series : TSeries +{ + private readonly List _buffer = new(); + private List _weights; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + + //core constructors + public DWMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"DWMA({period})"; + _len = 0; + _weights = CalculateWeights(_period); + } + + public DWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public DWMA_Series() : this(0, false) + { + } + + public DWMA_Series(int period) : this(period, false) + { + } + + public DWMA_Series(TBars source) : this(source.Close, 0, false) + { + } + + public DWMA_Series(TBars source, int period) : this(source.Close, period, false) + { + } + + public DWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) + { + } + + public DWMA_Series(TSeries source, int period) : this(source, period, false) + { + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(_buffer, TValue.v, _period, update); + if (_period == 0) + { + _len++; + _weights = CalculateWeights(_len); + } + + double _dwma = 0, _wsum = 0; + var bufferCount = _buffer.Count; + + var lockObj = new object(); + Parallel.For(0, bufferCount, i => + { + var temp = _buffer[i] * _weights[i]; + lock (lockObj) + { + _dwma += temp; + _wsum += _weights[i]; + } + }); + _dwma /= _wsum; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dwma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) + { + return (DateTime.Today, double.NaN); + } + + foreach (var item in data) + { + Add(item, false); + } + + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return Add(_data.Last, update); + } + + public (DateTime t, double v) Add() + { + return Add(_data.Last, false); + } + + private new void Sub(object source, TSeriesEventArgs e) + { + Add(_data.Last, e.update); + } + + //calculating weights + private static List CalculateWeights(int period) + { + var weights = new List(period); + for (var i = 0; i < period; i++) + { + weights.Add((i + 1) * (i + 1)); + } + + return weights; + } + + //reset calculation + public override void Reset() + { + _len = 0; + _buffer.Clear(); + _weights = CalculateWeights(_period); + } } \ No newline at end of file diff --git a/Calculations/_Updated/EMA_Series.cs b/Calculations/_Updated/EMA_Series.cs index 138f04ea..26edf8ef 100644 --- a/Calculations/_Updated/EMA_Series.cs +++ b/Calculations/_Updated/EMA_Series.cs @@ -1,120 +1,136 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -EMA: Exponential Moving Average - EMA needs very short history buffer and calculates the EMA value using just the - previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1) - -Sources: - https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages - https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp - https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA - -Issues: - There is no consensus what the first EMA value should be - a zero, a first - datapoint, or an average of the initial Period bars. All three starting methods - converge within 20+ bars to the same moving average. Most implementations (including this one) - use SMA() for the first Period bars as a seeding value for EMA. - - */ - -public class EMA_Series : TSeries { - private double _k; - private double _lastema, _oldema; - private double _sum, _oldsum; - private int _len; - private readonly bool _useSMA; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - -//core constructors - - public EMA_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - _useSMA = useSMA; - Name = $"EMA({period})"; - _k = 2.0 / (_period + 1); - _len = 0; - _sum = _oldsum = _lastema = _oldema = 0; - } - public EMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public EMA_Series() : this(0, false, true) {} - public EMA_Series(int period) : this(period, false, true) {} - public EMA_Series(TBars source) : this(source.Close, 0, false) {} - public EMA_Series(TBars source, int period) : this(source.Close, period, false) {} - public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public EMA_Series(TSeries source, int period) : this(source, period, false, true) {} - public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {} - - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - _lastema = _oldema; - _sum = _oldsum; - } - else { - _oldema = _lastema; - _oldsum = _sum; - _len++; - } - - double _ema = 0; - if (_period == 0) { - _k = 2.0 / (_len + 1); - } - - if (Count == 0) { - _ema = _sum = TValue.v; - } - else if (_len <= _period && _useSMA && _period != 0) { - _sum += TValue.v; - if (_period != 0 && _len > _period) { - _sum -= _data[Count - _period - (update ? 1 : 0)].v; - } - - _ema = _sum / Math.Min(_len, _period); - } - else { - _ema = _k * (TValue.v - _lastema) + _lastema; - } - - _lastema = double.IsNaN(_ema) ? _lastema : _ema; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema); - return base.Add(res, update); - } - -//variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _sum = _oldsum = _lastema = _oldema = 0; - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +EMA: Exponential Moving Average + EMA needs very short history buffer and calculates the EMA value using just the + previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1) + +Sources: + https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages + https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp + https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA + +Issues: + There is no consensus what the first EMA value should be - a zero, a first + datapoint, or an average of the initial Period bars. All three starting methods + converge within 20+ bars to the same moving average. Most implementations (including this one) + use SMA() for the first Period bars as a seeding value for EMA. + + */ + +public class EMA_Series : TSeries +{ + private double _k; + private double _lastema, _oldema; + private double _sum, _oldsum; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + + public EMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"EMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema = _oldema = 0; + } + public EMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public EMA_Series() : this(0, false, true) { } + public EMA_Series(int period) : this(period, false, true) { } + public EMA_Series(TBars source) : this(source.Close, 0, false) { } + public EMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public EMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema = _oldema; + _sum = _oldsum; + } + else + { + _oldema = _lastema; + _oldsum = _sum; + _len++; + } + + double _ema = 0; + if (_period == 0) + { + _k = 2.0 / (_len + 1); + } + + if (Count == 0) + { + _ema = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + if (_period != 0 && _len > _period) + { + _sum -= _data[Count - _period - (update ? 1 : 0)].v; + } + + _ema = _sum / Math.Min(_len, _period); + } + else + { + _ema = _k * (TValue.v - _lastema) + _lastema; + } + + _lastema = double.IsNaN(_ema) ? _lastema : _ema; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema = _oldema = 0; + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/ENTROPY_Series.cs b/Calculations/_Updated/ENTROPY_Series.cs index 23523a50..6c3bd399 100644 --- a/Calculations/_Updated/ENTROPY_Series.cs +++ b/Calculations/_Updated/ENTROPY_Series.cs @@ -1,87 +1,97 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -ENTROPY: - Introduced by Claude Shannon in 1948, entropy measures the unpredictability - of the data, or equivalently, of its average information. - -Calculation: - P = close / Σ(close) - ENTROPY = Σ(-P * Log(P) / Log(base)) - -Sources: - https://en.wikipedia.org/wiki/Entropy_(information_theory) - https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples - - */ - -public class ENTROPY_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly double _logbase; - private readonly System.Collections.Generic.List _buffer = new(); - private readonly System.Collections.Generic.List _buff2 = new(); - - //core constructors - public ENTROPY_Series(int period, double logbase, bool useNaN) { - _period = period; - _NaN = useNaN; - _logbase = logbase; - Name = $"ENTROPY({period})"; - } - public ENTROPY_Series(TSeries source, int period, double logbase, bool useNaN) : this(period, logbase, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public ENTROPY_Series() : this(period: 0, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(int period) : this(period: period, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(TBars source) : this(source.Close, period: 0, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(TBars source, int period) : this(source.Close, period, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, logbase: 2.0, useNaN: useNaN) { } - public ENTROPY_Series(TSeries source) : this(source, period: 0, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(TSeries source, int period) : this(source: source, period: period, logbase: 2.0, useNaN: false) { } - public ENTROPY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, logbase: 2.0, useNaN: useNaN) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - double _sum = _buffer.Sum(); - double _pp = this._buffer[^1] / _sum; - double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase); - BufferTrim(_buff2, _ppp, _period, update); - double _entp = _buff2.Sum(); - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _entp); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - _buff2.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +ENTROPY: + Introduced by Claude Shannon in 1948, entropy measures the unpredictability + of the data, or equivalently, of its average information. + +Calculation: + P = close / Σ(close) + ENTROPY = Σ(-P * Log(P) / Log(base)) + +Sources: + https://en.wikipedia.org/wiki/Entropy_(information_theory) + https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples + + */ + +public class ENTROPY_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly double _logbase; + private readonly System.Collections.Generic.List _buffer = new(); + private readonly System.Collections.Generic.List _buff2 = new(); + + //core constructors + public ENTROPY_Series(int period, double logbase, bool useNaN) + { + _period = period; + _NaN = useNaN; + _logbase = logbase; + Name = $"ENTROPY({period})"; + } + public ENTROPY_Series(TSeries source, int period, double logbase, bool useNaN) : this(period, logbase, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public ENTROPY_Series() : this(period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(int period) : this(period: period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source) : this(source.Close, period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source, int period) : this(source.Close, period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, logbase: 2.0, useNaN: useNaN) { } + public ENTROPY_Series(TSeries source) : this(source, period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TSeries source, int period) : this(source: source, period: period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, logbase: 2.0, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _sum = _buffer.Sum(); + double _pp = this._buffer[^1] / _sum; + double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase); + BufferTrim(_buff2, _ppp, _period, update); + double _entp = _buff2.Sum(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _entp); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _buff2.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/FWMA_Series.cs b/Calculations/_Updated/FWMA_Series.cs index 78cfef0e..f9976814 100644 --- a/Calculations/_Updated/FWMA_Series.cs +++ b/Calculations/_Updated/FWMA_Series.cs @@ -1,94 +1,105 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Threading.Tasks; -using System.Numerics; -using System.Linq; - -/* -FWMA: Fibonacci's Weighted Moving Average is similar to a Weighted Moving Average - (WMA) where the weights are based on the Fibonacci Sequence. - - */ -public class FWMA_Series : TSeries { - private readonly List _buffer = new(); - private List _weights; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - protected int _len; - - public FWMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"FWMA({period})"; - _len = 0; - _weights = CalculateWeights(_period); - } - - public FWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - - public FWMA_Series() : this(period: 0, useNaN: false) { } - public FWMA_Series(int period) : this(period: period, useNaN: false) { } - public FWMA_Series(TBars source) : this(source.Close, 0, false) { } - public FWMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public FWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public FWMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - if (_period == 0) { - _len++; - _weights = CalculateWeights(_len); - } - double _fwma = 0; - double totalWeights = _weights.Sum(); - object lockObj = new object(); - Parallel.For(0, _buffer.Count, i => - { - double temp = _buffer[i] * _weights[i]; - lock (lockObj) { _fwma += temp; } - }); - _fwma /= totalWeights; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fwma); - return base.Add(res, update); - } - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - private static List CalculateWeights(int period) { - //to prevent overflow, max period can be no more than 1476 - period = (period > 1476) ? 1476 : period; - List weights = new List(period); - BigInteger a = 0; - BigInteger b = 1; - for (int i = 0; i < period; i++) { - BigInteger temp = a; - a = b; - b = temp + b; - weights.Add((double)Decimal.Parse(a.ToString())); - } - return weights; - } - - public override void Reset() { - _weights = CalculateWeights(_period); - _buffer.Clear(); - } -} +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Threading.Tasks; +using System.Numerics; +using System.Linq; + +/* +FWMA: Fibonacci's Weighted Moving Average is similar to a Weighted Moving Average + (WMA) where the weights are based on the Fibonacci Sequence. + + */ +public class FWMA_Series : TSeries +{ + private readonly List _buffer = new(); + private List _weights; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + + public FWMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"FWMA({period})"; + _len = 0; + _weights = CalculateWeights(_period); + } + + public FWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public FWMA_Series() : this(period: 0, useNaN: false) { } + public FWMA_Series(int period) : this(period: period, useNaN: false) { } + public FWMA_Series(TBars source) : this(source.Close, 0, false) { } + public FWMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public FWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public FWMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + if (_period == 0) + { + _len++; + _weights = CalculateWeights(_len); + } + double _fwma = 0; + double totalWeights = _weights.Sum(); + object lockObj = new object(); + Parallel.For(0, _buffer.Count, i => + { + double temp = _buffer[i] * _weights[i]; + lock (lockObj) { _fwma += temp; } + }); + _fwma /= totalWeights; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fwma); + return base.Add(res, update); + } + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + private static List CalculateWeights(int period) + { + //to prevent overflow, max period can be no more than 1476 + period = (period > 1476) ? 1476 : period; + List weights = new List(period); + BigInteger a = 0; + BigInteger b = 1; + for (int i = 0; i < period; i++) + { + BigInteger temp = a; + a = b; + b = temp + b; + weights.Add((double)Decimal.Parse(a.ToString())); + } + return weights; + } + + public override void Reset() + { + _weights = CalculateWeights(_period); + _buffer.Clear(); + } +} diff --git a/Calculations/_Updated/HEMA_Series.cs b/Calculations/_Updated/HEMA_Series.cs index b2f53114..c52f6c06 100644 --- a/Calculations/_Updated/HEMA_Series.cs +++ b/Calculations/_Updated/HEMA_Series.cs @@ -1,116 +1,133 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -HEMA: Hull-EMA Moving Average - a hybrid indicator - Modified HUll Moving Average; instead of using WMA (Weighted MA) for calculation, - HEMA uses EMA for Hull's formula: - -EMA1 = EMA(n/2) of price - where k = 4/(n/2 +1) -EMA2 = EMA(n) of price - where k = 3/(n+1) -Raw HMA = (2 * EMA1) - EMA2 -EMA3 = EMA(sqrt(n)) of Raw HMA - where k = 2/(sqrt(n)+1) - */ - -public class HEMA_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private double _k1, _k2, _k3; - private int _len; - private double _lastema1, _oldema1; - private double _lastema2, _oldema2; - private double _lasthema, _oldhema; - - //core constructors - public HEMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"HEMA({period})"; - (_k1, _k2, _k3) = CalculateK(_period); - _len = 0; - _lastema1 = _oldema1 = _lastema2 = _oldema2 = _lasthema = _oldhema = 0; - } - public HEMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public HEMA_Series() : this(period: 0, useNaN: false) { } - public HEMA_Series(int period) : this(period: period, useNaN: false) { } - public HEMA_Series(TBars source) : this(source.Close, 0, false) { } - public HEMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public HEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public HEMA_Series(TSeries source) : this(source, 0, false) { } - public HEMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - _lastema1 = _oldema1; - _lastema2 = _oldema2; - _lasthema = _oldhema; - } - else { - _oldema1 = _lastema1; - _oldema2 = _lastema2; - _oldhema = _lasthema; - } - double _ema1, _ema2, _hema; - if (_period == 0) { - _len++; - (_k1, _k2, _k3) = CalculateK(_len); - } - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, double.NaN), update); - } else if (this.Count == 0) { - _ema1 = _ema2 = _hema = TValue.v; - } - else { - _ema1 = _k1 * (TValue.v - _lastema1) + _lastema1; - _ema2 = _k2 * (TValue.v - _lastema2) + _lastema2; - _hema = _k3 * (((2 * _ema1) - _ema2) - _lasthema) + _lasthema; - } - - _lastema1 = _ema1; - _lastema2 = _ema2; - _lasthema = _hema; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hema); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _lastema1 = _lastema2 = _lasthema = 0; - _oldema1 = _oldema2 = _oldhema = 0; - _len = 0; - } - - public static (double k1, double k2, double k3) CalculateK(int len) { - double k1 = 8 / (double)(len + 7); - double k2 = 3 / (double)(len + 2); - double k3 = 2 / Math.Sqrt(len + 3); - - return (k1, k2, k3); - } - +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +HEMA: Hull-EMA Moving Average - a hybrid indicator + Modified HUll Moving Average; instead of using WMA (Weighted MA) for calculation, + HEMA uses EMA for Hull's formula: + +EMA1 = EMA(n/2) of price - where k = 4/(n/2 +1) +EMA2 = EMA(n) of price - where k = 3/(n+1) +Raw HMA = (2 * EMA1) - EMA2 +EMA3 = EMA(sqrt(n)) of Raw HMA - where k = 2/(sqrt(n)+1) + */ + +public class HEMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _k1, _k2, _k3; + private int _len; + private double _lastema1, _oldema1; + private double _lastema2, _oldema2; + private double _lasthema, _oldhema; + + //core constructors + public HEMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"HEMA({period})"; + (_k1, _k2, _k3) = CalculateK(_period); + _len = 0; + _lastema1 = _oldema1 = _lastema2 = _oldema2 = _lasthema = _oldhema = 0; + } + public HEMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HEMA_Series() : this(period: 0, useNaN: false) { } + public HEMA_Series(int period) : this(period: period, useNaN: false) { } + public HEMA_Series(TBars source) : this(source.Close, 0, false) { } + public HEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HEMA_Series(TSeries source) : this(source, 0, false) { } + public HEMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _lasthema = _oldhema; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldhema = _lasthema; + } + double _ema1, _ema2, _hema; + if (_period == 0) + { + _len++; + (_k1, _k2, _k3) = CalculateK(_len); + } + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + else if (this.Count == 0) + { + _ema1 = _ema2 = _hema = TValue.v; + } + else + { + _ema1 = _k1 * (TValue.v - _lastema1) + _lastema1; + _ema2 = _k2 * (TValue.v - _lastema2) + _lastema2; + _hema = _k3 * (((2 * _ema1) - _ema2) - _lasthema) + _lasthema; + } + + _lastema1 = _ema1; + _lastema2 = _ema2; + _lasthema = _hema; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hema); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastema1 = _lastema2 = _lasthema = 0; + _oldema1 = _oldema2 = _oldhema = 0; + _len = 0; + } + + public static (double k1, double k2, double k3) CalculateK(int len) + { + double k1 = 8 / (double)(len + 7); + double k2 = 3 / (double)(len + 2); + double k3 = 2 / Math.Sqrt(len + 3); + + return (k1, k2, k3); + } + } \ No newline at end of file diff --git a/Calculations/_Updated/HMA_Series.cs b/Calculations/_Updated/HMA_Series.cs index 9230a76b..4401886e 100644 --- a/Calculations/_Updated/HMA_Series.cs +++ b/Calculations/_Updated/HMA_Series.cs @@ -1,88 +1,98 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -HMA: Hull Moving Average - Developed by Alan Hull, an extremely fast and smooth moving average; almost - eliminates lag altogether and manages to improve smoothing at the same time. - -Sources: - https://alanhull.com/hull-moving-average - https://school.stockcharts.com/doku.php?id=technical_indicators:hull_moving_average - -WMA1 = WMA(n/2) of price -WMA2 = WMA(n) of price -Raw HMA = (2 * WMA1) - WMA2 -HMA = WMA(sqrt(n)) of Raw HMA - - */ - -public class HMA_Series : TSeries { - protected int _period, _period2, _psqrt; - protected readonly bool _NaN; - protected readonly TSeries _data; - protected WMA_Series _wma1, _wma2, _wma3; - - //core constructors - public HMA_Series(int period, bool useNaN) { - _period = period; - _period2 = period /2; - _psqrt = (int)Math.Sqrt(period); - _NaN = useNaN; - _wma1 = new(Math.Max(_period2,1), false); - _wma2 = new(Math.Max(_period,1), false); - _wma3 = new(Math.Max(_psqrt,1), useNaN); - Name = $"HMA({period})"; - } - public HMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public HMA_Series() : this(period: 0, useNaN: false) { } - public HMA_Series(int period) : this(period: period, useNaN: false) { } - public HMA_Series(TBars source) : this(source.Close, 0, false) { } - public HMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public HMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public HMA_Series(TSeries source) : this(source, 0, false) { } - public HMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (_period == 0) { - _wma1.Len = this.Count / 2; - _wma2.Len = this.Count; - _wma1.Len = (int)Math.Sqrt(this.Count); - } - double _w1 = _wma1.Add(TValue, update).v; - double _w2 = _wma2.Add(TValue, update).v; - double _hma = _wma3.Add((2 * _w1) - _w2, update).v; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hma); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _wma1.Reset(); - _wma2.Reset(); - _wma3.Reset(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +HMA: Hull Moving Average + Developed by Alan Hull, an extremely fast and smooth moving average; almost + eliminates lag altogether and manages to improve smoothing at the same time. + +Sources: + https://alanhull.com/hull-moving-average + https://school.stockcharts.com/doku.php?id=technical_indicators:hull_moving_average + +WMA1 = WMA(n/2) of price +WMA2 = WMA(n) of price +Raw HMA = (2 * WMA1) - WMA2 +HMA = WMA(sqrt(n)) of Raw HMA + + */ + +public class HMA_Series : TSeries +{ + protected int _period, _period2, _psqrt; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected WMA_Series _wma1, _wma2, _wma3; + + //core constructors + public HMA_Series(int period, bool useNaN) + { + _period = period; + _period2 = period / 2; + _psqrt = (int)Math.Sqrt(period); + _NaN = useNaN; + _wma1 = new(Math.Max(_period2, 1), false); + _wma2 = new(Math.Max(_period, 1), false); + _wma3 = new(Math.Max(_psqrt, 1), useNaN); + Name = $"HMA({period})"; + } + public HMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HMA_Series() : this(period: 0, useNaN: false) { } + public HMA_Series(int period) : this(period: period, useNaN: false) { } + public HMA_Series(TBars source) : this(source.Close, 0, false) { } + public HMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HMA_Series(TSeries source) : this(source, 0, false) { } + public HMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (_period == 0) + { + _wma1.Len = this.Count / 2; + _wma2.Len = this.Count; + _wma1.Len = (int)Math.Sqrt(this.Count); + } + double _w1 = _wma1.Add(TValue, update).v; + double _w2 = _wma2.Add(TValue, update).v; + double _hma = _wma3.Add((2 * _w1) - _w2, update).v; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _wma1.Reset(); + _wma2.Reset(); + _wma3.Reset(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/HWMA_Series.cs b/Calculations/_Updated/HWMA_Series.cs index f67249e2..6e3f7896 100644 --- a/Calculations/_Updated/HWMA_Series.cs +++ b/Calculations/_Updated/HWMA_Series.cs @@ -1,132 +1,146 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -HWMA: Holt-Winter Moving Average - Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving - average by the Holt-Winter method; Holt-Winters Exponential Smoothing is - used for forecasting time series data that exhibits both a trend and a - seasonal variation. - - -Sources: - https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/ - https://www.mql5.com/en/code/20856 - -nA - smoothed series (from 0 to 1) -nB - assess the trend (from 0 to 1) -nC - assess seasonality (from 0 to 1) - -Heuristic for determining alpha, beta, and gamma from period: - alpha = 2 / (1 + period) - beta = 1 / period - gamma = 1 / period - -F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i] -V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1]) -A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1]) -HWMA[i] = F[i] + V[i] + 0.5 * A[i] - - */ - -public class HWMA_Series : TSeries { - private int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - double _nA, _nB, _nC; - double _pF, _pV, _pA; - double _ppF, _ppV, _ppA; - - //core constructors - - public HWMA_Series(double nA, double nB, double nC, bool useNaN) { - _period = (int)((2 - nA) / nA); - _nA = nA; - _nB = nB; - _nC = nC; - _NaN = useNaN; - Name = $"HWMA({_period})"; - _len = 0; - } - public HWMA_Series(TSeries source, double nA, double nB, double nC, bool useNaN = false) : this(nA, nB, nC, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public HWMA_Series() : this(period: 0, useNaN: false) { } - public HWMA_Series(int period) : this(period, useNaN: false) { } - public HWMA_Series(int period, bool useNaN) : this(nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN) { - _period = period; - } - public HWMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } - public HWMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public HWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public HWMA_Series(TSeries source, int period) : this(source, period, false) { } - public HWMA_Series(TSeries source, int period, bool useNaN) : this(source, nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN: useNaN) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - double _F, _V, _A; - if (_len == 0) { _pF = TValue.v; _pA = _pV = 0; } - - if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; } - else { - _ppF = _pF; - _ppV = _pV; - _ppA = _pA; - _len++; - } - - if (_period == 0) { - _nA = 2 / (1 + (double)_len); - _nB = 1 / (double)_len; - _nC = 1 / (double)_len; - } - if (_period == 1) { - _nA = 1; - _nB = 0; - _nC = 0; - } - - _F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v; - _V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF); - _A = (1 - _nC) * _pA + _nC * (_V - _pV); - - double _hwma = _F + _V + 0.5 * _A; - _pF = _F; - _pV = _V; - _pA = _A; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hwma); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +HWMA: Holt-Winter Moving Average + Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving + average by the Holt-Winter method; Holt-Winters Exponential Smoothing is + used for forecasting time series data that exhibits both a trend and a + seasonal variation. + + +Sources: + https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/ + https://www.mql5.com/en/code/20856 + +nA - smoothed series (from 0 to 1) +nB - assess the trend (from 0 to 1) +nC - assess seasonality (from 0 to 1) + +Heuristic for determining alpha, beta, and gamma from period: + alpha = 2 / (1 + period) + beta = 1 / period + gamma = 1 / period + +F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i] +V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1]) +A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1]) +HWMA[i] = F[i] + V[i] + 0.5 * A[i] + + */ + +public class HWMA_Series : TSeries +{ + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + double _nA, _nB, _nC; + double _pF, _pV, _pA; + double _ppF, _ppV, _ppA; + + //core constructors + + public HWMA_Series(double nA, double nB, double nC, bool useNaN) + { + _period = (int)((2 - nA) / nA); + _nA = nA; + _nB = nB; + _nC = nC; + _NaN = useNaN; + Name = $"HWMA({_period})"; + _len = 0; + } + public HWMA_Series(TSeries source, double nA, double nB, double nC, bool useNaN = false) : this(nA, nB, nC, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HWMA_Series() : this(period: 0, useNaN: false) { } + public HWMA_Series(int period) : this(period, useNaN: false) { } + public HWMA_Series(int period, bool useNaN) : this(nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN) + { + _period = period; + } + public HWMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } + public HWMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HWMA_Series(TSeries source, int period) : this(source, period, false) { } + public HWMA_Series(TSeries source, int period, bool useNaN) : this(source, nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + double _F, _V, _A; + if (_len == 0) { _pF = TValue.v; _pA = _pV = 0; } + + if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; } + else + { + _ppF = _pF; + _ppV = _pV; + _ppA = _pA; + _len++; + } + + if (_period == 0) + { + _nA = 2 / (1 + (double)_len); + _nB = 1 / (double)_len; + _nC = 1 / (double)_len; + } + if (_period == 1) + { + _nA = 1; + _nB = 0; + _nC = 0; + } + + _F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v; + _V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF); + _A = (1 - _nC) * _pA + _nC * (_V - _pV); + + double _hwma = _F + _V + 0.5 * _A; + _pF = _F; + _pV = _V; + _pA = _A; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hwma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/JMA_Series.cs b/Calculations/_Updated/JMA_Series.cs index 8d5b9af0..517bbbe3 100644 --- a/Calculations/_Updated/JMA_Series.cs +++ b/Calculations/_Updated/JMA_Series.cs @@ -1,176 +1,176 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -JMA: Jurik Moving Average - Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the - underlying activity. It has extremely low lag, is very smooth and is responsive - to market gaps. - -Sources: - https://c.mql5.com/forextsd/forum/164/jurik_1.pdf - https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/ - -Issues: - Real JMA algorithm is not published and this formula is derived through - deduction and reverse analysis of JMA behavior. It is really close, but not - exact - published JMA tests against JMA.CSV fail with small deviation. The - original algo is slightly different, yet this approximation is close enough. - - */ - -public class JMA_Series : TSeries -{ - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly System.Collections.Generic.List volty_short = new(); - private readonly System.Collections.Generic.List vsum_buff = new(); - private readonly double pr; - private double upperBand, lowerBand, vsum, Kv; - private double prev_ma1, prev_det0, prev_det1, prev_vsum, prev_jma; - private double p_upperBand, p_lowerBand, p_Kv, p_prev_ma1, p_prev_det0, p_prev_det1, p_prev_vsum, p_prev_jma; - private readonly int _voltyS, _voltyL; - - //core constructors - public JMA_Series(int period, double phase, int vshort, int vlong, bool useNaN) - { - _period = period; - _NaN = useNaN; - Name = $"JMA({period})"; - upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; - pr = (phase * 0.01) + 1.5; - if (phase < -100) { pr = 0.5; } - if (phase > 100) { pr = 2.5; } - _voltyS = vshort; - _voltyL = vlong; - } - - public JMA_Series(TSeries source, int period, double phase, int vshort, int vlong, bool useNaN) : this(period, phase, vshort, vlong, useNaN) - { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public JMA_Series() : this(period: 0, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(int period) : this(period: period, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(TBars source) : this(source.Close, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(TBars source, int period) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } - public JMA_Series(TSeries source) : this(source, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(TSeries source, int period) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } - public JMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) - { - if (this.Count == 0) { prev_ma1 = prev_jma = TValue.v; } - if (update) - { - upperBand = p_upperBand; - lowerBand = p_lowerBand; - Kv = p_Kv; - prev_vsum = p_prev_vsum; - prev_ma1 = p_prev_ma1; - prev_det0 = p_prev_det0; - prev_det1 = p_prev_det1; - prev_jma = p_prev_jma; - } - else - { - p_upperBand = upperBand; - p_lowerBand = lowerBand; - p_Kv = Kv; - p_prev_vsum = prev_vsum; - p_prev_ma1 = prev_ma1; - p_prev_det0 = prev_det0; - p_prev_det1 = prev_det1; - p_prev_jma = prev_jma; - } - - if (double.IsNaN(TValue.v)) - { - return base.Add((TValue.t, double.NaN), update); - } - - // from Tvalue to volty - double del1 = TValue.v - upperBand; - double del2 = TValue.v - lowerBand; - upperBand = (del1 > 0) ? TValue.v : TValue.v - (Kv * del1); - lowerBand = (del2 < 0) ? TValue.v : TValue.v - (Kv * del2); - double volty = Math.Abs(del1) > Math.Abs(del2) ? Math.Abs(del1) : - (Math.Abs(del1) < Math.Abs(del2) ? Math.Abs(del2) : - Math.Abs(0.5 * (del1 + del2))); - - //// from volty to avolty - if (update) { volty_short[volty_short.Count - 1] = volty; } - else { volty_short.Add(volty); } - if (volty_short.Count > _voltyS) { volty_short.RemoveAt(0); } - vsum = prev_vsum + 0.1 * (volty - volty_short.First()); - prev_vsum = vsum; - if (update) { vsum_buff[vsum_buff.Count - 1] = vsum; } - else { vsum_buff.Add(vsum); } - if (vsum_buff.Count > _voltyL) { vsum_buff.RemoveAt(0); } - double avolty = 0; - for (int i = 0; i < vsum_buff.Count; i++) { avolty += vsum_buff[i]; } - avolty /= vsum_buff.Count; - - /// from avolty to rolty - double rvolty = (avolty != 0) ? volty / avolty : 0; - double len1 = (Math.Log(Math.Sqrt(_period)) / Math.Log(2.0)) + 2; - if (len1 < 0) { len1 = 0; } - - double pow1 = Math.Max(len1 - 2.0, 0.5); - if (rvolty > Math.Pow(len1, 1.0 / pow1)) { rvolty = Math.Pow(len1, 1.0 / pow1); } - if (rvolty < 1) { rvolty = 1; } - - //// from rvolty to second smoothing - double pow2 = Math.Pow(rvolty, pow1); - double beta = 0.45 * (_period - 1) / (0.45 * (_period - 1) + 2); - Kv = Math.Pow(beta, Math.Sqrt(pow2)); - double alpha = Math.Pow(beta, pow2); - double ma1 = (1 - alpha) * TValue.v + alpha * prev_ma1; - prev_ma1 = ma1; - - double det0 = (1 - beta) * (TValue.v - ma1) + beta * prev_det0; - prev_det0 = det0; - double ma2 = ma1 + pr * det0; - - double det1 = ((1 - alpha) * (1 - alpha) * (ma2 - prev_jma)) + (alpha * alpha * prev_det1); - prev_det1 = det1; - double jma = prev_jma + det1; - prev_jma = jma; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : jma); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) - { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) - { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() - { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) - { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() - { - upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +JMA: Jurik Moving Average + Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the + underlying activity. It has extremely low lag, is very smooth and is responsive + to market gaps. + +Sources: + https://c.mql5.com/forextsd/forum/164/jurik_1.pdf + https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/ + +Issues: + Real JMA algorithm is not published and this formula is derived through + deduction and reverse analysis of JMA behavior. It is really close, but not + exact - published JMA tests against JMA.CSV fail with small deviation. The + original algo is slightly different, yet this approximation is close enough. + + */ + +public class JMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly System.Collections.Generic.List volty_short = new(); + private readonly System.Collections.Generic.List vsum_buff = new(); + private readonly double pr; + private double upperBand, lowerBand, vsum, Kv; + private double prev_ma1, prev_det0, prev_det1, prev_vsum, prev_jma; + private double p_upperBand, p_lowerBand, p_Kv, p_prev_ma1, p_prev_det0, p_prev_det1, p_prev_vsum, p_prev_jma; + private readonly int _voltyS, _voltyL; + + //core constructors + public JMA_Series(int period, double phase, int vshort, int vlong, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"JMA({period})"; + upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; + pr = (phase * 0.01) + 1.5; + if (phase < -100) { pr = 0.5; } + if (phase > 100) { pr = 2.5; } + _voltyS = vshort; + _voltyL = vlong; + } + + public JMA_Series(TSeries source, int period, double phase, int vshort, int vlong, bool useNaN) : this(period, phase, vshort, vlong, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public JMA_Series() : this(period: 0, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(int period) : this(period: period, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source) : this(source.Close, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source, int period) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } + public JMA_Series(TSeries source) : this(source, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TSeries source, int period) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (this.Count == 0) { prev_ma1 = prev_jma = TValue.v; } + if (update) + { + upperBand = p_upperBand; + lowerBand = p_lowerBand; + Kv = p_Kv; + prev_vsum = p_prev_vsum; + prev_ma1 = p_prev_ma1; + prev_det0 = p_prev_det0; + prev_det1 = p_prev_det1; + prev_jma = p_prev_jma; + } + else + { + p_upperBand = upperBand; + p_lowerBand = lowerBand; + p_Kv = Kv; + p_prev_vsum = prev_vsum; + p_prev_ma1 = prev_ma1; + p_prev_det0 = prev_det0; + p_prev_det1 = prev_det1; + p_prev_jma = prev_jma; + } + + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + // from Tvalue to volty + double del1 = TValue.v - upperBand; + double del2 = TValue.v - lowerBand; + upperBand = (del1 > 0) ? TValue.v : TValue.v - (Kv * del1); + lowerBand = (del2 < 0) ? TValue.v : TValue.v - (Kv * del2); + double volty = Math.Abs(del1) > Math.Abs(del2) ? Math.Abs(del1) : + (Math.Abs(del1) < Math.Abs(del2) ? Math.Abs(del2) : + Math.Abs(0.5 * (del1 + del2))); + + //// from volty to avolty + if (update) { volty_short[volty_short.Count - 1] = volty; } + else { volty_short.Add(volty); } + if (volty_short.Count > _voltyS) { volty_short.RemoveAt(0); } + vsum = prev_vsum + 0.1 * (volty - volty_short.First()); + prev_vsum = vsum; + if (update) { vsum_buff[vsum_buff.Count - 1] = vsum; } + else { vsum_buff.Add(vsum); } + if (vsum_buff.Count > _voltyL) { vsum_buff.RemoveAt(0); } + double avolty = 0; + for (int i = 0; i < vsum_buff.Count; i++) { avolty += vsum_buff[i]; } + avolty /= vsum_buff.Count; + + /// from avolty to rolty + double rvolty = (avolty != 0) ? volty / avolty : 0; + double len1 = (Math.Log(Math.Sqrt(_period)) / Math.Log(2.0)) + 2; + if (len1 < 0) { len1 = 0; } + + double pow1 = Math.Max(len1 - 2.0, 0.5); + if (rvolty > Math.Pow(len1, 1.0 / pow1)) { rvolty = Math.Pow(len1, 1.0 / pow1); } + if (rvolty < 1) { rvolty = 1; } + + //// from rvolty to second smoothing + double pow2 = Math.Pow(rvolty, pow1); + double beta = 0.45 * (_period - 1) / (0.45 * (_period - 1) + 2); + Kv = Math.Pow(beta, Math.Sqrt(pow2)); + double alpha = Math.Pow(beta, pow2); + double ma1 = (1 - alpha) * TValue.v + alpha * prev_ma1; + prev_ma1 = ma1; + + double det0 = (1 - beta) * (TValue.v - ma1) + beta * prev_det0; + prev_det0 = det0; + double ma2 = ma1 + pr * det0; + + double det1 = ((1 - alpha) * (1 - alpha) * (ma2 - prev_jma)) + (alpha * alpha * prev_det1); + prev_det1 = det1; + double jma = prev_jma + det1; + prev_jma = jma; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : jma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/KAMA_Series.cs b/Calculations/_Updated/KAMA_Series.cs index a3bcf394..ae3a96cd 100644 --- a/Calculations/_Updated/KAMA_Series.cs +++ b/Calculations/_Updated/KAMA_Series.cs @@ -1,107 +1,118 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -KAMA: Kaufman's Adaptive Moving Average - Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as - Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972, - it was not until the popular book titled "Trading Systems and Methods" that it was made widely - available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive - Moving Average, considers market volatility apart from price fluctuations. - - KAMA[i] = KAMA[i-1] + SC * ( price - KAMA[i-1] ) - -Sources: - https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work - https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/ - https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average - -Remark: - If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards. - Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields - slightly different results for the first 50 bars - and then converges with the other one. - - */ - -public class KAMA_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - private double _lastkama, _lastlastkama; - private readonly double _scFast, _scSlow; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public KAMA_Series(int period, int fast, int slow, bool useNaN) { - _period = period; - _NaN = useNaN; - _scFast = 2.0 / (((period < fast) ? period : fast) + 1); - _scSlow = 2.0 / (slow + 1); - _lastkama = _lastlastkama = 0; - Name = $"KAMA({period})"; - } - public KAMA_Series(TSeries source, int period, int fast, int slow, bool useNaN) : this(period, fast, slow, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public KAMA_Series() : this(period: 0, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(int period) : this(period: period, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(TBars source) : this(source.Close, period: 0, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(TBars source, int period) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: useNaN) { } - public KAMA_Series(TSeries source) : this(source, period: 0, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(TSeries source, int period) : this(source: source, period: period, fast: 2, slow: 30, useNaN: false) { } - public KAMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, fast: 2, slow: 30, useNaN: useNaN) { } - public KAMA_Series(TSeries source, int period, int fast, int slow) : this(source: source, period: period, fast: fast, slow: slow, useNaN: false) { } - - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - - if (update) { _lastkama = _lastlastkama; } - else { _lastlastkama = _lastkama; } - BufferTrim(buffer: _buffer, value: TValue.v, period: _period + 1, update: update); - - double _kama = 0; - if (this.Count < _period) { _kama = TValue.v; } - else { - double _change = Math.Abs(_buffer[^1] - _buffer[(_buffer.Count > _period + 1) ? 1 : 0]); - double _sumpv = 0; - for (int i = 1; i < _buffer.Count; i++) { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); } - double _er = (_sumpv == 0) ? 0 : _change / _sumpv; - double _sc = (_er * (_scFast - _scSlow)) + _scSlow; - _kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama))); - } - _lastkama = _kama; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kama); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - _lastkama = _lastlastkama = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +KAMA: Kaufman's Adaptive Moving Average + Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as + Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972, + it was not until the popular book titled "Trading Systems and Methods" that it was made widely + available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive + Moving Average, considers market volatility apart from price fluctuations. + + KAMA[i] = KAMA[i-1] + SC * ( price - KAMA[i-1] ) + +Sources: + https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work + https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/ + https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average + +Remark: + If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards. + Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields + slightly different results for the first 50 bars - and then converges with the other one. + + */ + +public class KAMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private double _lastkama, _lastlastkama; + private readonly double _scFast, _scSlow; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public KAMA_Series(int period, int fast, int slow, bool useNaN) + { + _period = period; + _NaN = useNaN; + _scFast = 2.0 / (((period < fast) ? period : fast) + 1); + _scSlow = 2.0 / (slow + 1); + _lastkama = _lastlastkama = 0; + Name = $"KAMA({period})"; + } + public KAMA_Series(TSeries source, int period, int fast, int slow, bool useNaN) : this(period, fast, slow, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public KAMA_Series() : this(period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(int period) : this(period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source) : this(source.Close, period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source, int period) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: useNaN) { } + public KAMA_Series(TSeries source) : this(source, period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TSeries source, int period) : this(source: source, period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, fast: 2, slow: 30, useNaN: useNaN) { } + public KAMA_Series(TSeries source, int period, int fast, int slow) : this(source: source, period: period, fast: fast, slow: slow, useNaN: false) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + if (update) { _lastkama = _lastlastkama; } + else { _lastlastkama = _lastkama; } + BufferTrim(buffer: _buffer, value: TValue.v, period: _period + 1, update: update); + + double _kama = 0; + if (this.Count < _period) { _kama = TValue.v; } + else + { + double _change = Math.Abs(_buffer[^1] - _buffer[(_buffer.Count > _period + 1) ? 1 : 0]); + double _sumpv = 0; + for (int i = 1; i < _buffer.Count; i++) { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); } + double _er = (_sumpv == 0) ? 0 : _change / _sumpv; + double _sc = (_er * (_scFast - _scSlow)) + _scSlow; + _kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama))); + } + _lastkama = _kama; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kama); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _lastkama = _lastlastkama = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/KURTOSIS_Series.cs b/Calculations/_Updated/KURTOSIS_Series.cs index 71b8cfba..bfc972af 100644 --- a/Calculations/_Updated/KURTOSIS_Series.cs +++ b/Calculations/_Updated/KURTOSIS_Series.cs @@ -1,96 +1,107 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -KURTOSIS: Kurtosis of population - Kurtosis characterizes the relative peakedness or flatness of a distribution - compared with the normal distribution. Positive kurtosis indicates a relatively - peaked distribution. Negative kurtosis indicates a relatively flat distribution. - - The normal curve is called Mesokurtic curve. If the curve of a distribution is - more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then - it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or - lighter-tailed) than a normal curve, it is called as a platykurtic curve. - -Calculation: - sum4 = Σ(close-SMA)^4 - sum2 = (Σ(close-SMA)^2)^2 - KURTOSIS = length * (sum4/sum2) - -Sources: - https://en.wikipedia.org/wiki/Kurtosis - https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/ - - */ - -public class KURTOSIS_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly System.Collections.Generic.List _buffer = new(); - - //core constructors - public KURTOSIS_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"KURTOSIS({period})"; - } - public KURTOSIS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public KURTOSIS_Series() : this(period: 0, useNaN: false) { } - public KURTOSIS_Series(int period) : this(period: period, useNaN: false) { } - public KURTOSIS_Series(TSeries source) : this(source, period: 0, useNaN: false) { } - public KURTOSIS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - double _n = _buffer.Count; - double _avg = _buffer.Average(); - - double _s2 = 0; - double _s4 = 0; - for (int i = 0; i < this._buffer.Count; i++) { - _s2 += (_buffer[i] - _avg) * (_buffer[i] - _avg); - _s4 += (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg); - } - - double _Vx = _s2 / (_n - 1); - double _kurt = (_n > 3) ? - (_n * (_n + 1) * _s4) / (_Vx * _Vx * (_n - 3) * (_n - 1) * (_n - 2)) - (3 * (_n - 1) * (_n - 1) / ((_n - 2) * (_n - 3))) //using Sheskin Algo - : (_s2 * _s2) / _n - 3; //using Snedecor and Cochran (1967) algo - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kurt); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +KURTOSIS: Kurtosis of population + Kurtosis characterizes the relative peakedness or flatness of a distribution + compared with the normal distribution. Positive kurtosis indicates a relatively + peaked distribution. Negative kurtosis indicates a relatively flat distribution. + + The normal curve is called Mesokurtic curve. If the curve of a distribution is + more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then + it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or + lighter-tailed) than a normal curve, it is called as a platykurtic curve. + +Calculation: + sum4 = Σ(close-SMA)^4 + sum2 = (Σ(close-SMA)^2)^2 + KURTOSIS = length * (sum4/sum2) + +Sources: + https://en.wikipedia.org/wiki/Kurtosis + https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/ + + */ + +public class KURTOSIS_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly System.Collections.Generic.List _buffer = new(); + + //core constructors + public KURTOSIS_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"KURTOSIS({period})"; + } + public KURTOSIS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public KURTOSIS_Series() : this(period: 0, useNaN: false) { } + public KURTOSIS_Series(int period) : this(period: period, useNaN: false) { } + public KURTOSIS_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public KURTOSIS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _n = _buffer.Count; + double _avg = _buffer.Average(); + + double _s2 = 0; + double _s4 = 0; + for (int i = 0; i < this._buffer.Count; i++) + { + _s2 += (_buffer[i] - _avg) * (_buffer[i] - _avg); + _s4 += (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg); + } + + double _Vx = _s2 / (_n - 1); + double _kurt = (_n > 3) ? + (_n * (_n + 1) * _s4) / (_Vx * _Vx * (_n - 3) * (_n - 1) * (_n - 2)) - (3 * (_n - 1) * (_n - 1) / ((_n - 2) * (_n - 3))) //using Sheskin Algo + : (_s2 * _s2) / _n - 3; //using Snedecor and Cochran (1967) algo + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kurt); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MACD_Series.cs b/Calculations/_Updated/MACD_Series.cs index a81ce1a9..251f6577 100644 --- a/Calculations/_Updated/MACD_Series.cs +++ b/Calculations/_Updated/MACD_Series.cs @@ -1,78 +1,88 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MACD: Moving Average Convergence/Divergence - Moving average convergence divergence (MACD) is a trend-following momentum - indicator that shows the relationship between two moving averages of a series. - The MACD is calculated by subtracting the 26-period exponential moving average (EMA) - from the 12-period EMA. MACD Signal is 9-day EMA of MACD. - - */ - -public class MACD_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - - protected readonly int _slow, _fast, _signal; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly EMA_Series _TSlow; - private readonly EMA_Series _TFast; - public EMA_Series Signal { get; } - - //core constructors - public MACD_Series(int slow = 26, int fast = 12, int signal = 9, bool useNaN = false) { - _slow = slow; - _fast = fast; - _signal = signal; - _NaN = useNaN; - Name = $"MACD({slow},{fast},{signal})"; - _TSlow = new(slow, useNaN:false, useSMA:true); - _TFast = new(fast, useNaN: false, useSMA: true); - Signal = new(signal, useNaN: false, useSMA: true); - } - public MACD_Series(TSeries source, int slow, int fast, int signal, bool useNaN) : this(slow, fast, signal, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MACD_Series(TSeries source) : this(source:source, slow:26, fast:12, signal:9 , useNaN:false) { } - public MACD_Series(TSeries source, int slow, int fast, int signal) : this(source: source, slow: slow, fast:fast, signal:signal, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - - var _sslow = _TSlow.Add(TValue,update); - var _sfast = _TFast.Add(TValue, update); - Signal.Add((TValue.t, _sfast.v-_sslow.v)); - - var res = (TValue.t, Count < _fast - 1 && _NaN ? double.NaN : _sfast.v-_sslow.v); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MACD: Moving Average Convergence/Divergence + Moving average convergence divergence (MACD) is a trend-following momentum + indicator that shows the relationship between two moving averages of a series. + The MACD is calculated by subtracting the 26-period exponential moving average (EMA) + from the 12-period EMA. MACD Signal is 9-day EMA of MACD. + + */ + +public class MACD_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _slow, _fast, _signal; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _TSlow; + private readonly EMA_Series _TFast; + public EMA_Series Signal { get; } + + //core constructors + public MACD_Series(int slow = 26, int fast = 12, int signal = 9, bool useNaN = false) + { + _slow = slow; + _fast = fast; + _signal = signal; + _NaN = useNaN; + Name = $"MACD({slow},{fast},{signal})"; + _TSlow = new(slow, useNaN: false, useSMA: true); + _TFast = new(fast, useNaN: false, useSMA: true); + Signal = new(signal, useNaN: false, useSMA: true); + } + public MACD_Series(TSeries source, int slow, int fast, int signal, bool useNaN) : this(slow, fast, signal, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MACD_Series(TSeries source) : this(source: source, slow: 26, fast: 12, signal: 9, useNaN: false) { } + public MACD_Series(TSeries source, int slow, int fast, int signal) : this(source: source, slow: slow, fast: fast, signal: signal, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + var _sslow = _TSlow.Add(TValue, update); + var _sfast = _TFast.Add(TValue, update); + Signal.Add((TValue.t, _sfast.v - _sslow.v)); + + var res = (TValue.t, Count < _fast - 1 && _NaN ? double.NaN : _sfast.v - _sslow.v); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MAD_Series.cs b/Calculations/_Updated/MAD_Series.cs index 0378f1c5..3ba8ca1b 100644 --- a/Calculations/_Updated/MAD_Series.cs +++ b/Calculations/_Updated/MAD_Series.cs @@ -1,79 +1,88 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MAD: Mean Absolute Deviation - Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation - MAD defines the degree of variation across the series. - -Calculation: - MAD = Σ(|close-SMA|) / period - -Sources: - https://en.wikipedia.org/wiki/Average_absolute_deviation - - */ - -public class MAD_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MAD_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MAD({period})"; - } - public MAD_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MAD_Series() : this(period: 0, useNaN: false) { } - public MAD_Series(int period) : this(period: period, useNaN: false) { } - public MAD_Series(TBars source) : this(source.Close, 0, false) { } - public MAD_Series(TBars source, int period) : this(source.Close, period, false) { } - public MAD_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MAD_Series(TSeries source) : this(source, 0, false) { } - public MAD_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - double _mad = 0; - for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); } - _mad /= this._buffer.Count; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mad); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAD: Mean Absolute Deviation + Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation + MAD defines the degree of variation across the series. + +Calculation: + MAD = Σ(|close-SMA|) / period + +Sources: + https://en.wikipedia.org/wiki/Average_absolute_deviation + + */ + +public class MAD_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAD_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAD({period})"; + } + public MAD_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAD_Series() : this(period: 0, useNaN: false) { } + public MAD_Series(int period) : this(period: period, useNaN: false) { } + public MAD_Series(TBars source) : this(source.Close, 0, false) { } + public MAD_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAD_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAD_Series(TSeries source) : this(source, 0, false) { } + public MAD_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + double _mad = 0; + for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); } + _mad /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mad); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MAE_Series.cs b/Calculations/_Updated/MAE_Series.cs index b01839d2..7a8d05f6 100644 --- a/Calculations/_Updated/MAE_Series.cs +++ b/Calculations/_Updated/MAE_Series.cs @@ -1,77 +1,86 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MAE: Mean Absolute Error - Defined as a Mean (Average) of the absolute difference between actual and estimated values. - MAE = (1/n) * Σ|y_i - MA_i| - -Sources: - https://en.wikipedia.org/wiki/Mean_absolute_error - - */ - -public class MAE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MAE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MSE({period})"; - } - public MAE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MAE_Series() : this(period: 0, useNaN: false) { } - public MAE_Series(int period) : this(period: period, useNaN: false) { } - public MAE_Series(TBars source) : this(source.Close, 0, false) { } - public MAE_Series(TBars source, int period) : this(source.Close, period, false) { } - public MAE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MAE_Series(TSeries source) : this(source, 0, false) { } - public MAE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _mae = 0; - for (int i = 0; i < _buffer.Count; i++) { _mae += Math.Abs(_buffer[i] - _sma); } - _mae /= this._buffer.Count; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mae); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAE: Mean Absolute Error + Defined as a Mean (Average) of the absolute difference between actual and estimated values. + MAE = (1/n) * Σ|y_i - MA_i| + +Sources: + https://en.wikipedia.org/wiki/Mean_absolute_error + + */ + +public class MAE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MSE({period})"; + } + public MAE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAE_Series() : this(period: 0, useNaN: false) { } + public MAE_Series(int period) : this(period: period, useNaN: false) { } + public MAE_Series(TBars source) : this(source.Close, 0, false) { } + public MAE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAE_Series(TSeries source) : this(source, 0, false) { } + public MAE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mae = 0; + for (int i = 0; i < _buffer.Count; i++) { _mae += Math.Abs(_buffer[i] - _sma); } + _mae /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mae); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MAMA_Series.cs b/Calculations/_Updated/MAMA_Series.cs index 52bd5d7b..e450337a 100644 --- a/Calculations/_Updated/MAMA_Series.cs +++ b/Calculations/_Updated/MAMA_Series.cs @@ -1,189 +1,207 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -MAMA: MESA Adaptive Moving Average - Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of - high/low price that uses classic electrical radio-frequency signal processing algorithms - to reduce noise. - - KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 ) - -Sources: - https://mesasoftware.com/papers/MAMA.pdf - https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/ - - */ - -public class MAMA_Series : TSeries { - private int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - private double sumPr; - private double fastl, slowl; - private (double i, double i1, double i2, double i3, double i4, double i5, double i6, double io) pr, i1, q1, sm, dt; - private (double i, double i1, double io) i2, q2, re, im, pd, ph, mama, fama; - public TSeries Fama { get; } - private double mamaseed, famaseed; - - //core constructors - - public MAMA_Series(double fastlimit, double slowlimit, bool useNaN) { - _period = (int)(2 / fastlimit) - 1; - fastl = fastlimit; - slowl = slowlimit; - Fama = new TSeries(); - _NaN = useNaN; - Name = $"MAMA({_period})"; - _len = 0; - } - public MAMA_Series(TSeries source, double fastlimit, double slowlimit, bool useNaN = false) : this(fastlimit, slowlimit, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MAMA_Series() : this(period: 0, useNaN: false) { } - public MAMA_Series(int period) : this(period, useNaN: false) { } - public MAMA_Series(int period, bool useNaN) : this(fastlimit: 2 / (period + 1), slowlimit: 0.2 / (period + 1), useNaN) { - _period = period; - } - public MAMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } - public MAMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public MAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MAMA_Series(TSeries source, int period) : this(source, period, false) { } - public MAMA_Series(TSeries source, int period, bool useNaN) : this(source, fastlimit: 2 / ((double)period + 1), slowlimit: 0.2 / ((double)period + 1), useNaN: useNaN) { } - - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - if (!update) { - // roll forward (oldx = x) - pr.io = pr.i6; pr.i6 = pr.i5; pr.i5 = pr.i4; pr.i4 = pr.i3; pr.i3 = pr.i2; pr.i2 = pr.i1; pr.i1 = pr.i; - i1.io = i1.i6; i1.i6 = i1.i5; i1.i5 = i1.i4; i1.i4 = i1.i3; i1.i3 = i1.i2; i1.i2 = i1.i1; i1.i1 = i1.i; - q1.io = q1.i6; q1.i6 = q1.i5; q1.i5 = q1.i4; q1.i4 = q1.i3; q1.i3 = q1.i2; q1.i2 = q1.i1; q1.i1 = q1.i; - dt.io = dt.i6; dt.i6 = dt.i5; dt.i5 = dt.i4; dt.i4 = dt.i3; dt.i3 = dt.i2; dt.i2 = dt.i1; dt.i1 = dt.i; - sm.io = sm.i6; sm.i6 = sm.i5; sm.i5 = sm.i4; sm.i4 = sm.i3; sm.i3 = sm.i2; sm.i2 = sm.i1; sm.i1 = sm.i; - i2.io = i2.i1; i2.i1 = i2.i; q2.io = q2.i1; q2.i1 = q2.i; - re.io = re.i1; re.i1 = re.i; im.io = im.i1; im.i1 = im.i; - pd.io = pd.i1; pd.i1 = pd.i; ph.io = ph.i1; ph.i1 = ph.i; - mama.io = mama.i1; mama.i1 = mama.i; - fama.io = fama.i1; - fama.i1 = fama.i; - _len++; - } - if (_period == 0) { - fastl = 2 / (double)_len; - slowl = fastl * 0.1; - } - if (_period == 1) { - fastl = 1; - slowl = 1; - } - var i = _len - 1; - pr.i = TValue.v; - if (i > 5) { - var adj = 0.075 * pd.i1 + 0.54; - - // smooth and detrender - sm.i = (4 * pr.i + 3 * pr.i1 + 2 * pr.i2 + pr.i3) / 10; - dt.i = (0.0962 * sm.i + 0.5769 * sm.i2 - 0.5769 * sm.i4 - 0.0962 * sm.i6) * adj; - - // in-phase and quadrature - q1.i = (0.0962 * dt.i + 0.5769 * dt.i2 - 0.5769 * dt.i4 - 0.0962 * dt.i6) * adj; - i1.i = dt.i3; - - // advance the phases by 90 degrees - double jI = (0.0962 * i1.i + 0.5769 * i1.i2 - 0.5769 * i1.i4 - 0.0962 * i1.i6) * adj; - double jQ = (0.0962 * q1.i + 0.5769 * q1.i2 - 0.5769 * q1.i4 - 0.0962 * q1.i6) * adj; - - // phasor addition for 3-bar averaging - i2.i = i1.i - jQ; - q2.i = q1.i + jI; - - i2.i = 0.2 * i2.i + 0.8 * i2.i1; // smoothing it - q2.i = 0.2 * q2.i + 0.8 * q2.i1; - - // homodyne discriminator - re.i = i2.i * i2.i1 + q2.i * q2.i1; - im.i = i2.i * q2.i1 - q2.i * i2.i1; - - re.i = 0.2 * re.i + 0.8 * re.i1; // smoothing it - im.i = 0.2 * im.i + 0.8 * im.i1; - - // calculate period - pd.i = im.i != 0 && re.i != 0 ? 6.283185307179586 / Math.Atan(im.i / re.i) : 0d; - - // adjust period to thresholds - pd.i = pd.i > 1.5 * pd.i1 ? 1.5 * pd.i1 : pd.i; - pd.i = pd.i < 0.67 * pd.i1 ? 0.67 * pd.i1 : pd.i; - pd.i = pd.i < 6d ? 6d : pd.i; - pd.i = pd.i > 50d ? 50d : pd.i; - - // smooth the period - pd.i = 0.2 * pd.i + 0.8 * pd.i1; - - // determine phase position - ph.i = i1.i != 0 ? Math.Atan(q1.i / i1.i) * 57.29577951308232 : 0; - - // change in phase - var delta = Math.Max(ph.i1 - ph.i, 1d); - - // adaptive alpha value - var alpha = Math.Max(fastl / delta, slowl); - - // final indicators - mama.i = alpha * (pr.i - mama.i1) + mama.i1; - fama.i = 0.5d * alpha * (mama.i - fama.i1) + fama.i1; - } - else { - sumPr += pr.i; - pd.i = sm.i = dt.i = i1.i = q1.i = i2.i = q2.i = re.i = im.i = ph.i = 0; - mama.i = fama.i = sumPr / (i + 1); - - if (_len == 1) { - mamaseed = famaseed = TValue.v; - } - else { - mamaseed = fastl * (TValue.v - mamaseed) + mamaseed; - famaseed = slowl * (TValue.v - famaseed) + famaseed; - } - } - - double _fama = (i > 5) ? fama.i : famaseed; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fama); - Fama.Add(res, update); - double _mama = (i > 5) ? mama.i : mamaseed; - res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mama); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +MAMA: MESA Adaptive Moving Average + Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of + high/low price that uses classic electrical radio-frequency signal processing algorithms + to reduce noise. + + KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 ) + +Sources: + https://mesasoftware.com/papers/MAMA.pdf + https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/ + + */ + +public class MAMA_Series : TSeries +{ + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + private double sumPr; + private double fastl, slowl; + private (double i, double i1, double i2, double i3, double i4, double i5, double i6, double io) pr, i1, q1, sm, dt; + private (double i, double i1, double io) i2, q2, re, im, pd, ph, mama, fama; + public TSeries Fama { get; } + private double mamaseed, famaseed; + + //core constructors + + public MAMA_Series(double fastlimit, double slowlimit, bool useNaN) + { + _period = (int)(2 / fastlimit) - 1; + fastl = fastlimit; + slowl = slowlimit; + Fama = new TSeries(); + _NaN = useNaN; + Name = $"MAMA({_period})"; + _len = 0; + } + public MAMA_Series(TSeries source, double fastlimit, double slowlimit, bool useNaN = false) : this(fastlimit, slowlimit, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAMA_Series() : this(period: 0, useNaN: false) { } + public MAMA_Series(int period) : this(period, useNaN: false) { } + public MAMA_Series(int period, bool useNaN) : this(fastlimit: 2 / (period + 1), slowlimit: 0.2 / (period + 1), useNaN) + { + _period = period; + } + public MAMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } + public MAMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAMA_Series(TSeries source, int period) : this(source, period, false) { } + public MAMA_Series(TSeries source, int period, bool useNaN) : this(source, fastlimit: 2 / ((double)period + 1), slowlimit: 0.2 / ((double)period + 1), useNaN: useNaN) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (!update) + { + // roll forward (oldx = x) + pr.io = pr.i6; pr.i6 = pr.i5; pr.i5 = pr.i4; pr.i4 = pr.i3; pr.i3 = pr.i2; pr.i2 = pr.i1; pr.i1 = pr.i; + i1.io = i1.i6; i1.i6 = i1.i5; i1.i5 = i1.i4; i1.i4 = i1.i3; i1.i3 = i1.i2; i1.i2 = i1.i1; i1.i1 = i1.i; + q1.io = q1.i6; q1.i6 = q1.i5; q1.i5 = q1.i4; q1.i4 = q1.i3; q1.i3 = q1.i2; q1.i2 = q1.i1; q1.i1 = q1.i; + dt.io = dt.i6; dt.i6 = dt.i5; dt.i5 = dt.i4; dt.i4 = dt.i3; dt.i3 = dt.i2; dt.i2 = dt.i1; dt.i1 = dt.i; + sm.io = sm.i6; sm.i6 = sm.i5; sm.i5 = sm.i4; sm.i4 = sm.i3; sm.i3 = sm.i2; sm.i2 = sm.i1; sm.i1 = sm.i; + i2.io = i2.i1; i2.i1 = i2.i; q2.io = q2.i1; q2.i1 = q2.i; + re.io = re.i1; re.i1 = re.i; im.io = im.i1; im.i1 = im.i; + pd.io = pd.i1; pd.i1 = pd.i; ph.io = ph.i1; ph.i1 = ph.i; + mama.io = mama.i1; mama.i1 = mama.i; + fama.io = fama.i1; + fama.i1 = fama.i; + _len++; + } + if (_period == 0) + { + fastl = 2 / (double)_len; + slowl = fastl * 0.1; + } + if (_period == 1) + { + fastl = 1; + slowl = 1; + } + var i = _len - 1; + pr.i = TValue.v; + if (i > 5) + { + var adj = 0.075 * pd.i1 + 0.54; + + // smooth and detrender + sm.i = (4 * pr.i + 3 * pr.i1 + 2 * pr.i2 + pr.i3) / 10; + dt.i = (0.0962 * sm.i + 0.5769 * sm.i2 - 0.5769 * sm.i4 - 0.0962 * sm.i6) * adj; + + // in-phase and quadrature + q1.i = (0.0962 * dt.i + 0.5769 * dt.i2 - 0.5769 * dt.i4 - 0.0962 * dt.i6) * adj; + i1.i = dt.i3; + + // advance the phases by 90 degrees + double jI = (0.0962 * i1.i + 0.5769 * i1.i2 - 0.5769 * i1.i4 - 0.0962 * i1.i6) * adj; + double jQ = (0.0962 * q1.i + 0.5769 * q1.i2 - 0.5769 * q1.i4 - 0.0962 * q1.i6) * adj; + + // phasor addition for 3-bar averaging + i2.i = i1.i - jQ; + q2.i = q1.i + jI; + + i2.i = 0.2 * i2.i + 0.8 * i2.i1; // smoothing it + q2.i = 0.2 * q2.i + 0.8 * q2.i1; + + // homodyne discriminator + re.i = i2.i * i2.i1 + q2.i * q2.i1; + im.i = i2.i * q2.i1 - q2.i * i2.i1; + + re.i = 0.2 * re.i + 0.8 * re.i1; // smoothing it + im.i = 0.2 * im.i + 0.8 * im.i1; + + // calculate period + pd.i = im.i != 0 && re.i != 0 ? 6.283185307179586 / Math.Atan(im.i / re.i) : 0d; + + // adjust period to thresholds + pd.i = pd.i > 1.5 * pd.i1 ? 1.5 * pd.i1 : pd.i; + pd.i = pd.i < 0.67 * pd.i1 ? 0.67 * pd.i1 : pd.i; + pd.i = pd.i < 6d ? 6d : pd.i; + pd.i = pd.i > 50d ? 50d : pd.i; + + // smooth the period + pd.i = 0.2 * pd.i + 0.8 * pd.i1; + + // determine phase position + ph.i = i1.i != 0 ? Math.Atan(q1.i / i1.i) * 57.29577951308232 : 0; + + // change in phase + var delta = Math.Max(ph.i1 - ph.i, 1d); + + // adaptive alpha value + var alpha = Math.Max(fastl / delta, slowl); + + // final indicators + mama.i = alpha * (pr.i - mama.i1) + mama.i1; + fama.i = 0.5d * alpha * (mama.i - fama.i1) + fama.i1; + } + else + { + sumPr += pr.i; + pd.i = sm.i = dt.i = i1.i = q1.i = i2.i = q2.i = re.i = im.i = ph.i = 0; + mama.i = fama.i = sumPr / (i + 1); + + if (_len == 1) + { + mamaseed = famaseed = TValue.v; + } + else + { + mamaseed = fastl * (TValue.v - mamaseed) + mamaseed; + famaseed = slowl * (TValue.v - famaseed) + famaseed; + } + } + + double _fama = (i > 5) ? fama.i : famaseed; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fama); + Fama.Add(res, update); + double _mama = (i > 5) ? mama.i : mamaseed; + res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mama); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/MAPE_Series.cs b/Calculations/_Updated/MAPE_Series.cs index 43a6a9c6..b2869c63 100644 --- a/Calculations/_Updated/MAPE_Series.cs +++ b/Calculations/_Updated/MAPE_Series.cs @@ -1,85 +1,95 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MAPE: Mean Absolute Percentage Error - Measures the size of the error in percentage terms - -Calculation: - MAPE = Σ(|close – SMA| / |close|) / n - -Sources: - https://en.wikipedia.org/wiki/Mean_absolute_percentage_error - -Remark: - returns infinity if any of observations is 0. - Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE - - */ - -public class MAPE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MAPE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MAPE({period})"; - } - public MAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MAPE_Series() : this(period: 0, useNaN: false) { } - public MAPE_Series(int period) : this(period: period, useNaN: false) { } - public MAPE_Series(TBars source) : this(source.Close, 0, false) { } - public MAPE_Series(TBars source, int period) : this(source.Close, period, false) { } - public MAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MAPE_Series(TSeries source) : this(source, 0, false) { } - public MAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _mape = 0; - for (int i = 0; i < _buffer.Count; i++) { - _mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity; - } - _mape /= (_buffer.Count > 0) ? _buffer.Count : 1; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mape); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAPE: Mean Absolute Percentage Error + Measures the size of the error in percentage terms + +Calculation: + MAPE = Σ(|close – SMA| / |close|) / n + +Sources: + https://en.wikipedia.org/wiki/Mean_absolute_percentage_error + +Remark: + returns infinity if any of observations is 0. + Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE + + */ + +public class MAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAPE({period})"; + } + public MAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAPE_Series() : this(period: 0, useNaN: false) { } + public MAPE_Series(int period) : this(period: period, useNaN: false) { } + public MAPE_Series(TBars source) : this(source.Close, 0, false) { } + public MAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAPE_Series(TSeries source) : this(source, 0, false) { } + public MAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mape = 0; + for (int i = 0; i < _buffer.Count; i++) + { + _mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity; + } + _mape /= (_buffer.Count > 0) ? _buffer.Count : 1; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MAX_Series.cs b/Calculations/_Updated/MAX_Series.cs index f199a329..4c96e41e 100644 --- a/Calculations/_Updated/MAX_Series.cs +++ b/Calculations/_Updated/MAX_Series.cs @@ -1,68 +1,77 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MAX - Maximum value in the given period in the series. - If period = 0 => period = full length of the series - - */ - -public class MAX_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MAX_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MAX({period})"; - } - public MAX_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MAX_Series() : this(period: 0, useNaN: false) { } - public MAX_Series(int period) : this(period: period, useNaN: false) { } - public MAX_Series(TBars source) : this(source.Close, 0, false) { } - public MAX_Series(TBars source, int period) : this(source.Close, period, false) { } - public MAX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MAX_Series(TSeries source) : this(source, 0, false) { } - public MAX_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _max= _buffer.Max(); - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAX - Maximum value in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MAX_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAX_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAX({period})"; + } + public MAX_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAX_Series() : this(period: 0, useNaN: false) { } + public MAX_Series(int period) : this(period: period, useNaN: false) { } + public MAX_Series(TBars source) : this(source.Close, 0, false) { } + public MAX_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAX_Series(TSeries source) : this(source, 0, false) { } + public MAX_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Max(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MEDIAN_Series.cs b/Calculations/_Updated/MEDIAN_Series.cs index 0df36399..e896d1ce 100644 --- a/Calculations/_Updated/MEDIAN_Series.cs +++ b/Calculations/_Updated/MEDIAN_Series.cs @@ -1,86 +1,95 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MED - Median value - Median of numbers is the middlemost value of the given set of numbers. - It separates the higher half and the lower half of a given data sample. - At least half of the observations are smaller than or equal to median - and at least half of the observations are greater than or equal to the median. - - If the number of values is odd, the middlemost observation of the sorted - list is the median of the given data. If the number of values is even, - median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list. - - If period = 0 => period is max - -Sources: - https://corporatefinanceinstitute.com/resources/knowledge/other/median/ - https://en.wikipedia.org/wiki/Median - - */ - -public class MEDIAN_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MEDIAN_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MEDIAN({period})"; - } - public MEDIAN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MEDIAN_Series() : this(period: 0, useNaN: false) { } - public MEDIAN_Series(int period) : this(period: period, useNaN: false) { } - public MEDIAN_Series(TBars source) : this(source.Close, 0, false) { } - public MEDIAN_Series(TBars source, int period) : this(source.Close, period, false) { } - public MEDIAN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MEDIAN_Series(TSeries source) : this(source, 0, false) { } - public MEDIAN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - System.Collections.Generic.List _s = new(this._buffer); - _s.Sort(); - int _p1 = _s.Count / 2; - int _p2 = Math.Max(0, (_s.Count / 2) - 1); - double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _med); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MED - Median value + Median of numbers is the middlemost value of the given set of numbers. + It separates the higher half and the lower half of a given data sample. + At least half of the observations are smaller than or equal to median + and at least half of the observations are greater than or equal to the median. + + If the number of values is odd, the middlemost observation of the sorted + list is the median of the given data. If the number of values is even, + median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list. + + If period = 0 => period is max + +Sources: + https://corporatefinanceinstitute.com/resources/knowledge/other/median/ + https://en.wikipedia.org/wiki/Median + + */ + +public class MEDIAN_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MEDIAN_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MEDIAN({period})"; + } + public MEDIAN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MEDIAN_Series() : this(period: 0, useNaN: false) { } + public MEDIAN_Series(int period) : this(period: period, useNaN: false) { } + public MEDIAN_Series(TBars source) : this(source.Close, 0, false) { } + public MEDIAN_Series(TBars source, int period) : this(source.Close, period, false) { } + public MEDIAN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MEDIAN_Series(TSeries source) : this(source, 0, false) { } + public MEDIAN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + System.Collections.Generic.List _s = new(this._buffer); + _s.Sort(); + int _p1 = _s.Count / 2; + int _p2 = Math.Max(0, (_s.Count / 2) - 1); + double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _med); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MIDPOINT_Series.cs b/Calculations/_Updated/MIDPOINT_Series.cs index 46bdd221..ca27243a 100644 --- a/Calculations/_Updated/MIDPOINT_Series.cs +++ b/Calculations/_Updated/MIDPOINT_Series.cs @@ -1,72 +1,81 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MIDPOINT: Midpoint value (max+min)/2 in the given period in the series. - If period = 0 => period = full length of the series - -Sources: - https://thefaqblog.com/what-is-the-midpoint-in-statistics/ - - */ - -public class MIDPOINT_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MIDPOINT_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MIDPOINT({period})"; - } - public MIDPOINT_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MIDPOINT_Series() : this(period: 0, useNaN: false) { } - public MIDPOINT_Series(int period) : this(period: period, useNaN: false) { } - public MIDPOINT_Series(TBars source) : this(source.Close, 0, false) { } - public MIDPOINT_Series(TBars source, int period) : this(source.Close, period, false) { } - public MIDPOINT_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MIDPOINT_Series(TSeries source) : this(source, 0, false) { } - public MIDPOINT_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _max= _buffer.Max(); - double _min = _buffer.Min(); - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : (_max+_min)*0.5); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MIDPOINT: Midpoint value (max+min)/2 in the given period in the series. + If period = 0 => period = full length of the series + +Sources: + https://thefaqblog.com/what-is-the-midpoint-in-statistics/ + + */ + +public class MIDPOINT_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MIDPOINT_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MIDPOINT({period})"; + } + public MIDPOINT_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MIDPOINT_Series() : this(period: 0, useNaN: false) { } + public MIDPOINT_Series(int period) : this(period: period, useNaN: false) { } + public MIDPOINT_Series(TBars source) : this(source.Close, 0, false) { } + public MIDPOINT_Series(TBars source, int period) : this(source.Close, period, false) { } + public MIDPOINT_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MIDPOINT_Series(TSeries source) : this(source, 0, false) { } + public MIDPOINT_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Max(); + double _min = _buffer.Min(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : (_max + _min) * 0.5); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MIDPRICE_Series.cs b/Calculations/_Updated/MIDPRICE_Series.cs index 1bb914a8..099f90c8 100644 --- a/Calculations/_Updated/MIDPRICE_Series.cs +++ b/Calculations/_Updated/MIDPRICE_Series.cs @@ -1,65 +1,74 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -MIDPRICE: Midpoint price (highhest high + lowest low)/2 in the given period in the series. - If period = 0 => period = full length of the series - - */ - -public class MIDPRICE_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TBars _data; - private readonly System.Collections.Generic.List _bufferhi = new(); - private readonly System.Collections.Generic.List _bufferlo = new(); - - //core constructors - public MIDPRICE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MIDPRICE({period})"; - } - public MIDPRICE_Series(TBars source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(data: _data); - } - public MIDPRICE_Series() : this(period: 2, useNaN: false) { } - public MIDPRICE_Series(int period) : this(period: period, useNaN: false) { } - public MIDPRICE_Series(TBars source) : this(source, period: 2, useNaN: false) { } - public MIDPRICE_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - BufferTrim(_bufferhi, TBar.h, _period, update); - BufferTrim(_bufferlo, TBar.l, _period, update); - double _mid = (_bufferhi.Max() + _bufferlo.Min()) * 0.5; - - var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _mid); - return base.Add(res, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _bufferhi.Clear(); - _bufferlo.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +MIDPRICE: Midpoint price (highhest high + lowest low)/2 in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MIDPRICE_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private readonly System.Collections.Generic.List _bufferhi = new(); + private readonly System.Collections.Generic.List _bufferlo = new(); + + //core constructors + public MIDPRICE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MIDPRICE({period})"; + } + public MIDPRICE_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public MIDPRICE_Series() : this(period: 2, useNaN: false) { } + public MIDPRICE_Series(int period) : this(period: period, useNaN: false) { } + public MIDPRICE_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public MIDPRICE_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + BufferTrim(_bufferhi, TBar.h, _period, update); + BufferTrim(_bufferlo, TBar.l, _period, update); + double _mid = (_bufferhi.Max() + _bufferlo.Min()) * 0.5; + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _mid); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _bufferhi.Clear(); + _bufferlo.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MIN_Series.cs b/Calculations/_Updated/MIN_Series.cs index 3f30ffbe..4bb44ca9 100644 --- a/Calculations/_Updated/MIN_Series.cs +++ b/Calculations/_Updated/MIN_Series.cs @@ -1,68 +1,77 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MIN - Minimum value in the given period in the series. - If period = 0 => period = full length of the series - - */ - -public class MIN_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MIN_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MAX({period})"; - } - public MIN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MIN_Series() : this(period: 0, useNaN: false) { } - public MIN_Series(int period) : this(period: period, useNaN: false) { } - public MIN_Series(TBars source) : this(source.Close, 0, false) { } - public MIN_Series(TBars source, int period) : this(source.Close, period, false) { } - public MIN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MIN_Series(TSeries source) : this(source, 0, false) { } - public MIN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _max= _buffer.Min(); - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MIN - Minimum value in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MIN_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MIN_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAX({period})"; + } + public MIN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MIN_Series() : this(period: 0, useNaN: false) { } + public MIN_Series(int period) : this(period: period, useNaN: false) { } + public MIN_Series(TBars source) : this(source.Close, 0, false) { } + public MIN_Series(TBars source, int period) : this(source.Close, period, false) { } + public MIN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MIN_Series(TSeries source) : this(source, 0, false) { } + public MIN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Min(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/MSE_Series.cs b/Calculations/_Updated/MSE_Series.cs index 262cbd6a..7be1a30a 100644 --- a/Calculations/_Updated/MSE_Series.cs +++ b/Calculations/_Updated/MSE_Series.cs @@ -1,76 +1,85 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -MSE: Mean Square Error - Defined as a Mean (Average) of the Square of the difference between actual and estimated values. - -Sources: - https://en.wikipedia.org/wiki/Mean_squared_error - - */ - -public class MSE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public MSE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"MSE({period})"; - } - public MSE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public MSE_Series() : this(period: 0, useNaN: false) { } - public MSE_Series(int period) : this(period: period, useNaN: false) { } - public MSE_Series(TBars source) : this(source.Close, 0, false) { } - public MSE_Series(TBars source, int period) : this(source.Close, period, false) { } - public MSE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public MSE_Series(TSeries source) : this(source, 0, false) { } - public MSE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _mse = 0; - for (int i = 0; i < _buffer.Count; i++) { _mse += (_buffer[i] - _sma) * (_buffer[i] - _sma); } - _mse /= this._buffer.Count; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mse); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MSE: Mean Square Error + Defined as a Mean (Average) of the Square of the difference between actual and estimated values. + +Sources: + https://en.wikipedia.org/wiki/Mean_squared_error + + */ + +public class MSE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MSE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MSE({period})"; + } + public MSE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MSE_Series() : this(period: 0, useNaN: false) { } + public MSE_Series(int period) : this(period: period, useNaN: false) { } + public MSE_Series(TBars source) : this(source.Close, 0, false) { } + public MSE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MSE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MSE_Series(TSeries source) : this(source, 0, false) { } + public MSE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mse = 0; + for (int i = 0; i < _buffer.Count; i++) { _mse += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _mse /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mse); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/OBV_Series.cs b/Calculations/_Updated/OBV_Series.cs index 57531bdd..f589cc3b 100644 --- a/Calculations/_Updated/OBV_Series.cs +++ b/Calculations/_Updated/OBV_Series.cs @@ -1,96 +1,106 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -OBV: On-Balance Volume - On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict - changes in stock price. Joseph Granville first developed the OBV metric in the 1963 book - Granville's New Key to Stock Market Profits. - - | +volume; if close > close[previous] - OBV = OBV[previous] + | 0; if close = close[previous] - | -volume; if close < close[previous] - -Sources: - https://www.investopedia.com/terms/o/onbalancevolume.asp - https://www.tradingview.com/wiki/On_Balance_Volume_(OBV) - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/ - https://www.motivewave.com/studies/on_balance_volume.htm - -Note: - There is no consensus on what is the first OBV value in the series: - - TA-LIB uses the first volume: OBV[0] = volume[0] - - Skender stock library uses 0: OBV[0] = 0 - - */ - -public class OBV_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TBars _data; - private double _lastobv, _lastlastobv; - private double _lastclose, _lastlastclose; - - //core constructors - public OBV_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"OBV({period})"; - this._lastobv = this._lastlastobv = 0; - this._lastclose = this._lastlastclose = 0; - } - public OBV_Series(TBars source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(data: _data); - } - public OBV_Series() : this(period: 2, useNaN: false) { } - public OBV_Series(int period) : this(period: period, useNaN: false) { } - public OBV_Series(TBars source) : this(source, period: 2, useNaN: false) { } - public OBV_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - - if (update) { - this._lastobv = this._lastlastobv; - this._lastclose = this._lastlastclose; - } - - double _obv = this._lastobv; - if (TBar.c > this._lastclose) { _obv += TBar.v; } - if (TBar.c < this._lastclose) { _obv -= TBar.v; } - - this._lastlastobv = this._lastobv; - this._lastobv = _obv; - - this._lastlastclose = this._lastclose; - this._lastclose = TBar.c; - - var res = (TBar.t, (this.Count < this._period && this._NaN) ? double.NaN : _obv); - return base.Add(res, update); - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - this._lastobv = this._lastlastobv = 0; - this._lastclose = this._lastlastclose = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +OBV: On-Balance Volume + On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict + changes in stock price. Joseph Granville first developed the OBV metric in the 1963 book + Granville's New Key to Stock Market Profits. + + | +volume; if close > close[previous] + OBV = OBV[previous] + | 0; if close = close[previous] + | -volume; if close < close[previous] + +Sources: + https://www.investopedia.com/terms/o/onbalancevolume.asp + https://www.tradingview.com/wiki/On_Balance_Volume_(OBV) + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/ + https://www.motivewave.com/studies/on_balance_volume.htm + +Note: + There is no consensus on what is the first OBV value in the series: + - TA-LIB uses the first volume: OBV[0] = volume[0] + - Skender stock library uses 0: OBV[0] = 0 + + */ + +public class OBV_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _lastobv, _lastlastobv; + private double _lastclose, _lastlastclose; + + //core constructors + public OBV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"OBV({period})"; + this._lastobv = this._lastlastobv = 0; + this._lastclose = this._lastlastclose = 0; + } + public OBV_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public OBV_Series() : this(period: 2, useNaN: false) { } + public OBV_Series(int period) : this(period: period, useNaN: false) { } + public OBV_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public OBV_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + this._lastobv = this._lastlastobv; + this._lastclose = this._lastlastclose; + } + + double _obv = this._lastobv; + if (TBar.c > this._lastclose) { _obv += TBar.v; } + if (TBar.c < this._lastclose) { _obv -= TBar.v; } + + this._lastlastobv = this._lastobv; + this._lastobv = _obv; + + this._lastlastclose = this._lastclose; + this._lastclose = TBar.c; + + var res = (TBar.t, (this.Count < this._period && this._NaN) ? double.NaN : _obv); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + this._lastobv = this._lastlastobv = 0; + this._lastclose = this._lastlastclose = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/RMA_Series.cs b/Calculations/_Updated/RMA_Series.cs index bd84105f..217c895d 100644 --- a/Calculations/_Updated/RMA_Series.cs +++ b/Calculations/_Updated/RMA_Series.cs @@ -1,116 +1,133 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -RMA: wildeR Moving Average - J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is - set as 1/period, giving less weight to the new data compared to EMA. - -Sources: - https://archive.org/details/newconceptsintec00wild/page/23/mode/2up - https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing - https://www.incrediblecharts.com/indicators/wilder_moving_average.php - -Issues: - Pandas-TA library calculates RMA using straight Exponential Weighted Mean: - pandas.ewm().mean() and returns incorrect first (period) of bars compared to - published formula. This implementation passess the validation test in Wilder's book. - - */ - -public class RMA_Series : TSeries { - private double _k; - private double _lastrma, _oldrma; - private double _sum, _oldsum; - private readonly bool _useSMA; - private int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - -//core constructor - public RMA_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - _useSMA = useSMA; - Name = $"RMA({period})"; - _k = 1.0 / (double)(this._period); - _len = 0; - _sum = _oldsum = _lastrma = _oldrma = 0; - } - //generic constructors (source) - - public RMA_Series() : this(0, false, true) {} - public RMA_Series(int period) : this(period, false, true) {} - public RMA_Series(TBars source) : this(source.Close, 0, false) {} - public RMA_Series(TBars source, int period) : this(source.Close, period, false) {} - public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public RMA_Series(TSeries source, int period) : this(source, period, false, true) {} - public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {} - public RMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - -// core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - _lastrma = _oldrma; - _sum = _oldsum; - } - else { - _oldrma = _lastrma; - _oldsum = _sum; - _len++; - } - - double _rma = 0; - if (_period == 0) { - _k = 1.0 / (double)(this._len); - } - - if (Count == 0) { - _rma = _sum = TValue.v; - - } else if (_len <= _period && _useSMA && _period != 0) { - _sum += TValue.v; - if (_period != 0 && _len > _period) { - _sum -= _data[Count - _period - (update ? 1 : 0)].v; - } - _rma = _sum / Math.Min(_len, _period); - } - else { - _rma = _k * (TValue.v - _lastrma) + _lastrma; - } - - _lastrma = double.IsNaN(_rma) ? _lastrma : _rma; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma); - return base.Add(res, update); - } - -//variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _sum = _oldsum = _lastrma = _oldrma = 0; - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +RMA: wildeR Moving Average + J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is + set as 1/period, giving less weight to the new data compared to EMA. + +Sources: + https://archive.org/details/newconceptsintec00wild/page/23/mode/2up + https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing + https://www.incrediblecharts.com/indicators/wilder_moving_average.php + +Issues: + Pandas-TA library calculates RMA using straight Exponential Weighted Mean: + pandas.ewm().mean() and returns incorrect first (period) of bars compared to + published formula. This implementation passess the validation test in Wilder's book. + + */ + +public class RMA_Series : TSeries +{ + private double _k; + private double _lastrma, _oldrma; + private double _sum, _oldsum; + private readonly bool _useSMA; + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public RMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"RMA({period})"; + _k = 1.0 / (double)(this._period); + _len = 0; + _sum = _oldsum = _lastrma = _oldrma = 0; + } + //generic constructors (source) + + public RMA_Series() : this(0, false, true) { } + public RMA_Series(int period) : this(period, false, true) { } + public RMA_Series(TBars source) : this(source.Close, 0, false) { } + public RMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public RMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public RMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastrma = _oldrma; + _sum = _oldsum; + } + else + { + _oldrma = _lastrma; + _oldsum = _sum; + _len++; + } + + double _rma = 0; + if (_period == 0) + { + _k = 1.0 / (double)(this._len); + } + + if (Count == 0) + { + _rma = _sum = TValue.v; + + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + if (_period != 0 && _len > _period) + { + _sum -= _data[Count - _period - (update ? 1 : 0)].v; + } + _rma = _sum / Math.Min(_len, _period); + } + else + { + _rma = _k * (TValue.v - _lastrma) + _lastrma; + } + + _lastrma = double.IsNaN(_rma) ? _lastrma : _rma; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastrma = _oldrma = 0; + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/RSI_Series.cs b/Calculations/_Updated/RSI_Series.cs index 7acb6daa..ee9c883b 100644 --- a/Calculations/_Updated/RSI_Series.cs +++ b/Calculations/_Updated/RSI_Series.cs @@ -1,120 +1,134 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -RSI: Relative Strength Index - Created by J. Welles Wilder, the Relative Strength Index measures strength - of the winning/losing streak over N lookback periods on a scale of 0 to 100, - to depict overbought and oversold conditions. - -Sources: - https://www.investopedia.com/terms/r/rsi.asp - - */ - -public class RSI_Series : TSeries { - private readonly System.Collections.Generic.List _gain = new(); - private readonly System.Collections.Generic.List _loss = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private double _avgGain, _avgLoss, _lastValue; - private double _avgGain_o, _avgLoss_o, _lastValue_o; - private int i; - - //core constructors - public RSI_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"RSI({period})"; - i = 0; - } - public RSI_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public RSI_Series() : this(period: 0, useNaN: false) { } - public RSI_Series(int period) : this(period: period, useNaN: false) { } - public RSI_Series(TBars source) : this(source.Close, 0, false) { } - public RSI_Series(TBars source, int period) : this(source.Close, period, false) { } - public RSI_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public RSI_Series(TSeries source) : this(source, 0, false) { } - public RSI_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - - double _rsi = 0; - if (update) { - _lastValue = _lastValue_o; - _avgGain = _avgGain_o; - _avgLoss = _avgLoss_o; - } - else { - _lastValue_o = _lastValue; - _avgGain_o = _avgGain; - _avgLoss_o = _avgLoss; - } - - if (i == 0) { _lastValue = TValue.v; } - - double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0; - BufferTrim(_gain, _gainval, _period, update); - double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0; - BufferTrim(_loss, _lossval, _period, update); - _lastValue = TValue.v; - - // calculate RSI - if (i > _period && _period != 0) { - _avgGain = ((_avgGain * (_period - 1)) + _gain[^1]) / _period; - _avgLoss = ((_avgLoss * (_period - 1)) + _loss[^1]) / _period; - if (_avgLoss > 0) { - double rs = _avgGain / _avgLoss; - _rsi = 100 - (100 / (1 + rs)); - } - else { _rsi = 100; } - } - // initialize average gain - else { - double _sumGain = 0; - for (int p = 0; p < _gain.Count; p++) { _sumGain += _gain[p]; } - double _sumLoss = 0; - for (int p = 0; p < _loss.Count; p++) { _sumLoss += _loss[p]; } - - _avgGain = _sumGain / _gain.Count; - _avgLoss = _sumLoss / _loss.Count; - - _rsi = (_avgLoss > 0) ? 100 - (100 / (1 + (_avgGain / _avgLoss))) : 100; - } - if (!update) { i++; } - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rsi); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - i = 0; - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +RSI: Relative Strength Index + Created by J. Welles Wilder, the Relative Strength Index measures strength + of the winning/losing streak over N lookback periods on a scale of 0 to 100, + to depict overbought and oversold conditions. + +Sources: + https://www.investopedia.com/terms/r/rsi.asp + + */ + +public class RSI_Series : TSeries +{ + private readonly System.Collections.Generic.List _gain = new(); + private readonly System.Collections.Generic.List _loss = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _avgGain, _avgLoss, _lastValue; + private double _avgGain_o, _avgLoss_o, _lastValue_o; + private int i; + + //core constructors + public RSI_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"RSI({period})"; + i = 0; + } + public RSI_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public RSI_Series() : this(period: 0, useNaN: false) { } + public RSI_Series(int period) : this(period: period, useNaN: false) { } + public RSI_Series(TBars source) : this(source.Close, 0, false) { } + public RSI_Series(TBars source, int period) : this(source.Close, period, false) { } + public RSI_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public RSI_Series(TSeries source) : this(source, 0, false) { } + public RSI_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + + double _rsi = 0; + if (update) + { + _lastValue = _lastValue_o; + _avgGain = _avgGain_o; + _avgLoss = _avgLoss_o; + } + else + { + _lastValue_o = _lastValue; + _avgGain_o = _avgGain; + _avgLoss_o = _avgLoss; + } + + if (i == 0) { _lastValue = TValue.v; } + + double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0; + BufferTrim(_gain, _gainval, _period, update); + double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0; + BufferTrim(_loss, _lossval, _period, update); + _lastValue = TValue.v; + + // calculate RSI + if (i > _period && _period != 0) + { + _avgGain = ((_avgGain * (_period - 1)) + _gain[^1]) / _period; + _avgLoss = ((_avgLoss * (_period - 1)) + _loss[^1]) / _period; + if (_avgLoss > 0) + { + double rs = _avgGain / _avgLoss; + _rsi = 100 - (100 / (1 + rs)); + } + else { _rsi = 100; } + } + // initialize average gain + else + { + double _sumGain = 0; + for (int p = 0; p < _gain.Count; p++) { _sumGain += _gain[p]; } + double _sumLoss = 0; + for (int p = 0; p < _loss.Count; p++) { _sumLoss += _loss[p]; } + + _avgGain = _sumGain / _gain.Count; + _avgLoss = _sumLoss / _loss.Count; + + _rsi = (_avgLoss > 0) ? 100 - (100 / (1 + (_avgGain / _avgLoss))) : 100; + } + if (!update) { i++; } + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rsi); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + i = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/SDEV_Series.cs b/Calculations/_Updated/SDEV_Series.cs index 4b5a91be..b7b5cc1a 100644 --- a/Calculations/_Updated/SDEV_Series.cs +++ b/Calculations/_Updated/SDEV_Series.cs @@ -1,82 +1,91 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -SDEV: Population Standard Deviation - Population Standard Deviation is the square root of the biased variance, also knons as - Uncorrected Sample Standard Deviation - -Sources: - https://en.wikipedia.org/wiki/Standard_deviation#Uncorrected_sample_standard_deviation - -Remark: - SDEV (Population Standard Deviation) is also known as a biased/uncorrected Standard Deviation. - For unbiased version that uses Bessel's correction, use SDEV instead. - - */ - -public class SDEV_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public SDEV_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SDEV({period})"; - } - public SDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SDEV_Series() : this(period: 0, useNaN: false) { } - public SDEV_Series(int period) : this(period: period, useNaN: false) { } - public SDEV_Series(TBars source) : this(source.Close, 0, false) { } - public SDEV_Series(TBars source, int period) : this(source.Close, period, false) { } - public SDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SDEV_Series(TSeries source) : this(source, 0, false) { } - public SDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _var = 0; - for (int i = 0; i < _buffer.Count; i++) { _var += (_buffer[i] - _sma) * (_buffer[i] - _sma); } - _var /= this._buffer.Count; - double _sdev = Math.Sqrt(_var); - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sdev); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SDEV: Population Standard Deviation + Population Standard Deviation is the square root of the biased variance, also knons as + Uncorrected Sample Standard Deviation + +Sources: + https://en.wikipedia.org/wiki/Standard_deviation#Uncorrected_sample_standard_deviation + +Remark: + SDEV (Population Standard Deviation) is also known as a biased/uncorrected Standard Deviation. + For unbiased version that uses Bessel's correction, use SDEV instead. + + */ + +public class SDEV_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SDEV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SDEV({period})"; + } + public SDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SDEV_Series() : this(period: 0, useNaN: false) { } + public SDEV_Series(int period) : this(period: period, useNaN: false) { } + public SDEV_Series(TBars source) : this(source.Close, 0, false) { } + public SDEV_Series(TBars source, int period) : this(source.Close, period, false) { } + public SDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SDEV_Series(TSeries source) : this(source, 0, false) { } + public SDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _var = 0; + for (int i = 0; i < _buffer.Count; i++) { _var += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _var /= this._buffer.Count; + double _sdev = Math.Sqrt(_var); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sdev); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/SLOPE_Series.cs b/Calculations/_Updated/SLOPE_Series.cs index c9fc3eae..8d7580c2 100644 --- a/Calculations/_Updated/SLOPE_Series.cs +++ b/Calculations/_Updated/SLOPE_Series.cs @@ -1,122 +1,130 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -SLOPE: Slope of linear regression (using Least Square Method) - Linear Regression provides a slope of a straight line that is the best approximation of the given set of data. - The method of least squares is a standard approach in linear regression analysis to approximate the solution - by minimizing the sum of the squares of the residuals made in the results of each individual equation. - -Additional outputs provided by LINREG: - .Intercept - y-intercept point of the best fit line - .RSquared - R-Squared (R²), Coefficient of Determination - .StdDev - Standard Deviation of data over given periods - - y = Slope * x + Intercept - -Sources: - https://en.wikipedia.org/wiki/Least_squares - - */ - -public class SLOPE_Series : TSeries { - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly TSeries p_Intercept = new(); - private readonly TSeries p_RSquared = new(); - private readonly TSeries p_StdDev = new(); - private readonly System.Collections.Generic.List _buffer = new(); - public TSeries Intercept => p_Intercept; - public TSeries RSquared => p_RSquared; - public TSeries StdDev => p_StdDev; - //core constructors - public SLOPE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SLOPE({period})"; - } - public SLOPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SLOPE_Series() : this(period: 0, useNaN: false) { } - public SLOPE_Series(int period) : this(period: period, useNaN: false) { } - public SLOPE_Series(TBars source) : this(source.Close, 0, false) { } - public SLOPE_Series(TBars source, int period) : this(source.Close, period, false) { } - public SLOPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SLOPE_Series(TSeries source) : this(source, 0, false) { } - public SLOPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - int _len = this._buffer.Count; - - // get averages for period - double sumX = 0; - double sumY = 0; - - for (int p = 0; p < _len; p++) { - sumX += this.Count - _len + 2 + p; - sumY += _buffer[p]; - } - double avgX = sumX / _len; - double avgY = sumY / _len; - - // least squares method - double sumSqX = 0; - double sumSqY = 0; - double sumSqXY = 0; - - for (int p = 0; p < _len; p++) { - double devX = this.Count - _len + 2 + p - avgX; - double devY = _buffer[p] - avgY; - - sumSqX += devX * devX; - sumSqY += devY * devY; - sumSqXY += devX * devY; - } - - double _slope = sumSqXY / sumSqX; - double _intercept = avgY - (_slope * avgX); - - // calculate Standard Deviation and R-Squared - double stdDevX = Math.Sqrt(sumSqX / _len); - double stdDevY = Math.Sqrt(sumSqY / _len); - double _StdDev = stdDevY; - - double arrr = (stdDevX * stdDevY != 0) ? sumSqXY / (stdDevX * stdDevY) / _len : 0; - double _RSquared = arrr * arrr; - - var ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _intercept); - p_Intercept.Add(ret, update); - - ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _StdDev); - p_StdDev.Add(ret, update); - - ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _RSquared); - p_RSquared.Add(ret, update); - - ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _slope); - return base.Add(ret, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SLOPE: Slope of linear regression (using Least Square Method) + Linear Regression provides a slope of a straight line that is the best approximation of the given set of data. + The method of least squares is a standard approach in linear regression analysis to approximate the solution + by minimizing the sum of the squares of the residuals made in the results of each individual equation. + +Additional outputs provided by LINREG: + .Intercept - y-intercept point of the best fit line + .RSquared - R-Squared (R²), Coefficient of Determination + .StdDev - Standard Deviation of data over given periods + + y = Slope * x + Intercept + +Sources: + https://en.wikipedia.org/wiki/Least_squares + + */ + +public class SLOPE_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly TSeries p_Intercept = new(); + private readonly TSeries p_RSquared = new(); + private readonly TSeries p_StdDev = new(); + private readonly System.Collections.Generic.List _buffer = new(); + public TSeries Intercept => p_Intercept; + public TSeries RSquared => p_RSquared; + public TSeries StdDev => p_StdDev; + //core constructors + public SLOPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SLOPE({period})"; + } + public SLOPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SLOPE_Series() : this(period: 0, useNaN: false) { } + public SLOPE_Series(int period) : this(period: period, useNaN: false) { } + public SLOPE_Series(TBars source) : this(source.Close, 0, false) { } + public SLOPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public SLOPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SLOPE_Series(TSeries source) : this(source, 0, false) { } + public SLOPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + int _len = this._buffer.Count; + + // get averages for period + double sumX = 0; + double sumY = 0; + + for (int p = 0; p < _len; p++) + { + sumX += this.Count - _len + 2 + p; + sumY += _buffer[p]; + } + double avgX = sumX / _len; + double avgY = sumY / _len; + + // least squares method + double sumSqX = 0; + double sumSqY = 0; + double sumSqXY = 0; + + for (int p = 0; p < _len; p++) + { + double devX = this.Count - _len + 2 + p - avgX; + double devY = _buffer[p] - avgY; + + sumSqX += devX * devX; + sumSqY += devY * devY; + sumSqXY += devX * devY; + } + + double _slope = sumSqXY / sumSqX; + double _intercept = avgY - (_slope * avgX); + + // calculate Standard Deviation and R-Squared + double stdDevX = Math.Sqrt(sumSqX / _len); + double stdDevY = Math.Sqrt(sumSqY / _len); + double _StdDev = stdDevY; + + double arrr = (stdDevX * stdDevY != 0) ? sumSqXY / (stdDevX * stdDevY) / _len : 0; + double _RSquared = arrr * arrr; + + var ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _intercept); + p_Intercept.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _StdDev); + p_StdDev.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _RSquared); + p_RSquared.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _slope); + return base.Add(ret, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/SMAPE_Series.cs b/Calculations/_Updated/SMAPE_Series.cs index 00ff1237..082fcd26 100644 --- a/Calculations/_Updated/SMAPE_Series.cs +++ b/Calculations/_Updated/SMAPE_Series.cs @@ -1,75 +1,84 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -SMAPE: Symmetric Mean Absolute Percentage Error - Measures the size of the error in percentage terms - -Sources: - https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error - - */ - -public class SMAPE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public SMAPE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SMAPE({period})"; - } - public SMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SMAPE_Series() : this(period: 0, useNaN: false) { } - public SMAPE_Series(int period) : this(period: period, useNaN: false) { } - public SMAPE_Series(TBars source) : this(source.Close, 0, false) { } - public SMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } - public SMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SMAPE_Series(TSeries source) : this(source, 0, false) { } - public SMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - double _smape = 0; - for (int i = 0; i < _buffer.Count; i++) { _smape += Math.Abs(_buffer[i] - _sma) / (Math.Abs(_buffer[i]) + Math.Abs(_sma)); } - _smape /= this._buffer.Count; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smape); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SMAPE: Symmetric Mean Absolute Percentage Error + Measures the size of the error in percentage terms + +Sources: + https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error + + */ + +public class SMAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SMAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SMAPE({period})"; + } + public SMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMAPE_Series() : this(period: 0, useNaN: false) { } + public SMAPE_Series(int period) : this(period: period, useNaN: false) { } + public SMAPE_Series(TBars source) : this(source.Close, 0, false) { } + public SMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMAPE_Series(TSeries source) : this(source, 0, false) { } + public SMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + double _smape = 0; + for (int i = 0; i < _buffer.Count; i++) { _smape += Math.Abs(_buffer[i] - _sma) / (Math.Abs(_buffer[i]) + Math.Abs(_sma)); } + _smape /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/SMA_Series.cs b/Calculations/_Updated/SMA_Series.cs index ba25a3f6..0c5a19fa 100644 --- a/Calculations/_Updated/SMA_Series.cs +++ b/Calculations/_Updated/SMA_Series.cs @@ -1,96 +1,112 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -SMA: Simple Moving Average - The weights are equally distributed across the period, resulting in a mean() of - the data within the period - -Sources: - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ - https://stats.stackexchange.com/a/24739 - -Remark: - This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB - implementation, but it does allow incremental additions of inputs and real-time calculations of SMA() - - */ -public class SMA_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - - private double _sum, _oldsum; - private readonly int _period; - private readonly TSeries _data; - protected readonly bool _NaN; - - //core constructor - public SMA_Series(int period, bool useNaN) { - _period = Math.Max(0, period); - _NaN = useNaN; - Name = $"SMA({period})"; - _sum = _oldsum = 0; - } - public SMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SMA_Series() : this(0, false) {} - public SMA_Series(int period) : this(period, false) {} - public SMA_Series(TBars source) : this(source.Close, 0, false) {} - public SMA_Series(TBars source, int period) : this(source.Close, period, false) {} - public SMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public SMA_Series(TSeries source) : this(source, 0, false) {} - public SMA_Series(TSeries source, int period) : this(source, period, false) {} - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { return (TValue.t, double.NaN); - } else { - if (update && _buffer.Count > 0) { - _sum -= _buffer[^1]; - _buffer[^1] = TValue.v; - _oldsum = _sum; - } - else { - _buffer.Add(TValue.v); - _oldsum = _sum; - } - - _sum += TValue.v; - if (_period != 0 && _buffer.Count > _period) { - _sum -= _buffer[0]; - _buffer.RemoveAt(0); - } - } - - double _div = _period == 0 ? _buffer.Count : Math.Min(_buffer.Count, _period); - var _sma = _sum / _div; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sma); - return base.Add(res, update); - } - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - - //reset calculation - public override void Reset() { - _sum = _oldsum = 0; - _buffer.Clear(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SMA: Simple Moving Average + The weights are equally distributed across the period, resulting in a mean() of + the data within the period + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ + https://stats.stackexchange.com/a/24739 + +Remark: + This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB + implementation, but it does allow incremental additions of inputs and real-time calculations of SMA() + + */ +public class SMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + private double _sum, _oldsum; + private readonly int _period; + private readonly TSeries _data; + protected readonly bool _NaN; + + //core constructor + public SMA_Series(int period, bool useNaN) + { + _period = Math.Max(0, period); + _NaN = useNaN; + Name = $"SMA({period})"; + _sum = _oldsum = 0; + } + public SMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMA_Series() : this(0, false) { } + public SMA_Series(int period) : this(period, false) { } + public SMA_Series(TBars source) : this(source.Close, 0, false) { } + public SMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMA_Series(TSeries source) : this(source, 0, false) { } + public SMA_Series(TSeries source, int period) : this(source, period, false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return (TValue.t, double.NaN); + } + else + { + if (update && _buffer.Count > 0) + { + _sum -= _buffer[^1]; + _buffer[^1] = TValue.v; + _oldsum = _sum; + } + else + { + _buffer.Add(TValue.v); + _oldsum = _sum; + } + + _sum += TValue.v; + if (_period != 0 && _buffer.Count > _period) + { + _sum -= _buffer[0]; + _buffer.RemoveAt(0); + } + } + + double _div = _period == 0 ? _buffer.Count : Math.Min(_buffer.Count, _period); + var _sma = _sum / _div; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sma); + return base.Add(res, update); + } + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + + //reset calculation + public override void Reset() + { + _sum = _oldsum = 0; + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/SMMA_Series.cs b/Calculations/_Updated/SMMA_Series.cs index f653eab3..46b975c5 100644 --- a/Calculations/_Updated/SMMA_Series.cs +++ b/Calculations/_Updated/SMMA_Series.cs @@ -1,93 +1,105 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Linq; - -/* -SMMA: Smoothed Moving Average - The Smoothed Moving Average (SMMA) is a combination of a SMA and an EMA. It gives the recent prices - an equal weighting as the historic prices as it takes all available price data into account. - The main advantage of a smoothed moving average is that it removes short-term fluctuations. - - SMMA(i) = (SMMA-1*(N-1) + CLOSE (i)) / N - -Sources: - https://blog.earn2trade.com/smoothed-moving-average - https://guide.traderevolution.com/traderevolution/mobile-applications/phone/android/technical-indicators/moving-averages/smma-smoothed-moving-average - https://www.chartmill.com/documentation/technical-analysis-indicators/217-MOVING-AVERAGES-%7C-The-Smoothed-Moving-Average-%28SMMA%29 - - */ - -public class SMMA_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private double _lastsmma, _lastlastsmma; - - //core constructors - public SMMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SMMA({period})"; - } - public SMMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SMMA_Series() : this(period: 0, useNaN: false) { } - public SMMA_Series(int period) : this(period: period, useNaN: false) { } - public SMMA_Series(TBars source) : this(source.Close, 0, false) { } - public SMMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public SMMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SMMA_Series(TSeries source) : this(source, 0, false) { } - public SMMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, double.NaN),update); - } - - double _smma = 0; - if (update) { this._lastsmma = this._lastlastsmma; } - - if (this.Count < this._period) { - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - _smma = _buffer.Average(); - } - else { - _smma = ((_lastsmma * (_period - 1)) + TValue.v) / _period; - } - - this._lastlastsmma = this._lastsmma; - this._lastsmma = _smma; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smma); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - this._lastsmma = this._lastlastsmma = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +SMMA: Smoothed Moving Average + The Smoothed Moving Average (SMMA) is a combination of a SMA and an EMA. It gives the recent prices + an equal weighting as the historic prices as it takes all available price data into account. + The main advantage of a smoothed moving average is that it removes short-term fluctuations. + + SMMA(i) = (SMMA-1*(N-1) + CLOSE (i)) / N + +Sources: + https://blog.earn2trade.com/smoothed-moving-average + https://guide.traderevolution.com/traderevolution/mobile-applications/phone/android/technical-indicators/moving-averages/smma-smoothed-moving-average + https://www.chartmill.com/documentation/technical-analysis-indicators/217-MOVING-AVERAGES-%7C-The-Smoothed-Moving-Average-%28SMMA%29 + + */ + +public class SMMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _lastsmma, _lastlastsmma; + + //core constructors + public SMMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SMMA({period})"; + } + public SMMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMMA_Series() : this(period: 0, useNaN: false) { } + public SMMA_Series(int period) : this(period: period, useNaN: false) { } + public SMMA_Series(TBars source) : this(source.Close, 0, false) { } + public SMMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMMA_Series(TSeries source) : this(source, 0, false) { } + public SMMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + double _smma = 0; + if (update) { this._lastsmma = this._lastlastsmma; } + + if (this.Count < this._period) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + _smma = _buffer.Average(); + } + else + { + _smma = ((_lastsmma * (_period - 1)) + TValue.v) / _period; + } + + this._lastlastsmma = this._lastsmma; + this._lastsmma = _smma; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + this._lastsmma = this._lastlastsmma = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/SSDEV_Series.cs b/Calculations/_Updated/SSDEV_Series.cs index 20d41b95..490c579c 100644 --- a/Calculations/_Updated/SSDEV_Series.cs +++ b/Calculations/_Updated/SSDEV_Series.cs @@ -1,82 +1,91 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -SSDEV: (Corrected) Sample Standard Deviation - Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance. - -Sources: - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation - Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction - -Remark: - SSDEV (Sample Standard Deviation) is also known as a unbiased/corrected Standard Deviation. - For a population/biased/uncorrected Standard Deviation, use PSDEV instead - - */ - -public class SSDEV_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public SSDEV_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SSDEV({period})"; - } - public SSDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SSDEV_Series() : this(period: 0, useNaN: false) { } - public SSDEV_Series(int period) : this(period: period, useNaN: false) { } - public SSDEV_Series(TBars source) : this(source.Close, 0, false) { } - public SSDEV_Series(TBars source, int period) : this(source.Close, period, false) { } - public SSDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SSDEV_Series(TSeries source) : this(source, 0, false) { } - public SSDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _svar = 0; - for (int i = 0; i < this._buffer.Count; i++) { _svar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } - _svar /= (_buffer.Count > 1) ? _buffer.Count - 1 : 1; // Bessel's correction - double _ssdev = Math.Sqrt(_svar); - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ssdev); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SSDEV: (Corrected) Sample Standard Deviation + Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance. + +Sources: + https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + SSDEV (Sample Standard Deviation) is also known as a unbiased/corrected Standard Deviation. + For a population/biased/uncorrected Standard Deviation, use PSDEV instead + + */ + +public class SSDEV_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SSDEV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SSDEV({period})"; + } + public SSDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SSDEV_Series() : this(period: 0, useNaN: false) { } + public SSDEV_Series(int period) : this(period: period, useNaN: false) { } + public SSDEV_Series(TBars source) : this(source.Close, 0, false) { } + public SSDEV_Series(TBars source, int period) : this(source.Close, period, false) { } + public SSDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SSDEV_Series(TSeries source) : this(source, 0, false) { } + public SSDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _svar = 0; + for (int i = 0; i < this._buffer.Count; i++) { _svar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _svar /= (_buffer.Count > 1) ? _buffer.Count - 1 : 1; // Bessel's correction + double _ssdev = Math.Sqrt(_svar); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ssdev); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/SVAR_Series.cs b/Calculations/_Updated/SVAR_Series.cs index b121fe8b..01020519 100644 --- a/Calculations/_Updated/SVAR_Series.cs +++ b/Calculations/_Updated/SVAR_Series.cs @@ -1,81 +1,90 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -VAR: Population Variance - Population variance without Bessel's correction - -Sources: - https://en.wikipedia.org/wiki/Variance - Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction - -Remark: - VAR (Population Variance) is also known as a biased Sample Variance. For unbiased - sample variance use SVAR instead. - - */ - -public class SVAR_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public SVAR_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"SVAR({period})"; - } - public SVAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public SVAR_Series() : this(period: 0, useNaN: false) { } - public SVAR_Series(int period) : this(period: period, useNaN: false) { } - public SVAR_Series(TBars source) : this(source.Close, 0, false) { } - public SVAR_Series(TBars source, int period) : this(source.Close, period, false) { } - public SVAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public SVAR_Series(TSeries source) : this(source, 0, false) { } - public SVAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _svar = 0; - for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); } - _svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _svar); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +VAR: Population Variance + Population variance without Bessel's correction + +Sources: + https://en.wikipedia.org/wiki/Variance + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + VAR (Population Variance) is also known as a biased Sample Variance. For unbiased + sample variance use SVAR instead. + + */ + +public class SVAR_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SVAR_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SVAR({period})"; + } + public SVAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SVAR_Series() : this(period: 0, useNaN: false) { } + public SVAR_Series(int period) : this(period: period, useNaN: false) { } + public SVAR_Series(TBars source) : this(source.Close, 0, false) { } + public SVAR_Series(TBars source, int period) : this(source.Close, period, false) { } + public SVAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SVAR_Series(TSeries source) : this(source, 0, false) { } + public SVAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _svar = 0; + for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); } + _svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _svar); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/T3_Series.cs b/Calculations/_Updated/T3_Series.cs index 1ffac970..6f87ea07 100644 --- a/Calculations/_Updated/T3_Series.cs +++ b/Calculations/_Updated/T3_Series.cs @@ -1,161 +1,173 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Numerics; - -/* -T3: Tillson T3 Moving Average - Tim Tillson described it in "Technical Analysis of Stocks and Commodities", January 1998 in the - article "Better Moving Averages". Tillson’s moving average becomes a popular indicator of - technical analysis as it gets less lag with the price chart and its curve is considerably smoother. - -Sources: - https://technicalindicators.net/indicators-technical-analysis/150-t3-moving-average - http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/ - */ - -public class T3_Series : TSeries { - private readonly double _k, _k1m, _c1, _c2, _c3, _c4; - private readonly System.Collections.Generic.List _buffer1 = new(); - private readonly System.Collections.Generic.List _buffer2 = new(); - private readonly System.Collections.Generic.List _buffer3 = new(); - private readonly System.Collections.Generic.List _buffer4 = new(); - private readonly System.Collections.Generic.List _buffer5 = new(); - private readonly System.Collections.Generic.List _buffer6 = new(); - private readonly bool _useSMA; - private double _lastema1, _lastema2, _lastema3, _lastema4, _lastema5, _lastema6; - private double _llastema1, _llastema2, _llastema3, _llastema4, _llastema5, _llastema6; - protected int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public T3_Series(int period, double vfactor, bool useSMA, bool useNaN) { - _period = period; - _len = 0; - _NaN = useNaN; - Name = $"T3({period})"; - _useSMA = useSMA; - double _a = vfactor; //0.7; //0.618 - _c1 = -_a * _a * _a; - _c2 = 3 * _a * _a + 3 * _a * _a * _a; - _c3 = -6 * _a * _a - 3 * _a - 3 * _a * _a * _a; - _c4 = 1 + 3 * _a + _a * _a * _a + 3 * _a * _a; - - _k = 2.0 / (_period + 1); - _k1m = 1.0 - _k; - _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; - } - public T3_Series(TSeries source, int period, double vfactor, bool useSMA, bool useNaN) : this(period, vfactor, useSMA, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public T3_Series() : this(period: 0, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(int period) : this(period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(TBars source) : this(source.Close, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(TBars source, int period) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(TBars source, int period, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } - public T3_Series(TBars source, int period, double vfactor, bool useNaN) : this(source.Close, period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } - public T3_Series(TBars source, int period, bool useSMA, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: useSMA, useNaN: useNaN) { } - public T3_Series(TSeries source) : this(source, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(TSeries source, int period) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } - public T3_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } - public T3_Series(TSeries source, int period, double vfactor) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: false) { } - public T3_Series(TSeries source, int period, double vfactor, bool useNaN) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - double _ema1, _ema2, _ema3, _ema4, _ema5, _ema6; - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN),update); - } - - if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; _lastema4 = _llastema4; _lastema5 = _llastema5; _lastema6 = _llastema6; } - else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _llastema4 = _lastema4; _llastema5 = _lastema5; _llastema6 = _lastema6; } - - if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = _lastema4 = _lastema5 = _lastema6 = TValue.v; } - - - if ((_len < _period) && _useSMA) { - BufferTrim(_buffer1, TValue.v, _period, update); - _ema1 = 0; - for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } - _ema1 /= _buffer1.Count; - - BufferTrim(_buffer2, _ema1, _period, update); - _ema2 = 0; - for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } - _ema2 /= _buffer2.Count; - - BufferTrim(_buffer3, _ema2, _period, update); - _ema3 = 0; - for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } - _ema3 /= _buffer3.Count; - - BufferTrim(_buffer4, _ema3, _period, update); - _ema4 = 0; - for (int i = 0; i < _buffer4.Count; i++) { _ema4 += _buffer4[i]; } - _ema4 /= _buffer4.Count; - - BufferTrim(_buffer5, _ema4, _period, update); - _ema5 = 0; - for (int i = 0; i < _buffer5.Count; i++) { _ema5 += _buffer5[i]; } - _ema5 /= _buffer5.Count; - - BufferTrim(_buffer6, _ema5, _period, update); - _ema6 = 0; - for (int i = 0; i < _buffer6.Count; i++) { _ema6 += _buffer6[i]; } - _ema6 /= _buffer6.Count; - } - else { - _ema1 = (TValue.v * this._k) + (this._lastema1 * this._k1m); - _ema2 = (_ema1 * this._k) + (this._lastema2 * this._k1m); - _ema3 = (_ema2 * this._k) + (this._lastema3 * this._k1m); - _ema4 = (_ema3 * this._k) + (this._lastema4 * this._k1m); - _ema5 = (_ema4 * this._k) + (this._lastema5 * this._k1m); - _ema6 = (_ema5 * this._k) + (this._lastema6 * this._k1m); - } - _len++; - _lastema1 = _ema1; - _lastema2 = _ema2; - _lastema3 = _ema3; - _lastema4 = _ema4; - _lastema5 = _ema5; - _lastema6 = _ema6; - - double _T3 = _c1 * _ema6 + _c2 * _ema5 + _c3 * _ema4 + _c4 * _ema3; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _T3); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; - _buffer1.Clear(); - _buffer2.Clear(); - _buffer3.Clear(); - _buffer4.Clear(); - _buffer5.Clear(); - _buffer6.Clear(); - _len = 0; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Numerics; + +/* +T3: Tillson T3 Moving Average + Tim Tillson described it in "Technical Analysis of Stocks and Commodities", January 1998 in the + article "Better Moving Averages". Tillson’s moving average becomes a popular indicator of + technical analysis as it gets less lag with the price chart and its curve is considerably smoother. + +Sources: + https://technicalindicators.net/indicators-technical-analysis/150-t3-moving-average + http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/ + */ + +public class T3_Series : TSeries +{ + private readonly double _k, _k1m, _c1, _c2, _c3, _c4; + private readonly System.Collections.Generic.List _buffer1 = new(); + private readonly System.Collections.Generic.List _buffer2 = new(); + private readonly System.Collections.Generic.List _buffer3 = new(); + private readonly System.Collections.Generic.List _buffer4 = new(); + private readonly System.Collections.Generic.List _buffer5 = new(); + private readonly System.Collections.Generic.List _buffer6 = new(); + private readonly bool _useSMA; + private double _lastema1, _lastema2, _lastema3, _lastema4, _lastema5, _lastema6; + private double _llastema1, _llastema2, _llastema3, _llastema4, _llastema5, _llastema6; + protected int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public T3_Series(int period, double vfactor, bool useSMA, bool useNaN) + { + _period = period; + _len = 0; + _NaN = useNaN; + Name = $"T3({period})"; + _useSMA = useSMA; + double _a = vfactor; //0.7; //0.618 + _c1 = -_a * _a * _a; + _c2 = 3 * _a * _a + 3 * _a * _a * _a; + _c3 = -6 * _a * _a - 3 * _a - 3 * _a * _a * _a; + _c4 = 1 + 3 * _a + _a * _a * _a + 3 * _a * _a; + + _k = 2.0 / (_period + 1); + _k1m = 1.0 - _k; + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; + } + public T3_Series(TSeries source, int period, double vfactor, bool useSMA, bool useNaN) : this(period, vfactor, useSMA, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public T3_Series() : this(period: 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(int period) : this(period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source) : this(source.Close, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source, int period) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source, int period, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } + public T3_Series(TBars source, int period, double vfactor, bool useNaN) : this(source.Close, period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } + public T3_Series(TBars source, int period, bool useSMA, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: useSMA, useNaN: useNaN) { } + public T3_Series(TSeries source) : this(source, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } + public T3_Series(TSeries source, int period, double vfactor) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period, double vfactor, bool useNaN) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + double _ema1, _ema2, _ema3, _ema4, _ema5, _ema6; + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; _lastema4 = _llastema4; _lastema5 = _llastema5; _lastema6 = _llastema6; } + else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _llastema4 = _lastema4; _llastema5 = _lastema5; _llastema6 = _lastema6; } + + if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = _lastema4 = _lastema5 = _lastema6 = TValue.v; } + + + if ((_len < _period) && _useSMA) + { + BufferTrim(_buffer1, TValue.v, _period, update); + _ema1 = 0; + for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } + _ema1 /= _buffer1.Count; + + BufferTrim(_buffer2, _ema1, _period, update); + _ema2 = 0; + for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } + _ema2 /= _buffer2.Count; + + BufferTrim(_buffer3, _ema2, _period, update); + _ema3 = 0; + for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } + _ema3 /= _buffer3.Count; + + BufferTrim(_buffer4, _ema3, _period, update); + _ema4 = 0; + for (int i = 0; i < _buffer4.Count; i++) { _ema4 += _buffer4[i]; } + _ema4 /= _buffer4.Count; + + BufferTrim(_buffer5, _ema4, _period, update); + _ema5 = 0; + for (int i = 0; i < _buffer5.Count; i++) { _ema5 += _buffer5[i]; } + _ema5 /= _buffer5.Count; + + BufferTrim(_buffer6, _ema5, _period, update); + _ema6 = 0; + for (int i = 0; i < _buffer6.Count; i++) { _ema6 += _buffer6[i]; } + _ema6 /= _buffer6.Count; + } + else + { + _ema1 = (TValue.v * this._k) + (this._lastema1 * this._k1m); + _ema2 = (_ema1 * this._k) + (this._lastema2 * this._k1m); + _ema3 = (_ema2 * this._k) + (this._lastema3 * this._k1m); + _ema4 = (_ema3 * this._k) + (this._lastema4 * this._k1m); + _ema5 = (_ema4 * this._k) + (this._lastema5 * this._k1m); + _ema6 = (_ema5 * this._k) + (this._lastema6 * this._k1m); + } + _len++; + _lastema1 = _ema1; + _lastema2 = _ema2; + _lastema3 = _ema3; + _lastema4 = _ema4; + _lastema5 = _ema5; + _lastema6 = _ema6; + + double _T3 = _c1 * _ema6 + _c2 * _ema5 + _c3 * _ema4 + _c4 * _ema3; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _T3); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; + _buffer1.Clear(); + _buffer2.Clear(); + _buffer3.Clear(); + _buffer4.Clear(); + _buffer5.Clear(); + _buffer6.Clear(); + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/TBars.cs b/Calculations/_Updated/TBars.cs index 920dd17c..7b9af4ce 100644 --- a/Calculations/_Updated/TBars.cs +++ b/Calculations/_Updated/TBars.cs @@ -1,138 +1,153 @@ -namespace QuanTAlib; -using System; - -/* -TBars class - includes all series for common data used in indicators and other calculations. - Has a bit limited overloading and casting (compared to TSeries) - Includes Select(int) method to simplify choosing the most optimal data source for indicators - Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4 - (it is 'cheaper' to calculate them once during data capture than each time during data analysis) - - */ - -public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)> -{ - public string Name { get; set; } - private readonly TSeries _open = new("open"); - private readonly TSeries _high = new("high"); - private readonly TSeries _low = new("low"); - private readonly TSeries _close = new("close"); - private readonly TSeries _volume = new("volume"); - private readonly TSeries _hl2 = new("HL2"); - private readonly TSeries _oc2 = new("OC2"); - private readonly TSeries _ohl3 = new("OHL3"); - private readonly TSeries _hlc3 = new("HLC3"); - private readonly TSeries _ohlc4 = new("OHLC4"); - private readonly TSeries _hlcc4 = new("HLCC4"); - - public TSeries Open => this._open; - public TSeries High => this._high; - public TSeries Low => this._low; - public TSeries Close => this._close; - public TSeries Volume => this._volume; - public TSeries HL2 => this._hl2; - public TSeries OC2 => this._oc2; - public TSeries OHL3 => this._ohl3; - public TSeries HLC3 => this._hlc3; - public TSeries OHLC4 => this._ohlc4; - public TSeries HLCC4 => this._hlcc4; - - public TBars() { } - - public TBars(string Name) { - this.Name = Name; - } - - public (DateTime t, double o, double h, double l, double c, double v) Last => this[^1]; - public TBars Tail(int count = 10) - { - TBars outBars = new(); - if (count > this.Count) { count = this.Count; } - for (int i = this.Count - count; i < this.Count; i++) { outBars.Add(this[i]); } - return outBars; - } - public TSeries Select(int source) - { - return source switch - { - 0 => _open, - 1 => _high, - 2 => _low, - 3 => _close, - 4 => _hl2, - 5 => _oc2, - 6 => _ohl3, - 7 => _hlc3, - 8 => _ohlc4, - _ => _hlcc4, - }; - } - public static string SelectStr(int source) - { - return source switch - { - 0 => "Open", - 1 => "High", - 2 => "Low", - 3 => "Close", - 4 => "HL2", - 5 => "OC2", - 6 => "OHL3", - 7 => "HLC3", - 8 => "OHLC4", - _ => "HLCC4", - }; - } - - public virtual (DateTime t, double v) Add((double o, double h, double l, double c, double v) p, bool update = false) => - Add((t: (this.Count == 0) ? DateTime.Today : this[^1].t.AddDays(1),p.o,p.h,p.l,p.c,p.v),update); - - public virtual (DateTime t, double v) Add(double o, double h, double l, double c, double v, bool update = false) => - Add((o,h,l,c,v),update); - - public virtual (DateTime t, double v) Add(DateTime t, double o, double h, double l, double c, double v, bool update = false) => - this.Add((t, o, h, l, c, v), update); - - public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - if (update) { this[^1] = TBar; } else { base.Add(TBar); } - - _open.Add((TBar.t, TBar.o), update); - _high.Add((TBar.t, TBar.h), update); - _low.Add((TBar.t, TBar.l), update); - _close.Add((TBar.t, TBar.c), update); - _volume.Add((TBar.t, TBar.v), update); - _hl2.Add((TBar.t, (TBar.h + TBar.l) * 0.5), update); - _oc2.Add((TBar.t, (TBar.o + TBar.c) * 0.5), update); - _ohl3.Add((TBar.t, (TBar.o + TBar.h + TBar.l) * 0.333333333333333), update); - _hlc3.Add((TBar.t, (TBar.h + TBar.l + TBar.c) * 0.333333333333333), update); - _ohlc4.Add((TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25), update); - _hlcc4.Add((TBar.t, (TBar.h + TBar.l + TBar.c + TBar.c) * 0.25), update); - - this.OnEvent(update); - return (TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25); - } - - public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); - public event NewDataEventHandler Pub; - protected virtual void OnEvent(bool update = false) { if (Pub != null && Pub.Target != this) { - Pub(this, new TSeriesEventArgs { update = update }); } } - - public void Sub(object source, TSeriesEventArgs e) { TBars ss = (TBars)source; if (ss.Count > 1) { - for (int i = 0; i < ss.Count; i++) { this.Add(ss[i]); } - } else { - this.Add(ss[^1], e.update); - } - } - - /// common helpers - public static void BufferTrim(System.Collections.Generic.List buffer, double value, int period, bool update) { - if (!update) { - buffer.Add(value); - if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } - return; - } - buffer[^1] = value; - } - public virtual void Reset() { - } -} +namespace QuanTAlib; +using System; + +/* +TBars class - includes all series for common data used in indicators and other calculations. + Has a bit limited overloading and casting (compared to TSeries) + Includes Select(int) method to simplify choosing the most optimal data source for indicators + Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4 + (it is 'cheaper' to calculate them once during data capture than each time during data analysis) + + */ + +public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)> +{ + public string Name { get; set; } + private readonly TSeries _open = new("open"); + private readonly TSeries _high = new("high"); + private readonly TSeries _low = new("low"); + private readonly TSeries _close = new("close"); + private readonly TSeries _volume = new("volume"); + private readonly TSeries _hl2 = new("HL2"); + private readonly TSeries _oc2 = new("OC2"); + private readonly TSeries _ohl3 = new("OHL3"); + private readonly TSeries _hlc3 = new("HLC3"); + private readonly TSeries _ohlc4 = new("OHLC4"); + private readonly TSeries _hlcc4 = new("HLCC4"); + + public TSeries Open => this._open; + public TSeries High => this._high; + public TSeries Low => this._low; + public TSeries Close => this._close; + public TSeries Volume => this._volume; + public TSeries HL2 => this._hl2; + public TSeries OC2 => this._oc2; + public TSeries OHL3 => this._ohl3; + public TSeries HLC3 => this._hlc3; + public TSeries OHLC4 => this._ohlc4; + public TSeries HLCC4 => this._hlcc4; + + public TBars() { } + + public TBars(string Name) + { + this.Name = Name; + } + + public (DateTime t, double o, double h, double l, double c, double v) Last => this[^1]; + public TBars Tail(int count = 10) + { + TBars outBars = new(); + if (count > this.Count) { count = this.Count; } + for (int i = this.Count - count; i < this.Count; i++) { outBars.Add(this[i]); } + return outBars; + } + public TSeries Select(int source) + { + return source switch + { + 0 => _open, + 1 => _high, + 2 => _low, + 3 => _close, + 4 => _hl2, + 5 => _oc2, + 6 => _ohl3, + 7 => _hlc3, + 8 => _ohlc4, + _ => _hlcc4, + }; + } + public static string SelectStr(int source) + { + return source switch + { + 0 => "Open", + 1 => "High", + 2 => "Low", + 3 => "Close", + 4 => "HL2", + 5 => "OC2", + 6 => "OHL3", + 7 => "HLC3", + 8 => "OHLC4", + _ => "HLCC4", + }; + } + + public virtual (DateTime t, double v) Add((double o, double h, double l, double c, double v) p, bool update = false) => + Add((t: (this.Count == 0) ? DateTime.Today : this[^1].t.AddDays(1), p.o, p.h, p.l, p.c, p.v), update); + + public virtual (DateTime t, double v) Add(double o, double h, double l, double c, double v, bool update = false) => + Add((o, h, l, c, v), update); + + public virtual (DateTime t, double v) Add(DateTime t, double o, double h, double l, double c, double v, bool update = false) => + this.Add((t, o, h, l, c, v), update); + + public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { this[^1] = TBar; } else { base.Add(TBar); } + + _open.Add((TBar.t, TBar.o), update); + _high.Add((TBar.t, TBar.h), update); + _low.Add((TBar.t, TBar.l), update); + _close.Add((TBar.t, TBar.c), update); + _volume.Add((TBar.t, TBar.v), update); + _hl2.Add((TBar.t, (TBar.h + TBar.l) * 0.5), update); + _oc2.Add((TBar.t, (TBar.o + TBar.c) * 0.5), update); + _ohl3.Add((TBar.t, (TBar.o + TBar.h + TBar.l) * 0.333333333333333), update); + _hlc3.Add((TBar.t, (TBar.h + TBar.l + TBar.c) * 0.333333333333333), update); + _ohlc4.Add((TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25), update); + _hlcc4.Add((TBar.t, (TBar.h + TBar.l + TBar.c + TBar.c) * 0.25), update); + + this.OnEvent(update); + return (TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25); + } + + public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); + public event NewDataEventHandler Pub; + protected virtual void OnEvent(bool update = false) + { + if (Pub != null && Pub.Target != this) + { + Pub(this, new TSeriesEventArgs { update = update }); + } + } + + public void Sub(object source, TSeriesEventArgs e) + { + TBars ss = (TBars)source; if (ss.Count > 1) + { + for (int i = 0; i < ss.Count; i++) { this.Add(ss[i]); } + } + else + { + this.Add(ss[^1], e.update); + } + } + + /// common helpers + public static void BufferTrim(System.Collections.Generic.List buffer, double value, int period, bool update) + { + if (!update) + { + buffer.Add(value); + if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } + return; + } + buffer[^1] = value; + } + public virtual void Reset() + { + } +} diff --git a/Calculations/_Updated/TEMA_Series.cs b/Calculations/_Updated/TEMA_Series.cs index a9653f6b..88218161 100644 --- a/Calculations/_Updated/TEMA_Series.cs +++ b/Calculations/_Updated/TEMA_Series.cs @@ -1,120 +1,134 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -TEMA: Triple Exponential Moving Average - TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average. - -Sources: - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/ - -Remark: - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - ema3 = EMA(ema2, length) - TEMA = 3 * (ema1 - ema2) + ema3 - - */ - -public class TEMA_Series : TSeries { - private double _k; - private double _sum, _oldsum; - private double _lastema1, _oldema1, _lastema2, _oldema2, _lastema3, _oldema3; - private int _len; - private readonly bool _useSMA; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - -//core constructor - public TEMA_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - _useSMA = useSMA; - Name = $"TEMA({period})"; - _k = 2.0 / (_period + 1); - _len = 0; - _sum = _oldsum = _lastema1 = _lastema2 = _lastema3 = 0; - } - public TEMA_Series() : this(0, false, true) {} - public TEMA_Series(int period) : this(period, false, true) {} - public TEMA_Series(TBars source) : this(source.Close, 0, false) {} - public TEMA_Series(TBars source, int period) : this(source.Close, period, false) {} - public TEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public TEMA_Series(TSeries source, int period) : this(source, period, false, true) {} - public TEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {} - public TEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - -// core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - _lastema1 = _oldema1; - _lastema2 = _oldema2; - _lastema3 = _oldema3; - _sum = _oldsum; - } - else { - _oldema1 = _lastema1; - _oldema2 = _lastema2; - _oldema3 = _lastema3; - _oldsum = _sum; - _len++; - } - - if (_period == 0) { _k = 2.0 / (_len + 1); } - - double _ema1, _ema2, _ema3, _tema; - if (this.Count == 0) { - _ema1 = _ema2 = _ema3 =_sum = TValue.v; - } - else if (_len <= _period && _useSMA && _period != 0) { - _sum += TValue.v; - _ema1 = _sum / Math.Min(_len, _period); - _ema2 = _ema1; - _ema3 = _ema2; - } - else { - _ema1 = (TValue.v - _lastema1) * _k + _lastema1; - _ema2 = (_ema1 - _lastema2) * _k + _lastema2; - _ema3 = (_ema2 - _lastema3) * _k + _lastema3; - } - - _tema = (3 * (_ema1 - _ema2)) + _ema3; - - _lastema1 = Double.IsNaN(_ema1)?_lastema1:_ema1; - _lastema2 = Double.IsNaN(_ema2)?_lastema2:_ema2; - _lastema3 = Double.IsNaN(_ema3) ? _lastema3 : _ema3; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _tema); - return base.Add(res, update); - } - -//variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _sum = _oldsum = _lastema1 = _lastema2 = 0; - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +TEMA: Triple Exponential Moving Average + TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average. + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/ + +Remark: + ema1 = EMA(close, length) + ema2 = EMA(ema1, length) + ema3 = EMA(ema2, length) + TEMA = 3 * (ema1 - ema2) + ema3 + + */ + +public class TEMA_Series : TSeries +{ + private double _k; + private double _sum, _oldsum; + private double _lastema1, _oldema1, _lastema2, _oldema2, _lastema3, _oldema3; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public TEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"TEMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema1 = _lastema2 = _lastema3 = 0; + } + public TEMA_Series() : this(0, false, true) { } + public TEMA_Series(int period) : this(period, false, true) { } + public TEMA_Series(TBars source) : this(source.Close, 0, false) { } + public TEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public TEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public TEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public TEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _lastema3 = _oldema3; + _sum = _oldsum; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldema3 = _lastema3; + _oldsum = _sum; + _len++; + } + + if (_period == 0) { _k = 2.0 / (_len + 1); } + + double _ema1, _ema2, _ema3, _tema; + if (this.Count == 0) + { + _ema1 = _ema2 = _ema3 = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + _ema1 = _sum / Math.Min(_len, _period); + _ema2 = _ema1; + _ema3 = _ema2; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + _ema3 = (_ema2 - _lastema3) * _k + _lastema3; + } + + _tema = (3 * (_ema1 - _ema2)) + _ema3; + + _lastema1 = Double.IsNaN(_ema1) ? _lastema1 : _ema1; + _lastema2 = Double.IsNaN(_ema2) ? _lastema2 : _ema2; + _lastema3 = Double.IsNaN(_ema3) ? _lastema3 : _ema3; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _tema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema1 = _lastema2 = 0; + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/TRIMA_Series.cs b/Calculations/_Updated/TRIMA_Series.cs index 210be772..703f594d 100644 --- a/Calculations/_Updated/TRIMA_Series.cs +++ b/Calculations/_Updated/TRIMA_Series.cs @@ -1,84 +1,94 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -TRIMA: Triangular Moving Average - A weighted moving average where the shape of the weights are triangular and the greatest - weight is in the middle of the period, - -Sources: - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/ - -Remark: - trima = sma(sma(signal, n/2), n/2) - - */ - -public class TRIMA_Series : TSeries { - private readonly int _p1a, _p1b; - private readonly SMA_Series sma, trima; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public TRIMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"xMA({period})"; - _p1a = (int)Math.Floor((period * 0.5) + 1); - _p1b = (int)Math.Ceiling(0.5 * period); - sma = new(_p1a); - trima = new(_p1b); - - } - public TRIMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public TRIMA_Series() : this(period: 0, useNaN: false) { } - public TRIMA_Series(int period) : this(period: period, useNaN: false) { } - public TRIMA_Series(TBars source) : this(source.Close, 0, false) { } - public TRIMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public TRIMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public TRIMA_Series(TSeries source) : this(source, 0, false) { } - public TRIMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - - var _sma = sma.Add(TValue, update); - var _trima = trima.Add(_sma, update); - - var res = (_trima.t, Count < _period - 1 && _NaN ? double.NaN : _trima.v); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - sma.Reset(); - trima.Reset(); - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +TRIMA: Triangular Moving Average + A weighted moving average where the shape of the weights are triangular and the greatest + weight is in the middle of the period, + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/ + +Remark: + trima = sma(sma(signal, n/2), n/2) + + */ + +public class TRIMA_Series : TSeries +{ + private readonly int _p1a, _p1b; + private readonly SMA_Series sma, trima; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public TRIMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"xMA({period})"; + _p1a = (int)Math.Floor((period * 0.5) + 1); + _p1b = (int)Math.Ceiling(0.5 * period); + sma = new(_p1a); + trima = new(_p1b); + + } + public TRIMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public TRIMA_Series() : this(period: 0, useNaN: false) { } + public TRIMA_Series(int period) : this(period: period, useNaN: false) { } + public TRIMA_Series(TBars source) : this(source.Close, 0, false) { } + public TRIMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public TRIMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TRIMA_Series(TSeries source) : this(source, 0, false) { } + public TRIMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + var _sma = sma.Add(TValue, update); + var _trima = trima.Add(_sma, update); + + var res = (_trima.t, Count < _period - 1 && _NaN ? double.NaN : _trima.v); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + sma.Reset(); + trima.Reset(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/TRIX_Series.cs b/Calculations/_Updated/TRIX_Series.cs index 30d9f58e..049c23da 100644 --- a/Calculations/_Updated/TRIX_Series.cs +++ b/Calculations/_Updated/TRIX_Series.cs @@ -1,118 +1,132 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -TRIX: Triple Exponential Average Oscillator - Developed by Jack Hutson in the early 1980s, the triple exponential average (TRIX) - has become a popular technical analysis tool to aid chartists in spotting diversions - and directional cues in stock trading patterns. - -Sources: - https://www.investopedia.com/terms/t/trix.asp - - */ - -public class TRIX_Series : TSeries { - private readonly double _k; - private readonly System.Collections.Generic.List _buffer1 = new(); - private readonly System.Collections.Generic.List _buffer2 = new(); - private readonly System.Collections.Generic.List _buffer3 = new(); - private double _lastema1, _lastema2, _lastema3; - private double _llastema1, _llastema2, _llastema3; - private int _len; - private readonly bool _useSMA; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - -//core constructors - - public TRIX_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - _useSMA = useSMA; - Name = $"TRIX({period})"; - _k = 2.0 / (_period + 1); - _len = 0; - _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = 0; - } - public TRIX_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public TRIX_Series() : this(0, false, true) {} - public TRIX_Series(int period) : this(period, false, true) {} - public TRIX_Series(TBars source) : this(source.Close, 0, false) {} - public TRIX_Series(TBars source, int period) : this(source.Close, period, false) {} - public TRIX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {} - public TRIX_Series(TSeries source, int period) : this(source, period, false, true) {} - public TRIX_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {} - - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (double.IsNaN(TValue.v)) { - return base.Add((TValue.t, Double.NaN), update); - } - if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = TValue.v; } - if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; } - else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _len++; - } - - double _ema1, _ema2, _ema3; - if ((this.Count < _period) && _useSMA) { - BufferTrim(_buffer1, TValue.v, _period, update); - _ema1 = 0; - for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } - _ema1 /= _buffer1.Count; - - BufferTrim(_buffer2, _ema1, _period, update); - _ema2 = 0; - for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } - _ema2 /= _buffer2.Count; - - BufferTrim(_buffer3, _ema2, _period, update); - _ema3 = 0; - for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } - _ema3 /= _buffer3.Count; - } - else { - _ema1 = (TValue.v - _lastema1) * _k + _lastema1; - _ema2 = (_ema1 - _lastema2) * _k + _lastema2; - _ema3 = (_ema2 - _lastema3) * _k + _lastema3; - } - double _trix = 100 * (_ema3 - _lastema3) / _lastema3; - _lastema1 = _ema1; - _lastema2 = _ema2; - _lastema3 = _ema3; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _trix); - return base.Add(res, update); - } - -//variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _len = 0; - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +TRIX: Triple Exponential Average Oscillator + Developed by Jack Hutson in the early 1980s, the triple exponential average (TRIX) + has become a popular technical analysis tool to aid chartists in spotting diversions + and directional cues in stock trading patterns. + +Sources: + https://www.investopedia.com/terms/t/trix.asp + + */ + +public class TRIX_Series : TSeries +{ + private readonly double _k; + private readonly System.Collections.Generic.List _buffer1 = new(); + private readonly System.Collections.Generic.List _buffer2 = new(); + private readonly System.Collections.Generic.List _buffer3 = new(); + private double _lastema1, _lastema2, _lastema3; + private double _llastema1, _llastema2, _llastema3; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + + public TRIX_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"TRIX({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = 0; + } + public TRIX_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public TRIX_Series() : this(0, false, true) { } + public TRIX_Series(int period) : this(period, false, true) { } + public TRIX_Series(TBars source) : this(source.Close, 0, false) { } + public TRIX_Series(TBars source, int period) : this(source.Close, period, false) { } + public TRIX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TRIX_Series(TSeries source, int period) : this(source, period, false, true) { } + public TRIX_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = TValue.v; } + if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; } + else + { + _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _len++; + } + + double _ema1, _ema2, _ema3; + if ((this.Count < _period) && _useSMA) + { + BufferTrim(_buffer1, TValue.v, _period, update); + _ema1 = 0; + for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } + _ema1 /= _buffer1.Count; + + BufferTrim(_buffer2, _ema1, _period, update); + _ema2 = 0; + for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } + _ema2 /= _buffer2.Count; + + BufferTrim(_buffer3, _ema2, _period, update); + _ema3 = 0; + for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } + _ema3 /= _buffer3.Count; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + _ema3 = (_ema2 - _lastema3) * _k + _lastema3; + } + double _trix = 100 * (_ema3 - _lastema3) / _lastema3; + _lastema1 = _ema1; + _lastema2 = _ema2; + _lastema3 = _ema3; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _trix); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } } \ No newline at end of file diff --git a/Calculations/_Updated/TR_Series.cs b/Calculations/_Updated/TR_Series.cs index e3c61058..308e08e8 100644 --- a/Calculations/_Updated/TR_Series.cs +++ b/Calculations/_Updated/TR_Series.cs @@ -1,79 +1,91 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -TR: True Range - True Range was introduced by J. Welles Wilder in his book New Concepts in Technical Trading Systems. - It measures the daily range plus any gap from the closing price of the preceding day. - -Calculation: - d1 = ABS(High - Low) - d2 = ABS(High - Previous close) - d3 = ABS(Previous close - Low) - TR = MAX(d1,d2,d3) - -Sources: - https://www.macroption.com/true-range/ - - */ - -public class TR_Series : TSeries { - protected readonly TBars _data; - private double _cm1, _cm1_o; - - //core constructors - public TR_Series() { - Name = $"TR()"; - _cm1 = _cm1_o = double.NaN; - } - public TR_Series(TBars source) { - _data = source; - Name = $"TR({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _cm1 = _cm1_o = double.NaN; - _data.Pub += Sub; - Add(data: _data); - } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - - if (update) { - _cm1 = _cm1_o; - } - else { - _cm1_o = _cm1; - } - - if (_cm1 is double.NaN) { - _cm1 = TBar.c; - } - - double d1 = Math.Abs(TBar.h - TBar.l); - double d2 = Math.Abs(_cm1 - TBar.h); - double d3 = Math.Abs(_cm1 - TBar.l); - _cm1 = TBar.c; - var ret = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); - return base.Add(ret, update); - - } - - public new void Add(TBars data) { - foreach (var item in data) { Add(item, false); } - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TBar: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TBar: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TBar: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _cm1 = _cm1_o = double.NaN; - } +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +TR: True Range + True Range was introduced by J. Welles Wilder in his book New Concepts in Technical Trading Systems. + It measures the daily range plus any gap from the closing price of the preceding day. + +Calculation: + d1 = ABS(High - Low) + d2 = ABS(High - Previous close) + d3 = ABS(Previous close - Low) + TR = MAX(d1,d2,d3) + +Sources: + https://www.macroption.com/true-range/ + + */ + +public class TR_Series : TSeries +{ + protected readonly TBars _data; + private double _cm1, _cm1_o; + + //core constructors + public TR_Series() + { + Name = $"TR()"; + _cm1 = _cm1_o = double.NaN; + } + public TR_Series(TBars source) + { + _data = source; + Name = $"TR({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _cm1 = _cm1_o = double.NaN; + _data.Pub += Sub; + Add(data: _data); + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + _cm1 = _cm1_o; + } + else + { + _cm1_o = _cm1; + } + + if (_cm1 is double.NaN) + { + _cm1 = TBar.c; + } + + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + _cm1 = TBar.c; + var ret = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + return base.Add(ret, update); + + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _cm1 = _cm1_o = double.NaN; + } } \ No newline at end of file diff --git a/Calculations/_Updated/TSeries.cs b/Calculations/_Updated/TSeries.cs index ccde468d..00ba50a2 100644 --- a/Calculations/_Updated/TSeries.cs +++ b/Calculations/_Updated/TSeries.cs @@ -1,103 +1,137 @@ -namespace QuanTAlib; -using System; -using System.Collections.Generic; -using System.Collections.ObjectModel; -using System.Data; -using System.Linq; - -/* -TSeries is the cornerstone of all QuanTAlib classes. - TSeries is a single List of tuples (time, value) and contains several operators, casts, overloads - and other helpers that simplify usage of library. - Think of TSeries as an equivalent of Numpy array. - - - includes Length property (to mimic array's method) - - includes publishing and subscribing methods that attach to events - - */ -public class TSeriesEventArgs : EventArgs { - public bool update { get; set; } -} - -public class TSeries : List<(DateTime t, double v)> { - private readonly (DateTime t, double v) Default = (DateTime.MinValue, double.NaN); - public IEnumerable t => this.Select(item => item.t); - public IEnumerable v => this.Select(item => item.v); - public (DateTime t, double v) Last => Count > 0 ? this[^1] : Default; - - public int Length => Count; - public string Name { get; set; } - - public TSeries() { - this.Name = "data"; - } - - public TSeries(string Name) { - this.Name = Name; - } - - public virtual (DateTime t, double v) Add(double v, bool update = false) { - return Add((t: Count == 0 ? DateTime.Today : this[^1].t.AddDays(1), v), update); - } - - public virtual (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - if (update) { - this[^1] = TValue; - } - else { - base.Add(TValue); - } - - OnEvent(update); - return TValue; - } - - public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) { - if (update) { - this[this.Count - 1] = (TBar.t, TBar.c); - } - else { - base.Add((TBar.t, TBar.c)); - } - - OnEvent(update); - return (TBar.t, TBar.c); - } - - public virtual (DateTime t, double v) Add(TSeries data) { - foreach (var item in data) { Add(item); } - return data.Last; - } - - public virtual (DateTime t, double v) Add(TBars data) { - foreach (var item in data) { Add(item.c, false); } - return (data.Last.t, data.Last.c); - } - - public void Sub(object source, TSeriesEventArgs e) { - var data = (TSeries) source; - if (data == null) { return; } - foreach (var item in data) { Add(item); } - } - - public delegate void NewEventHandler(object source, TSeriesEventArgs args); - - public event NewEventHandler Pub; - - protected virtual void OnEvent(bool update = false) - { - Pub?.Invoke(this, new TSeriesEventArgs {update = update}); - } - - /// common helpers - public static void BufferTrim(List buffer, double value, int period, bool update) { - if (!update) { - buffer.Add(value); - if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } - return; - } - buffer[^1] = value; - } - public virtual void Reset() { - } -} +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Collections.ObjectModel; +using System.Data; +using System.Linq; + +/* +TSeries is the cornerstone of all QuanTAlib classes. + TSeries is a single List of tuples (time, value) and contains several operators, casts, overloads + and other helpers that simplify usage of library. + Think of TSeries as an equivalent of Numpy array. + + - includes Length property (to mimic array's method) + - includes publishing and subscribing methods that attach to events + + */ +public class TSeriesEventArgs : EventArgs +{ + public bool update { get; set; } +} + +public class TSeries : List<(DateTime t, double v)> +{ + private readonly (DateTime t, double v) Default = (DateTime.MinValue, double.NaN); + public IEnumerable t => this.Select(item => item.t); + public IEnumerable v => this.Select(item => item.v); + public (DateTime t, double v) Last => Count > 0 ? this[^1] : Default; + + public int Length => Count; + public string Name { get; set; } + public int Keep = 0; + + public TSeries() + { + this.Name = "data"; + } + + public TSeries(string Name) + { + this.Name = Name; + } + + public virtual (DateTime t, double v) Add(double v, bool update = false) + { + return Add((t: Count == 0 ? DateTime.Today : this[^1].t.AddDays(1), v), update); + } + + public virtual (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + this[^1] = TValue; + } + else + { + base.Add(TValue); + } + + OnEvent(update); + return TValue; + } + + public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) + { + this[this.Count - 1] = (TBar.t, TBar.c); + } + else + { + base.Add((TBar.t, TBar.c)); + } + + OnEvent(update); + return (TBar.t, TBar.c); + } + + public virtual (DateTime t, double v) Add(TSeries data) + { + foreach (var item in data) { Add(item); } + return data.Last; + } + + public virtual (DateTime t, double v) Add(TBars data) + { + foreach (var item in data) { Add(item.c, false); } + return (data.Last.t, data.Last.c); + } + + public void Sub(object source, TSeriesEventArgs e) + { + var data = (TSeries)source; + if (data == null) { return; } + foreach (var item in data) { Add(item); } + } + + public delegate void NewEventHandler(object source, TSeriesEventArgs args); + + public event NewEventHandler Pub; + + protected virtual void OnEvent(bool update = false) + { + if (Keep > 0) + { + TrimToSize(keep: Keep); + } + Pub?.Invoke(this, new TSeriesEventArgs { update = update }); + } + + /// common helpers + public static void BufferTrim(List buffer, double value, int period, bool update) + { + if (!update) + { + buffer.Add(value); + if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } + return; + } + buffer[^1] = value; + } + public virtual void Reset() + { + } + + public void TrimToSize(int keep) + { + if (keep >= this.Count) + { + return; // No need to trim if the series is already smaller than or equal to n + } + + // Remove elements from the beginning of the list + int elementsToRemove = this.Count - keep; + RemoveRange(0, elementsToRemove); + } +} diff --git a/Calculations/_Updated/VAR_Series.cs b/Calculations/_Updated/VAR_Series.cs index 72562277..20b7a556 100644 --- a/Calculations/_Updated/VAR_Series.cs +++ b/Calculations/_Updated/VAR_Series.cs @@ -1,81 +1,90 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -VAR: Population Variance - Population variance without Bessel's correction - -Sources: - https://en.wikipedia.org/wiki/Variance - Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction - -Remark: - VAR (Population Variance) is also known as a biased Sample Variance. For unbiased - sample variance use SVAR instead. - - */ - -public class VAR_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public VAR_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"VAR({period})"; - } - public VAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public VAR_Series() : this(period: 0, useNaN: false) { } - public VAR_Series(int period) : this(period: period, useNaN: false) { } - public VAR_Series(TBars source) : this(source.Close, 0, false) { } - public VAR_Series(TBars source, int period) : this(source.Close, period, false) { } - public VAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public VAR_Series(TSeries source) : this(source, 0, false) { } - public VAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _pvar = 0; - for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } - _pvar /= this._buffer.Count; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pvar); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +VAR: Population Variance + Population variance without Bessel's correction + +Sources: + https://en.wikipedia.org/wiki/Variance + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + VAR (Population Variance) is also known as a biased Sample Variance. For unbiased + sample variance use SVAR instead. + + */ + +public class VAR_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public VAR_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"VAR({period})"; + } + public VAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public VAR_Series() : this(period: 0, useNaN: false) { } + public VAR_Series(int period) : this(period: period, useNaN: false) { } + public VAR_Series(TBars source) : this(source.Close, 0, false) { } + public VAR_Series(TBars source, int period) : this(source.Close, period, false) { } + public VAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public VAR_Series(TSeries source) : this(source, 0, false) { } + public VAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _pvar = 0; + for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _pvar /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pvar); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/WMAPE_Series.cs b/Calculations/_Updated/WMAPE_Series.cs index b0479cb8..7dfc95cb 100644 --- a/Calculations/_Updated/WMAPE_Series.cs +++ b/Calculations/_Updated/WMAPE_Series.cs @@ -1,82 +1,92 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -WMAPE: Weighted Mean Absolute Percentage Error - Measures the size of the error in percentage terms. Improves problems with MAPE - when there are zero or close-to-zero values because there would be a division by zero - or values of MAPE tending to infinity. - -Sources: - https://en.wikipedia.org/wiki/WMAPE - - */ - -public class WMAPE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public WMAPE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"WMAPE({period})"; - } - public WMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public WMAPE_Series() : this(period: 0, useNaN: false) { } - public WMAPE_Series(int period) : this(period: period, useNaN: false) { } - public WMAPE_Series(TBars source) : this(source.Close, 0, false) { } - public WMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } - public WMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public WMAPE_Series(TSeries source) : this(source, 0, false) { } - public WMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - - double _sma = _buffer.Average(); - - double _div = 0; - double _wmape = 0; - for (int i = 0; i < _buffer.Count; i++) { - _wmape += Math.Abs(_buffer[i] - _sma); - _div += Math.Abs(_buffer[i]); - } - _wmape = (_div != 0) ? _wmape / _div : double.PositiveInfinity; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wmape); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +WMAPE: Weighted Mean Absolute Percentage Error + Measures the size of the error in percentage terms. Improves problems with MAPE + when there are zero or close-to-zero values because there would be a division by zero + or values of MAPE tending to infinity. + +Sources: + https://en.wikipedia.org/wiki/WMAPE + + */ + +public class WMAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public WMAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"WMAPE({period})"; + } + public WMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public WMAPE_Series() : this(period: 0, useNaN: false) { } + public WMAPE_Series(int period) : this(period: period, useNaN: false) { } + public WMAPE_Series(TBars source) : this(source.Close, 0, false) { } + public WMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public WMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public WMAPE_Series(TSeries source) : this(source, 0, false) { } + public WMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _div = 0; + double _wmape = 0; + for (int i = 0; i < _buffer.Count; i++) + { + _wmape += Math.Abs(_buffer[i] - _sma); + _div += Math.Abs(_buffer[i]); + } + _wmape = (_div != 0) ? _wmape / _div : double.PositiveInfinity; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wmape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/WMA_Series.cs b/Calculations/_Updated/WMA_Series.cs index 48679fe4..957660fd 100644 --- a/Calculations/_Updated/WMA_Series.cs +++ b/Calculations/_Updated/WMA_Series.cs @@ -1,104 +1,117 @@ -namespace QuanTAlib; - -using System; -using System.Collections.Generic; -using System.Linq; -using System.Threading; -using System.Threading.Tasks; - -/* -WMA: (linearly) Weighted Moving Average - The weights are linearly decreasing over the period and the most recent data has - the heaviest weight. - -Sources: - https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/weighted-moving-average-wma/ - https://www.technicalindicators.net/indicators-technical-analysis/83-moving-averages-simple-exponential-weighted - - */ - -public class WMA_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - private System.Collections.Generic.List _weights; - protected int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - protected int _len; - public int Len { - get { return _len; } - set { _len = value; } - } - - //core constructors - public WMA_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"WMA({period})"; - _len = 1; - _weights = CalculateWeights(_period); - } - public WMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public WMA_Series() : this(period: 0, useNaN: false) { } - public WMA_Series(int period) : this(period: period, useNaN: false) { } - public WMA_Series(TBars source) : this(source.Close, 0, false) { } - public WMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public WMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public WMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update=false) { - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - if (_period == 0) { - _weights = CalculateWeights(_len); - _len++; - } - double _wma = 0; - double totalWeights = (_buffer.Count * (_buffer.Count + 1)) * 0.5; - object lockObj = new object(); - Parallel.For(0, _buffer.Count, i => - { - double temp = _buffer[i] * this._weights[i]; - lock (lockObj) { _wma += temp; } - }); - _wma /= totalWeights; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wma); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //calculating weights - private static List CalculateWeights(int period) { - List weights = new List(period); - for (int i = 0; i < period; i++) { - weights.Add(i + 1); - } - return weights; - } - - //reset calculation - public override void Reset() { - _len = 0; - _weights = CalculateWeights(_period); - _buffer.Clear(); - } +namespace QuanTAlib; + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Threading; +using System.Threading.Tasks; + +/* +WMA: (linearly) Weighted Moving Average + The weights are linearly decreasing over the period and the most recent data has + the heaviest weight. + +Sources: + https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/weighted-moving-average-wma/ + https://www.technicalindicators.net/indicators-technical-analysis/83-moving-averages-simple-exponential-weighted + + */ + +public class WMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private System.Collections.Generic.List _weights; + protected int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + public int Len + { + get { return _len; } + set { _len = value; } + } + + //core constructors + public WMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"WMA({period})"; + _len = 1; + _weights = CalculateWeights(_period); + } + public WMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public WMA_Series() : this(period: 0, useNaN: false) { } + public WMA_Series(int period) : this(period: period, useNaN: false) { } + public WMA_Series(TBars source) : this(source.Close, 0, false) { } + public WMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public WMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public WMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + if (_period == 0) + { + _weights = CalculateWeights(_len); + _len++; + } + double _wma = 0; + double totalWeights = (_buffer.Count * (_buffer.Count + 1)) * 0.5; + object lockObj = new object(); + Parallel.For(0, _buffer.Count, i => + { + double temp = _buffer[i] * this._weights[i]; + lock (lockObj) { _wma += temp; } + }); + _wma /= totalWeights; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //calculating weights + private static List CalculateWeights(int period) + { + List weights = new List(period); + for (int i = 0; i < period; i++) + { + weights.Add(i + 1); + } + return weights; + } + + //reset calculation + public override void Reset() + { + _len = 0; + _weights = CalculateWeights(_period); + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/ZLEMA_Series.cs b/Calculations/_Updated/ZLEMA_Series.cs index 512b5cc0..d0c2158d 100644 --- a/Calculations/_Updated/ZLEMA_Series.cs +++ b/Calculations/_Updated/ZLEMA_Series.cs @@ -1,95 +1,105 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -ZLEMA: Zero Lag Exponential Moving Average - The Zero lag exponential moving average (ZLEMA) indicator was created by John - Ehlers and Ric Way. - -The formula for a given N-Day period and for a given Data series is: - Lag = (Period-1)/2 - Ema Data = {Data+(Data-Data(Lag days ago)) - ZLEMA = EMA (EmaData,Period) - -Remark: - The idea is do a regular exponential moving average (EMA) calculation but on a - de-lagged data instead of doing it on the regular data. Data is de-lagged by - removing the data from "lag" days ago thus removing (or attempting to remove) - the cumulative lag effect of the moving average. - - */ - -public class ZLEMA_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - private int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly EMA_Series _ema; - - //core constructor - public ZLEMA_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - Name = $"ZLEMA({period})"; - _len = 1; - _ema = new(period); - } - //generic constructors (source) - - public ZLEMA_Series() : this(0, false, true) { } - public ZLEMA_Series(int period) : this(period, false, true) { } - public ZLEMA_Series(TBars source) : this(source.Close, 0, false) { } - public ZLEMA_Series(TBars source, int period) : this(source.Close, period, false) { } - public ZLEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public ZLEMA_Series(TSeries source, int period) : this(source, period, false, true) { } - public ZLEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } - public ZLEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - int _lag; - if (_period == 0) { - _lag = (int)((_len - 1) * 0.5); - _len++; - } - else { _lag = (int)((_period - 1) * 0.5); } - _lag = Math.Min(_lag, _buffer.Count - 1); - _lag = Math.Max(_lag, 0) + 1; - double _zlValue = 2 * TValue.v - _buffer[^_lag]; - double _zlema = _ema.Add((TValue.t, _zlValue), update).v; - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlema); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - _ema.Reset(); - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +ZLEMA: Zero Lag Exponential Moving Average + The Zero lag exponential moving average (ZLEMA) indicator was created by John + Ehlers and Ric Way. + +The formula for a given N-Day period and for a given Data series is: + Lag = (Period-1)/2 + Ema Data = {Data+(Data-Data(Lag days ago)) + ZLEMA = EMA (EmaData,Period) + +Remark: + The idea is do a regular exponential moving average (EMA) calculation but on a + de-lagged data instead of doing it on the regular data. Data is de-lagged by + removing the data from "lag" days ago thus removing (or attempting to remove) + the cumulative lag effect of the moving average. + + */ + +public class ZLEMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _ema; + + //core constructor + public ZLEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + Name = $"ZLEMA({period})"; + _len = 1; + _ema = new(period); + } + //generic constructors (source) + + public ZLEMA_Series() : this(0, false, true) { } + public ZLEMA_Series(int period) : this(period, false, true) { } + public ZLEMA_Series(TBars source) : this(source.Close, 0, false) { } + public ZLEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZLEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZLEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public ZLEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public ZLEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + int _lag; + if (_period == 0) + { + _lag = (int)((_len - 1) * 0.5); + _len++; + } + else { _lag = (int)((_period - 1) * 0.5); } + _lag = Math.Min(_lag, _buffer.Count - 1); + _lag = Math.Max(_lag, 0) + 1; + double _zlValue = 2 * TValue.v - _buffer[^_lag]; + double _zlema = _ema.Add((TValue.t, _zlValue), update).v; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _ema.Reset(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/ZL_Series.cs b/Calculations/_Updated/ZL_Series.cs index 0f6af1aa..f7f6088b 100644 --- a/Calculations/_Updated/ZL_Series.cs +++ b/Calculations/_Updated/ZL_Series.cs @@ -1,90 +1,100 @@ -namespace QuanTAlib; - -using System; -using System.Linq; - -/* -ZL: Zero Lag - Data is de-lagged by removing the data from “lag” days ago, thus removing - (or attempting to) the cumulative effect of the moving average. - -Calculation: - Lag = (Period-1)/2 - ZL = Data + (Data - Data(Lag days ago) ) - -Sources: - https://mudrex.com/blog/zero-lag-ema-trading-strategy/ - - */ - -public class ZL_Series: TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - private int _len; - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - private readonly EMA_Series _ema; - - //core constructor - public ZL_Series(int period, bool useNaN, bool useSMA) { - _period = period; - _NaN = useNaN; - Name = $"ZL({period})"; - _len = 1; - _ema = new(period); - } - //generic constructors (source) - - public ZL_Series() : this(0, false, true) { } - public ZL_Series(int period) : this(period, false, true) { } - public ZL_Series(TBars source) : this(source.Close, 0, false) { } - public ZL_Series(TBars source, int period) : this(source.Close, period, false) { } - public ZL_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public ZL_Series(TSeries source, int period) : this(source, period, false, true) { } - public ZL_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } - public ZL_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); - int _lag; - if (_period == 0) { - _lag = (int)((_len - 1) * 0.5); - _len++; - } - else { _lag = (int)((_period - 1) * 0.5); } - _lag = Math.Min(_lag, _buffer.Count - 1); - _lag = Math.Max(_lag, 0) + 1; - double _zlValue = 2 * TValue.v - _buffer[^_lag]; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlValue); - return base.Add(res, update); - } - - //variation of Add() - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - _ema.Reset(); - } +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +ZL: Zero Lag + Data is de-lagged by removing the data from “lag” days ago, thus removing + (or attempting to) the cumulative effect of the moving average. + +Calculation: + Lag = (Period-1)/2 + ZL = Data + (Data - Data(Lag days ago) ) + +Sources: + https://mudrex.com/blog/zero-lag-ema-trading-strategy/ + + */ + +public class ZL_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _ema; + + //core constructor + public ZL_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + Name = $"ZL({period})"; + _len = 1; + _ema = new(period); + } + //generic constructors (source) + + public ZL_Series() : this(0, false, true) { } + public ZL_Series(int period) : this(period, false, true) { } + public ZL_Series(TBars source) : this(source.Close, 0, false) { } + public ZL_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZL_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZL_Series(TSeries source, int period) : this(source, period, false, true) { } + public ZL_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public ZL_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + int _lag; + if (_period == 0) + { + _lag = (int)((_len - 1) * 0.5); + _len++; + } + else { _lag = (int)((_period - 1) * 0.5); } + _lag = Math.Min(_lag, _buffer.Count - 1); + _lag = Math.Max(_lag, 0) + 1; + double _zlValue = 2 * TValue.v - _buffer[^_lag]; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlValue); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _ema.Reset(); + } } \ No newline at end of file diff --git a/Calculations/_Updated/ZSCORE_Series.cs b/Calculations/_Updated/ZSCORE_Series.cs index c3ab4288..d8e5699d 100644 --- a/Calculations/_Updated/ZSCORE_Series.cs +++ b/Calculations/_Updated/ZSCORE_Series.cs @@ -1,88 +1,97 @@ -using System.Linq; - -namespace QuanTAlib; -using System; -using System.Collections.Generic; - -/* -ZSCORE: number of standard deviations from SMA - Z-score describes a value's relationship to the mean of a series, as measured in - terms of standard deviations from the mean. If a Z-score is 0, it indicates that - the data point's score is identical to the mean score. A Z-score of 1.0 would - indicate a value that is one standard deviation from the mean. Z-scores may be - positive or negative, with a positive value indicating the score is above the - mean and a negative score indicating it is below the mean. - -Sources: - https://en.wikipedia.org/wiki/Z-score - https://www.investopedia.com/terms/z/zscore.asp - -Calculation: - std = std * STDEV(close, length) - mean = SMA(close, length) - ZSCORE = (close - mean) / std - - */ - -public class ZSCORE_Series : TSeries { - private readonly System.Collections.Generic.List _buffer = new(); - protected readonly int _period; - protected readonly bool _NaN; - protected readonly TSeries _data; - - //core constructors - public ZSCORE_Series(int period, bool useNaN) { - _period = period; - _NaN = useNaN; - Name = $"ZSCORE({period})"; - } - public ZSCORE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) { - _data = source; - Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; - _data.Pub += Sub; - Add(_data); - } - public ZSCORE_Series() : this(period: 0, useNaN: false) { } - public ZSCORE_Series(int period) : this(period: period, useNaN: false) { } - public ZSCORE_Series(TBars source) : this(source.Close, 0, false) { } - public ZSCORE_Series(TBars source, int period) : this(source.Close, period, false) { } - public ZSCORE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } - public ZSCORE_Series(TSeries source) : this(source, 0, false) { } - public ZSCORE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } - - ////////////////// - // core Add() algo - public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { - BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update); - double _sma = _buffer.Average(); - - double _pvar = 0; - for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } - _pvar /= this._buffer.Count; - double _psdev = Math.Sqrt(_pvar); - double _zscore = (_psdev == 0) ? 1 : (TValue.v - _sma) / _psdev; - - var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zscore); - return base.Add(res, update); - } - - public override (DateTime t, double v) Add(TSeries data) { - if (data == null) { return (DateTime.Today, Double.NaN); } - foreach (var item in data) { Add(item, false); } - return _data.Last; - } - public (DateTime t, double v) Add(bool update) { - return this.Add(TValue: _data.Last, update: update); - } - public (DateTime t, double v) Add() { - return Add(TValue: _data.Last, update: false); - } - private new void Sub(object source, TSeriesEventArgs e) { - Add(TValue: _data.Last, update: e.update); - } - - //reset calculation - public override void Reset() { - _buffer.Clear(); - } +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ZSCORE: number of standard deviations from SMA + Z-score describes a value's relationship to the mean of a series, as measured in + terms of standard deviations from the mean. If a Z-score is 0, it indicates that + the data point's score is identical to the mean score. A Z-score of 1.0 would + indicate a value that is one standard deviation from the mean. Z-scores may be + positive or negative, with a positive value indicating the score is above the + mean and a negative score indicating it is below the mean. + +Sources: + https://en.wikipedia.org/wiki/Z-score + https://www.investopedia.com/terms/z/zscore.asp + +Calculation: + std = std * STDEV(close, length) + mean = SMA(close, length) + ZSCORE = (close - mean) / std + + */ + +public class ZSCORE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public ZSCORE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"ZSCORE({period})"; + } + public ZSCORE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public ZSCORE_Series() : this(period: 0, useNaN: false) { } + public ZSCORE_Series(int period) : this(period: period, useNaN: false) { } + public ZSCORE_Series(TBars source) : this(source.Close, 0, false) { } + public ZSCORE_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZSCORE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZSCORE_Series(TSeries source) : this(source, 0, false) { } + public ZSCORE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _sma = _buffer.Average(); + + double _pvar = 0; + for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _pvar /= this._buffer.Count; + double _psdev = Math.Sqrt(_pvar); + double _zscore = (_psdev == 0) ? 1 : (TValue.v - _sma) / _psdev; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zscore); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } } \ No newline at end of file diff --git a/Indicators/Charts/2MACross_chart.cs b/Indicators/Charts/2MACross_chart.cs index 2bc81b8e..19ead156 100644 --- a/Indicators/Charts/2MACross_chart.cs +++ b/Indicators/Charts/2MACross_chart.cs @@ -1,278 +1,298 @@ -using System; -using System.Drawing; -using System.Linq; -using TradingPlatform.BusinessLayer; -namespace QuanTAlib; - -public class MovingAverage_chart : Indicator { - #region Parameters - [InputParameter("MA1: Type:", 0, variants: new object[] - { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, - "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] - private int MA1type = 15; - - [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] - private int MA1Period = 10; - - [InputParameter("MA1: Data source:", 2, variants: new object[] - { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, - "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] - private int MA1DataSource = 3; - - [InputParameter("MA2: Type:", 3, variants: new object[] - { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, - "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] - private int MA2type = 16; - - [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] - private int MA2Period = 50; - - [InputParameter("MA2: Data source:", 5, variants: new object[] - { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, - "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] - private int MA2DataSource = 8; - - [InputParameter("Long trades", 6)] - private bool LongTrades = true; - - [InputParameter("Short trades", 6)] - private bool ShortTrades = true; - - #endregion Parameters - - protected HistoricalData History; - private TBars bars; - - /////// - private TSeries MA1, MA2; - private CROSS_Series trades; - private COMPARE_Series overunder; - - /////// - - public MovingAverage_chart() { - this.SeparateWindow = false; - this.Name = "MAs Crossover"; - this.AddLineSeries("MA1", Color.LimeGreen, 2, LineStyle.Solid); - this.AddLineSeries("MA2", Color.OrangeRed, 2, LineStyle.Solid); - } - - protected override void OnInit() { - this.bars = new(); - this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); - for (int i = this.History.Count - 1; i >= 0; i--) { - var rec = this.History[i, SeekOriginHistory.Begin]; - bars.Add(rec.TimeLeft, rec[PriceType.Open], - rec[PriceType.High], rec[PriceType.Low], - rec[PriceType.Close], rec[PriceType.Volume]); - } - this.Name = "MAs Cross: [ "; - switch (MA1type) { - case 0: - MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"SMA"; - break; - case 1: - MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"EMA"; - break; - case 2: - MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"WMA"; - break; - case 3: - MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"T3"; - break; - case 4: - MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"SMMA"; - break; - case 5: - MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"TRIMA"; - break; - case 6: - MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"DWMA"; - break; - case 7: - MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); - this.Name += $"FWMA"; - break; - case 8: - MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"DEMA"; - break; - case 9: - MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"TEMA"; - break; - case 10: - MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"ALMA"; - break; - case 11: - MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"HMA"; - break; - case 12: - MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"HEMA"; - break; - case 13: - double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); - MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); - this.Name += $"MAMA"; - break; - case 14: - MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"KAMA"; - break; - case 15: - MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"ZLEMA"; - break; - default: - MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"JMA"; - break; - } - - this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; - - switch (MA2type) { - case 0: - MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"SMA"; - break; - case 1: - MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"EMA"; - break; - case 2: - MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"WMA"; - break; - case 3: - MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"T3"; - break; - case 4: - MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"SMMA"; - break; - case 5: - MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"TRIMA"; - break; - case 6: - MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"DWMA"; - break; - case 7: - MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); - this.Name += $"FWMA"; - break; - case 8: - MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"DEMA"; - break; - case 9: - MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"TEMA"; - break; - case 10: - MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"ALMA"; - break; - case 11: - MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"HMA"; - break; - case 12: - MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"HEMA"; - break; - case 13: - double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); - MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); - this.Name += $"MAMA"; - break; - case 14: - MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"KAMA"; - break; - case 15: - MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"ZLEMA"; - break; - default: - MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"JMA"; - break; - } - this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; - - overunder = new(MA1, MA2); - trades = new(MA1, MA2); - } - - protected override void OnUpdate(UpdateArgs args) { - bool update = !(args.Reason == UpdateReason.NewBar || - args.Reason == UpdateReason.HistoricalBar); - this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), - this.GetPrice(PriceType.High), - this.GetPrice(PriceType.Low), - this.GetPrice(PriceType.Close), - this.GetPrice(PriceType.Volume), update); - this.SetValue(this.MA1[^1].v, lineIndex: 0); - this.SetValue(this.MA2[^1].v, lineIndex: 1); - - if (trades[^1].v == 1) { - this.EndCloud(0, 1, Color.Empty); - if (LongTrades) { - this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); - this.BeginCloud(0, 1, Color.FromArgb(127, Color.Green)); - } - if (ShortTrades) { - this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); - } - } - if (trades[^1].v == -1) { - this.EndCloud(0, 1, Color.Empty); - if (ShortTrades) { - this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); - this.BeginCloud(0, 1, Color.FromArgb(127, Color.Red)); - } - if (LongTrades) { - this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); - } - } - } - public override void OnPaintChart(PaintChartEventArgs args) { - base.OnPaintChart(args); - if (this.CurrentChart == null) {return;} - Graphics graphics = args.Graphics; - var mainWindow = this.CurrentChart.MainWindow; - int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); - int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); - int historycount = HistoricalData.Count; - int ymax = mainWindow.ClientRectangle.Height; - int xmax = mainWindow.ClientRectangle.Width; - - /* - for (int i = leftIndex; i <= rightIndex; i++) { - int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); - int width = this.CurrentChart.BarsWidth; - int height = (int)((equity[i+historycount].v) *proportion); - - Brush bb = Brushes.DarkSlateGray; - bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; - bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; - - graphics.FillRectangle(bb, xi, ymax - height, width, height); - } - */ - } -} +using System; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class MovingAverage_chart : Indicator +{ + #region Parameters + [InputParameter("MA1: Type:", 0, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA1type = 15; + + [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] + private int MA1Period = 10; + + [InputParameter("MA1: Data source:", 2, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA1DataSource = 3; + + [InputParameter("MA2: Type:", 3, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA2type = 16; + + [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] + private int MA2Period = 50; + + [InputParameter("MA2: Data source:", 5, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA2DataSource = 8; + + [InputParameter("Long trades", 6)] + private bool LongTrades = true; + + [InputParameter("Short trades", 6)] + private bool ShortTrades = true; + + #endregion Parameters + + protected HistoricalData History; + private TBars bars; + + /////// + private TSeries MA1, MA2; + private CROSS_Series trades; + private COMPARE_Series overunder; + + /////// + + public MovingAverage_chart() + { + this.SeparateWindow = false; + this.Name = "MAs Crossover"; + this.AddLineSeries("MA1", Color.LimeGreen, 2, LineStyle.Solid); + this.AddLineSeries("MA2", Color.OrangeRed, 2, LineStyle.Solid); + } + + protected override void OnInit() + { + this.bars = new(); + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + this.Name = "MAs Cross: [ "; + switch (MA1type) + { + case 0: + MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); + this.Name += $"FWMA"; + break; + case 8: + MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); + MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"JMA"; + break; + } + + this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; + + switch (MA2type) + { + case 0: + MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); + this.Name += $"FWMA"; + break; + case 8: + MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); + MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"JMA"; + break; + } + this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; + + int maxKeep = Math.Max(Math.Max(this.MA1Period, this.MA2Period), 100); + MA1.Keep = maxKeep; + MA2.Keep = maxKeep; + trades.Keep = maxKeep; + overunder.Keep = maxKeep; + + overunder = new(MA1, MA2); + trades = new(MA1, MA2); + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), + this.GetPrice(PriceType.High), + this.GetPrice(PriceType.Low), + this.GetPrice(PriceType.Close), + this.GetPrice(PriceType.Volume), update); + this.SetValue(this.MA1[^1].v, lineIndex: 0); + this.SetValue(this.MA2[^1].v, lineIndex: 1); + + if (trades[^1].v == 1) + { + this.EndCloud(0, 1, Color.Empty); + if (LongTrades) + { + this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); + this.BeginCloud(0, 1, Color.FromArgb(127, Color.Green)); + } + if (ShortTrades) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); + } + } + if (trades[^1].v == -1) + { + this.EndCloud(0, 1, Color.Empty); + if (ShortTrades) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + this.BeginCloud(0, 1, Color.FromArgb(127, Color.Red)); + } + if (LongTrades) + { + this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + } + } + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) { return; } + Graphics graphics = args.Graphics; + var mainWindow = this.CurrentChart.MainWindow; + int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); + int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); + int historycount = HistoricalData.Count; + int ymax = mainWindow.ClientRectangle.Height; + int xmax = mainWindow.ClientRectangle.Width; + + /* + for (int i = leftIndex; i <= rightIndex; i++) { + int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); + int width = this.CurrentChart.BarsWidth; + int height = (int)((equity[i+historycount].v) *proportion); + + Brush bb = Brushes.DarkSlateGray; + bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; + bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; + + graphics.FillRectangle(bb, xi, ymax - height, width, height); + } + */ + } +} diff --git a/Indicators/Charts/2MASlope_chart.cs b/Indicators/Charts/2MASlope_chart.cs index 547dc860..c031e513 100644 --- a/Indicators/Charts/2MASlope_chart.cs +++ b/Indicators/Charts/2MASlope_chart.cs @@ -1,298 +1,320 @@ -using System; -using System.Drawing; -using System.Linq; -using TradingPlatform.BusinessLayer; -namespace QuanTAlib; - -public class MovingAverageSlope_chart : Indicator { - #region Parameters - [InputParameter("MA1: Type:", 0, variants: new object[] - { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, - "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] - private int MA1type = 16; - - [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] - private int MA1Period = 10; - - [InputParameter("MA1: Data source:", 2, variants: new object[] - { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, - "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] - private int MA1DataSource = 3; - - [InputParameter("MA2: Type:", 3, variants: new object[] - { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, - "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] - private int MA2type = 6; - - [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] - private int MA2Period = 50; - - [InputParameter("MA2: Data source:", 5, variants: new object[] - { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, - "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] - private int MA2DataSource = 8; - - [InputParameter("Data required for slope calc:", 6, 2, 10, 1, 1)] - private int SlopePeriod = 3; - - [InputParameter("Long trades", 7)] - private bool LongTrades = true; - - [InputParameter("Short trades", 8)] - private bool ShortTrades; - - #endregion Parameters - - protected HistoricalData History; - private TBars bars; - - /////// - private TSeries MA1, MA2; - private SLOPE_Series sMA1, sMA2; - private CROSS_Series sig1, sig2; - - private bool inLong, inShort; - /////// - - public MovingAverageSlope_chart() { - this.SeparateWindow = false; - this.Name = "Slopes convergence"; - this.AddLineSeries("MA1", Color.DarkSlateGray, 2, LineStyle.Solid); - this.AddLineSeries("MA2", Color.DarkSlateGray, 2, LineStyle.Solid); - } - - protected override void OnInit() { - this.bars = new(); - this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); - for (int i = this.History.Count - 1; i >= 0; i--) { - var rec = this.History[i, SeekOriginHistory.Begin]; - bars.Add(rec.TimeLeft, rec[PriceType.Open], - rec[PriceType.High], rec[PriceType.Low], - rec[PriceType.Close], rec[PriceType.Volume]); - } - this.Name = "Slopes convergence: [ "; - switch (MA1type) { - case 0: - MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"SMA"; - break; - case 1: - MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"EMA"; - break; - case 2: - MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"WMA"; - break; - case 3: - MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"T3"; - break; - case 4: - MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"SMMA"; - break; - case 5: - MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"TRIMA"; - break; - case 6: - MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"DWMA"; - break; - case 7: - MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); - this.Name += $"FWMA"; - break; - case 8: - MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"DEMA"; - break; - case 9: - MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"TEMA"; - break; - case 10: - MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"ALMA"; - break; - case 11: - MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"HMA"; - break; - case 12: - MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"HEMA"; - break; - case 13: - double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); - MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); - this.Name += $"MAMA"; - break; - case 14: - MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"KAMA"; - break; - case 15: - MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"ZLEMA"; - break; - default: - MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); - this.Name += $"JMA"; - break; - } - - this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; - - switch (MA2type) { - case 0: - MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"SMA"; - break; - case 1: - MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"EMA"; - break; - case 2: - MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"WMA"; - break; - case 3: - MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"T3"; - break; - case 4: - MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"SMMA"; - break; - case 5: - MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"TRIMA"; - break; - case 6: - MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"DWMA"; - break; - case 7: - MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); - this.Name += $"FWMA"; - break; - case 8: - MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"DEMA"; - break; - case 9: - MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"TEMA"; - break; - case 10: - MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"ALMA"; - break; - case 11: - MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"HMA"; - break; - case 12: - MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"HEMA"; - break; - case 13: - double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); - MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); - this.Name += $"MAMA"; - break; - case 14: - MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"KAMA"; - break; - case 15: - MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"ZLEMA"; - break; - default: - MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); - this.Name += $"JMA"; - break; - } - this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; - - sMA1 = new(MA1, SlopePeriod); - sMA2 = new(MA2, SlopePeriod); - sig1 = new(sMA1, 0); - sig2 = new(sMA2, 0); - } - - protected override void OnUpdate(UpdateArgs args) { - bool update = !(args.Reason == UpdateReason.NewBar || - args.Reason == UpdateReason.HistoricalBar); - this.bars.Add(this.Time(),this.Open(), this.High(), this.Low(), this.Close(), this.Volume(), update); - this.SetValue(this.MA1[^1].v, lineIndex: 0); - this.SetValue(this.MA2[^1].v, lineIndex: 1); - - Color s1Color= (this.sMA1[^1].v > 0)?Color.LimeGreen:Color.OrangeRed; - Color s2Color = (this.sMA2[^1].v > 0) ? Color.LimeGreen : Color.OrangeRed; - - this.LinesSeries[0].SetMarker(0,s1Color); - this.LinesSeries[1].SetMarker(0,s2Color); - - if (sig1[^1].v > 0 || sig2[^1].v > 0) { - if (sMA1[^1].v >= 0 && sMA2[^1].v >= 0 && LongTrades) - { - inLong = true; - this.BeginCloud(0, 1, Color.FromArgb(127, Color.DarkGreen)); - this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v)? 0 : 1 ].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); - } - else { - this.EndCloud(0, 1, Color.Empty); - if (inShort) - { - this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v) ? 1 : 0].SetMarker(1, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); - inShort = false; - } - } - } - - if (sig1[^1].v < 0 || sig2[^1].v < 0) { - if (sMA1[^1].v <= 0 && sMA2[^1].v <= 0 && ShortTrades) - { - inShort = true; - this.BeginCloud(0, 1, Color.FromArgb(100, Color.Red)); - this.LinesSeries[(this.MA1[^1].v > this.MA2[^1].v) ? 0 : 1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); - } - else { - this.EndCloud(0, 1, Color.Empty); - if (inLong) { - LinesSeries[(this.MA1[^1].v > this.MA2[^1].v)?1:0].SetMarker(1, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); - inLong = false; - } - } - } - } - public override void OnPaintChart(PaintChartEventArgs args) { - base.OnPaintChart(args); - if (this.CurrentChart == null) {return;} - Graphics graphics = args.Graphics; - var mainWindow = this.CurrentChart.MainWindow; - int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); - int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); -/* - int historycount = HistoricalData.Count; - int ymax = mainWindow.ClientRectangle.Height; - - - for (int i = leftIndex; i <= rightIndex; i++) { - int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); - int width = this.CurrentChart.BarsWidth; - int height = (int)((equity[i+historycount].v) *proportion); - - Brush bb = Brushes.DarkSlateGray; - bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; - bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; - - graphics.FillRectangle(bb, xi, ymax - height, width, height); - } -*/ - } -} +using System; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class MovingAverageSlope_chart : Indicator +{ + #region Parameters + [InputParameter("MA1: Type:", 0, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA1type = 16; + + [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] + private int MA1Period = 10; + + [InputParameter("MA1: Data source:", 2, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA1DataSource = 3; + + [InputParameter("MA2: Type:", 3, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA2type = 6; + + [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] + private int MA2Period = 50; + + [InputParameter("MA2: Data source:", 5, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA2DataSource = 8; + + [InputParameter("Data required for slope calc:", 6, 2, 10, 1, 1)] + private int SlopePeriod = 3; + + [InputParameter("Long trades", 7)] + private bool LongTrades = true; + + [InputParameter("Short trades", 8)] + private bool ShortTrades; + + #endregion Parameters + + protected HistoricalData History; + private TBars bars; + + /////// + private TSeries MA1, MA2; + private SLOPE_Series sMA1, sMA2; + private CROSS_Series sig1, sig2; + + private bool inLong, inShort; + /////// + + public MovingAverageSlope_chart() + { + this.SeparateWindow = false; + this.Name = "Slopes convergence"; + this.AddLineSeries("MA1", Color.DarkSlateGray, 2, LineStyle.Solid); + this.AddLineSeries("MA2", Color.DarkSlateGray, 2, LineStyle.Solid); + } + + protected override void OnInit() + { + this.bars = new(); + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + this.Name = "Slopes convergence: [ "; + switch (MA1type) + { + case 0: + MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); + this.Name += $"FWMA"; + break; + case 8: + MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); + MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"JMA"; + break; + } + + this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; + + switch (MA2type) + { + case 0: + MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); + this.Name += $"FWMA"; + break; + case 8: + MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); + MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"JMA"; + break; + } + this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; + + sMA1 = new(MA1, SlopePeriod); + sMA2 = new(MA2, SlopePeriod); + sig1 = new(sMA1, 0); + sig2 = new(sMA2, 0); + + int maxKeep = Math.Max(Math.Max(this.MA1Period, this.MA2Period), 100); + + MA1.Keep = maxKeep; + MA2.Keep = maxKeep; + sMA1.Keep = maxKeep; + sMA2.Keep = maxKeep; + sig1.Keep = maxKeep; + sig2.Keep = maxKeep; + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.Open(), this.High(), this.Low(), this.Close(), this.Volume(), update); + this.SetValue(this.MA1[^1].v, lineIndex: 0); + this.SetValue(this.MA2[^1].v, lineIndex: 1); + + Color s1Color = (this.sMA1[^1].v > 0) ? Color.LimeGreen : Color.OrangeRed; + Color s2Color = (this.sMA2[^1].v > 0) ? Color.LimeGreen : Color.OrangeRed; + + this.LinesSeries[0].SetMarker(0, s1Color); + this.LinesSeries[1].SetMarker(0, s2Color); + + if (sig1[^1].v > 0 || sig2[^1].v > 0) + { + if (sMA1[^1].v >= 0 && sMA2[^1].v >= 0 && LongTrades) + { + inLong = true; + this.BeginCloud(0, 1, Color.FromArgb(127, Color.DarkGreen)); + this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v) ? 0 : 1].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); + } + else + { + this.EndCloud(0, 1, Color.Empty); + if (inShort && this.Count > 1) + { + this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v) ? 1 : 0].SetMarker(1, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); + inShort = false; + } + } + } + + if (sig1[^1].v < 0 || sig2[^1].v < 0) + { + if (sMA1[^1].v <= 0 && sMA2[^1].v <= 0 && ShortTrades) + { + inShort = true; + this.BeginCloud(0, 1, Color.FromArgb(100, Color.Red)); + this.LinesSeries[(this.MA1[^1].v > this.MA2[^1].v) ? 0 : 1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + } + else + { + this.EndCloud(0, 1, Color.Empty); + if (inLong && this.Count > 1) + { + LinesSeries[(this.MA1[^1].v > this.MA2[^1].v) ? 1 : 0].SetMarker(1, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + inLong = false; + } + } + } + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) { return; } + Graphics graphics = args.Graphics; + var mainWindow = this.CurrentChart.MainWindow; + int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); + int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); + /* + int historycount = HistoricalData.Count; + int ymax = mainWindow.ClientRectangle.Height; + + + for (int i = leftIndex; i <= rightIndex; i++) { + int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); + int width = this.CurrentChart.BarsWidth; + int height = (int)((equity[i+historycount].v) *proportion); + + Brush bb = Brushes.DarkSlateGray; + bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; + bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; + + graphics.FillRectangle(bb, xi, ymax - height, width, height); + } + */ + } +} diff --git a/Indicators/Charts/JMA_chart.cs b/Indicators/Charts/JMA_chart.cs index 7d5e5341..d35174cf 100644 --- a/Indicators/Charts/JMA_chart.cs +++ b/Indicators/Charts/JMA_chart.cs @@ -1,96 +1,104 @@ -using System; -using System.Diagnostics; -using System.Drawing; -using System.Linq; -using TradingPlatform.BusinessLayer; -using TradingPlatform.BusinessLayer.Chart; -namespace QuanTAlib; - -public class JMA_chart : Indicator { - #region Parameters - - [InputParameter("Data source", 0, variants: new object[] - { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, - "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] - private int DataSource = 3; - - [InputParameter("Smoothing period", 1, 1, 999, 1, 1)] - private int Period = 9; - - [InputParameter("Volatility short", 2, 3, 50, 1, 1)] - private int Vshort = 10; - - [InputParameter("Volatility long", 3, 20, 500, 1, 1)] - private int Vlong = 65; - - [InputParameter("Phase", 4, -100, 100, 1, 2)] - private double Jphase; - - #endregion Parameters - - /////// - private JMA_Series indicator; - /////// - - protected TBars bars; - protected IChartWindow mainWindow; - protected Graphics graphics; - protected int firstOnScreenBarIndex, lastOnScreenBarIndex; - protected HistoricalData History; - protected int HistPeriod; - public JMA_chart() { - Name = "JMA - Jurik Moving Avg"; - Description = "Jurik Moving Average description"; - AddLineSeries(lineName: "JMA", lineColor: Color.Yellow, lineWidth: 3,lineStyle: LineStyle.Solid); - SeparateWindow = false; - HistPeriod = Period; - } - - - protected override void OnInit() { - base.OnInit(); - bars = new(); - var dur1 = this.HistoricalData.FromTime; - var dur = this.HistoricalData.Period.Duration.TotalSeconds * (HistPeriod * 4); //seconds of two periods - - this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); - - for (int i = this.History.Count - 1; i >= 0; i--) { - - var rec = this.History[i, SeekOriginHistory.Begin]; - - bars.Add(rec.TimeLeft, rec[PriceType.Open], - rec[PriceType.High], rec[PriceType.Low], - rec[PriceType.Close], rec[PriceType.Volume]); - } - - indicator = new(source: bars.Select(DataSource), period: Period, phase: Jphase, vshort: Vshort, vlong: Vlong, useNaN: true); - } - - protected override void OnUpdate(UpdateArgs args) { - base.OnUpdate(args); - bars.Add(Time(), GetPrice(PriceType.Open), - GetPrice(PriceType.High), - GetPrice(PriceType.Low), - GetPrice(PriceType.Close), - GetPrice(PriceType.Volume), - update: !(args.Reason == UpdateReason.NewBar || args.Reason == UpdateReason.HistoricalBar)); - - this.SetValue(indicator[^1].v, lineIndex: 0); - } - public override void OnPaintChart(PaintChartEventArgs args) { - base.OnPaintChart(args); - if (this.CurrentChart == null) { - return; - } - - graphics = args.Graphics; - mainWindow = this.CurrentChart.MainWindow; - - DateTime leftTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left); - DateTime rightTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right); - firstOnScreenBarIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(leftTime); - lastOnScreenBarIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(rightTime)); - } - -} +using System; +using System.Diagnostics; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +using TradingPlatform.BusinessLayer.Chart; +namespace QuanTAlib; + +public class JMA_chart : Indicator +{ + #region Parameters + + [InputParameter("Data source", 0, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int DataSource = 3; + + [InputParameter("Smoothing period", 1, 1, 999, 1, 1)] + private int Period = 9; + + [InputParameter("Volatility short", 2, 3, 50, 1, 1)] + private int Vshort = 10; + + [InputParameter("Volatility long", 3, 20, 500, 1, 1)] + private int Vlong = 65; + + [InputParameter("Phase", 4, -100, 100, 1, 2)] + private double Jphase; + + #endregion Parameters + + /////// + private JMA_Series indicator; + /////// + + protected TBars bars; + protected IChartWindow mainWindow; + protected Graphics graphics; + protected int firstOnScreenBarIndex, lastOnScreenBarIndex; + protected HistoricalData History; + protected int HistPeriod; + public JMA_chart() + { + Name = "JMA - Jurik Moving Avg"; + Description = "Jurik Moving Average description"; + AddLineSeries(lineName: "JMA", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + SeparateWindow = false; + HistPeriod = Period; + } + + + protected override void OnInit() + { + base.OnInit(); + bars = new(); + var dur1 = this.HistoricalData.FromTime; + var dur = this.HistoricalData.Period.Duration.TotalSeconds * (HistPeriod * 4); //seconds of two periods + + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + + for (int i = this.History.Count - 1; i >= 0; i--) + { + + var rec = this.History[i, SeekOriginHistory.Begin]; + + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + + indicator = new(source: bars.Select(DataSource), period: Period, phase: Jphase, vshort: Vshort, vlong: Vlong, useNaN: true); + indicator.Keep = Math.Max(Period, 100); + } + + protected override void OnUpdate(UpdateArgs args) + { + base.OnUpdate(args); + bars.Add(Time(), GetPrice(PriceType.Open), + GetPrice(PriceType.High), + GetPrice(PriceType.Low), + GetPrice(PriceType.Close), + GetPrice(PriceType.Volume), + update: !(args.Reason == UpdateReason.NewBar || args.Reason == UpdateReason.HistoricalBar)); + + this.SetValue(indicator[^1].v, lineIndex: 0); + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) + { + return; + } + + graphics = args.Graphics; + mainWindow = this.CurrentChart.MainWindow; + + DateTime leftTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left); + DateTime rightTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right); + firstOnScreenBarIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(leftTime); + lastOnScreenBarIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(rightTime)); + } + +} diff --git a/Indicators/Charts/TrailingStop.cs b/Indicators/Charts/TrailingStop.cs index 29aee917..8ee9f476 100644 --- a/Indicators/Charts/TrailingStop.cs +++ b/Indicators/Charts/TrailingStop.cs @@ -1,95 +1,102 @@ -using System; -using System.Diagnostics; -using System.Drawing; -using System.Linq; -using TradingPlatform.BusinessLayer; -namespace QuanTAlib; - -public class TrailingStop_chart : Indicator { - #region Parameters - - [InputParameter("Period", 0, 1, 100, 1, 1)] - protected int _period = 30; - - [InputParameter("Factor", 1, 1, 100, 0.1, 1)] - protected double _factor = 10; - - [InputParameter("Long TS", 2)] - private bool _LongTS = true; - - [InputParameter("Short TS", 3)] - private bool _ShortTS = true; - - #endregion Parameters - - /////// - private HistoricalData History; - private TBars bars; - private ATR_Series _atr; - private double _tslineL, _ratchetL, _tslineS, _ratchetS; - - /////// - - public TrailingStop_chart() { - Name = $"ATR Trailing Stop"; - AddLineSeries(lineName: "TrailingATR Long", lineColor: Color.Yellow, lineWidth: 1,lineStyle: LineStyle.Dot); - AddLineSeries(lineName: "Ratchet Long", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); - - AddLineSeries(lineName: "TrailingATR Short", lineColor: Color.Yellow, lineWidth: 1, lineStyle: LineStyle.Dot); - AddLineSeries(lineName: "Ratchet Short", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); - - SeparateWindow = false; - } - - - protected override void OnInit() { - this.Name = $"Trailing Stop (ATR:{_period}, Mult:{_factor:f2})"; - this.bars = new(); - - this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); - for (int i = this.History.Count - 1; i >= 0; i--) { - var rec = this.History[i, SeekOriginHistory.Begin]; - bars.Add(rec.TimeLeft, rec[PriceType.Open], - rec[PriceType.High], rec[PriceType.Low], - rec[PriceType.Close], rec[PriceType.Volume]); - } - _atr = new(source: bars, _period, useNaN: true); - _ratchetL = Double.NegativeInfinity; - _ratchetS = Double.PositiveInfinity; - - this.LinesSeries[0].Visible = _LongTS; - this.LinesSeries[1].Visible = _LongTS; - this.LinesSeries[2].Visible = _ShortTS; - this.LinesSeries[3].Visible = _ShortTS; - } - - protected override void OnUpdate(UpdateArgs args) { - bool update = !(args.Reason == UpdateReason.NewBar || - args.Reason == UpdateReason.HistoricalBar); - this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), - this.GetPrice(PriceType.High), - this.GetPrice(PriceType.Low), - this.GetPrice(PriceType.Close), - this.GetPrice(PriceType.Volume), update); - - _tslineL = bars.High[^1].v - (_factor * _atr[^1].v); - _ratchetL = Math.Max(_tslineL,_ratchetL); - if (_ratchetL > bars.Low[^1].v) { - this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.Yellow, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); - _ratchetL = _tslineL; - } - - _tslineS = bars.High[^1].v + (_factor * _atr[^1].v); - _ratchetS = Math.Min(_tslineS, _ratchetS); - if (_ratchetS < bars.High[^1].v) { - this.LinesSeries[3].SetMarker(0, new IndicatorLineMarker(Color.Yellow, upperIcon: IndicatorLineMarkerIconType.UpArrow)); - _ratchetS = _tslineS; - } - - this.SetValue(_tslineL, lineIndex: 0); - this.SetValue(_ratchetL, lineIndex: 1); - this.SetValue(_tslineS, lineIndex: 2); - this.SetValue(_ratchetS, lineIndex: 3); - } -} - +using System; +using System.Diagnostics; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class TrailingStop_chart : Indicator +{ + #region Parameters + + [InputParameter("Period", 0, 1, 100, 1, 1)] + protected int _period = 30; + + [InputParameter("Factor", 1, 1, 100, 0.1, 1)] + protected double _factor = 10; + + [InputParameter("Long TS", 2)] + private bool _LongTS = true; + + [InputParameter("Short TS", 3)] + private bool _ShortTS = true; + + #endregion Parameters + + /////// + private HistoricalData History; + private TBars bars; + private ATR_Series _atr; + private double _tslineL, _ratchetL, _tslineS, _ratchetS; + + /////// + + public TrailingStop_chart() + { + Name = $"ATR Trailing Stop"; + AddLineSeries(lineName: "TrailingATR Long", lineColor: Color.Yellow, lineWidth: 1, lineStyle: LineStyle.Dot); + AddLineSeries(lineName: "Ratchet Long", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + + AddLineSeries(lineName: "TrailingATR Short", lineColor: Color.Yellow, lineWidth: 1, lineStyle: LineStyle.Dot); + AddLineSeries(lineName: "Ratchet Short", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + + SeparateWindow = false; + } + + + protected override void OnInit() + { + this.Name = $"Trailing Stop (ATR:{_period}, Mult:{_factor:f2})"; + this.bars = new(); + + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + _atr = new(source: bars, _period, useNaN: true); + _ratchetL = Double.NegativeInfinity; + _ratchetS = Double.PositiveInfinity; + + this.LinesSeries[0].Visible = _LongTS; + this.LinesSeries[1].Visible = _LongTS; + this.LinesSeries[2].Visible = _ShortTS; + this.LinesSeries[3].Visible = _ShortTS; + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), + this.GetPrice(PriceType.High), + this.GetPrice(PriceType.Low), + this.GetPrice(PriceType.Close), + this.GetPrice(PriceType.Volume), update); + + _tslineL = bars.High[^1].v - (_factor * _atr[^1].v); + _ratchetL = Math.Max(_tslineL, _ratchetL); + if (_ratchetL > bars.Low[^1].v) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.Yellow, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + _ratchetL = _tslineL; + } + + _tslineS = bars.High[^1].v + (_factor * _atr[^1].v); + _ratchetS = Math.Min(_tslineS, _ratchetS); + if (_ratchetS < bars.High[^1].v) + { + this.LinesSeries[3].SetMarker(0, new IndicatorLineMarker(Color.Yellow, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + _ratchetS = _tslineS; + } + + this.SetValue(_tslineL, lineIndex: 0); + this.SetValue(_ratchetL, lineIndex: 1); + this.SetValue(_tslineS, lineIndex: 2); + this.SetValue(_ratchetS, lineIndex: 3); + } +} + diff --git a/Indicators/Indicators.csproj b/Indicators/Indicators.csproj index 5b9da593..eaa8bbb0 100644 --- a/Indicators/Indicators.csproj +++ b/Indicators/Indicators.csproj @@ -1,56 +1,56 @@ - - - net7.0 - preview - false - AnyCPU - Indicator - QuanTAlib_Indicators - QuanTAlib - embedded - AnyCPU - disable - False - ..\.sonarlint\mihakralj_quantalibcsharp.ruleset - 0.2.1.0 - 0.2.1.0 - 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d - 0.2.1-dev.2 - NETSDK1057 - true - NETSDK1057 - - - True - 3 - True - anycpu - full - - - embedded - True - 3 - True - anycpu - - - - - - - - - - - - - QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) - - - - - ..\.github\TradingPlatform.BusinessLayer.dll - - + + + net7.0 + preview + false + AnyCPU + Indicator + QuanTAlib_Indicators + QuanTAlib + embedded + AnyCPU + disable + False + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1.0 + 0.2.1.0 + 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d + 0.2.1-dev.2 + NETSDK1057 + true + NETSDK1057 + + + True + 3 + True + anycpu + full + + + embedded + True + 3 + True + anycpu + + + + + + + + + + + + + QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) + + + + + ..\.github\TradingPlatform.BusinessLayer.dll + + \ No newline at end of file diff --git a/LICENSE b/LICENSE index 261eeb9e..29f81d81 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,201 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/QuanTAlib.sln b/QuanTAlib.sln index d0b6ce59..ab71a1e8 100644 --- a/QuanTAlib.sln +++ b/QuanTAlib.sln @@ -1,74 +1,24 @@ - -Microsoft Visual Studio Solution File, Format Version 12.00 +Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 17 VisualStudioVersion = 17.2.32210.308 MinimumVisualStudioVersion = 10.0.40219.1 -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Calculations", "Calculations\Calculations.csproj", "{AAE21F8A-9BC2-4647-A9EB-4DC86C569080}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tests", "Tests\Tests.csproj", "{283EACC9-3AF6-4DAE-9C1C-0F7F8C8CD70D}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Indicators", "Indicators\Indicators.csproj", "{43AD2D78-024C-4D96-A70B-915CF519965A}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Strategies", "Strategies\Strategies.csproj", "{FA526AF6-95BC-4AC0-8B46-A304FD06689D}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Docs", "Docs", "{47B6ACDB-F535-4FEB-9A0A-C427CAE8C28E}" - ProjectSection(SolutionItems) = preProject - docs\.nojekyll = docs\.nojekyll - docs\ALMA.md = docs\ALMA.md - docs\DEMA.md = docs\DEMA.md - docs\DWMA.md = docs\DWMA.md - docs\EMA.md = docs\EMA.md - docs\FMA.md = docs\FMA.md - docs\getting_started.ipynb = docs\getting_started.ipynb - docs\HEMA.md = docs\HEMA.md - docs\HMA.md = docs\HMA.md - docs\HWMA.md = docs\HWMA.md - docs\index.html = docs\index.html - docs\indicators.md = docs\indicators.md - docs\JMA.md = docs\JMA.md - docs\KAMA.md = docs\KAMA.md - docs\LICENSE = docs\LICENSE - docs\MAMA.md = docs\MAMA.md - docs\QA.md = docs\QA.md - docs\readme.md = docs\readme.md - docs\RMA.md = docs\RMA.md - docs\SMA.md = docs\SMA.md - docs\SMMA.md = docs\SMMA.md - docs\T3.md = docs\T3.md - docs\TEMA.md = docs\TEMA.md - docs\TRIMA.md = docs\TRIMA.md - docs\WMA.md = docs\WMA.md - docs\ZLEMA.md = docs\ZLEMA.md - docs\_sidebar.md = docs\_sidebar.md - EndProjectSection +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Calculations", "v2\calculations.csproj", "{AAE21F8A-9BC2-4647-A9EB-4DC86C569080}" EndProject Global - GlobalSection(SolutionConfigurationPlatforms) = preSolution - Debug|Any CPU = Debug|Any CPU - Release|Any CPU = Release|Any CPU - EndGlobalSection - GlobalSection(ProjectConfigurationPlatforms) = postSolution - {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.Build.0 = Debug|Any CPU - {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.ActiveCfg = Release|Any CPU - {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.Build.0 = Release|Any CPU - {283EACC9-3AF6-4DAE-9C1C-0F7F8C8CD70D}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {283EACC9-3AF6-4DAE-9C1C-0F7F8C8CD70D}.Debug|Any CPU.Build.0 = Debug|Any CPU - {283EACC9-3AF6-4DAE-9C1C-0F7F8C8CD70D}.Release|Any CPU.ActiveCfg = Release|Any CPU - {283EACC9-3AF6-4DAE-9C1C-0F7F8C8CD70D}.Release|Any CPU.Build.0 = Release|Any CPU - {43AD2D78-024C-4D96-A70B-915CF519965A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {43AD2D78-024C-4D96-A70B-915CF519965A}.Debug|Any CPU.Build.0 = Debug|Any CPU - {43AD2D78-024C-4D96-A70B-915CF519965A}.Release|Any CPU.ActiveCfg = Release|Any CPU - {43AD2D78-024C-4D96-A70B-915CF519965A}.Release|Any CPU.Build.0 = Release|Any CPU - {FA526AF6-95BC-4AC0-8B46-A304FD06689D}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {FA526AF6-95BC-4AC0-8B46-A304FD06689D}.Debug|Any CPU.Build.0 = Debug|Any CPU - {FA526AF6-95BC-4AC0-8B46-A304FD06689D}.Release|Any CPU.ActiveCfg = Release|Any CPU - {FA526AF6-95BC-4AC0-8B46-A304FD06689D}.Release|Any CPU.Build.0 = Release|Any CPU - EndGlobalSection - GlobalSection(SolutionProperties) = preSolution - HideSolutionNode = FALSE - EndGlobalSection - GlobalSection(ExtensibilityGlobals) = postSolution - SolutionGuid = {E5592DC2-0542-45B2-A0CF-C6B1EDC72B87} - EndGlobalSection -EndGlobal + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug|Any CPU = Debug|Any CPU + Release|Any CPU = Release|Any CPU + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.Build.0 = Debug|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.ActiveCfg = Release|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.Build.0 = Release|Any CPU + EndGlobalSection + GlobalSection(SolutionProperties) = preSolution + HideSolutionNode = FALSE + EndGlobalSection + GlobalSection(ExtensibilityGlobals) = postSolution + SolutionGuid = {E5592DC2-0542-45B2-A0CF-C6B1EDC72B87} + EndGlobalSection +EndGlobal \ No newline at end of file diff --git a/Strategies/Strategies.csproj b/Strategies/Strategies.csproj index e665dc50..5cf21f57 100644 --- a/Strategies/Strategies.csproj +++ b/Strategies/Strategies.csproj @@ -1,53 +1,53 @@ - - - net7.0 - preview - false - AnyCPU - Strategy - QuanTAlib_Strategies - QuanTAlib - embedded - AnyCPU - disable - False - ..\.sonarlint\mihakralj_quantalibcsharp.ruleset - 0.2.1.0 - 0.2.1.0 - 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d - 0.2.1-dev.2 - NETSDK1057 - true - NETSDK1057 - - - True - 3 - True - anycpu - full - - - embedded - True - 3 - True - anycpu - - - - - - - QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) - - - - - - - - ..\.github\TradingPlatform.BusinessLayer.dll - - + + + net7.0 + preview + false + AnyCPU + Strategy + QuanTAlib_Strategies + QuanTAlib + embedded + AnyCPU + disable + False + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1.0 + 0.2.1.0 + 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d + 0.2.1-dev.2 + NETSDK1057 + true + NETSDK1057 + + + True + 3 + True + anycpu + full + + + embedded + True + 3 + True + anycpu + + + + + + + QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) + + + + + + + + ..\.github\TradingPlatform.BusinessLayer.dll + + \ No newline at end of file diff --git a/Tests/Basic tests/Indicators.cs b/Tests/Basic tests/Indicators.cs index de49baae..0bac1c17 100644 --- a/Tests/Basic tests/Indicators.cs +++ b/Tests/Basic tests/Indicators.cs @@ -1,155 +1,156 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Basics; -#nullable disable -public class Indicators -{ - private static Type[] maSeriesTypes = new Type[] - { - typeof(SMA_Series), - typeof(EMA_Series), - typeof(DEMA_Series), - typeof(TEMA_Series), - typeof(WMA_Series), - typeof(ALMA_Series), - typeof(DWMA_Series), - typeof(FWMA_Series), - typeof(HMA_Series), - typeof(ZLEMA_Series), - typeof(RMA_Series), - typeof(HEMA_Series), - typeof(JMA_Series), - typeof(CUSUM_Series), - typeof(SMMA_Series), - typeof(T3_Series), - typeof(KAMA_Series), - typeof(TRIMA_Series), - typeof(MAMA_Series), - typeof(HWMA_Series), -}; - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Name_exists(Type classType) - { - TSeries data = new("Data") {1,2,3}; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - Assert.NotEmpty(MA_Series.Name); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Series_Length(Type classType) - { - GBM_Feed feed = new(1000); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - Assert.Equal(1000, MA_Series.Count); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Return_data(Type classType) - { - TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - var result = MA_Series.Add(20); - Assert.Equal(result.v, MA_Series.Last.v); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Update(Type classType) - { - TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - var pre_update = MA_Series.Last.v; - - double pre_data = data.Last.v; - data.Add(20, true); - data.Add(pre_data, true); - - Assert.Equal(pre_update, MA_Series.Last.v); - Assert.Equal(data.Count, MA_Series.Count); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Period_zero(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; - Assert.Equal(data.Count, MA_Series.Count); - Assert.False(double.IsNaN(MA_Series.Last.v)); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Reset(Type classType) - { - GBM_Feed feed = new(10); - TSeries data = feed.OHLC4; - var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; - MA_Series.Reset(); - data.Add(0); - Assert.Equal(data.Last.v, MA_Series.Last.v); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Period_one(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; - Assert.InRange(MA_Series.Last.v - data.Last.v, -10e-6, 10e-6); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void NaN_test(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.True(double.IsNaN(MA_Series[0].v)); - Assert.True(double.IsNaN(MA_Series[8].v)); - Assert.False(double.IsNaN(MA_Series[9].v)); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Edge_numbers(Type classType) - { - TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; - var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.Equal(4, MA_Series.Count); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void handling_NaN(Type classType) { - TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; -var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.False(double.IsNaN(MA_Series.Last.v)); - } - -public static IEnumerable MASeriesData() - { - foreach (var type in maSeriesTypes) - { - yield return new object[] { type }; - } - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class Indicators +{ + private static Type[] maSeriesTypes = new Type[] + { + typeof(SMA_Series), + typeof(EMA_Series), + typeof(DEMA_Series), + typeof(TEMA_Series), + typeof(WMA_Series), + typeof(ALMA_Series), + typeof(DWMA_Series), + typeof(FWMA_Series), + typeof(HMA_Series), + typeof(ZLEMA_Series), + typeof(RMA_Series), + typeof(HEMA_Series), + typeof(JMA_Series), + typeof(CUSUM_Series), + typeof(SMMA_Series), + typeof(T3_Series), + typeof(KAMA_Series), + typeof(TRIMA_Series), + typeof(MAMA_Series), + typeof(HWMA_Series), + }; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + TSeries data = new("Data") { 1, 2, 3 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed feed = new(1000); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var result = MA_Series.Add(20); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var pre_update = MA_Series.Last.v; + + double pre_data = data.Last.v; + data.Add(20, true); + data.Add(pre_data, true); + + Assert.Equal(pre_update, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_zero(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; + Assert.Equal(data.Count, MA_Series.Count); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed feed = new(10); + TSeries data = feed.OHLC4; + var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; + MA_Series.Reset(); + data.Add(0); + Assert.Equal(data.Last.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_one(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; + Assert.InRange(MA_Series.Last.v - data.Last.v, -10e-6, 10e-6); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void NaN_test(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.True(double.IsNaN(MA_Series[0].v)); + Assert.True(double.IsNaN(MA_Series[8].v)); + Assert.False(double.IsNaN(MA_Series[9].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Edge_numbers(Type classType) + { + TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.Equal(4, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void handling_NaN(Type classType) + { + TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} #nullable restore \ No newline at end of file diff --git a/Tests/Basic tests/Oscillators.cs b/Tests/Basic tests/Oscillators.cs index 7d87e784..61911d66 100644 --- a/Tests/Basic tests/Oscillators.cs +++ b/Tests/Basic tests/Oscillators.cs @@ -1,160 +1,161 @@ -using Xunit; -using System; -using System.Runtime.InteropServices; -using QuanTAlib; - -namespace Basics; -#nullable disable -public class Oscillators -{ - private static Type[] maSeriesTypes = new[] - { - typeof(BIAS_Series), - typeof(MAX_Series), - typeof(MIN_Series), - typeof(MIDPOINT_Series), - typeof(ZL_Series), - typeof(DECAY_Series), - typeof(ENTROPY_Series), - typeof(KURTOSIS_Series), - typeof(MAD_Series), - typeof(MAPE_Series), - typeof(MAE_Series), - typeof(MSE_Series), - typeof(SDEV_Series), - typeof(SMAPE_Series), - typeof(WMAPE_Series), - typeof(SSDEV_Series), - typeof(VAR_Series), - typeof(SVAR_Series), - typeof(MEDIAN_Series), - typeof(ZSCORE_Series), - typeof(CMO_Series), - typeof(RSI_Series), - typeof(TRIX_Series), - typeof(BBANDS_Series), -}; - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Name_exists(Type classType) - { - TSeries data = new("Data") {1,2,3}; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - Assert.NotEmpty(MA_Series.Name); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Series_Length(Type classType) - { - GBM_Feed feed = new(1000); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - Assert.Equal(1000, MA_Series.Count); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Return_data(Type classType) - { - TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - var result = MA_Series.Add(20); - Assert.Equal(result.v, MA_Series.Last.v); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Update(Type classType) - { - TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; - - var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; - var pre_update = MA_Series.Last.v; - - double pre_data = data.Last.v; - data.Add(20, true); - data.Add(pre_data, true); - - Assert.Equal(pre_update, MA_Series.Last.v); - Assert.Equal(data.Count, MA_Series.Count); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Period_zero(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; - Assert.Equal(data.Count, MA_Series.Count); - Assert.False(double.IsNaN(MA_Series.Last.v)); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Reset(Type classType) - { - GBM_Feed feed = new(10); - TSeries data = feed.OHLC4; - var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; - MA_Series.Reset(); - data.Add(1); - Assert.False(double.IsNaN(MA_Series.Last.v)); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Period_one(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; - Assert.False(double.IsNaN(MA_Series[^1].v)); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void NaN_test(Type classType) - { - GBM_Feed feed = new(100); - TSeries data = feed.OHLC4; - - var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.True(double.IsNaN(MA_Series[0].v)); - Assert.True(double.IsNaN(MA_Series[8].v)); - Assert.False(double.IsNaN(MA_Series[9].v)); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Edge_numbers(Type classType) - { - TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; - var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.Equal(4, MA_Series.Count); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void handling_NaN(Type classType) { - TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; -var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; - Assert.False(double.IsNaN(MA_Series.Last.v)); - } - -public static IEnumerable MASeriesData() - { - foreach (var type in maSeriesTypes) - { - yield return new object[] { type }; - } - } -} +using Xunit; +using System; +using System.Runtime.InteropServices; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class Oscillators +{ + private static Type[] maSeriesTypes = new[] + { + typeof(BIAS_Series), + typeof(MAX_Series), + typeof(MIN_Series), + typeof(MIDPOINT_Series), + typeof(ZL_Series), + typeof(DECAY_Series), + typeof(ENTROPY_Series), + typeof(KURTOSIS_Series), + typeof(MAD_Series), + typeof(MAPE_Series), + typeof(MAE_Series), + typeof(MSE_Series), + typeof(SDEV_Series), + typeof(SMAPE_Series), + typeof(WMAPE_Series), + typeof(SSDEV_Series), + typeof(VAR_Series), + typeof(SVAR_Series), + typeof(MEDIAN_Series), + typeof(ZSCORE_Series), + typeof(CMO_Series), + typeof(RSI_Series), + typeof(TRIX_Series), + typeof(BBANDS_Series), +}; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + TSeries data = new("Data") { 1, 2, 3 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed feed = new(1000); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var result = MA_Series.Add(20); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var pre_update = MA_Series.Last.v; + + double pre_data = data.Last.v; + data.Add(20, true); + data.Add(pre_data, true); + + Assert.Equal(pre_update, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_zero(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; + Assert.Equal(data.Count, MA_Series.Count); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed feed = new(10); + TSeries data = feed.OHLC4; + var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; + MA_Series.Reset(); + data.Add(1); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_one(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; + Assert.False(double.IsNaN(MA_Series[^1].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void NaN_test(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.True(double.IsNaN(MA_Series[0].v)); + Assert.True(double.IsNaN(MA_Series[8].v)); + Assert.False(double.IsNaN(MA_Series[9].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Edge_numbers(Type classType) + { + TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.Equal(4, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void handling_NaN(Type classType) + { + TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} #nullable restore \ No newline at end of file diff --git a/Tests/Basic tests/TBars_input.cs b/Tests/Basic tests/TBars_input.cs index f3ee38b2..39850ef6 100644 --- a/Tests/Basic tests/TBars_input.cs +++ b/Tests/Basic tests/TBars_input.cs @@ -1,96 +1,97 @@ -using Xunit; -using System; -using System.Runtime.InteropServices; -using QuanTAlib; - -namespace Basics; -#nullable disable -public class TBars -{ - private static Type[] maSeriesTypes = new Type[] - { - typeof(ATR_Series), - typeof(ATRP_Series), - typeof(TR_Series), - typeof(ADL_Series), - typeof(CCI_Series), - typeof(OBV_Series), - typeof(ADOSC_Series), - typeof(MIDPRICE_Series), -}; - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Name_exists(Type classType) - { - GBM_Feed data = new(10); - - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - Assert.NotEmpty(MA_Series.Name); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Series_Length(Type classType) - { - GBM_Feed data = new(1000); - - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - Assert.Equal(1000, MA_Series.Count); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Return_data(Type classType) - { - GBM_Feed data = new(10); - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - var result = MA_Series.Add((DateTime.Today, 1,2,3,4,5)); - Assert.Equal(result.v, MA_Series.Last.v); - } - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Update(Type classType) - { - GBM_Feed data = new(10); - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - var pre_update = MA_Series.Last; - - var pre_data = data.Last; - data.Add((DateTime.Today, 1, 2, 3, 4, 5), true); - data.Add(pre_data, true); - - Assert.Equal(pre_update.v, MA_Series.Last.v); - Assert.Equal(data.Count, MA_Series.Count); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Reset(Type classType) - { - GBM_Feed data = new(10); - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - MA_Series.Reset(); - data.Add(); - Assert.False(double.IsNaN(MA_Series.Last.v)); -} - - [Theory] - [MemberData(nameof(MASeriesData))] - public void Period_default(Type classType) { - GBM_Feed data = new(100); - - var MA_Series = Activator.CreateInstance(classType, data) as TSeries; - Assert.False(double.IsNaN(MA_Series.Last.v)); - } - - public static IEnumerable MASeriesData() - { - foreach (var type in maSeriesTypes) - { - yield return new object[] { type }; - } - } -} +using Xunit; +using System; +using System.Runtime.InteropServices; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class TBars +{ + private static Type[] maSeriesTypes = new Type[] + { + typeof(ATR_Series), + typeof(ATRP_Series), + typeof(TR_Series), + typeof(ADL_Series), + typeof(CCI_Series), + typeof(OBV_Series), + typeof(ADOSC_Series), + typeof(MIDPRICE_Series), + }; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + GBM_Feed data = new(10); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed data = new(1000); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + var result = MA_Series.Add((DateTime.Today, 1, 2, 3, 4, 5)); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + var pre_update = MA_Series.Last; + + var pre_data = data.Last; + data.Add((DateTime.Today, 1, 2, 3, 4, 5), true); + data.Add(pre_data, true); + + Assert.Equal(pre_update.v, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + MA_Series.Reset(); + data.Add(); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_default(Type classType) + { + GBM_Feed data = new(100); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} #nullable restore \ No newline at end of file diff --git a/Tests/Pairs/ADD_Test.cs b/Tests/Pairs/ADD_Test.cs index 2c86e5a8..fa687709 100644 --- a/Tests/Pairs/ADD_Test.cs +++ b/Tests/Pairs/ADD_Test.cs @@ -1,64 +1,64 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Pairs; -public class ADD_Test -{ - [Fact] - public void ADDSeriesSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - ADD_Series c = new(a, b); - Assert.Equal(5, c.Last().v); - } - - [Fact] - public void ADDSeriesDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - ADD_Series c = new(a, 10.0); - Assert.Equal(15, c.Last().v); - } - - [Fact] - public void ADDDoubleSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - ADD_Series c = new(10.0, a); - Assert.Equal(15, c.Last().v); - } - - [Fact] - public void ADDEventing_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - ADD_Series c = new(a, b); - a.Add(2); - b.Add(2); - Assert.Equal(4, c.Last().v); - } - - [Fact] - public void ADDUpdateDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - double b = 10; - ADD_Series c = new(a, b); - a.Add(0, true); - Assert.Equal(10, c.Last().v); - } - - [Fact] - public void ADDUpdating_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - ADD_Series c = new(a, b); - a.Add(10, true); - b.Add(10, true); - Assert.Equal(20, c.Last().v); - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class ADD_Test +{ + [Fact] + public void ADDSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void ADDSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + ADD_Series c = new(a, 10.0); + Assert.Equal(15, c.Last().v); + } + + [Fact] + public void ADDDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + ADD_Series c = new(10.0, a); + Assert.Equal(15, c.Last().v); + } + + [Fact] + public void ADDEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + a.Add(2); + b.Add(2); + Assert.Equal(4, c.Last().v); + } + + [Fact] + public void ADDUpdateDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + double b = 10; + ADD_Series c = new(a, b); + a.Add(0, true); + Assert.Equal(10, c.Last().v); + } + + [Fact] + public void ADDUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + a.Add(10, true); + b.Add(10, true); + Assert.Equal(20, c.Last().v); + } +} diff --git a/Tests/Pairs/DIV_Test.cs b/Tests/Pairs/DIV_Test.cs index a8286017..824bd553 100644 --- a/Tests/Pairs/DIV_Test.cs +++ b/Tests/Pairs/DIV_Test.cs @@ -1,64 +1,64 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Pairs; -public class DIV_Test -{ - [Fact] - public void DIVSeriesSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15 }; - TSeries b = new() { 5, 4, 3, 2, 1, 3 }; - DIV_Series c = new(a, b); - Assert.Equal(5, c.Last().v); - } - - [Fact] - public void DIVSeriesDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; - DIV_Series c = new(a, 0); - Assert.Equal(double.PositiveInfinity, c.Last().v); - } - - [Fact] - public void DIVDoubleSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 3.0 }; - DIV_Series c = new(12.0, a); - Assert.Equal(4.0, c.Last().v); - } - - [Fact] - public void DIVEventing_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - DIV_Series c = new(a, b); - a.Add(12.0); - b.Add(2); - Assert.Equal(6.0, c.Last().v); - } - - [Fact] - public void DIVUpdatewDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15 }; - double b = 2; - DIV_Series c = new(a, b); - a.Add(10, true); - Assert.Equal(5, c.Last().v); - } - - [Fact] - public void DIVUpdating_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 1 }; - DIV_Series c = new(a, b); - a.Add(10, true); - b.Add(2, true); - Assert.Equal(5, c.Last().v); - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class DIV_Test +{ + [Fact] + public void DIVSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + TSeries b = new() { 5, 4, 3, 2, 1, 3 }; + DIV_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void DIVSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + DIV_Series c = new(a, 0); + Assert.Equal(double.PositiveInfinity, c.Last().v); + } + + [Fact] + public void DIVDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 3.0 }; + DIV_Series c = new(12.0, a); + Assert.Equal(4.0, c.Last().v); + } + + [Fact] + public void DIVEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + DIV_Series c = new(a, b); + a.Add(12.0); + b.Add(2); + Assert.Equal(6.0, c.Last().v); + } + + [Fact] + public void DIVUpdatewDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + double b = 2; + DIV_Series c = new(a, b); + a.Add(10, true); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void DIVUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + DIV_Series c = new(a, b); + a.Add(10, true); + b.Add(2, true); + Assert.Equal(5, c.Last().v); + } +} diff --git a/Tests/Pairs/MUL_Test.cs b/Tests/Pairs/MUL_Test.cs index 90ad3888..583422b8 100644 --- a/Tests/Pairs/MUL_Test.cs +++ b/Tests/Pairs/MUL_Test.cs @@ -1,64 +1,64 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Pairs; -public class MUL_Test -{ - [Fact] - public void MULSeriesSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 1 }; - MUL_Series c = new(a, b); - Assert.Equal(5, c.Last().v); - } - - [Fact] - public void MULSeriesDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - MUL_Series c = new(a, 10.0); - Assert.Equal(50, c.Last().v); - } - - [Fact] - public void MULDoubleSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - MUL_Series c = new(5.0, a); - Assert.Equal(25, c.Last().v); - } - - [Fact] - public void MULEventing_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - MUL_Series c = new(a, b); - a.Add(2); - b.Add(5); - Assert.Equal(10, c.Last().v); - } - - [Fact] - public void MULUpdateDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - double b = 10; - MUL_Series c = new(a, b); - a.Add(2, true); - Assert.Equal(20, c.Last().v); - } - - [Fact] - public void MULUpdating_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - MUL_Series c = new(a, b); - a.Add(10, true); - b.Add(10, true); - Assert.Equal(100, c.Last().v); - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class MUL_Test +{ + [Fact] + public void MULSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + MUL_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void MULSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + MUL_Series c = new(a, 10.0); + Assert.Equal(50, c.Last().v); + } + + [Fact] + public void MULDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + MUL_Series c = new(5.0, a); + Assert.Equal(25, c.Last().v); + } + + [Fact] + public void MULEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + MUL_Series c = new(a, b); + a.Add(2); + b.Add(5); + Assert.Equal(10, c.Last().v); + } + + [Fact] + public void MULUpdateDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + double b = 10; + MUL_Series c = new(a, b); + a.Add(2, true); + Assert.Equal(20, c.Last().v); + } + + [Fact] + public void MULUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + MUL_Series c = new(a, b); + a.Add(10, true); + b.Add(10, true); + Assert.Equal(100, c.Last().v); + } +} diff --git a/Tests/Pairs/SUB_Test.cs b/Tests/Pairs/SUB_Test.cs index 04775464..e2f3d6af 100644 --- a/Tests/Pairs/SUB_Test.cs +++ b/Tests/Pairs/SUB_Test.cs @@ -1,64 +1,64 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Pairs; -public class SUB_Test -{ - [Fact] - public void SUBSeriesSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 1 }; - SUB_Series c = new(a, b); - Assert.Equal(4, c.Last().v); - } - - [Fact] - public void SUBSeriesDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; - SUB_Series c = new(a, 10.0); - Assert.Equal(5.0, c.Last().v); - } - - [Fact] - public void SUBDoubleSeries_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; - SUB_Series c = new(10.0, a); - Assert.Equal(-5.0, c.Last().v); - } - - [Fact] - public void SUBEventing_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 0 }; - SUB_Series c = new(a, b); - a.Add(7.0); - b.Add(2); - Assert.Equal(5.0, c.Last().v); - } - - [Fact] - public void SUBUpdatewDouble_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 15 }; - double b = 10; - SUB_Series c = new(a, b); - a.Add(1, true); - Assert.Equal(-9, c.Last().v); - } - - [Fact] - public void SUBUpdating_Test() - { - TSeries a = new() { 0, 1, 2, 3, 4, 5 }; - TSeries b = new() { 5, 4, 3, 2, 1, 1 }; - SUB_Series c = new(a, b); - a.Add(10, true); - b.Add(0, true); - Assert.Equal(10, c.Last().v); - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class SUB_Test +{ + [Fact] + public void SUBSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + SUB_Series c = new(a, b); + Assert.Equal(4, c.Last().v); + } + + [Fact] + public void SUBSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + SUB_Series c = new(a, 10.0); + Assert.Equal(5.0, c.Last().v); + } + + [Fact] + public void SUBDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + SUB_Series c = new(10.0, a); + Assert.Equal(-5.0, c.Last().v); + } + + [Fact] + public void SUBEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + SUB_Series c = new(a, b); + a.Add(7.0); + b.Add(2); + Assert.Equal(5.0, c.Last().v); + } + + [Fact] + public void SUBUpdatewDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + double b = 10; + SUB_Series c = new(a, b); + a.Add(1, true); + Assert.Equal(-9, c.Last().v); + } + + [Fact] + public void SUBUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + SUB_Series c = new(a, b); + a.Add(10, true); + b.Add(0, true); + Assert.Equal(10, c.Last().v); + } +} diff --git a/Tests/Pairs/TBars_Test.cs b/Tests/Pairs/TBars_Test.cs index 531703bc..47b97c89 100644 --- a/Tests/Pairs/TBars_Test.cs +++ b/Tests/Pairs/TBars_Test.cs @@ -1,112 +1,112 @@ -using Xunit; -using System; -using QuanTAlib; - -namespace Bars; -public class TBars_Test -{ - [Fact] - public void InsertingTuple() - { - TBars s = new() { (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, c: Double.NegativeInfinity, v: Double.PositiveInfinity) }; - var tup = (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, - c: Double.NegativeInfinity, v: Double.PositiveInfinity); - Assert.Equal(tup, s[^1]); - } - - [Fact] - public void Casting_Parameters() - { - TBars s = new() - { - { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false } - }; - Assert.Equal(0.1, s[^1].o); - Assert.Equal(1.1, s[^1].h); - Assert.Equal(2.1, s[^1].l); - Assert.Equal(3.1, s[^1].c); - Assert.Equal(4.1, s[^1].v); - Assert.Equal(DateTime.Today, s[^1].t); - Assert.Single(s); - } - - [Fact] - public void Updating_Value() - { - TBars s = new() - { - { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 } - }; - s.Add(DateTime.Today, 1.0, 1.0, 1.0, 1.0, 1.0, update: false); - s.Add(DateTime.Today, 0.0, 0.0, 0.0, 0.0, 0.0, update: true); - Assert.Equal(0.0, s[^1].o); - Assert.Equal(0.0, s[^1].h); - Assert.Equal(0.0, s[^1].l); - Assert.Equal(0.0, s[^1].c); - Assert.Equal(0.0, s[^1].v); - Assert.Equal(2, s.Count); - } - [Fact] - public void Extracting_TSeries() - { - TBars s = new() - { - { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 }, - { DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1 } - }; - - TSeries t = s.Open; - Assert.Equal(t.t, s.Open.t); - Assert.Equal(t.v, s.Open.v); - - t = s.High; - Assert.Equal(t.t, s.High.t); - Assert.Equal(t.v, s.High.v); - - t = s.Low; - Assert.Equal(t.t, s.Low.t); - Assert.Equal(t.v, s.Low.v); - - t = s.Close; - Assert.Equal(t.t, s.Close.t); - Assert.Equal(t.v, s.Close.v); - - t = s.Volume; - Assert.Equal(t.t, s.Volume.t); - Assert.Equal(t.v, s.Volume.v); - - t = s.HL2; - Assert.Equal(t.t, s.HL2.t); - Assert.Equal(t.v, s.HL2.v); - - t = s.OC2; - Assert.Equal(t.t, s.OC2.t); - Assert.Equal(t.v, s.OC2.v); - - t = s.OHL3; - Assert.Equal(t.t, s.OHL3.t); - Assert.Equal(t.v, s.OHL3.v); - - t = s.HLC3; - Assert.Equal(t.t, s.HLC3.t); - Assert.Equal(t.v, s.HLC3.v); - - t = s.OHLC4; - Assert.Equal(t.t, s.OHLC4.t); - Assert.Equal(t.v, s.OHLC4.v); - - t = s.HLCC4; - Assert.Equal(t.t, s.HLCC4.t); - Assert.Equal(t.v, s.HLCC4.v); - } - [Fact] - public void Broadcasting_Events() - { - TBars s = new() { (DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1) }; - TSeries t = new(); - s.Close.Pub += t.Sub; - s.Add(DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false); - Assert.Equal(s.Close.v, t.v); - Assert.Equal(s.Close.Count, t.Count); - } -} +using Xunit; +using System; +using QuanTAlib; + +namespace Bars; +public class TBars_Test +{ + [Fact] + public void InsertingTuple() + { + TBars s = new() { (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, c: Double.NegativeInfinity, v: Double.PositiveInfinity) }; + var tup = (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, + c: Double.NegativeInfinity, v: Double.PositiveInfinity); + Assert.Equal(tup, s[^1]); + } + + [Fact] + public void Casting_Parameters() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false } + }; + Assert.Equal(0.1, s[^1].o); + Assert.Equal(1.1, s[^1].h); + Assert.Equal(2.1, s[^1].l); + Assert.Equal(3.1, s[^1].c); + Assert.Equal(4.1, s[^1].v); + Assert.Equal(DateTime.Today, s[^1].t); + Assert.Single(s); + } + + [Fact] + public void Updating_Value() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 } + }; + s.Add(DateTime.Today, 1.0, 1.0, 1.0, 1.0, 1.0, update: false); + s.Add(DateTime.Today, 0.0, 0.0, 0.0, 0.0, 0.0, update: true); + Assert.Equal(0.0, s[^1].o); + Assert.Equal(0.0, s[^1].h); + Assert.Equal(0.0, s[^1].l); + Assert.Equal(0.0, s[^1].c); + Assert.Equal(0.0, s[^1].v); + Assert.Equal(2, s.Count); + } + [Fact] + public void Extracting_TSeries() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 }, + { DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1 } + }; + + TSeries t = s.Open; + Assert.Equal(t.t, s.Open.t); + Assert.Equal(t.v, s.Open.v); + + t = s.High; + Assert.Equal(t.t, s.High.t); + Assert.Equal(t.v, s.High.v); + + t = s.Low; + Assert.Equal(t.t, s.Low.t); + Assert.Equal(t.v, s.Low.v); + + t = s.Close; + Assert.Equal(t.t, s.Close.t); + Assert.Equal(t.v, s.Close.v); + + t = s.Volume; + Assert.Equal(t.t, s.Volume.t); + Assert.Equal(t.v, s.Volume.v); + + t = s.HL2; + Assert.Equal(t.t, s.HL2.t); + Assert.Equal(t.v, s.HL2.v); + + t = s.OC2; + Assert.Equal(t.t, s.OC2.t); + Assert.Equal(t.v, s.OC2.v); + + t = s.OHL3; + Assert.Equal(t.t, s.OHL3.t); + Assert.Equal(t.v, s.OHL3.v); + + t = s.HLC3; + Assert.Equal(t.t, s.HLC3.t); + Assert.Equal(t.v, s.HLC3.v); + + t = s.OHLC4; + Assert.Equal(t.t, s.OHLC4.t); + Assert.Equal(t.v, s.OHLC4.v); + + t = s.HLCC4; + Assert.Equal(t.t, s.HLCC4.t); + Assert.Equal(t.v, s.HLCC4.v); + } + [Fact] + public void Broadcasting_Events() + { + TBars s = new() { (DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1) }; + TSeries t = new(); + s.Close.Pub += t.Sub; + s.Add(DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false); + Assert.Equal(s.Close.v, t.v); + Assert.Equal(s.Close.Count, t.Count); + } +} diff --git a/Tests/Validations/Trends/Pandas_TA.cs b/Tests/Validations/Trends/Pandas_TA.cs index f6953454..471d7753 100644 --- a/Tests/Validations/Trends/Pandas_TA.cs +++ b/Tests/Validations/Trends/Pandas_TA.cs @@ -7,395 +7,463 @@ using Python.Runtime; namespace Validations; -public class PandasTA : IDisposable { - private bool disposed = false; - private readonly GBM_Feed bars; - private readonly Random rnd = new(); - private readonly int period, skip; - private readonly int digits; - private readonly dynamic np; - private readonly dynamic ta; - private readonly dynamic pd; - private readonly dynamic df; +public class PandasTA : IDisposable +{ + private bool disposed = false; + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, skip; + private readonly int digits; + private readonly dynamic np; + private readonly dynamic ta; + private readonly dynamic pd; + private readonly dynamic df; - public PandasTA() { - bars = new GBM_Feed(5000, 0.8, 0.0); - period = rnd.Next(28) + 3; - skip = period + 50; - digits = 8; + public PandasTA() + { + bars = new GBM_Feed(5000, 0.8, 0.0); + period = rnd.Next(28) + 3; + skip = period + 50; + digits = 8; - var pythonDLL = PythonLibrary.Locate(); - Runtime.PythonDLL = pythonDLL; - PythonEngine.Initialize(); + var pythonDLL = PythonLibrary.Locate(); + Runtime.PythonDLL = pythonDLL; + PythonEngine.Initialize(); - np = Py.Import("numpy"); - pd = Py.Import("pandas"); - ta = Py.Import("pandas_ta"); + np = Py.Import("numpy"); + pd = Py.Import("pandas"); + ta = Py.Import("pandas_ta"); - string[] cols = {"open", "high", "low", "close", "volume"}; - var ary = new double[bars.Count, 5]; - for (var i = 0; i < bars.Count; i++) { - ary[i, 0] = bars.Open[i].v; - ary[i, 1] = bars.High[i].v; - ary[i, 2] = bars.Low[i].v; - ary[i, 3] = bars.Close[i].v; - ary[i, 4] = bars.Volume[i].v; - } + string[] cols = { "open", "high", "low", "close", "volume" }; + var ary = new double[bars.Count, 5]; + for (var i = 0; i < bars.Count; i++) + { + ary[i, 0] = bars.Open[i].v; + ary[i, 1] = bars.High[i].v; + ary[i, 2] = bars.Low[i].v; + ary[i, 3] = bars.Close[i].v; + ary[i, 4] = bars.Volume[i].v; + } - df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols)); - } + df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols)); + } - public void Dispose() { - Dispose(true); - PythonEngine.Shutdown(); - GC.SuppressFinalize(this); - } + public void Dispose() + { + Dispose(true); + PythonEngine.Shutdown(); + GC.SuppressFinalize(this); + } - ~PandasTA() { - Dispose(false); - } + ~PandasTA() + { + Dispose(false); + } - protected virtual void Dispose(bool disposing) { - if (!disposed) { - disposed = true; - } - } + protected virtual void Dispose(bool disposing) + { + if (!disposed) + { + disposed = true; + } + } - [Fact] - private void ADL() { - ADL_Series QL = new(bars); - var pta = df.ta.ad(high: df.high, low: df.low, close: df.close, volume: df.volume); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void ADL() + { + ADL_Series QL = new(bars); + var pta = df.ta.ad(high: df.high, low: df.low, close: df.close, volume: df.volume); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void BBANDS() { - BBANDS_Series QL = new(bars.Close, period); - var pta = df.ta.bbands(close: df.close, length: period).to_numpy(); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL.Lower[i].v; - var PanTA_item = (double) pta[i][0]; //lower - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Mid[i].v; - PanTA_item = (double) pta[i][1]; //mid - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Upper[i].v; - PanTA_item = (double) pta[i][2]; //upper - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void BBANDS() + { + BBANDS_Series QL = new(bars.Close, period); + var pta = df.ta.bbands(close: df.close, length: period).to_numpy(); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL.Lower[i].v; + var PanTA_item = (double)pta[i][0]; //lower + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + PanTA_item = (double)pta[i][1]; //mid + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Upper[i].v; + PanTA_item = (double)pta[i][2]; //upper + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void BIAS() { - BIAS_Series QL = new(bars.Close, period, false); - var pta = df.ta.bias(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void BIAS() + { + BIAS_Series QL = new(bars.Close, period, false); + var pta = df.ta.bias(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void CCI() { - CCI_Series QL = new(bars, period, false); - var pta = df.ta.cci(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void CCI() + { + CCI_Series QL = new(bars, period, false); + var pta = df.ta.cci(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void DEMA() { - DEMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.dema(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void DEMA() + { + DEMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.dema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void EMA() { - EMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.ema(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void EMA() + { + EMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.ema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void ENTROPY() { - ENTROPY_Series QL = new(bars.Close, period, false); - var pta = df.ta.entropy(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void ENTROPY() + { + ENTROPY_Series QL = new(bars.Close, period, false); + var pta = df.ta.entropy(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void HL2() { - var pta = df.ta.hl2(high: df.high, low: df.low); - for (var i = bars.HL2.Length - 1; i > skip; i--) { - var QL_item = bars.HL2[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void HL2() + { + var pta = df.ta.hl2(high: df.high, low: df.low); + for (var i = bars.HL2.Length - 1; i > skip; i--) + { + var QL_item = bars.HL2[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void HLC3() { - var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close); - for (var i = bars.HLC3.Length; i > skip; i--) { - var QL_item = bars.HLC3[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void HLC3() + { + var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close); + for (var i = bars.HLC3.Length; i > skip; i--) + { + var QL_item = bars.HLC3[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void HMA() { - HMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.hma(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void HMA() + { + HMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.hma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void KURTOSIS() { - KURTOSIS_Series QL = new(bars.Close, period, false); - var pta = df.ta.kurtosis(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void KURTOSIS() + { + KURTOSIS_Series QL = new(bars.Close, period, false); + var pta = df.ta.kurtosis(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void MACD() { - MACD_Series QL = new(bars.Close, 26, 12, 9, false); - var pta = df.ta.macd(close: df.close).to_numpy(); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1][0]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Signal[i - 1].v; - PanTA_item = (double) pta[i - 1][2]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void MACD() + { + MACD_Series QL = new(bars.Close, 26, 12, 9, false); + var pta = df.ta.macd(close: df.close).to_numpy(); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1][0]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Signal[i - 1].v; + PanTA_item = (double)pta[i - 1][2]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void MAD() { - MAD_Series QL = new(bars.Close, period, false); - var pta = df.ta.mad(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void MAD() + { + MAD_Series QL = new(bars.Close, period, false); + var pta = df.ta.mad(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void MEDIAN() { - MEDIAN_Series QL = new(bars.Close, period); - var pta = df.ta.median(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void MEDIAN() + { + MEDIAN_Series QL = new(bars.Close, period); + var pta = df.ta.median(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void OBV() { - OBV_Series QL = new(bars); - var pta = df.ta.obv(close: df.close, volume: df.volume); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void OBV() + { + OBV_Series QL = new(bars); + var pta = df.ta.obv(close: df.close, volume: df.volume); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void OHLC4() { - var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close); - for (var i = bars.OHLC4.Length; i > skip; i--) { - var QL_item = bars.OHLC4[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void OHLC4() + { + var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close); + for (var i = bars.OHLC4.Length; i > skip; i--) + { + var QL_item = bars.OHLC4[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void SDEV() { - SDEV_Series QL = new(bars.Close, period, false); - var pta = df.ta.stdev(close: df.close, length: period, ddof: 0); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void SDEV() + { + SDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void SMA() { - SMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.sma(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void SMA() + { + SMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.sma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void SSDEV() { - SSDEV_Series QL = new(bars.Close, period, false); - var pta = df.ta.stdev(close: df.close, length: period, ddof: 1); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void SSDEV() + { + SSDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void SVARIANCE() { - SVAR_Series QL = new(bars.Close, period); - var pta = df.ta.variance(close: df.close, length: period, ddof: 1); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void SVARIANCE() + { + SVAR_Series QL = new(bars.Close, period); + var pta = df.ta.variance(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void TEMA() { - TEMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.tema(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void TEMA() + { + TEMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.tema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void TR() { - TR_Series QL = new(bars); - var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void TR() + { + TR_Series QL = new(bars); + var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void TRIMA() { - // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right - TRIMA_Series QL = new(bars.Close, 11); - var pta = df.ta.trima(close: df.close, length: 11); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void TRIMA() + { + // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right + TRIMA_Series QL = new(bars.Close, 11); + var pta = df.ta.trima(close: df.close, length: 11); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void VARIANCE() { - VAR_Series QL = new(bars.Close, period); - var pta = df.ta.variance(close: df.close, length: period, ddof: 0); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void VARIANCE() + { + VAR_Series QL = new(bars.Close, period); + var pta = df.ta.variance(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void WMA() { - WMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.wma(close: df.close, length: period); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.wma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - [Fact] - private void ZSCORE() { - ZSCORE_Series QL = new(bars.Close, period, false); - var pta = df.ta.zscore(close: df.close, length: period, ddof: 0); - for (var i = QL.Length - 1; i > skip; i--) { - var QL_item = QL[i - 1].v; - var PanTA_item = (double) pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] + private void ZSCORE() + { + ZSCORE_Series QL = new(bars.Close, period, false); + var pta = df.ta.zscore(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } } -public static class PythonLibrary { - public static string Locate() { - if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) { - string[] paths = Environment.GetEnvironmentVariable("PATH")?.Split(';') ?? Array.Empty(); - foreach (string path in paths) { - string[] pythonDLLs = Directory.GetFiles(path, "python3*.dll"); - if (pythonDLLs.Length > 0) { - foreach (string item in pythonDLLs) { - if (!item.EndsWith("python3.dll", StringComparison.OrdinalIgnoreCase)) { - return item; - } - } +public static class PythonLibrary +{ + public static string Locate() + { + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { + string[] paths = Environment.GetEnvironmentVariable("PATH")?.Split(';') ?? Array.Empty(); + foreach (string path in paths) + { + string[] pythonDLLs = Directory.GetFiles(path, "python3*.dll"); + if (pythonDLLs.Length > 0) + { + foreach (string item in pythonDLLs) + { + if (!item.EndsWith("python3.dll", StringComparison.OrdinalIgnoreCase)) + { + return item; + } + } - } - } - throw new FileNotFoundException("Python library not found in PATH"); - } - else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) { + } + } + throw new FileNotFoundException("Python library not found in PATH"); + } + else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) + { return "/usr/lib/x86_64-linux-gnu/libpython3.10.so"; -/* - List pythonLibraries = new List(); - List directoriesToSearch = new List { "/home/runner/.local/lib" }; // Add more directories as needed - string filePattern = "libpython3.*.so"; - SearchFiles(directoriesToSearch, filePattern, pythonLibraries); + /* + List pythonLibraries = new List(); + List directoriesToSearch = new List { "/home/runner/.local/lib" }; // Add more directories as needed + string filePattern = "libpython3.*.so"; + SearchFiles(directoriesToSearch, filePattern, pythonLibraries); - if (pythonLibraries.Count > 0) { - return pythonLibraries[0]; - } - else { - throw new FileNotFoundException("Python library not found"); - } -*/ - } + if (pythonLibraries.Count > 0) { + return pythonLibraries[0]; + } + else { + throw new FileNotFoundException("Python library not found"); + } + */ + } - else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) { - throw new NotSupportedException("Not supported yet"); - } + else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) + { + throw new NotSupportedException("Not supported yet"); + } - else { throw new NotSupportedException("Unsupported operating system"); } - } - static void SearchFiles(List directoriesToSearch, string filePattern, List foundFiles) + else { throw new NotSupportedException("Unsupported operating system"); } + } + static void SearchFiles(List directoriesToSearch, string filePattern, List foundFiles) { foreach (string directory in directoriesToSearch) { diff --git a/Tests/Validations/Trends/Skender.cs b/Tests/Validations/Trends/Skender.cs index d413a391..d26f48e4 100644 --- a/Tests/Validations/Trends/Skender.cs +++ b/Tests/Validations/Trends/Skender.cs @@ -1,486 +1,489 @@ -using System; -using QuanTAlib; -using Skender.Stock.Indicators; -using Xunit; - -namespace Validations; -public class Skender -{ - private readonly GBM_Feed bars; - private readonly Random rnd = new(); - private readonly int period, digits, skip; - private readonly IEnumerable quotes; - - - public Skender() - { - bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2); - period = rnd.Next(30) + 5; - digits = 6; //minimizing rounding errors in type conversions - skip = period+2; - - quotes = bars.Select(q => new Quote - { - Date = q.t, - Open = (decimal)q.o, - High = (decimal)q.h, - Low = (decimal)q.l, - Close = (decimal)q.c, - Volume = (decimal)q.v - }); - } - -/* - [Fact] - public void ADL() - { - ADL_Series QL = new(bars); - var SK = quotes.GetAdl().Select(i => i.Adl); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1)!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - */ - [Fact] - public void ALMA() - { - ALMA_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetAlma(period).Select(i => i.Alma.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void ATR() - { - ATR_Series QL = new(bars, period:period,useNaN: false); - var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void ATRP() - { - ATRP_Series QL = new(bars, period, false); - var SK = quotes.GetAtr(period).Select(i => i.Atrp.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void BBANDS() - { - BBANDS_Series QL = new(bars.Close, period, 2.0, useNaN: false); - var SK = quotes.GetBollingerBands(period, 2.0); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL.Mid[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Sma!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - QL_item = QL.Upper[i - 1].v; - SK_item = SK.ElementAt(i - 1).UpperBand!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - QL_item = QL.Lower[i - 1].v; - SK_item = SK.ElementAt(i - 1).LowerBand!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - QL_item = QL.Bandwidth[i - 1].v; - SK_item = SK.ElementAt(i - 1).Width!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - QL_item = QL.PercentB[i - 1].v; - SK_item = SK.ElementAt(i - 1).PercentB!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - QL_item = QL.Zscore[i - 1].v; - SK_item = SK.ElementAt(i - 1).ZScore!.Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void CCI() - { - CCI_Series QL = new(bars, period, false); - var SK = quotes.GetCci(period).Select(i => i.Cci.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void CMO() - { - CMO_Series QL = new(bars.Close, period, false); - var SK = quotes.GetCmo(period).Select(i => i.Cmo.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void CORR() - { - CORR_Series QL = new(bars.High, bars.Low, period, false); - var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Correlation.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void COVAR() - { - COVAR_Series QL = new(bars.High, bars.Low, period, false); - var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Covariance.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void DEMA() - { - DEMA_Series QL = new(bars.Close, period, false, useSMA: true); - var SK = quotes.GetDema(period).Select(i => i.Dema.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void EMA() - { - EMA_Series QL = new(bars.Close, period, false); - var SK = quotes.GetEma(lookbackPeriods: period).Select(i => i.Ema.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void HL2() - { - TSeries QL = bars.HL2; - var SK = quotes.GetBaseQuote(CandlePart.HL2).ToList(); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void HLC3() - { - TSeries QL = bars.HLC3; - var SK = quotes.GetBaseQuote(CandlePart.HLC3).ToList(); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void HMA() - { - HMA_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetHma(period).Select(i => i.Hma.Null2NaN()!); - for (int i = QL.Length; i > skip*2; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - - [Fact] - public void KAMA() - { - // TODO: check precision of KAMA() - KAMA_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetKama(period).Select(i => i.Kama.Null2NaN()!); - for (int i = QL.Length; i > skip+2; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void SLOPE() - { - SLOPE_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetSlope(period); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = (double)SK.ElementAt(i - 1).Slope!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - QL_item = QL.Intercept[i - 1].v; - SK_item = (double)SK.ElementAt(i - 1).Intercept!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - QL_item = QL.RSquared[i - 1].v; - SK_item = (double)SK.ElementAt(i - 1).RSquared!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - QL_item = QL.StdDev[i - 1].v; - SK_item = (double)SK.ElementAt(i - 1).StdDev!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void MACD() - { - MACD_Series QL = new(bars.Close, 26, 12, 9, useNaN: false); - var SK = quotes.GetMacd(12, 26, 9); - for (int i = QL.Length; i > 27; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Macd.Null2NaN()!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - //QL_item = QL.Signal[i - 1].v; - //SK_item = SK.ElementAt(i - 1).Signal.Null2NaN()!; - //Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void MAD() - { - MAD_Series QL = new(bars.Close, period, false); - var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mad.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void MAMA() - { - MAMA_Series QL = new(bars.HL2, fastlimit: 0.5, slowlimit: 0.05); - var SK = quotes.GetMama(fastLimit: 0.5, slowLimit: 0.05); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Mama.Null2NaN()!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - QL_item = QL.Fama[i - 1].v; - SK_item = SK.ElementAt(i - 1).Fama.Null2NaN()!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void MAPE() - { - MAPE_Series QL = new(bars.Close, period, false); - var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mape.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void MSE() - { - MSE_Series QL = new(bars.Close, period, false); - var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mse.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void OBV() - { - OBV_Series QL = new(bars, period, false); - var SK = quotes.GetObv(period).Select(i => i.Obv!); - for (int i = QL.Length; i > skip; i--) { - double QL_item = QL.Last().v; - // adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB - double SK_item = SK.Last()! + (double)quotes.First().Volume!; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void OC2() - { - TSeries QL = bars.OC2; - var SK = quotes.GetBaseQuote(CandlePart.OC2).ToList(); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void OHL3() - { - TSeries QL = bars.OHL3; - var SK = quotes.GetBaseQuote(CandlePart.OHL3).ToList(); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void OHLC4() - { - TSeries QL = bars.OHLC4; - var SK = quotes.GetBaseQuote(CandlePart.OHLC4).ToList(); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1).Value; - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void RSI() - { - RSI_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetRsi(period).Select(i => i.Rsi.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void SDEV() - { - SDEV_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetStdDev(period).Select(i => i.StdDev.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void SMA() - { - SMA_Series QL = new(bars.Close, period, false); - var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void SMMA() - { - SMMA_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetSmma(period).Select(i => i.Smma.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void T3() - { - T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, false); - var SK = quotes.GetT3(lookbackPeriods: period, volumeFactor: 0.7).Select(i => i.T3.Null2NaN()!); - for (int i = QL.Length; i > period*15; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void TRIX() { - TRIX_Series QL = new(bars.Close, period, false); - var SK = quotes.GetTrix(period).Select(i => i.Trix.Null2NaN()!); - for (int i = QL.Length; i > period*12; i--) { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); - } - } - [Fact] - public void TEMA() - { - TEMA_Series QL = new(bars.Close, period, false); - var SK = quotes.GetTema(period).Select(i => i.Tema.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void TR() - { - TR_Series QL = new(bars); - var SK = quotes.GetTr().Select(i => i.Tr.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void WMA() - { - WMA_Series QL = new(bars.Close, period, false); - var SK = quotes.GetWma(period).Select(i => i.Wma.Null2NaN()!); - for (int i = QL.Length; i > skip*2; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - [Fact] - public void ZSCORE() - { - ZSCORE_Series QL = new(bars.Close, period, useNaN: false); - var SK = quotes.GetStdDev(period).Select(i => i.ZScore.Null2NaN()!); - for (int i = QL.Length; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double SK_item = SK.ElementAt(i - 1); - Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); - } - } - -} +using System; +using QuanTAlib; +using Skender.Stock.Indicators; +using Xunit; + +namespace Validations; +public class Skender +{ + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly IEnumerable quotes; + + + public Skender() + { + bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2); + period = rnd.Next(30) + 5; + digits = 6; //minimizing rounding errors in type conversions + skip = period + 2; + + quotes = bars.Select(q => new Quote + { + Date = q.t, + Open = (decimal)q.o, + High = (decimal)q.h, + Low = (decimal)q.l, + Close = (decimal)q.c, + Volume = (decimal)q.v + }); + } + + /* + [Fact] + public void ADL() + { + ADL_Series QL = new(bars); + var SK = quotes.GetAdl().Select(i => i.Adl); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1)!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); + } + } + */ + [Fact] + public void ALMA() + { + ALMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetAlma(period).Select(i => i.Alma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ATR() + { + ATR_Series QL = new(bars, period: period, useNaN: false); + var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ATRP() + { + ATRP_Series QL = new(bars, period, false); + var SK = quotes.GetAtr(period).Select(i => i.Atrp.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void BBANDS() + { + BBANDS_Series QL = new(bars.Close, period, 2.0, useNaN: false); + var SK = quotes.GetBollingerBands(period, 2.0); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL.Mid[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Sma!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Upper[i - 1].v; + SK_item = SK.ElementAt(i - 1).UpperBand!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Lower[i - 1].v; + SK_item = SK.ElementAt(i - 1).LowerBand!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Bandwidth[i - 1].v; + SK_item = SK.ElementAt(i - 1).Width!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.PercentB[i - 1].v; + SK_item = SK.ElementAt(i - 1).PercentB!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Zscore[i - 1].v; + SK_item = SK.ElementAt(i - 1).ZScore!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CCI() + { + CCI_Series QL = new(bars, period, false); + var SK = quotes.GetCci(period).Select(i => i.Cci.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CMO() + { + CMO_Series QL = new(bars.Close, period, false); + var SK = quotes.GetCmo(period).Select(i => i.Cmo.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CORR() + { + CORR_Series QL = new(bars.High, bars.Low, period, false); + var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Correlation.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void COVAR() + { + COVAR_Series QL = new(bars.High, bars.Low, period, false); + var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Covariance.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void DEMA() + { + DEMA_Series QL = new(bars.Close, period, false, useSMA: true); + var SK = quotes.GetDema(period).Select(i => i.Dema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void EMA() + { + EMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetEma(lookbackPeriods: period).Select(i => i.Ema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HL2() + { + TSeries QL = bars.HL2; + var SK = quotes.GetBaseQuote(CandlePart.HL2).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HLC3() + { + TSeries QL = bars.HLC3; + var SK = quotes.GetBaseQuote(CandlePart.HLC3).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HMA() + { + HMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetHma(period).Select(i => i.Hma.Null2NaN()!); + for (int i = QL.Length; i > skip * 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + + [Fact] + public void KAMA() + { + // TODO: check precision of KAMA() + KAMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetKama(period).Select(i => i.Kama.Null2NaN()!); + for (int i = QL.Length; i > skip + 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SLOPE() + { + SLOPE_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetSlope(period); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = (double)SK.ElementAt(i - 1).Slope!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Intercept[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).Intercept!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.RSquared[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).RSquared!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.StdDev[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).StdDev!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MACD() + { + MACD_Series QL = new(bars.Close, 26, 12, 9, useNaN: false); + var SK = quotes.GetMacd(12, 26, 9); + for (int i = QL.Length; i > 27; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Macd.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + //QL_item = QL.Signal[i - 1].v; + //SK_item = SK.ElementAt(i - 1).Signal.Null2NaN()!; + //Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); + } + } + [Fact] + public void MAD() + { + MAD_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mad.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MAMA() + { + MAMA_Series QL = new(bars.HL2, fastlimit: 0.5, slowlimit: 0.05); + var SK = quotes.GetMama(fastLimit: 0.5, slowLimit: 0.05); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Mama.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Fama[i - 1].v; + SK_item = SK.ElementAt(i - 1).Fama.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MAPE() + { + MAPE_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mape.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MSE() + { + MSE_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mse.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OBV() + { + OBV_Series QL = new(bars, period, false); + var SK = quotes.GetObv(period).Select(i => i.Obv!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL.Last().v; + // adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB + double SK_item = SK.Last()! + (double)quotes.First().Volume!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OC2() + { + TSeries QL = bars.OC2; + var SK = quotes.GetBaseQuote(CandlePart.OC2).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OHL3() + { + TSeries QL = bars.OHL3; + var SK = quotes.GetBaseQuote(CandlePart.OHL3).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OHLC4() + { + TSeries QL = bars.OHLC4; + var SK = quotes.GetBaseQuote(CandlePart.OHLC4).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void RSI() + { + RSI_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetRsi(period).Select(i => i.Rsi.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SDEV() + { + SDEV_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetStdDev(period).Select(i => i.StdDev.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SMA() + { + SMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SMMA() + { + SMMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetSmma(period).Select(i => i.Smma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void T3() + { + T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, false); + var SK = quotes.GetT3(lookbackPeriods: period, volumeFactor: 0.7).Select(i => i.T3.Null2NaN()!); + for (int i = QL.Length; i > period * 15; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TRIX() + { + TRIX_Series QL = new(bars.Close, period, false); + var SK = quotes.GetTrix(period).Select(i => i.Trix.Null2NaN()!); + for (int i = QL.Length; i > period * 12; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TEMA() + { + TEMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetTema(period).Select(i => i.Tema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TR() + { + TR_Series QL = new(bars); + var SK = quotes.GetTr().Select(i => i.Tr.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetWma(period).Select(i => i.Wma.Null2NaN()!); + for (int i = QL.Length; i > skip * 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ZSCORE() + { + ZSCORE_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetStdDev(period).Select(i => i.ZScore.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + +} diff --git a/Tests/Validations/Trends/TA_LIB.cs b/Tests/Validations/Trends/TA_LIB.cs index 01e40c2a..9c8e27f9 100644 --- a/Tests/Validations/Trends/TA_LIB.cs +++ b/Tests/Validations/Trends/TA_LIB.cs @@ -6,478 +6,482 @@ using QuanTAlib; namespace Validations; public class Ta_Lib { - private readonly GBM_Feed bars; - private readonly Random rnd = new(); - private readonly int period, digits, skip; - private readonly double[] TALIB; - private readonly double[] TALIB2; - private readonly double[] inopen; - private readonly double[] inhigh; - private readonly double[] inlow; - private readonly double[] inclose; - private readonly double[] involume; + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly double[] TALIB; + private readonly double[] TALIB2; + private readonly double[] inopen; + private readonly double[] inhigh; + private readonly double[] inlow; + private readonly double[] inclose; + private readonly double[] involume; - public Ta_Lib() - { - bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); - period = rnd.Next(28) + 3; - skip = period+2; - digits = 9; + public Ta_Lib() + { + bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); + period = rnd.Next(28) + 3; + skip = period + 2; + digits = 9; - TALIB = new double[bars.Count]; - TALIB2 = new double[bars.Count]; - inopen = bars.Open.v.ToArray(); - inhigh = bars.High.v.ToArray(); - inlow = bars.Low.v.ToArray(); - inclose = bars.Close.v.ToArray(); - involume = bars.Volume.v.ToArray(); - } + TALIB = new double[bars.Count]; + TALIB2 = new double[bars.Count]; + inopen = bars.Open.v.ToArray(); + inhigh = bars.High.v.ToArray(); + inlow = bars.Low.v.ToArray(); + inclose = bars.Close.v.ToArray(); + involume = bars.Volume.v.ToArray(); + } - [Fact] - public void ADD() - { - ADD_Series QL = new(bars.Open, bars.Close); - Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void ADD() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + ADD_Series QL = new(bars.Open, bars.Close); + Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void ADL() - { - ADL_Series QL = new(bars); - Core.Ad(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > 0; i--) + [Fact] + public void ADL() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void ADOSC() - { - ADOSC_Series QL = new(bars, 3, 10, false); - Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip*2; i--) - { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + ADL_Series QL = new(bars); + Core.Ad(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > 0; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void ATR() - { - ATR_Series QL = new(bars, period:period, useNaN: false); - Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void ADOSC() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + ADOSC_Series QL = new(bars, 3, 10, false); + Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip * 2; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ATR() + { + ATR_Series QL = new(bars, period: period, useNaN: false); + Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void BBANDS() - { - double[] outMiddle = new double[bars.Count]; - double[] outUpper = new double[bars.Count]; - double[] outLower = new double[bars.Count]; - BBANDS_Series QL = new(bars.Close, period: period, multiplier: 2.0, false); - Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: period, optInNbDevUp: 2.0, optInNbDevDn: 2.0); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void BBANDS() { - double QL_item = QL.Upper[i].v; - double TA_item = outUpper[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); - QL_item = QL.Mid[i].v; - TA_item = outMiddle[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); - QL_item = QL.Lower[i].v; - TA_item = outLower[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + double[] outMiddle = new double[bars.Count]; + double[] outUpper = new double[bars.Count]; + double[] outLower = new double[bars.Count]; + BBANDS_Series QL = new(bars.Close, period: period, multiplier: 2.0, false); + Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: period, optInNbDevUp: 2.0, optInNbDevDn: 2.0); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL.Upper[i].v; + double TA_item = outUpper[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + TA_item = outMiddle[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + QL_item = QL.Lower[i].v; + TA_item = outLower[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + } } - } - [Fact] - public void CCI() - { - CCI_Series QL = new(bars, period, false); - Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void CCI() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + CCI_Series QL = new(bars, period, false); + Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - /* CMO in TA-LIB is not valid - [Fact] - public void CMO() { - CMO_Series QL = new(bars.Close, period, false); - Core.Cmo(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - */ - [Fact] - public void CORR() - { - CORR_Series QL = new(bars.Open, bars.Close, period); - Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period); - for (int i = QL.Length - 1; i > skip; i--) + /* CMO in TA-LIB is not valid + [Fact] + public void CMO() { + CMO_Series QL = new(bars.Close, period, false); + Core.Cmo(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + */ + [Fact] + public void CORR() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + CORR_Series QL = new(bars.Open, bars.Close, period); + Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void DEMA() - { - DEMA_Series QL = new(bars.Close, period, false, useSMA: false); - Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > period*10; i--) + [Fact] + public void DEMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + DEMA_Series QL = new(bars.Close, period, false, useSMA: false); + Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void DIV() - { - DIV_Series QL = new(bars.Open, bars.Close); - Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void DIV() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + DIV_Series QL = new(bars.Open, bars.Close); + Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void EMA() - { - EMA_Series QL = new(bars.Close, period, false); - Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void EMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + EMA_Series QL = new(bars.Close, period, false); + Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void HL2() - { - TSeries QL = bars.HL2; - Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void HL2() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TSeries QL = bars.HL2; + Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void HLC3() - { - TSeries QL = bars.HLC3; - Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void HLC3() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TSeries QL = bars.HLC3; + Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void HLCC4() - { - TSeries QL = bars.HLCC4; - Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void HLCC4() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TSeries QL = bars.HLCC4; + Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void KAMA() { - KAMA_Series QL = new(bars.Close, period, fast: 2, slow: 30); - Core.Kama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period); - for (int i = QL.Length - 1; i > skip * 15; i--) { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void MACD() - { - double[] macdSignal = new double[bars.Count]; - double[] macdHist = new double[bars.Count]; - MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false); - // TA-LIB runs EMA without SMA, leaving first 100 values for convergence - Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _, optInFastPeriod: 12, optInSlowPeriod: 26, optInSignalPeriod: 9); - for (int i = QL.Length - 1; i > 100; i--) + [Fact] + public void KAMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Signal[i].v; - TA_item = macdSignal[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + KAMA_Series QL = new(bars.Close, period, fast: 2, slow: 30); + Core.Kama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - /* - [Fact] - public void MAMA() - { - MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05); - Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05); - for (int i = QL.Length - 1; i > skip * 10; i--) + [Fact] + public void MACD() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits-1), Math.Exp(-digits-1)); + double[] macdSignal = new double[bars.Count]; + double[] macdHist = new double[bars.Count]; + MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false); + // TA-LIB runs EMA without SMA, leaving first 100 values for convergence + Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _, optInFastPeriod: 12, optInSlowPeriod: 26, optInSignalPeriod: 9); + for (int i = QL.Length - 1; i > 100; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Signal[i].v; + TA_item = macdSignal[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - */ - [Fact] - public void MAX() - { - MAX_Series QL = new(bars.Close, period, false); - Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + /* + [Fact] + public void MAMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05); + Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05); + for (int i = QL.Length - 1; i > skip * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits-1), Math.Exp(-digits-1)); + } } - } - [Fact] - public void MIDPOINT() - { - MIDPOINT_Series QL = new(bars.Close, period, false); - Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + */ + [Fact] + public void MAX() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MAX_Series QL = new(bars.Close, period, false); + Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void MIDPRICE() - { - MIDPRICE_Series QL = new(bars, period, false); - Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void MIDPOINT() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MIDPOINT_Series QL = new(bars.Close, period, false); + Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void MIN() - { - MIN_Series QL = new(bars.Close, period, false); - Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void MIDPRICE() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MIDPRICE_Series QL = new(bars, period, false); + Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void MUL() - { - MUL_Series QL = new(bars.Open, bars.Close); - Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void MIN() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MIN_Series QL = new(bars.Close, period, false); + Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void OBV() - { - OBV_Series QL = new(bars, period, false); - Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void MUL() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + MUL_Series QL = new(bars.Open, bars.Close); + Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void OHLC4() - { - TSeries QL = bars.OHLC4; - Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void OBV() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + OBV_Series QL = new(bars, period, false); + Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void RSI() - { - RSI_Series QL = new(bars.Close, period, false); - Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void OHLC4() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TSeries QL = bars.OHLC4; + Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void SDEV() - { - SDEV_Series QL = new(bars.Close, period, false); - Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void RSI() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + RSI_Series QL = new(bars.Close, period, false); + Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void SMA() - { - SMA_Series QL = new(bars.Close, period, false); - Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void SDEV() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + SDEV_Series QL = new(bars.Close, period, false); + Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void SUB() - { - SUB_Series QL = new(bars.Open, bars.Close); - Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void SMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + SMA_Series QL = new(bars.Close, period, false); + Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void SUM() - { - CUSUM_Series QL = new(bars.Close, period, false); - Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void SUB() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + SUB_Series QL = new(bars.Open, bars.Close); + Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void T3() - { - T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false); - Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7); - for (int i = QL.Length - 1; i > period*10; i--) + [Fact] + public void SUM() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + CUSUM_Series QL = new(bars.Close, period, false); + Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void TEMA() - { - TEMA_Series QL = new(bars.Close, period, false); - Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip * 15; i--) + [Fact] + public void T3() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false); + Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void TR() - { - TR_Series QL = new(bars); - Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void TEMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TEMA_Series QL = new(bars.Close, period, false); + Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void TRIMA() - { - TRIMA_Series QL = new(bars.Close, period, false); - Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void TR() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TR_Series QL = new(bars); + Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void TRIX() { - TRIX_Series QL = new(bars.Close, period, useNaN: false, useSMA: true); - Core.Trix(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > period*10; i--) { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void VAR() - { - VAR_Series QL = new(bars.Close, period, false); - Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip * 15; i--) + [Fact] + public void TRIMA() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TRIMA_Series QL = new(bars.Close, period, false); + Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } - [Fact] - public void WMA() - { - WMA_Series QL = new(bars.Close, period, false); - Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); - for (int i = QL.Length - 1; i > skip; i--) + [Fact] + public void TRIX() { - double QL_item = QL[i].v; - double TA_item = TALIB[i - outBegIdx]; - Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + TRIX_Series QL = new(bars.Close, period, useNaN: false, useSMA: true); + Core.Trix(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void VAR() + { + VAR_Series QL = new(bars.Close, period, false); + Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - } } diff --git a/Tests/Validations/Trends/Tulip.cs b/Tests/Validations/Trends/Tulip.cs index f16f0184..32774221 100644 --- a/Tests/Validations/Trends/Tulip.cs +++ b/Tests/Validations/Trends/Tulip.cs @@ -1,524 +1,578 @@ -using Xunit; -using System; -using Tulip; -using QuanTAlib; - -namespace Validations; -public class Tulip_Test -{ - private readonly GBM_Feed bars; - private readonly Random rnd = new(); - private readonly int period, digits, skip; - private readonly double[] outdata; - private readonly double[] inopen; - private readonly double[] inhigh; - private readonly double[] inlow; - private readonly double[] inclose; - private readonly double[] involume; - - public Tulip_Test() - { - bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); - period = rnd.Next(28) + 3; - skip = period+5; - digits = 8; - - outdata = new double[bars.Count]; - inopen = bars.Open.v.ToArray(); - inhigh = bars.High.v.ToArray(); - inlow = bars.Low.v.ToArray(); - inclose = bars.Close.v.ToArray()!; - involume = bars.Volume.v.ToArray()!; - - } - [Fact] - public void ADL() - { - double[][] arrin = {inhigh, inlow, inclose, involume }; - double[][] arrout = { outdata }; - ADL_Series QL = new(bars); - Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void ADD() - { - double[][] arrin = { inhigh, inlow }; - double[][] arrout = { outdata }; - ADD_Series QL = new(bars.High, bars.Low); - Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void ADOSC() - { - double[][] arrin = { inhigh, inlow, inclose, involume }; - double[][] arrout = { outdata }; - int s = 3; - ADOSC_Series QL = new(bars, s, period, false); - Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i-period+1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void ATR() - { - double[][] arrin = { inhigh, inlow, inclose }; - double[][] arrout = { outdata }; - - ATR_Series QL = new(bars, period:period, useNaN:false); - Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - //Tulip ATR doesn't use warm-up SMA, compensating with 200 warming bars - for (int i = QL.Length - 1; i > 200+skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void BBANDS() - { - double[][] arrin = { inclose }; - double[] outmid = new double[bars.Count]; - double[] outlower = new double[bars.Count]; - double[] outupper = new double[bars.Count]; - double[][] arrout = { outlower, outmid, outupper}; - BBANDS_Series QL = new(bars.Close, period, 2, false); - Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL.Lower[i].v; - double TU_item = outlower[i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Mid[i].v; - TU_item = outmid[i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - QL_item = QL.Upper[i].v; - TU_item = outupper[i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - /* - [Fact] - public void CCI() { - double[][] arrin = { inhigh, inlow, inclose }; - double[][] arrout = { outdata }; - CCI_Series QL = new(bars, period, useNaN: false); - Tulip.Indicators.cci.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = outdata[i - period + 1]; - Assert.Equal(QL_item,TU_item); - //Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - */ - [Fact] - public void CMO() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - CMO_Series QL = new(bars.Close, period, useNaN: false); - Tulip.Indicators.cmo.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void DECAY() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - DECAY_Series QL = new(bars.Close, period, useNaN: false); - Tulip.Indicators.decay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip + 200; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void DEMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - DEMA_Series QL = new(bars.Close, period, useNaN: false, useSMA: false); - Tulip.Indicators.dema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip+200; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i-(period+period-2)]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void DIV() { - double[][] arrin = { inhigh, inlow }; - double[][] arrout = { outdata }; - DIV_Series QL = new(bars.High, bars.Low); - Tulip.Indicators.div.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void EDECAY() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - DECAY_Series QL = new(bars.Close, period, exponential: true, useNaN: false); - Tulip.Indicators.edecay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip + 200; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void EMA() - { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - // Tulip EMA doesn't use SMA to warm-up - EMA_Series QL = new(bars.Close, period, false, useSMA: false); - Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void HL2() { - double[][] arrin = { inhigh, inlow }; - double[][] arrout = { outdata }; - - TSeries QL = bars.HL2; - Tulip.Indicators.medprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void HLC3() { - double[][] arrin = { inhigh, inlow, inclose }; - double[][] arrout = { outdata }; - - TSeries QL = bars.HLC3; - Tulip.Indicators.typprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void HLCC4() { - double[][] arrin = { inhigh, inlow, inclose }; - double[][] arrout = { outdata }; - - TSeries QL = bars.HLCC4; - Tulip.Indicators.wcprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] - public void HMA() { - int p = 10; - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - HMA_Series QL = new(bars.Close, p, false); - Tulip.Indicators.hma.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); - for (int i = QL.Length - 1; i > skip+2; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - p - 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits-2), Math.Exp(-digits-2)); - } - } - - [Fact] - public void KAMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - KAMA_Series QL = new(bars.Close, period); - Tulip.Indicators.kama.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > 250; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] - public void LINREG() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - SLOPE_Series QL = new(bars.Close, period); - Tulip.Indicators.linregslope.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period+1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void MACD() { - - double[] outsignal = new double[bars.Count]; - double[] outhist = new double[bars.Count]; - double[][] arrin = { inclose }; - double[][] arrout = { outdata, outsignal, outhist }; - MACD_Series QL = new(bars.Close, slow: 26,fast: 10, signal: 9); - Tulip.Indicators.macd.Run(inputs: arrin, options: new double[] { 10,26,9 }, outputs: arrout); - for (int i = QL.Length - 1; i > 150; i--) { - double QL_item = QL[i].v; - double TU_item =outdata[i - 26+1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void MAX() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - MAX_Series QL = new(bars.Close, period, false); - Tulip.Indicators.max.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i-period+1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void MIN() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - MIN_Series QL = new(bars.Close, period, false); - Tulip.Indicators.min.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void MUL() { - double[][] arrin = { inhigh, inlow }; - double[][] arrout = { outdata }; - MUL_Series QL = new(bars.High, bars.Low); - Tulip.Indicators.mul.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void OBV() { - double[][] arrin = { inclose, involume }; - double[][] arrout = { outdata }; - OBV_Series QL = new(bars, period, false); - Tulip.Indicators.obv.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i] + arrin[1][0]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void OHLC4() - { - double[][] arrin = { inopen, inhigh, inlow, inclose }; - double[][] arrout = { outdata }; - - TSeries QL = bars.OHLC4; - Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void RMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - RMA_Series QL = new(bars.Close, period, false); - Tulip.Indicators.wilders.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void RSI() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - RSI_Series QL = new(bars.Close, period, false); - Tulip.Indicators.rsi.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void SMA() - { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - SMA_Series QL = new(bars.Close, period, false); - Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) - { - double QL_item = QL[i].v; - double TU_item = arrout[0][i-period+1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void SDEV() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - SDEV_Series QL = new(bars.Close, period, false); - Tulip.Indicators.stddev.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void SUB() { - double[][] arrin = { inhigh, inlow }; - double[][] arrout = { outdata }; - SUB_Series QL = new(bars.High, bars.Low); - Tulip.Indicators.sub.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void SUM() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - CUSUM_Series QL = new(bars.Close, period, false); - Tulip.Indicators.sum.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void TR() { - double[][] arrin = { inhigh,inlow,inclose }; - double[][] arrout = { outdata }; - TR_Series QL = new(bars); - Tulip.Indicators.tr.Run(inputs: arrin, options: new double[] {}, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void TEMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - TEMA_Series QL = new(bars.Close, period, false); - Tulip.Indicators.tema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip+200; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - (period-1)*3]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void TRIMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - TRIMA_Series QL = new(bars.Close, period, false); - Tulip.Indicators.trima.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - /* - [Fact] - public void TRIX() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - TRIX_Series QL = new(bars.Close, period); - Tulip.Indicators.trix.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > period+200; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - (period*3) + 2]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits+2), Math.Exp(-digits+2)); - } - } - */ - [Fact] - public void VAR() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - VAR_Series QL = new(bars.Close, period, false); - Tulip.Indicators.var.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void WMA() { - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - WMA_Series QL = new(bars.Close, period, false); - Tulip.Indicators.wma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); - for (int i = QL.Length - 1; i > skip; i--) { - double QL_item = QL[i].v; - double TU_item = arrout[0][i - period + 1]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] - public void ZLEMA() { - int p = 4; - double[][] arrin = { inclose }; - double[][] arrout = { outdata }; - ZLEMA_Series QL = new(bars.Close, p, false); - Tulip.Indicators.zlema.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); - for (int i = QL.Length - 1; i > skip+20; i--) { - double QL_item = QL[i].v; - double TU_item = outdata[i]; - Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits-2), Math.Exp(-digits-2)); - } - } -} +using Xunit; +using System; +using Tulip; +using QuanTAlib; + +namespace Validations; +public class Tulip_Test +{ + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly double[] outdata; + private readonly double[] inopen; + private readonly double[] inhigh; + private readonly double[] inlow; + private readonly double[] inclose; + private readonly double[] involume; + + public Tulip_Test() + { + bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); + period = rnd.Next(28) + 3; + skip = period + 5; + digits = 8; + + outdata = new double[bars.Count]; + inopen = bars.Open.v.ToArray(); + inhigh = bars.High.v.ToArray(); + inlow = bars.Low.v.ToArray(); + inclose = bars.Close.v.ToArray()!; + involume = bars.Volume.v.ToArray()!; + + } + [Fact] + public void ADL() + { + double[][] arrin = { inhigh, inlow, inclose, involume }; + double[][] arrout = { outdata }; + ADL_Series QL = new(bars); + Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADD() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + ADD_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADOSC() + { + double[][] arrin = { inhigh, inlow, inclose, involume }; + double[][] arrout = { outdata }; + int s = 3; + ADOSC_Series QL = new(bars, s, period, false); + Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ATR() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + ATR_Series QL = new(bars, period: period, useNaN: false); + Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + //Tulip ATR doesn't use warm-up SMA, compensating with 200 warming bars + for (int i = QL.Length - 1; i > 200 + skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void BBANDS() + { + double[][] arrin = { inclose }; + double[] outmid = new double[bars.Count]; + double[] outlower = new double[bars.Count]; + double[] outupper = new double[bars.Count]; + double[][] arrout = { outlower, outmid, outupper }; + BBANDS_Series QL = new(bars.Close, period, 2, false); + Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL.Lower[i].v; + double TU_item = outlower[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + TU_item = outmid[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Upper[i].v; + TU_item = outupper[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* + [Fact] + public void CCI() { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + CCI_Series QL = new(bars, period, useNaN: false); + Tulip.Indicators.cci.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) { + double QL_item = QL[i].v; + double TU_item = outdata[i - period + 1]; + Assert.Equal(QL_item,TU_item); + //Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + */ + [Fact] + public void CMO() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + CMO_Series QL = new(bars.Close, period, useNaN: false); + Tulip.Indicators.cmo.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DECAY() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DECAY_Series QL = new(bars.Close, period, useNaN: false); + Tulip.Indicators.decay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DEMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DEMA_Series QL = new(bars.Close, period, useNaN: false, useSMA: false); + Tulip.Indicators.dema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period + period - 2)]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DIV() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + DIV_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.div.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void EDECAY() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DECAY_Series QL = new(bars.Close, period, exponential: true, useNaN: false); + Tulip.Indicators.edecay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void EMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + // Tulip EMA doesn't use SMA to warm-up + EMA_Series QL = new(bars.Close, period, false, useSMA: false); + Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HL2() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HL2; + Tulip.Indicators.medprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLC3() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HLC3; + Tulip.Indicators.typprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLCC4() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HLCC4; + Tulip.Indicators.wcprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + public void HMA() + { + int p = 10; + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + HMA_Series QL = new(bars.Close, p, false); + Tulip.Indicators.hma.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 2; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - p - 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits - 2), Math.Exp(-digits - 2)); + } + } + + [Fact] + public void KAMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + KAMA_Series QL = new(bars.Close, period); + Tulip.Indicators.kama.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > 250; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + public void LINREG() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SLOPE_Series QL = new(bars.Close, period); + Tulip.Indicators.linregslope.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MACD() + { + + double[] outsignal = new double[bars.Count]; + double[] outhist = new double[bars.Count]; + double[][] arrin = { inclose }; + double[][] arrout = { outdata, outsignal, outhist }; + MACD_Series QL = new(bars.Close, slow: 26, fast: 10, signal: 9); + Tulip.Indicators.macd.Run(inputs: arrin, options: new double[] { 10, 26, 9 }, outputs: arrout); + for (int i = QL.Length - 1; i > 150; i--) + { + double QL_item = QL[i].v; + double TU_item = outdata[i - 26 + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MAX() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + MAX_Series QL = new(bars.Close, period, false); + Tulip.Indicators.max.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MIN() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + MIN_Series QL = new(bars.Close, period, false); + Tulip.Indicators.min.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MUL() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + MUL_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.mul.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OBV() + { + double[][] arrin = { inclose, involume }; + double[][] arrout = { outdata }; + OBV_Series QL = new(bars, period, false); + Tulip.Indicators.obv.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i] + arrin[1][0]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OHLC4() + { + double[][] arrin = { inopen, inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.OHLC4; + Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void RMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + RMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.wilders.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void RSI() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + RSI_Series QL = new(bars.Close, period, false); + Tulip.Indicators.rsi.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SDEV() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SDEV_Series QL = new(bars.Close, period, false); + Tulip.Indicators.stddev.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUB() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + SUB_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.sub.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUM() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + CUSUM_Series QL = new(bars.Close, period, false); + Tulip.Indicators.sum.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TR() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + TR_Series QL = new(bars); + Tulip.Indicators.tr.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TEMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TEMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.tema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period - 1) * 3]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TRIMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TRIMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.trima.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* + [Fact] + public void TRIX() { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TRIX_Series QL = new(bars.Close, period); + Tulip.Indicators.trix.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > period+200; i--) { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period*3) + 2]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits+2), Math.Exp(-digits+2)); + } + } + */ + [Fact] + public void VAR() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + VAR_Series QL = new(bars.Close, period, false); + Tulip.Indicators.var.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void WMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + WMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.wma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ZLEMA() + { + int p = 4; + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + ZLEMA_Series QL = new(bars.Close, p, false); + Tulip.Indicators.zlema.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 20; i--) + { + double QL_item = QL[i].v; + double TU_item = outdata[i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits - 2), Math.Exp(-digits - 2)); + } + } +} diff --git a/docs/To illuminate and standardize the vast d.md b/docs/To illuminate and standardize the vast d.md index 5d9fcdd3..941489a5 100644 --- a/docs/To illuminate and standardize the vast d.md +++ b/docs/To illuminate and standardize the vast d.md @@ -1,2 +1,2 @@ -"To use unique insights from EPAM's history, expertise, and innovative spirit we want to recalibrate technology strategies to deliver solutions that are not just innovative, but driven by value creation. We envision a future where every client engagement is delivers integrated value from strategy to optimization, and where our technical thought leadership is a benchmark for the industry, ensuring that EPAM is synonymous with transformative digital engineering." - +"To use unique insights from EPAM's history, expertise, and innovative spirit we want to recalibrate technology strategies to deliver solutions that are not just innovative, but driven by value creation. We envision a future where every client engagement is delivers integrated value from strategy to optimization, and where our technical thought leadership is a benchmark for the industry, ensuring that EPAM is synonymous with transformative digital engineering." + diff --git a/docs/Trading_example.ipynb b/docs/Trading_example.ipynb index 020d62b0..6b1af863 100644 --- a/docs/Trading_example.ipynb +++ b/docs/Trading_example.ipynb @@ -79,7 +79,7 @@ "TSeries data = bars.Close; //we need just one average value - (Open+High+Low+CLose)/4\n", "\n", "//make a chart\n", - "var d = Chart2D.Chart.Candlestick(bars.Open.v.Skip(warmup).ToList(), bars.High.v.Skip(warmup).ToList(), \n", + "var d = Chart2D.Chart.Candlestick(bars.Open.v.Skip(warmup).ToList(), bars.High.v.Skip(warmup).ToList(),\n", "bars.Low.v.Skip(warmup).ToList(), bars.Close.v.Skip(warmup).ToList(), bars.Open.t.Skip(warmup).ToList(), symbol)\n", " .WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false)).WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle(symbol);\n", "d" @@ -344,7 +344,7 @@ } ], "source": [ - "EQUITY_Series folio = new(trades, data, Long:true, Short:false, Warmup:warmup); //generate equity curve from trades and \n", + "EQUITY_Series folio = new(trades, data, Long:true, Short:false, Warmup:warmup); //generate equity curve from trades and\n", "\n", "//make a chart\n", "var cbars = Chart2D.Chart.Area(folio.t.Skip(warmup).ToList(), folio.v.Skip(warmup).ToList(),false ).WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false))\n", diff --git a/v2/GBM_Feed.cs b/v2/GBM_Feed.cs new file mode 100644 index 00000000..0b643838 --- /dev/null +++ b/v2/GBM_Feed.cs @@ -0,0 +1,57 @@ +//namespace QuanTAlib; +public class GBM_Feed +{ + private readonly double _mu; + private readonly double _sigma; + private readonly Random _random; + private double _lastClose; + private double _lastHigh; + private double _lastLow; + + public GBM_Feed(double initialPrice, double mu, double sigma) + { + _lastClose = initialPrice; + _lastHigh = initialPrice; + _lastLow = initialPrice; + _mu = mu; + _sigma = sigma; + _random = Random.Shared; + } + + public TBar Generate(bool IsNew = true) + { + DateTime time = DateTime.UtcNow; + double dt = 1.0 / 252; // Assuming daily steps in a trading year of 252 days + double drift = (_mu - 0.5 * _sigma * _sigma) * dt; + double diffusion = _sigma * Math.Sqrt(dt) * NormalRandom(); + double newClose = _lastClose * Math.Exp(drift + diffusion); + + double open = _lastClose; + double high = Math.Max(open, newClose) * (1 + _random.NextDouble() * 0.01); + double low = Math.Min(open, newClose) * (1 - _random.NextDouble() * 0.01); + double volume = 1000 + _random.NextDouble() * 1000; // Random volume between 1000 and 2000 + + if (!IsNew) + { + high = Math.Max(_lastHigh, high); + low = Math.Min(_lastLow, low); + } + else + { + _lastClose = newClose; + } + + _lastHigh = high; + _lastLow = low; + + return new TBar(time, open, high, low, newClose, volume, IsNew); + } + + private double NormalRandom() + { + // Box-Muller transform to generate standard normal random variable + double u1 = 1.0 - _random.NextDouble(); // Uniform(0,1] random doubles + double u2 = 1.0 - _random.NextDouble(); + return Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); + } +} \ No newline at end of file diff --git a/v2/Indicators/EMA.cs b/v2/Indicators/EMA.cs new file mode 100644 index 00000000..da9699fc --- /dev/null +++ b/v2/Indicators/EMA.cs @@ -0,0 +1,71 @@ +public class EMA +{ + private readonly int _period; + private int _index, _hotIndex; + public TValue Value { get; private set; } + public bool IsHot => _index > _period; + public int Period => Math.Min(_index, _period); + private double _k; + private double _lastEMA, _lastEMACandidate; + + public EMA(int period) { + _period = period; + Init(); + } + + public EMA(object source, int period) : this(period) { + var sourceType = source.GetType(); + var updateMethod = sourceType.GetMethod("Update", new[] { typeof(TValue) }); + if (updateMethod != null) { + var pubEvent = sourceType.GetEvent("Pub"); + if (pubEvent != null && pubEvent.EventHandlerType == typeof(Signal)) { + pubEvent.AddEventHandler(source, new Signal(Sub)); + } else { + throw new ArgumentException("Source object must have a Pub event of type NewValue."); + } + } else { + throw new ArgumentException("Source object must have an Update(TValue) method."); + } + } + + public void Init() { + Value = default; + _index = _hotIndex = 0; + _k = 2.0 / (_period + 1); + _lastEMA = 0; + _lastEMACandidate = 0; + } + + public TValue Update(TValue input) { + if (!input.IsHot && input.IsNew) { _hotIndex++; } + + if (double.IsNaN(input.Value) || double.IsInfinity(input.Value)) { + Value = new TValue(input.Time, _lastEMA, input.IsNew, _index > _period); + Pub?.Invoke(this, Value); + return Value; + } + if (input.IsNew) { + if (_index < 1) { _lastEMA = input.Value; } + _lastEMACandidate = _lastEMA; + _index++; + } else { + if (_index <= 1) { _lastEMACandidate = input.Value; } + _lastEMA = _lastEMACandidate; + } + + double kk = (_index <= _period) ? (2.0 / (_index + 1)) : _k; + + double ema = (input.Value - _lastEMA) * kk + _lastEMA; + _lastEMA = ema; + + Value = new TValue(input.Time, ema, input.IsNew, _index > (_period + _hotIndex)); + Pub?.Invoke(this, Value); + return Value; + } + + public void Sub(object source, TValue arg) { + Update(arg); + } + + public event Signal Pub; +} \ No newline at end of file diff --git a/v2/Indicators/SMA.cs b/v2/Indicators/SMA.cs new file mode 100644 index 00000000..a4da6232 --- /dev/null +++ b/v2/Indicators/SMA.cs @@ -0,0 +1,80 @@ + +public class SMA +{ + private readonly int _period; + private int _index, _hotIndex; + public TValue Value { get; private set; } + public bool IsHot => _index >= _period; + public int Period => Math.Min(_index, _period); + private double _sum; + private double _lastValidSMA; + private CircularBuffer _buffer; + private double _lastAddedValue; + + public SMA(int period) { + _period = period; + Init(); + } + + public SMA(object source, int period) : this(period) { + var sourceType = source.GetType(); + var updateMethod = sourceType.GetMethod("Update", new[] { typeof(TValue) }); + + if (updateMethod != null) { + var pubEvent = sourceType.GetEvent("Pub"); + if (pubEvent != null && pubEvent.EventHandlerType == typeof(Signal)) { + pubEvent.AddEventHandler(source, new Signal(Sub)); + } else { + throw new ArgumentException("Source object must have a Pub event of type NewValue."); + } + } else { + throw new ArgumentException("Source object must have an Update(TValue) method."); + } + } + + public void Init() { + _buffer = new CircularBuffer(_period); + _sum = 0; + _lastValidSMA = 0; + Value = default; + _index = _hotIndex = 0; + _lastAddedValue = 0; + } + + public TValue Update(TValue input) { + if (!input.IsHot && input.IsNew) { _hotIndex++; } + + if (double.IsNaN(input.Value) || double.IsInfinity(input.Value)) { + Value = new TValue(input.Time, _lastValidSMA, input.IsNew, _index > _period); + Pub?.Invoke(this, Value); + return Value; + } + + if (input.IsNew) { + if (_buffer.Count == _buffer.Capacity) { + _sum -= _buffer[0]; + } + _buffer.Add(input.Value, true); + _sum += input.Value; + _lastAddedValue = input.Value; + _index++; + } else { + _sum = _sum - _lastAddedValue + input.Value; + _buffer[_buffer.Count - 1] = input.Value; + _lastAddedValue = input.Value; + } + + double sma = _sum / _buffer.Count; + _lastValidSMA = sma; + + Value = new TValue(input.Time, sma, input.IsNew, _index > (_period + _hotIndex)); + Pub?.Invoke(this, Value); + return Value; + } + + public void Sub(object source, TValue arg) { + Update(arg); + } + + public event Signal Pub; +} \ No newline at end of file diff --git a/v2/Indicators/Template.cs b/v2/Indicators/Template.cs new file mode 100644 index 00000000..1f070f53 --- /dev/null +++ b/v2/Indicators/Template.cs @@ -0,0 +1,40 @@ +public class Template +{ + private CircularBuffer buffer = null!; + private readonly int period; + private int index; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public Template(int Period) { + this.period = Period; + Init(); + } + + public void Init() { + this.buffer = new CircularBuffer(period); + this.IsHot = false; + this.Value = default; + this.index = 0; + } + + public TValue Update(TValue Input, bool IsNew = true) { + this.buffer.Add(Input,IsNew); + if (this.index == 0) { + if (IsNew) { this.index++; } + this.Value = new TValue(Input.Time, Input.Value, IsNew, true); + return this.Value; + } + + if (IsNew) { + // starting a new bar, fresh calc + index++; + } else { + // updating existing bar, recalc + } + double ma = Input.Value; + + this.Value = new TValue(Input.Time, ma, IsNew, buffer.Count >= period); + return this.Value; + } +} \ No newline at end of file diff --git a/v2/Indicators/WMA.cs b/v2/Indicators/WMA.cs new file mode 100644 index 00000000..f7673f7d --- /dev/null +++ b/v2/Indicators/WMA.cs @@ -0,0 +1,62 @@ +public class WMA +{ + private CircularBuffer buffer = null!; + private CircularBuffer weights = null!; + private int period; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public WMA(int period) + { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.buffer = new CircularBuffer(period); + this.weights = new CircularBuffer(period); + CalculateWeights(); + this.IsHot = false; + this.Value = default; + } + + public TValue Update(TValue input, bool IsNew = true) + { + if (IsNew) + { + buffer.Add(input); + } + else if (buffer.Count > 0) + { + buffer[buffer.Count - 1] = input; + } + else + { + buffer.Add(input); + } + + double wma = 0; + double totalWeights = 0; + + for (int i = 0; i < buffer.Count; i++) + { + wma += buffer[i] * weights[i]; + totalWeights += weights[i]; + } + + wma /= totalWeights; + + IsHot = buffer.Count >= period; + Value = new TValue(input.Time, wma, IsNew, IsHot); + return Value; + } + + private void CalculateWeights() + { + for (int i = 1; i <= period; i++) + { + weights.Add(i); + } + } +} \ No newline at end of file diff --git a/v2/basics/CircularBuffer.cs b/v2/basics/CircularBuffer.cs new file mode 100644 index 00000000..cc457009 --- /dev/null +++ b/v2/basics/CircularBuffer.cs @@ -0,0 +1,90 @@ +using System; +using System.Collections; +using System.Collections.Generic; +using System.Runtime.CompilerServices; + +public class CircularBuffer: IEnumerable +{ + private double[] _buffer = null!; + private int _start; + private int _size; + + public int Capacity => _buffer.Length; + public int Count => _size; + + public CircularBuffer(int capacity) + { + + _buffer = new double[capacity]; + _start = 0; + _size = 0; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public void Add(double item, bool isNew = true) { + if (_size == 0 || isNew) { + // If buffer is empty or isNew is true, add new item + if (_size < Capacity) { + _buffer[(_start + _size) % Capacity] = item; + _size++; + } else { + _buffer[_start] = item; + _start = (_start + 1) % Capacity; + } + } else { + // If isNew is false, just update the last item + if (_size > 0) { + _buffer[(_start + _size - 1) % Capacity] = item; + } else { + _buffer[_start] = item; + _size = 1; + } + } + } + + public double this[int index] { + get { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + return _buffer[(_start + index) % Capacity]; + } set { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + _buffer[(_start + index) % Capacity] = value; + } + } + + public Enumerator GetEnumerator() => new Enumerator(this); + IEnumerator IEnumerable.GetEnumerator() => GetEnumerator(); + IEnumerator IEnumerable.GetEnumerator() => GetEnumerator(); + public struct Enumerator : IEnumerator { + private readonly CircularBuffer _buffer; + private int _index; + private double _current; + + internal Enumerator(CircularBuffer buffer) { + _buffer = buffer; + _index = -1; + _current = default; + } + + public bool MoveNext() { + if (_index + 1 >= _buffer._size) + return false; + + _index++; + _current = _buffer[_index]; + return true; + } + + public double Current => _current; + object IEnumerator.Current => Current; + + public void Reset() { + _index = -1; + _current = default; + } + + public void Dispose() { } + } +} \ No newline at end of file diff --git a/v2/basics/TBar.cs b/v2/basics/TBar.cs new file mode 100644 index 00000000..d5ea9a81 --- /dev/null +++ b/v2/basics/TBar.cs @@ -0,0 +1,17 @@ +//namespace QuanTAlib; +public readonly record struct TBar(DateTime Time, double Open, double High, double Low, double Close, double Volume, bool IsNew = true) +{ + public DateTime Time { get; init; } = Time; + public double Open { get; init; } = Open; + public double High { get; init; } = High; + public double Low { get; init; } = Low; + public double Close { get; init; } = Close; + public double Volume { get; init; } = Volume; + public bool IsNew { get; init; } = IsNew; + + public TBar() : this(DateTime.UtcNow, 0, 0, 0, 0, 0) { } + public TBar(double open, double high, double low, double close, double volume) : this(DateTime.UtcNow, open, high, low, close, volume) { } + public TBar((DateTime time, double open, double high, double low, double close, double volume) tuple) : this(tuple.time, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume) { } + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: O={Open:F2}, H={High:F2}, L={Low:F2}, C={Close:F2}, V={Volume:F2}]"; +} \ No newline at end of file diff --git a/v2/basics/TValue.cs b/v2/basics/TValue.cs new file mode 100644 index 00000000..d92d2878 --- /dev/null +++ b/v2/basics/TValue.cs @@ -0,0 +1,21 @@ +//namespace QuanTAlib; + +public readonly record struct TValue(DateTime Time, double Value, bool IsNew = true, bool IsHot = true) +{ + public DateTime Time { get; init; } = Time; + public double Value { get; init; } = Value; + public bool IsNew { get; init; } = IsNew; + public bool IsHot { get; init; } = IsHot; + + public TValue() : this(DateTime.UtcNow, 0) { } + public TValue(double value) : this(DateTime.UtcNow, value) { } + public TValue((DateTime time, double value) tuple) : this(tuple.time, tuple.value) { } + + public static implicit operator double(TValue tv) => tv.Value; + public static implicit operator DateTime(TValue tv) => tv.Time; + public static implicit operator TValue(double value) => new TValue(DateTime.UtcNow, value); + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: {Value:F2}]"; +} + +public delegate void Signal(object source, TValue args); \ No newline at end of file diff --git a/v2/calculations.csproj b/v2/calculations.csproj new file mode 100644 index 00000000..a7ad53bd --- /dev/null +++ b/v2/calculations.csproj @@ -0,0 +1,31 @@ + + + + net8.0 + enable + enable + preview + false + Indicator + git + Apache-2.0 + false + true + true + true + D:\Quantower\TradingPlatform + v1.140.8 + + + + $(QuantowerPath)\$(QuantowerVersion)\bin\TradingPlatform.BusinessLayer.dll + + + + + + TradingPlatform.BusinessLayer.xml + + + + \ No newline at end of file diff --git a/v2/circularbuffer.dib b/v2/circularbuffer.dib new file mode 100644 index 00000000..ac976a0d --- /dev/null +++ b/v2/circularbuffer.dib @@ -0,0 +1,162 @@ +#!meta + +{"kernelInfo":{"defaultKernelName":"csharp","items":[{"aliases":[],"name":"csharp"}]}} + +#!csharp + +#r ".\bin\Debug\calculations.dll" +using QuanTAlib; + +#!csharp + +using System.Runtime.CompilerServices; + +public class CircularBuffer: IEnumerable +{ + private double[] _buffer = null!; + private int _start; + private int _size; + + public int Capacity => _buffer.Length; + public int Count => _size; + + public CircularBuffer(int capacity) + { + + _buffer = new double[capacity]; + _start = 0; + _size = 0; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public void Add(double item, bool isNew = true) { + if (_size == 0 || isNew) { + // If buffer is empty or isNew is true, add new item + if (_size < Capacity) { + _buffer[(_start + _size) % Capacity] = item; + _size++; + } else { + _buffer[_start] = item; + _start = (_start + 1) % Capacity; + } + } else { + // If isNew is false, just update the last item + _buffer[(_start + _size - 1) % Capacity] = item; + } + } + + public double this[int index] { + get { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + return _buffer[(_start + index) % Capacity]; + } set { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + _buffer[(_start + index) % Capacity] = value; + } + } + + public Enumerator GetEnumerator() => new Enumerator(this); + IEnumerator IEnumerable.GetEnumerator() => GetEnumerator(); + IEnumerator IEnumerable.GetEnumerator() => GetEnumerator(); + public struct Enumerator : IEnumerator { + private readonly CircularBuffer _buffer; + private int _index; + private double _current; + + internal Enumerator(CircularBuffer buffer) { + _buffer = buffer; + _index = -1; + _current = default; + } + + public bool MoveNext() { + if (_index + 1 >= _buffer._size) + return false; + + _index++; + _current = _buffer[_index]; + return true; + } + + public double Current => _current; + object IEnumerator.Current => Current; + + public void Reset() { + _index = -1; + _current = default; + } + + public void Dispose() { } + } +} + +#!csharp + +public class SMA +{ + private CircularBuffer buffer; + private readonly int period; + private double sum; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public SMA(int period) + { + this.period = period; + Init(); + } + + public void Init() + { + this.buffer = new CircularBuffer(period); + this.sum = 0; + this.IsHot = false; + this.Value = default; + } + + public TValue Update(TValue input, bool isNew = true) + { + double oldValue = 0; + if (buffer.Count == period && isNew) + { + oldValue = buffer[0]; + } + + buffer.Add(input.Value, isNew); + + // Update sum + if (isNew) + { + if (buffer.Count <= period) + { + sum += input.Value; + } + else + { + sum = sum - oldValue + input.Value; + } + } + else if (buffer.Count > 0) + { + // If not new, update the sum by replacing the last value + sum = sum - buffer[buffer.Count - 1] + input.Value; + } + + double sma = buffer.Count > 0 ? sum / buffer.Count : double.NaN; + IsHot = buffer.Count >= period; + Value = new TValue(input.Time, sma, isNew, IsHot); + return Value; + } +} + +#!csharp + +CircularBuffer buffer = new(3); +buffer.Add(1, false); +buffer.Add(2, false); +buffer.Add(3, true); +for (int i=0; i _index >= _period; + public int Period => Math.Min(_index, _period); + private double _sum; + private double _lastValidValue; + private double _lastAddedValue; + + public Template(int period) { + _period = period; + Init(); + } + + public Template(object source, int period) : this(period) { + var sourceType = source.GetType(); + var updateMethod = sourceType.GetMethod("Update", new[] { typeof(TValue), typeof(bool) }); + + if (updateMethod != null) { + var pubEvent = sourceType.GetEvent("Pub"); + if (pubEvent != null && pubEvent.EventHandlerType == typeof(Signal)) { + pubEvent.AddEventHandler(source, new Signal(Sub)); + } else { + throw new ArgumentException("Source object must have a Pub event of type NewValue."); + } + } else { + throw new ArgumentException("Source object must have an Update(TValue, bool) method."); + } + } + + public void Init() { + _buffer = new CircularBuffer(_period); + _sum = 0; + _lastValidValue = 0; + Value = default; + _index = _hotIndex = 0; + _lastAddedValue = 0; + } + + public TValue Update(TValue input, bool isNew = true) { + if (!input.IsHot && isNew) { _hotIndex++; } + + if (double.IsNaN(input.Value) || double.IsInfinity(input.Value)) { + Value = new TValue(input.Time, _lastValidValue, isNew, _index > _period); + Pub?.Invoke(this, Value); + return Value; + } + + if (isNew) { + if (_buffer.Count == _buffer.Capacity) { + _sum -= _buffer[0]; + } + _buffer.Add(input.Value, true); + _sum += input.Value; + _lastAddedValue = input.Value; + _index++; + } else { + _sum = _sum - _lastAddedValue + input.Value; + _buffer[_buffer.Count - 1] = input.Value; + _lastAddedValue = input.Value; + } + + double result = _sum / _buffer.Count; + _lastValidValue = result; + + Value = new TValue(input.Time, result, isNew, _index > (_period + _hotIndex)); + Pub?.Invoke(this, Value); + return Value; + } + + public void Sub(object source, TValue arg) { + Update(arg); + } + + public event Signal Pub; +} + +#!csharp + +//public delegate void Signal(object source, TValue args); + +public class SMA +{ + private readonly int _period; + private int _index, _hotIndex; + public TValue Value { get; private set; } + public bool IsHot => _index >= _period; + public int Period => Math.Min(_index, _period); + private double _sum; + private double _lastValidSMA; + private CircularBuffer _buffer; + private double _lastAddedValue; + + public SMA(int period) { + _period = period; + Init(); + } + + public SMA(object source, int period) : this(period) { + var sourceType = source.GetType(); + var updateMethod = sourceType.GetMethod("Update", new[] { typeof(TValue) }); + + if (updateMethod != null) { + var pubEvent = sourceType.GetEvent("Pub"); + if (pubEvent != null && pubEvent.EventHandlerType == typeof(Signal)) { + pubEvent.AddEventHandler(source, new Signal(Sub)); + } else { + throw new ArgumentException("Source object must have a Pub event of type NewValue."); + } + } else { + throw new ArgumentException("Source object must have an Update(TValue) method."); + } + } + + public void Init() { + _buffer = new CircularBuffer(_period); + _sum = 0; + _lastValidSMA = 0; + Value = default; + _index = _hotIndex = 0; + _lastAddedValue = 0; + } + + public TValue Update(TValue input) { + if (!input.IsHot && input.IsNew) { _hotIndex++; } + + if (double.IsNaN(input.Value) || double.IsInfinity(input.Value)) { + Value = new TValue(input.Time, _lastValidSMA, input.IsNew, _index > _period); + Pub?.Invoke(this, Value); + return Value; + } + + if (input.IsNew) { + if (_buffer.Count == _buffer.Capacity) { + _sum -= _buffer[0]; + } + _buffer.Add(input.Value, true); + _sum += input.Value; + _lastAddedValue = input.Value; + _index++; + } else { + _sum = _sum - _lastAddedValue + input.Value; + _buffer[_buffer.Count - 1] = input.Value; + _lastAddedValue = input.Value; + } + + double sma = _sum / _buffer.Count; + _lastValidSMA = sma; + + Value = new TValue(input.Time, sma, input.IsNew, _index > (_period + _hotIndex)); + Pub?.Invoke(this, Value); + return Value; + } + + public void Sub(object source, TValue arg) { + Update(arg); + } + + public event Signal Pub; +} + +#!csharp + +public class EMA +{ + private readonly int _period; + private int _index, _hotIndex; + public TValue Value { get; private set; } + public bool IsHot => _index > _period; + public int Period => Math.Min(_index, _period); + private double _k; + private double _lastEMA, _lastEMACandidate; + + public EMA(int period) { + _period = period; + Init(); + } + + public EMA(object source, int period) : this(period) { + var sourceType = source.GetType(); + var updateMethod = sourceType.GetMethod("Update", new[] { typeof(TValue) }); + if (updateMethod != null) { + var pubEvent = sourceType.GetEvent("Pub"); + if (pubEvent != null && pubEvent.EventHandlerType == typeof(Signal)) { + pubEvent.AddEventHandler(source, new Signal(Sub)); + } else { + throw new ArgumentException("Source object must have a Pub event of type NewValue."); + } + } else { + throw new ArgumentException("Source object must have an Update(TValue) method."); + } + } + + public void Init() { + Value = default; + _index = _hotIndex = 0; + _k = 2.0 / (_period + 1); + _lastEMA = 0; + _lastEMACandidate = 0; + } + + public TValue Update(TValue input) { + if (!input.IsHot && input.IsNew) { _hotIndex++; } + + if (double.IsNaN(input.Value) || double.IsInfinity(input.Value)) { + Value = new TValue(input.Time, _lastEMA, input.IsNew, _index > _period); + Pub?.Invoke(this, Value); + return Value; + } + if (input.IsNew) { + if (_index < 1) { _lastEMA = input.Value; } + _lastEMACandidate = _lastEMA; + _index++; + } else { + if (_index <= 1) { _lastEMACandidate = input.Value; } + _lastEMA = _lastEMACandidate; + } + + double kk = (_index <= _period) ? (2.0 / (_index + 1)) : _k; + + double ema = (input.Value - _lastEMA) * kk + _lastEMA; + _lastEMA = ema; + + Value = new TValue(input.Time, ema, input.IsNew, _index > (_period + _hotIndex)); + Pub?.Invoke(this, Value); + return Value; + } + + public void Sub(object source, TValue arg) { + Update(arg); + } + + public event Signal Pub; +} + +#!csharp + +EMA ma = new(3); +SMA ma1 = new(ma, 3); +Console.WriteLine($"{"Close",5} {"MA()",10}"); +for (int i = 1; i < 10; i=i+1) +{ + TValue tv = new(DateTime.Now, 1000, IsNew: true); + ma.Update(tv); + tv = new(DateTime.Now, i, IsNew: false); + ma.Update(tv); + + Console.WriteLine($"{i} {(double)ma.Value,10:F2} {(double)ma1.Value,10:F2} {ma1.Value.IsHot}"); +} + +#!csharp + +public class Emitter { + private Random random = new Random(); + public event EventHandler> Pub; + public void Emit() { + DateTime now = DateTime.Now; + double randomValue = random.NextDouble() * 100; // Generates a random number between 0 and 100 + TValue value = new TValue(now, randomValue); + + EventArg eventArg = new EventArg(value, true, true); + OnValuePub(eventArg); + } + protected virtual void OnValuePub(EventArg eventArg) { + Pub?.Invoke(this, eventArg); + } +} + +public class BarEmitter +{ + private Random random = new Random(); + public event EventHandler> Pub; + private double lastClose = 100.0; // Starting price + + public void Emit() + { + double open = lastClose; + double close = open * (1 + (random.NextDouble() - 0.5) * 0.02); // +/- 1% change + double high = Math.Max(open, close) * (1 + random.NextDouble() * 0.005); // Up to 0.5% higher + double low = Math.Min(open, close) * (1 - random.NextDouble() * 0.005); // Up to 0.5% lower + double volume = random.NextDouble() * 1000000; // Random volume between 0 and 1,000,000 + + TBar bar = new TBar(DateTime.Now, open, high, low, close, volume); + lastClose = close; + + EventArg eventArg = new EventArg(bar, true, true); + OnBarPub(eventArg); + } + + protected virtual void OnBarPub(EventArg eventArg) + { + Pub?.Invoke(this, eventArg); + } +} + + +public class Listener +{ + public void Sub(object sender, EventArgs e) + { + if (e is EventArg tValueArg) { + Console.WriteLine($"TValue: {tValueArg.Data.Value:F2}"); + } else if (e is EventArg tBarArg) { + Console.WriteLine($"TBar: o={tBarArg.Data.Open:F2}, v={tBarArg.Data.Volume:F2}"); + } else { + Console.WriteLine($"Unknown type: {e.GetType().Name}"); + } + } +} + +#!csharp + +Emitter em1 = new(); +BarEmitter em2 = new(); +Listener list = new(); + +em1.Pub += list.Sub; +em2.Pub += list.Sub; + +// Emit 5 random values +for (int i = 0; i < 3; i++) { + em1.Emit(); + em2.Emit(); +} + +#!csharp + +public abstract class Indicator { + protected Indicator() { + Init(); } + public virtual void Init() {} + public virtual TValue Calc(TValue input, bool isNew=true, bool isHot=true) { + return new TValue(); + } +} + +public class EMA : Indicator +{ + private double lastEma, lastEmaCandidate, k; + private int period, i; + + public EMA(int period) { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.k = 2.0 / (period + 1); + this.lastEma = this.lastEmaCandidate = double.NaN; + this.i = 0; + } + + public override TValue Calc(TValue input, bool isNew = true, bool isHot = true) { + double ema; + + if (double.IsNaN(lastEma)) { lastEma = lastEmaCandidate = input.Value; } + + if (isNew) { + lastEma = lastEmaCandidate; + i++; + } + + double kk = (i>=period)?k:(2.0/(i+1)); + ema = lastEma + kk * (input.Value - lastEma); + lastEmaCandidate = ema; + + return new TValue(input.Timestamp, ema); + } +} + +#!csharp + +EMA ema = new(3); +display(ema.Calc(100)); +display(ema.Calc(0,false)); +display(ema.Calc(100,false)); +display(ema.Calc(0));