mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-15 17:18:05 +00:00
CORR - Pearson's Correlation Coefficient
This commit is contained in:
Miha Kralj
@@ -2,13 +2,13 @@
|
||||
using System;
|
||||
/* <summary>
|
||||
Abstract classes with all scaffolding required to build indicators.
|
||||
All abstracts support period, NaN, and all permutations of Add() methods.
|
||||
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.
|
||||
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.
|
||||
|
||||
@@ -42,11 +42,24 @@ public abstract class Single_TSeries_Indicator : TSeries
|
||||
|
||||
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)
|
||||
{
|
||||
this._p = period;
|
||||
this._NaN = useNaN;
|
||||
this._d1 = source1;
|
||||
this._d2 = source2;
|
||||
this._dd1 = double.NaN;
|
||||
this._dd2 = double.NaN;
|
||||
this._d1.Pub += this.Sub;
|
||||
this._d2.Pub += this.Sub;
|
||||
}
|
||||
protected Pair_TSeries_Indicator(TSeries source1, TSeries source2)
|
||||
{
|
||||
this._d1 = source1;
|
||||
|
||||
+46
-46
@@ -1,47 +1,47 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Text.Json;
|
||||
|
||||
/* <summary>
|
||||
Yahoo Finance - Free API feed to collect daily market quotes
|
||||
Parameters:
|
||||
Symbol: stock symbol (default: "IBM")
|
||||
Period: number of days of collected history (default: 252)
|
||||
Usage:
|
||||
Yahoo_Feed ticker = new("MSFT", 20)
|
||||
|
||||
</summary> */
|
||||
|
||||
public class Yahoo_Feed : TBars
|
||||
{
|
||||
public Yahoo_Feed(string Symbol = "IBM", int Period = 252) {
|
||||
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<JsonDocument>(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);
|
||||
|
||||
for (int i=0; i<datetime.GetArrayLength(); i++) {
|
||||
DateTime d = DateTimeOffset.FromUnixTimeSeconds(long.Parse(datetime[i].GetRawText())).DateTime;
|
||||
double o = Math.Round(double.Parse(open[i].GetRawText()),3);
|
||||
double h = Math.Round(double.Parse(high[i].GetRawText()),3);
|
||||
double l = Math.Round(double.Parse(low[i].GetRawText()),3);
|
||||
double c = Math.Round(double.Parse(close[i].GetRawText()),3);
|
||||
double v = Math.Round(double.Parse(volume[i].GetRawText()),3);
|
||||
base.Add(d, o, h, l, c, v);
|
||||
}
|
||||
}
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Text.Json;
|
||||
|
||||
/* <summary>
|
||||
Yahoo Finance - Free API feed to collect daily market quotes
|
||||
Parameters:
|
||||
Symbol: stock symbol (default: "IBM")
|
||||
Period: number of days of collected history (default: 252)
|
||||
Usage:
|
||||
Yahoo_Feed ticker = new("MSFT", 20)
|
||||
|
||||
</summary> */
|
||||
|
||||
public class Yahoo_Feed : TBars
|
||||
{
|
||||
public Yahoo_Feed(string Symbol = "IBM", int Period = 252) {
|
||||
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<JsonDocument>(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);
|
||||
|
||||
for (int i=0; i<datetime.GetArrayLength(); i++) {
|
||||
DateTime d = DateTimeOffset.FromUnixTimeSeconds(long.Parse(datetime[i].GetRawText())).DateTime;
|
||||
double o = Math.Round(double.Parse(open[i].GetRawText()),3);
|
||||
double h = Math.Round(double.Parse(high[i].GetRawText()),3);
|
||||
double l = Math.Round(double.Parse(low[i].GetRawText()),3);
|
||||
double c = Math.Round(double.Parse(close[i].GetRawText()),3);
|
||||
double v = Math.Round(double.Parse(volume[i].GetRawText()),3);
|
||||
base.Add(d, o, h, l, c, v);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
|
||||
/* <summary>
|
||||
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
|
||||
|
||||
</summary> */
|
||||
|
||||
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<double> _x = new();
|
||||
private readonly System.Collections.Generic.List<double> _xx = new();
|
||||
private readonly System.Collections.Generic.List<double> _y = new();
|
||||
private readonly System.Collections.Generic.List<double> _yy = new();
|
||||
private readonly System.Collections.Generic.List<double> _xy = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update)
|
||||
{
|
||||
if (update)
|
||||
{
|
||||
_x[_x.Count - 1] = TValue1.v;
|
||||
_xx[_xx.Count - 1] = TValue1.v * TValue1.v;
|
||||
_y[_y.Count - 1] = TValue2.v;
|
||||
_y[_yy.Count - 1] = TValue2.v * TValue2.v;
|
||||
_xy[_xy.Count - 1] = TValue1.v * TValue2.v;
|
||||
}
|
||||
else
|
||||
{
|
||||
_x.Add(TValue1.v);
|
||||
_xx.Add(TValue1.v * TValue1.v);
|
||||
_y.Add(TValue2.v);
|
||||
_yy.Add(TValue2.v * TValue2.v);
|
||||
_xy.Add(TValue1.v * TValue2.v);
|
||||
}
|
||||
if (_x.Count > this._p) { _x.RemoveAt(0); }
|
||||
if (_xx.Count > this._p) { _xx.RemoveAt(0); }
|
||||
if (_y.Count > this._p) { _y.RemoveAt(0); }
|
||||
if (_yy.Count > this._p) { _yy.RemoveAt(0); }
|
||||
if (_xy.Count > this._p) { _xy.RemoveAt(0); }
|
||||
|
||||
double _sumx = 0;
|
||||
for (int i = 0; i < _x.Count; i++) { _sumx += _x[i]; }
|
||||
double _sumxx = 0;
|
||||
for (int i = 0; i < _xx.Count; i++) { _sumxx += _xx[i]; }
|
||||
double _sumy = 0;
|
||||
for (int i = 0; i < _y.Count; i++) { _sumy += _y[i]; }
|
||||
double _sumyy = 0;
|
||||
for (int i = 0; i < _yy.Count; i++) { _sumyy += _yy[i]; }
|
||||
double _sumxy = 0;
|
||||
for (int i = 0; i < _xy.Count; i++) { _sumxy += _xy[i]; }
|
||||
|
||||
double _div = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p);
|
||||
double _cor = (_div != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_div) : 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); }
|
||||
}
|
||||
}
|
||||
@@ -1,93 +1,93 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
|
||||
/* <summary>
|
||||
LINREG: 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
|
||||
|
||||
</summary> */
|
||||
|
||||
public class LINREG_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public readonly TSeries Intercept = new();
|
||||
public readonly TSeries RSquared = new();
|
||||
public readonly TSeries StdDev = new();
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public LINREG_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((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
|
||||
else { this._buffer.Add(TValue.v); }
|
||||
if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
|
||||
|
||||
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._p - 1 && this._NaN ? double.NaN : _slope);
|
||||
base.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _intercept);
|
||||
Intercept.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _StdDev);
|
||||
StdDev.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _RSquared);
|
||||
RSquared.Add(ret, update);
|
||||
}
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
|
||||
/* <summary>
|
||||
LINREG: 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
|
||||
|
||||
</summary> */
|
||||
|
||||
public class LINREG_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public readonly TSeries Intercept = new();
|
||||
public readonly TSeries RSquared = new();
|
||||
public readonly TSeries StdDev = new();
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public LINREG_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((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
|
||||
else { this._buffer.Add(TValue.v); }
|
||||
if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
|
||||
|
||||
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._p - 1 && this._NaN ? double.NaN : _slope);
|
||||
base.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _intercept);
|
||||
Intercept.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _StdDev);
|
||||
StdDev.Add(ret, update);
|
||||
|
||||
ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _RSquared);
|
||||
RSquared.Add(ret, update);
|
||||
}
|
||||
}
|
||||
@@ -1,51 +1,51 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
|
||||
/* <summary>
|
||||
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
|
||||
|
||||
</summary> */
|
||||
|
||||
public class ZSCORE_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public ZSCORE_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
|
||||
else { _buffer.Add(TValue.v); }
|
||||
if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
|
||||
|
||||
double _sma = 0;
|
||||
for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
|
||||
_sma /= this._buffer.Count;
|
||||
|
||||
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) ? double.NaN : (TValue.v - _sma) / _psdev;
|
||||
|
||||
var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _zscore);
|
||||
base.Add(result, update);
|
||||
}
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
|
||||
/* <summary>
|
||||
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
|
||||
|
||||
</summary> */
|
||||
|
||||
public class ZSCORE_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public ZSCORE_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
|
||||
else { _buffer.Add(TValue.v); }
|
||||
if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
|
||||
|
||||
double _sma = 0;
|
||||
for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
|
||||
_sma /= this._buffer.Count;
|
||||
|
||||
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) ? double.NaN : (TValue.v - _sma) / _psdev;
|
||||
|
||||
var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _zscore);
|
||||
base.Add(result, update);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user