mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-19 02:58:05 +00:00
Miha Kralj
a9e72dae0d
- Updated Codacy instructions to streamline usage guidelines. - Refactored Bbands class to utilize ArrayPool for memory management, preventing stack overflow on large series. - Changed Fcb class to use long for monotonic deques to avoid truncation issues. - Enhanced Kchannel class to ensure safe defaults for non-finite values. - Improved Maenv class to prevent double-priming during calculations. - Modified Mmchannel class to ensure non-negative buffer indices and removed unnecessary state tracking. - Updated Pchannel class to correctly reference IsHot state. - Refined Regchannel class to avoid double-processing during calculations. - Enhanced Starchannel class to sanitize non-finite values during calculations. - Adjusted Stbands.Quantower.cs to allow finer control over multiplier precision. - Updated Ubands class to only update last valid values on new bars. - Modified Uchannel.Quantower.cs to allow for finer multiplier precision. - Enhanced Vwapbands classes to include standard deviation calculations and ensure consistent array lengths. - Refactored Vwapsd classes to include standard deviation outputs and ensure consistent array lengths. - Updated MonotonicDeque to use long for indices to prevent overflow. - Improved Mdape class to handle zero actual values with a substitute value for error calculation. - Enhanced Rae class to ensure correct state management during updates. - Refined Wmape class to simplify the logic for finding last valid actual and predicted values. - Updated Cmf.Quantower classes to ensure MinHistoryDepths reflects the current period.
474 lines
15 KiB
C#
474 lines
15 KiB
C#
using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// REGCHANNEL: Regression Channel
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/// Linear regression centerline with bands at ±multiplier × standard deviation of residuals.
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/// </summary>
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/// <remarks>
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/// The Regression Channel plots a linear regression line with parallel bands
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/// positioned at a specified number of standard deviations of the residuals above and below.
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///
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/// Calculation:
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/// 1. Compute linear regression line: y = mx + b using least squares
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/// 2. Calculate residuals: residual_i = y_i - predicted_i
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/// 3. Compute standard deviation of residuals: σ = √(Σ(residual²) / n)
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/// 4. Upper = regression + multiplier × σ
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/// 5. Lower = regression - multiplier × σ
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///
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/// Key characteristics:
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/// - Middle line is the linear regression endpoint (LSMA)
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/// - Bands measure dispersion around the regression line
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/// - Wider bands indicate more noise/volatility around the trend
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/// - Price touching bands suggests deviation from trend
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///
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/// Sources:
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/// https://www.investopedia.com/terms/l/linearregressionindicator.asp
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/// https://school.stockcharts.com/doku.php?id=technical_indicators:raff_regression_channel
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Regchannel : ITValuePublisher
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{
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private readonly int _period;
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private readonly double _multiplier;
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// Precomputed constants for linear regression
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private readonly double _sumX; // sum of x indices: 0 + 1 + ... + (n-1)
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private readonly double _denominator; // n * sumX2 - sumX²
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// Ring buffer for values
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private readonly double[] _buffer;
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private double[]? _p_buffer;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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int Head,
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int Count,
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double LastValid,
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double Slope,
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double StdDev,
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bool IsHot);
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private State _state;
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private State _p_state;
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private readonly TValuePublishedHandler _valueHandler;
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public string Name { get; }
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public int WarmupPeriod { get; }
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public TValue Last { get; private set; }
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public TValue Upper { get; private set; }
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public TValue Lower { get; private set; }
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public bool IsHot => _state.IsHot;
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/// <summary>
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/// The slope of the linear regression line
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/// </summary>
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public double Slope => _state.Slope;
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/// <summary>
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/// The standard deviation of residuals
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/// </summary>
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public double StdDev => _state.StdDev;
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public event TValuePublishedHandler? Pub;
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/// <summary>
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/// Initializes a new instance of the Regchannel indicator.
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/// </summary>
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/// <param name="period">Lookback period for regression (default 20, must be > 1)</param>
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/// <param name="multiplier">Standard deviation multiplier for bands (default 2.0, must be > 0)</param>
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public Regchannel(int period = 20, double multiplier = 2.0)
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{
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if (period <= 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than 1.");
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}
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if (multiplier <= 0)
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{
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throw new ArgumentOutOfRangeException(nameof(multiplier), "Multiplier must be greater than 0.");
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}
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_period = period;
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_multiplier = multiplier;
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_buffer = new double[period];
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_p_buffer = new double[period];
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WarmupPeriod = period;
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Name = $"Regchannel({period},{multiplier:F1})";
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_valueHandler = HandleValue;
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// Precompute constants
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// sumX = 0 + 1 + ... + (n-1) = n(n-1)/2
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_sumX = 0.5 * period * (period - 1);
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// sumX2 = 0² + 1² + ... + (n-1)² = (n-1)n(2n-1)/6
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double sumX2 = (period - 1.0) * period * (2.0 * period - 1.0) / 6.0;
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// denominator = n * sumX2 - sumX²
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_denominator = period * sumX2 - _sumX * _sumX;
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Reset();
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}
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public Regchannel(TSeries source, int period = 20, double multiplier = 2.0) : this(period, multiplier)
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{
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Prime(source);
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source.Pub += _valueHandler;
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}
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private void HandleValue(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void PubEvent(TValue value, bool isNew = true) =>
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Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew });
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public void Reset()
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{
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_state = new State(0, 0, double.NaN, 0, 0, false);
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_p_state = _state;
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Array.Fill(_buffer, 0.0);
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_p_buffer = (double[])_buffer.Clone();
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Last = default;
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Upper = default;
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Lower = default;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private double GetValid(double value, bool isNew)
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{
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if (double.IsFinite(value))
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{
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// Always update LastValid on finite input (including bar corrections)
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_state = _state with { LastValid = value };
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return value;
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}
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return double.IsFinite(_state.LastValid) ? _state.LastValid : 0.0;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(TValue input, bool isNew = true)
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{
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if (isNew)
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{
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_p_state = _state;
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Array.Copy(_buffer, _p_buffer!, _period);
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}
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else
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{
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_state = _p_state;
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Array.Copy(_p_buffer!, _buffer, _period);
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}
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double value = GetValid(input.Value, isNew);
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// Add to ring buffer
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int count = _state.Count;
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int head = _state.Head;
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if (count < _period)
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{
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count++;
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}
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_buffer[head] = value;
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int newHead = (head + 1) % _period;
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if (isNew)
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{
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_state = _state with { Head = newHead, Count = count };
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}
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// Calculate linear regression and std dev of residuals
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if (count <= 1)
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{
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Last = new TValue(input.Time, value);
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Upper = new TValue(input.Time, value);
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Lower = new TValue(input.Time, value);
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_state = _state with { Slope = 0, StdDev = 0 };
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PubEvent(Last, isNew);
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return Last;
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}
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// Build span of values in chronological order (oldest to newest)
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Span<double> values = stackalloc double[count];
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int readHead = (newHead - count + _period) % _period;
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for (int i = 0; i < count; i++)
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{
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values[i] = _buffer[(readHead + i) % _period];
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}
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// Calculate sums for linear regression
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double sumY = 0;
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double sumXY = 0;
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for (int i = 0; i < count; i++)
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{
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sumY += values[i];
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sumXY += i * values[i];
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}
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double n = count;
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double sx = _sumX;
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double denom = _denominator;
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// Adjust for partial window during warmup
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if (count < _period)
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{
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sx = 0.5 * n * (n - 1);
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double sx2 = (n - 1.0) * n * (2.0 * n - 1.0) / 6.0;
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denom = n * sx2 - sx * sx;
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}
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double slope, intercept, regression;
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if (Math.Abs(denom) < 1e-10)
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{
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slope = 0;
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intercept = sumY / n;
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regression = intercept;
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}
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else
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{
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slope = (n * sumXY - sx * sumY) / denom;
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intercept = (sumY - slope * sx) / n;
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// Regression value at current point (x = count - 1)
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regression = Math.FusedMultiplyAdd(slope, count - 1, intercept);
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}
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// Calculate standard deviation of residuals
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double sumResiduals2 = 0;
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for (int i = 0; i < count; i++)
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{
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double predicted = Math.FusedMultiplyAdd(slope, i, intercept);
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double residual = values[i] - predicted;
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sumResiduals2 = Math.FusedMultiplyAdd(residual, residual, sumResiduals2);
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}
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double stdDev = Math.Sqrt(sumResiduals2 / n);
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double band = _multiplier * stdDev;
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if (!_state.IsHot && count >= WarmupPeriod)
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{
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_state = _state with { IsHot = true };
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}
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_state = _state with { Slope = slope, StdDev = stdDev };
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Last = new TValue(input.Time, regression);
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Upper = new TValue(input.Time, regression + band);
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Lower = new TValue(input.Time, regression - band);
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PubEvent(Last, isNew);
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return Last;
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}
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public (TSeries Middle, TSeries Upper, TSeries Lower) Update(TSeries source)
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{
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if (source.Count == 0)
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{
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return (new TSeries([], []), new TSeries([], []), new TSeries([], []));
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}
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int len = source.Count;
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var tMiddle = new List<long>(len);
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var vMiddle = new List<double>(len);
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var tUpper = new List<long>(len);
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var vUpper = new List<double>(len);
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var tLower = new List<long>(len);
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var vLower = new List<double>(len);
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CollectionsMarshal.SetCount(tMiddle, len);
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CollectionsMarshal.SetCount(vMiddle, len);
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CollectionsMarshal.SetCount(tUpper, len);
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CollectionsMarshal.SetCount(vUpper, len);
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CollectionsMarshal.SetCount(tLower, len);
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CollectionsMarshal.SetCount(vLower, len);
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var tSpan = CollectionsMarshal.AsSpan(tMiddle);
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var vMiddleSpan = CollectionsMarshal.AsSpan(vMiddle);
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var vUpperSpan = CollectionsMarshal.AsSpan(vUpper);
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var vLowerSpan = CollectionsMarshal.AsSpan(vLower);
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Batch(source.Values, vMiddleSpan, vUpperSpan, vLowerSpan, _period, _multiplier);
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source.Times.CopyTo(tSpan);
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tSpan.CopyTo(CollectionsMarshal.AsSpan(tUpper));
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tSpan.CopyTo(CollectionsMarshal.AsSpan(tLower));
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// Prime internal state for continued streaming
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Prime(source);
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var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc);
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Last = new TValue(lastTime, vMiddleSpan[^1]);
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Upper = new TValue(lastTime, vUpperSpan[^1]);
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Lower = new TValue(lastTime, vLowerSpan[^1]);
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return (new TSeries(tMiddle, vMiddle), new TSeries(tUpper, vUpper), new TSeries(tLower, vLower));
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}
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public void Prime(TSeries source)
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{
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Reset();
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if (source.Count == 0)
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{
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return;
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}
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for (int i = 0; i < source.Count; i++)
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{
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Update(source[i], isNew: true);
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}
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}
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/// <summary>
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/// Batch calculation using spans.
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/// </summary>
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public static void Batch(
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ReadOnlySpan<double> source,
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Span<double> middle,
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Span<double> upper,
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Span<double> lower,
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int period,
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double multiplier = 2.0)
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{
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if (period <= 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than 1.");
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}
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if (multiplier <= 0)
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{
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throw new ArgumentOutOfRangeException(nameof(multiplier), "Multiplier must be greater than 0.");
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}
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if (middle.Length < source.Length || upper.Length < source.Length || lower.Length < source.Length)
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{
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throw new ArgumentException("Output spans must be at least as long as input", nameof(middle));
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}
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int len = source.Length;
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if (len == 0)
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{
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return;
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}
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// Precompute constants for full period
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double sumXFull = 0.5 * period * (period - 1);
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double sumX2Full = (period - 1.0) * period * (2.0 * period - 1.0) / 6.0;
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double denomFull = period * sumX2Full - sumXFull * sumXFull;
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for (int i = 0; i < len; i++)
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{
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int count = Math.Min(i + 1, period);
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int start = i - count + 1;
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if (count <= 1)
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{
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middle[i] = source[i];
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upper[i] = source[i];
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lower[i] = source[i];
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continue;
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}
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// Calculate sums for linear regression
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double sumY = 0;
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double sumXY = 0;
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for (int j = 0; j < count; j++)
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{
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double y = source[start + j];
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sumY += y;
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sumXY += j * y;
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}
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double n = count;
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double sx, denom;
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if (count < period)
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{
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sx = 0.5 * n * (n - 1);
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double sx2 = (n - 1.0) * n * (2.0 * n - 1.0) / 6.0;
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denom = n * sx2 - sx * sx;
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}
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else
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{
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sx = sumXFull;
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denom = denomFull;
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}
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double slope, intercept, regression;
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if (Math.Abs(denom) < 1e-10)
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{
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slope = 0;
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intercept = sumY / n;
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regression = intercept;
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}
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else
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{
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slope = (n * sumXY - sx * sumY) / denom;
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intercept = (sumY - slope * sx) / n;
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regression = Math.FusedMultiplyAdd(slope, count - 1, intercept);
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}
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// Calculate standard deviation of residuals
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double sumResiduals2 = 0;
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for (int j = 0; j < count; j++)
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{
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double predicted = Math.FusedMultiplyAdd(slope, j, intercept);
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double residual = source[start + j] - predicted;
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sumResiduals2 = Math.FusedMultiplyAdd(residual, residual, sumResiduals2);
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}
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double stdDev = Math.Sqrt(sumResiduals2 / n);
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double band = multiplier * stdDev;
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middle[i] = regression;
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upper[i] = regression + band;
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lower[i] = regression - band;
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}
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}
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public static (TSeries Middle, TSeries Upper, TSeries Lower) Batch(TSeries source, int period = 20, double multiplier = 2.0)
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{
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int len = source.Count;
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var tMiddle = new List<long>(len);
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var vMiddle = new List<double>(len);
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var tUpper = new List<long>(len);
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var vUpper = new List<double>(len);
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var tLower = new List<long>(len);
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var vLower = new List<double>(len);
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CollectionsMarshal.SetCount(tMiddle, len);
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CollectionsMarshal.SetCount(vMiddle, len);
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CollectionsMarshal.SetCount(tUpper, len);
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CollectionsMarshal.SetCount(vUpper, len);
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CollectionsMarshal.SetCount(tLower, len);
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CollectionsMarshal.SetCount(vLower, len);
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Batch(source.Values,
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CollectionsMarshal.AsSpan(vMiddle),
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CollectionsMarshal.AsSpan(vUpper),
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CollectionsMarshal.AsSpan(vLower),
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period, multiplier);
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source.Times.CopyTo(CollectionsMarshal.AsSpan(tMiddle));
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CollectionsMarshal.AsSpan(tMiddle).CopyTo(CollectionsMarshal.AsSpan(tUpper));
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CollectionsMarshal.AsSpan(tMiddle).CopyTo(CollectionsMarshal.AsSpan(tLower));
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return (new TSeries(tMiddle, vMiddle), new TSeries(tUpper, vUpper), new TSeries(tLower, vLower));
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}
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public static ((TSeries Middle, TSeries Upper, TSeries Lower) Results, Regchannel Indicator) Calculate(TSeries source, int period = 20, double multiplier = 2.0)
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{
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// Use parameterless constructor to avoid double-processing: new Regchannel(source, ...) calls Prime(source),
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// then Update(source) would call Prime again.
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var indicator = new Regchannel(period, multiplier);
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var results = indicator.Update(source);
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return (results, indicator);
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}
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}
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