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Update RSI_Series to check for period != 0 before calculating RSI

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This commit is contained in:
Miha Kralj
2023-04-27 22:30:13 -07:00
parent 3b71ac70c8
commit e1680e9d04
145 changed files with 4826 additions and 4207 deletions
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Calculations/Volatility/ATRP_Series.cs
+1 -1
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@@ -19,7 +19,7 @@ public class ATRP_Series : Single_TBars_Indicator {
public ATRP_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN) {
_period = period;
_k = 1.0 / (double)(_p);
_k = 1.0 / (double)(_period);
_lastatr = _lastlastatr = _cm1 = _lastcm1 = _sum = _oldsum = 0;
if (this._bars.Count > 0) { base.Add(this._bars); }
}
Calculations/Volatility/CMO_Series.cs
-46
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@@ -1,46 +0,0 @@
namespace QuanTAlib;
using System;
/* <summary>
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
</summary> */
public class CMO_Series : Single_TSeries_Indicator {
private readonly System.Collections.Generic.List<double> _buff_up = new();
private readonly System.Collections.Generic.List<double> _buff_dn = new();
private double _plast_value, _last_value;
public CMO_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) {
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update) {
if (this.Count == 0) { _plast_value = _last_value = TValue.v; }
if (update) {_last_value = _plast_value;} else {_plast_value = _last_value;}
Add_Replace_Trim(_buff_up, (TValue.v > _last_value) ? TValue.v-_last_value : 0, _p, update);
Add_Replace_Trim(_buff_dn, (TValue.v < _last_value) ? _last_value-TValue.v : 0, _p, 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;}
base.Add((TValue.t, _cmo), update, _NaN);
}
}
Calculations/Volatility/OBV_Series.cs
+59
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@@ -0,0 +1,59 @@
namespace QuanTAlib;
using System;
/* <summary>
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
</summary> */
public class OBV_Series : Single_TBars_Indicator
{
private double _lastobv, _lastlastobv;
private double _lastclose, _lastlastclose;
public OBV_Series(TBars source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN)
{
this._lastobv = this._lastlastobv = 0;
this._lastclose = this._lastlastclose = 0;
if (_bars.Count > 0) { base.Add(_bars); }
}
public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
{
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 result = (TBar.t, (this.Count < this._p && this._NaN) ? double.NaN : _obv);
base.Add(result, update);
}
}
Calculations/Volatility/RSI_Series.cs
-78
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@@ -1,78 +0,0 @@
namespace QuanTAlib;
using System;
/* <summary>
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
</summary> */
public class RSI_Series : Single_TSeries_Indicator
{
private readonly System.Collections.Generic.List<double> _gain = new();
private readonly System.Collections.Generic.List<double> _loss = new();
private double _avgGain, _avgLoss, _lastValue;
private double _avgGain_o, _avgLoss_o, _lastValue_o;
private int i;
public RSI_Series(TSeries source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN) {
i = 0;
if (source.Count > 0) { base.Add(source); }
}
public override void Add((System.DateTime t, double v) TValue, bool update) {
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;
Add_Replace_Trim(_gain, _gainval, _p, update);
double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0;
Add_Replace_Trim(_loss, _lossval, _p, update);
_lastValue = TValue.v;
// calculate RSI
if (i > _p)
{
_avgGain = ((_avgGain * (_p - 1)) + _gain[_gain.Count - 1]) / _p;
_avgLoss = ((_avgLoss * (_p - 1)) + _loss[_loss.Count - 1]) / _p;
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 result = (TValue.t, (this.Count < this._p && this._NaN) ? double.NaN : _rsi);
base.Add(result, update);
}
}