mirror of
https://github.com/mihakralj/QuanTAlib.git
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Covar, Kendall, Spearman
This commit is contained in:
Miha
@@ -68,6 +68,9 @@ public class EventingTests
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("Zscore", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Zscore", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Beta", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Beta", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Corr", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Corr", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Covar", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Kendall", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Spearman", new object[] { DefaultPeriod }, new object[] { new TSeries(), DefaultPeriod }),
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("Hv", new object[] { DefaultPeriod, false }, new object[] { new TSeries(), DefaultPeriod, false }),
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("Hv", new object[] { DefaultPeriod, false }, new object[] { new TSeries(), DefaultPeriod, false }),
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("Jvolty", new object[] { DefaultPeriod, 0 }, new object[] { new TSeries(), DefaultPeriod, 0 }),
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("Jvolty", new object[] { DefaultPeriod, 0 }, new object[] { new TSeries(), DefaultPeriod, 0 }),
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("Rv", new object[] { DefaultPeriod, false }, new object[] { new TSeries(), DefaultPeriod, false }),
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("Rv", new object[] { DefaultPeriod, false }, new object[] { new TSeries(), DefaultPeriod, false }),
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@@ -18,6 +18,13 @@ public class StatisticsUpdateTests : UpdateTestBase
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TestDualTValueUpdate(indicator, indicator.Calc);
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TestDualTValueUpdate(indicator, indicator.Calc);
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}
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}
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[Fact]
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public void Covar_Update()
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{
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var indicator = new Covar(period: 14);
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TestDualTValueUpdate(indicator, indicator.Calc);
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}
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[Fact]
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[Fact]
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public void Curvature_Update()
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public void Curvature_Update()
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{
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{
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@@ -39,6 +46,13 @@ public class StatisticsUpdateTests : UpdateTestBase
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TestTBarUpdate(indicator, indicator.Calc);
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TestTBarUpdate(indicator, indicator.Calc);
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}
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}
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[Fact]
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public void Kendall_Update()
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{
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var indicator = new Kendall(period: 14);
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TestDualTValueUpdate(indicator, indicator.Calc);
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}
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[Fact]
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[Fact]
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public void Kurtosis_Update()
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public void Kurtosis_Update()
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{
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{
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@@ -95,6 +109,13 @@ public class StatisticsUpdateTests : UpdateTestBase
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TestTValueUpdate(indicator, indicator.Calc);
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TestTValueUpdate(indicator, indicator.Calc);
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}
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}
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[Fact]
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public void Spearman_Update()
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{
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var indicator = new Spearman(period: 14);
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TestDualTValueUpdate(indicator, indicator.Calc);
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}
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[Fact]
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[Fact]
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public void Stddev_Update()
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public void Stddev_Update()
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{
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{
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+173
@@ -0,0 +1,173 @@
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# QuanTAlib Indicators Status
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## Implementation Status
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| Category | Done | Todo | Total |
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|------------|------|------|-------|
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| Averages | 33 | 0 | 33 |
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| Momentum | 17 | 0 | 17 |
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| Oscillators| 24 | 5 | 29 |
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| Patterns | 0 | 8 | 8 |
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| Statistics | 21 | 2 | 23 |
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| Volatility | 31 | 4 | 35 |
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| Total | 126 | 19 | 145 |
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## Indicators by Category
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### Averages (33/33)
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✔️ AFIRMA - Adaptive FIR Moving Average
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✔️ ALMA - Arnaud Legoux Moving Average
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✔️ CONVOLUTION - 1D Convolution with sliding kernel
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✔️ DEMA - Double Exponential Moving Average
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✔️ DSMA - Dynamic Simple Moving Average
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✔️ DWMA - Dynamic Weighted Moving Average
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✔️ EMA - Exponential Moving Average
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✔️ EPMA - Endpoint Moving Average
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✔️ FRAMA - Fractal Adaptive Moving Average
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✔️ FWMA - Forward Weighted Moving Average
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✔️ GMA - Gaussian Moving Average
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✔️ HMA - Hull Moving Average
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✔️ HTIT - Hilbert Transform Instantaneous Trendline
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✔️ HWMA - Hann Weighted Moving Average
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✔️ JMA - Jurik Moving Average
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✔️ KAMA - Kaufman Adaptive Moving Average
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✔️ LTMA - Linear Time Moving Average
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✔️ MAAF - Moving Average Adaptive Filter
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✔️ MAMA - MESA Adaptive Moving Average (MAMA, FAMA)
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✔️ MGDI - McGinley Dynamic Indicator
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✔️ MMA - Modified Moving Average
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✔️ PWMA - Parabolic Weighted Moving Average
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✔️ QEMA - Quick Exponential Moving Average
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✔️ REMA - Regularized Exponential Moving Average
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✔️ RMA - Running Moving Average
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✔️ SINEMA - Sine-weighted Moving Average
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✔️ SMA - Simple Moving Average
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✔️ SMMA - Smoothed Moving Average
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✔️ T3 - Triple Exponential Moving Average (T3)
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✔️ TEMA - Triple Exponential Moving Average
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✔️ TRIMA - Triangular Moving Average
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✔️ VIDYA - Variable Index Dynamic Average
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✔️ WMA - Weighted Moving Average
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✔️ ZLEMA - Zero-Lag Exponential Moving Average
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### Momentum (17/17)
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✔️ ADX - Average Directional Movement Index
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✔️ ADXR - Average Directional Movement Index Rating
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✔️ APO - Absolute Price Oscillator
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✔️ DMI - Directional Movement Index (DI+, DI-)
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✔️ DMX - Jurik Directional Movement Index
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✔️ DPO - Detrended Price Oscillator
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✔️ MACD - Moving Average Convergence/Divergence (MACD, Signal, Histogram)
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✔️ MOM - Momentum
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✔️ PMO - Price Momentum Oscillator
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✔️ PO - Price Oscillator
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✔️ PPO - Percentage Price Oscillator
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✔️ PRS - Price Relative Strength
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✔️ ROC - Rate of Change
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✔️ TSI - True Strength Index
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✔️ TRIX - 1-day ROC of TEMA
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✔️ VEL - Jurik Signal Velocity
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✔️ VORTEX - Vortex Indicator (VI+, VI-)
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### Oscillators (24/29)
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✔️ AC - Acceleration Oscillator
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✔️ AO - Awesome Oscillator
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✔️ AROON - Aroon oscillator (Up, Down)
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✔️ BOP - Balance of Power
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✔️ CCI - Commodity Channel Index
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✔️ CFO - Chande Forcast Oscillator
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✔️ CHOP - Choppiness Index
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✔️ CMO - Chande Momentum Oscillator
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✔️ COG - Ehler's Center of Gravity
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✔️ COPPOCK - Coppock Curve
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✔️ CRSI - Connor RSI
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✔️ CTI - Ehler's Correlation Trend Indicator
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✔️ DOSC - Derivative Oscillator
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✔️ FISHER - Fisher Transform
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✔️ EFI - Elder Ray's Force Index
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✔️ RSI - Relative Strength Index
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✔️ RSX - Jurik Trend Strength Index
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✔️ SMI - Stochastic Momentum Index
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✔️ SRSI - Stochastic RSI (SRSI, Signal)
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✔️ STC - Schaff Trend Cycle
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✔️ STOCH - Stochastic Oscillator (%K, %D)
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✔️ TSI - True Strength Index
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✔️ UO - Ultimate Oscillator
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✔️ WILLR - Larry Williams' %R
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FOSC - Forecast Oscillator
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GATOR - Williams Alliator Oscillator (Upper Jaw, Lower Jaw, Teeth)
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KDJ - KDJ Indicator (K, D, J lines)
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KRI - Kairi Relative Index
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RVGI - Relative Vigor Index (RVGI, Signal)
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### Patterns (0/8)
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DOJI - Doji Candlestick Pattern
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ER - Elder Ray Pattern (Bull Power, Bear Power)
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MARU - Marubozu Candlestick Pattern
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PIV - Pivot Points (Support 1-3, Pivot, Resistance 1-3)
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PP - Price Pivots (Support 1-3, Pivot, Resistance 1-3)
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RPP - Rolling Pivot Points (Support 1-3, Pivot, Resistance 1-3)
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WF - Williams Fractal
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ZZ - Zig Zag Pattern
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### Statistics (21/23)
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✔️ BETA - Beta coefficient measuring volatility relative to market
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✔️ CORR - Correlation coefficient between two series
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✔️ COVAR - Covariance between two series
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✔️ CURVATURE - Curvature of a time series
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✔️ ENTROPY - Information entropy of a series
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✔️ HURST - Hurst exponent for trend strength
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✔️ KENDALL - Kendall rank correlation
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✔️ KURTOSIS - Kurtosis measuring tail extremity
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✔️ MAX - Maximum value over period
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✔️ MEDIAN - Median value over period
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✔️ MIN - Minimum value over period
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✔️ MODE - Mode (most frequent value)
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✔️ PERCENTILE - Percentile rank calculation
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✔️ SKEW - Skewness measuring distribution asymmetry
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✔️ SLOPE - Linear regression slope
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✔️ SPEARMAN - Spearman rank correlation
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✔️ STDDEV - Standard deviation
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✔️ THEIL - Theil's U statistics for forecast accuracy
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✔️ TSF - Time series forecast
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✔️ VARIANCE - Statistical variance
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✔️ ZSCORE - Z-score standardization
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COINTEGRATION - Test for cointegrated series
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GRANGER - Granger causality test
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### Volatility (31/35)
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✔️ ADR - Average Daily Range
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✔️ AP - Andrew's Pitchfork
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✔️ ATR - Average True Range
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✔️ ATRP - Average True Range Percent
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✔️ ATRS - ATR Trailing Stop
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✔️ BBAND - Bollinger Bands® (Upper, Middle, Lower)
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✔️ CCV - Close-to-Close Volatility
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✔️ CE - Chandelier Exit
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✔️ CV - Conditional Volatility (ARCH/GARCH)
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✔️ CVI - Chaikin's Volatility
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✔️ DCHN - Donchian Channels (Upper, Middle, Lower)
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✔️ EWMA - Exponential Weighted Moving Average Volatility
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✔️ FCB - Fractal Chaos Bands
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✔️ GKV - Garman-Klass Volatility
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✔️ HLV - High-Low Volatility
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✔️ HV - Historical Volatility
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✔️ JVOLTY - Jurik Volatility (Jvolty, Upper band, Lower band)
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✔️ NATR - Normalized Average True Range
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✔️ PCH - Price Channel Indicator
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✔️ PV - Parkinson Volatility
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✔️ RSV - Rogers-Satchell Volatility
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✔️ RV - Realized Volatility
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✔️ RVI - Relative Volatility Index
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✔️ SV - Stochastic Volatility
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✔️ TR - True Range
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✔️ UI - Ulcer Index
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✔️ VC - Volatility Cone (Mean, Upper Bound, Lower Bound)
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✔️ VOV - Volatility of Volatility
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✔️ VR - Volatility Ratio
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✔️ VS - Volatility Stop (Long Stop, Short Stop)
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✔️ YZV - Yang-Zhang Volatility
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ICH - Ichimoku Cloud (Conversion, Base, Leading Span A, Leading Span B, Lagging Span)
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KC - Keltner Channels (Upper, Middle, Lower)
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PSAR - Parabolic Stop and Reverse (Value, Trend)
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STARC - Starc Bands (Upper, Middle, Lower)
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@@ -1,142 +0,0 @@
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# Stock Indicators List
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## Common Indicators (Both Libraries)
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| Indicator Name | Skender Method | QuanTAlib Class |
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|---------------|----------------|-----------------|
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| ADL - Accumulation/Distribution Line | GetAdl | Adl |
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| ADOSC - Accumulation/Distribution Oscillator | GetAdo | Adosc |
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| ALMA - Arnaud Legoux Moving Average | GetAlma | Alma |
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| AROON - Aroon Oscillator | GetAroon | Aroon |
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| ADX - Average Directional Index | GetAdx | Adx |
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| ATR - Average True Range | GetAtr | Atr |
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| AO - Awesome Oscillator | GetAwesome | Ao |
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| CMF - Chaikin Money Flow | GetCmf | Cmf |
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| CMO - Chande Momentum Oscillator | GetCmo | Cmo |
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| DEMA - Double Exponential Moving Average | GetDema | Dema |
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| EOM - Ease of Movement | GetEom | Eom |
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| EPMA - Endpoint Moving Average | GetEpma | Epma |
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| EMA - Exponential Moving Average | GetEma | Ema |
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| HTIT - Hilbert Transform Instantaneous Trendline | GetHtTrendline | Htit |
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| HMA - Hull Moving Average | GetHma | Hma |
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| KVO - Klinger Volume Oscillator | GetKvo | Kvo |
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| OBV - On-Balance Volume | GetObv | Aobv |
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| PMO - Price Momentum Oscillator | GetPmo | Pmo |
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| PRS - Price Relative Strength | GetPrs | Prs |
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| ROC - Rate of Change | GetRoc | Roc |
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| RSI - Relative Strength Index | GetRsi | Rsi |
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| SMA - Simple Moving Average | GetSma | Sma |
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| SMMA - Smoothed Moving Average | GetSmma | Smma |
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| SLOPE - Slope and Linear Regression | GetSlope | Slope |
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| STDEV - Standard Deviation | GetStdDev | Stddev |
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| TRIX - Triple EMA Oscillator | GetTrix | Trix |
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| TEMA - Triple Exponential Moving Average | GetTema | Tema |
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| WMA - Weighted Moving Average | GetWma | Wma |
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## Skender-Only Indicators
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| Indicator Name | Skender Method |
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|---------------|----------------|
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| ATRS - ATR Trailing Stop | GetAtrStop |
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| BOP - Balance of Power | GetBop |
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| BETA - Beta Coefficient | GetBeta |
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| BB - Bollinger Bands | GetBollingerBands |
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| CE - Chandelier Exit | GetChandelier |
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| CHOP - Choppiness Index | GetChop |
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| CCI - Commodity Channel Index | GetCci |
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| CRSI - Connors RSI | GetConnorsRsi |
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| CORR - Correlation Coefficient | GetCorrelation |
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| DPO - Detrended Price Oscillator | GetDpo |
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| DOJI - Doji Pattern | GetDoji |
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| DC - Donchian Channel | GetDonchian |
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| ER - Elder-Ray | GetElderRay |
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| FISH - Fisher Transform | GetFisherTransform |
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| FI - Force Index | GetForceIndex |
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| FCB - Fractal Chaos Bands | GetFcb |
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| GATOR - Gator Oscillator | GetGator |
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| HA - Heikin-Ashi | GetHeikinAshi |
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| HURST - Hurst Exponent | GetHurst |
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| ICH - Ichimoku Cloud | GetIchimoku |
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| KC - Keltner Channels | GetKeltner |
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| MARU - Marubozu Pattern | GetMarubozu |
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| MFI - Money Flow Index | GetMfi |
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| MAE - Moving Average Envelopes | GetMaEnvelopes |
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| PSAR - Parabolic SAR | GetParabolicSar |
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| PP - Pivot Points | GetPivotPoints |
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| PIV - Pivots | GetPivots |
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| PVO - Price Volume Oscillator | GetPvo |
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| RENKO-ATR - Renko Chart ATR | GetRenkoAtr |
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| RENKO - Renko Chart Standard | GetRenko |
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| RPP - Rolling Pivot Points | GetRollingPivots |
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| STC - Schaff Trend Cycle | GetStc |
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| SDC - Standard Deviation Channels | GetStdDevChannels |
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| STARC - Starc Bands | GetStarcBands |
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| SMI - Stochastic Momentum Index | GetSmi |
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| STOCH - Stochastic Oscillator | GetStoch |
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| STOCH-RSI - Stochastic RSI | GetStochRsi |
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| ST - Supertrend | GetSuperTrend |
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| TR - True Range | GetTr |
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| TSI - True Strength Index | GetTsi |
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| UI - Ulcer Index | GetUlcerIndex |
|
|
||||||
| UO - Ultimate Oscillator | GetUltimate |
|
|
||||||
| VSS - Volatility System/Stop | GetVolatilityStop |
|
|
||||||
| VWAP - Volume Weighted Average Price | GetVwap |
|
|
||||||
| VWMA - Volume Weighted Moving Average | GetVwma |
|
|
||||||
| VTX - Vortex Indicator | GetVortex |
|
|
||||||
| WAG - Williams Alligator | GetAlligator |
|
|
||||||
| WF - Williams Fractal | GetFractal |
|
|
||||||
| ZZ - Zig Zag | GetZigZag |
|
|
||||||
|
|
||||||
## QuanTAlib-Only Indicators
|
|
||||||
|
|
||||||
| Indicator Name | QuanTAlib Class |
|
|
||||||
|---------------|-----------------|
|
|
||||||
| AC - Acceleration Oscillator | Ac |
|
|
||||||
| AFIRMA - Adaptive Firman Moving Average | Afirma |
|
|
||||||
| APO - Absolute Price Oscillator | Apo |
|
|
||||||
| ADXR - ADX Rating | Adxr |
|
|
||||||
| CONV - Convolution Moving Average | Convolution |
|
|
||||||
| CURV - Curvature | Curvature |
|
|
||||||
| DMI - Directional Movement Index | Dmi |
|
|
||||||
| DMX - Directional Movement Extended | Dmx |
|
|
||||||
| DSMA - Double Smoothed Moving Average | Dsma |
|
|
||||||
| DWMA - Dynamic Weighted Moving Average | Dwma |
|
|
||||||
| ENT - Entropy | Entropy |
|
|
||||||
| FRAMA - Fractal Adaptive Moving Average | Frama |
|
|
||||||
| FWMA - Fibonacci Weighted Moving Average | Fwma |
|
|
||||||
| GMA - Gaussian Moving Average | Gma |
|
|
||||||
| HV - Historical Volatility | Hv |
|
|
||||||
| HWMA - Hybrid Weighted Moving Average | Hwma |
|
|
||||||
| JMA - Jurik Moving Average | Jma |
|
|
||||||
| JVOL - Jurik Volatility | Jvolty |
|
|
||||||
| KURT - Kurtosis | Kurtosis |
|
|
||||||
| KAMA - Kaufman Adaptive Moving Average | Kama |
|
|
||||||
| LTMA - Laguerre Time Moving Average | Ltma |
|
|
||||||
| MAX - Maximum Value | Max |
|
|
||||||
| MAAF - Median Adaptive Antifractal | Maaf |
|
|
||||||
| MAMA - Mesa Adaptive Moving Average | Mama |
|
|
||||||
| MGDI - McGinley Dynamic Indicator | Mgdi |
|
|
||||||
| MEDIAN - Median Value | Median |
|
|
||||||
| MIN - Minimum Value | Min |
|
|
||||||
| MMA - Modified Moving Average | Mma |
|
|
||||||
| MODE - Mode Value | Mode |
|
|
||||||
| MOM - Momentum | Mom |
|
|
||||||
| PCTL - Percentile | Percentile |
|
|
||||||
| PO - Price Oscillator | Po |
|
|
||||||
| PPO - Price Percentage Oscillator | Ppo |
|
|
||||||
| PWMA - Polynomial Weighted Moving Average | Pwma |
|
|
||||||
| QEMA - Quadratic Exponential Moving Average | Qema |
|
|
||||||
| REMA - Range-Normalized Exponential Moving Average | Rema |
|
|
||||||
| RSX - Relative Strength Extended | Rsx |
|
|
||||||
| RV - Realized Volatility | Rv |
|
|
||||||
| RVI - Relative Volatility Index | Rvi |
|
|
||||||
| RMA - Rolling Moving Average | Rma |
|
|
||||||
| SINEMA - Sine-Wave Exponential Moving Average | Sinema |
|
|
||||||
| SKEW - Skewness | Skew |
|
|
||||||
| T3 - Tillson T3 Moving Average | T3 |
|
|
||||||
| TRIMA - Triangular Moving Average | Trima |
|
|
||||||
| VAR - Variance | Variance |
|
|
||||||
| VIDYA - Variable Index Dynamic Average | Vidya |
|
|
||||||
| VEL - Velocity | Vel |
|
|
||||||
| ZLEMA - Zero-Lag Exponential Moving Average | Zlema |
|
|
||||||
| ZSCORE - Z-Score | Zscore |
|
|
||||||
@@ -6,7 +6,7 @@
|
|||||||
✔️ DMI - Directional Movement Index (DI+, DI-)
|
✔️ DMI - Directional Movement Index (DI+, DI-)
|
||||||
✔️ DMX - Jurik Directional Movement Index
|
✔️ DMX - Jurik Directional Movement Index
|
||||||
✔️ DPO - Detrended Price Oscillator
|
✔️ DPO - Detrended Price Oscillator
|
||||||
✔️ *MACD - Moving Average Convergence/Divergence (MACD, Signal, Histogram)
|
✔️ MACD - Moving Average Convergence/Divergence (MACD, Signal, Histogram)
|
||||||
✔️ MOM - Momentum
|
✔️ MOM - Momentum
|
||||||
✔️ PMO - Price Momentum Oscillator
|
✔️ PMO - Price Momentum Oscillator
|
||||||
✔️ PO - Price Oscillator
|
✔️ PO - Price Oscillator
|
||||||
@@ -16,4 +16,4 @@
|
|||||||
✔️ TSI - True Strength Index
|
✔️ TSI - True Strength Index
|
||||||
✔️ TRIX - 1-day ROC of TEMA
|
✔️ TRIX - 1-day ROC of TEMA
|
||||||
✔️ VEL - Jurik Signal Velocity
|
✔️ VEL - Jurik Signal Velocity
|
||||||
✔️ *VORTEX - Vortex Indicator (VI+, VI-)
|
✔️ VORTEX - Vortex Indicator (VI+, VI-)
|
||||||
|
|||||||
@@ -16,13 +16,8 @@ Done: 24, Todo: 5
|
|||||||
✔️ DOSC - Derivative Oscillator
|
✔️ DOSC - Derivative Oscillator
|
||||||
✔️ FISHER - Fisher Transform
|
✔️ FISHER - Fisher Transform
|
||||||
✔️ EFI - Elder Ray's Force Index
|
✔️ EFI - Elder Ray's Force Index
|
||||||
FOSC - Forecast Oscillator
|
|
||||||
*GATOR - Williams Alliator Oscillator (Upper Jaw, Lower Jaw, Teeth)
|
|
||||||
*KDJ - KDJ Indicator (K, D, J lines)
|
|
||||||
KRI - Kairi Relative Index
|
|
||||||
✔️ RSI - Relative Strength Index
|
✔️ RSI - Relative Strength Index
|
||||||
✔️ RSX - Jurik Trend Strength Index
|
✔️ RSX - Jurik Trend Strength Index
|
||||||
*RVGI - Relative Vigor Index (RVGI, Signal)
|
|
||||||
✔️ SMI - Stochastic Momentum Index
|
✔️ SMI - Stochastic Momentum Index
|
||||||
✔️ SRSI - Stochastic RSI (SRSI, Signal)
|
✔️ SRSI - Stochastic RSI (SRSI, Signal)
|
||||||
✔️ STC - Schaff Trend Cycle
|
✔️ STC - Schaff Trend Cycle
|
||||||
@@ -30,3 +25,8 @@ KRI - Kairi Relative Index
|
|||||||
✔️ TSI - True Strength Index
|
✔️ TSI - True Strength Index
|
||||||
✔️ UO - Ultimate Oscillator
|
✔️ UO - Ultimate Oscillator
|
||||||
✔️ WILLR - Larry Williams' %R
|
✔️ WILLR - Larry Williams' %R
|
||||||
|
FOSC - Forecast Oscillator
|
||||||
|
GATOR - Williams Alliator Oscillator (Upper Jaw, Lower Jaw, Teeth)
|
||||||
|
KDJ - KDJ Indicator (K, D, J lines)
|
||||||
|
KRI - Kairi Relative Index
|
||||||
|
RVGI - Relative Vigor Index (RVGI, Signal)
|
||||||
|
|||||||
@@ -2,10 +2,10 @@
|
|||||||
Done: 0, Todo: 8
|
Done: 0, Todo: 8
|
||||||
|
|
||||||
DOJI - Doji Candlestick Pattern
|
DOJI - Doji Candlestick Pattern
|
||||||
*ER - Elder Ray Pattern (Bull Power, Bear Power)
|
ER - Elder Ray Pattern (Bull Power, Bear Power)
|
||||||
MARU - Marubozu Candlestick Pattern
|
MARU - Marubozu Candlestick Pattern
|
||||||
*PIV - Pivot Points (Support 1-3, Pivot, Resistance 1-3)
|
PIV - Pivot Points (Support 1-3, Pivot, Resistance 1-3)
|
||||||
*PP - Price Pivots (Support 1-3, Pivot, Resistance 1-3)
|
PP - Price Pivots (Support 1-3, Pivot, Resistance 1-3)
|
||||||
*RPP - Rolling Pivot Points (Support 1-3, Pivot, Resistance 1-3)
|
RPP - Rolling Pivot Points (Support 1-3, Pivot, Resistance 1-3)
|
||||||
WF - Williams Fractal
|
WF - Williams Fractal
|
||||||
ZZ - Zig Zag Pattern
|
ZZ - Zig Zag Pattern
|
||||||
|
|||||||
@@ -0,0 +1,142 @@
|
|||||||
|
using System.Runtime.CompilerServices;
|
||||||
|
namespace QuanTAlib;
|
||||||
|
|
||||||
|
/// <summary>
|
||||||
|
/// COVAR: Covariance
|
||||||
|
/// A statistical measure that quantifies how two variables change together. Unlike correlation,
|
||||||
|
/// covariance is not normalized and therefore is scale-dependent. A positive covariance indicates
|
||||||
|
/// that variables tend to move in the same direction, while a negative covariance indicates
|
||||||
|
/// opposite movement.
|
||||||
|
/// </summary>
|
||||||
|
/// <remarks>
|
||||||
|
/// The Covariance calculation process:
|
||||||
|
/// 1. Calculates mean of both variables
|
||||||
|
/// 2. For each pair of points, multiply their deviations from their respective means
|
||||||
|
/// 3. Sum these products and divide by the number of observations
|
||||||
|
///
|
||||||
|
/// Key characteristics:
|
||||||
|
/// - Measures linear relationship
|
||||||
|
/// - Scale-dependent measure
|
||||||
|
/// - Sign indicates direction of relationship
|
||||||
|
/// - Magnitude depends on scale of variables
|
||||||
|
/// - Basis for correlation coefficient
|
||||||
|
///
|
||||||
|
/// Formula:
|
||||||
|
/// Cov(X,Y) = Σ((x - μx)(y - μy)) / n
|
||||||
|
/// where:
|
||||||
|
/// X, Y = variables
|
||||||
|
/// μx, μy = means of X and Y
|
||||||
|
/// n = number of observations
|
||||||
|
///
|
||||||
|
/// Market Applications:
|
||||||
|
/// - Portfolio risk analysis
|
||||||
|
/// - Pairs trading strategy development
|
||||||
|
/// - Asset relationship analysis
|
||||||
|
/// - Risk factor sensitivity analysis
|
||||||
|
/// - Multi-asset portfolio optimization
|
||||||
|
///
|
||||||
|
/// Sources:
|
||||||
|
/// https://en.wikipedia.org/wiki/Covariance
|
||||||
|
/// "Modern Portfolio Theory" - Harry Markowitz
|
||||||
|
///
|
||||||
|
/// Note: Scale-dependent nature means values should be interpreted in context of the data scales
|
||||||
|
/// </remarks>
|
||||||
|
[SkipLocalsInit]
|
||||||
|
public sealed class Covar : AbstractBase
|
||||||
|
{
|
||||||
|
private readonly int Period;
|
||||||
|
private readonly CircularBuffer _xValues;
|
||||||
|
private readonly CircularBuffer _yValues;
|
||||||
|
private const int MinimumPoints = 2;
|
||||||
|
|
||||||
|
/// <param name="period">The number of points to consider for covariance calculation.</param>
|
||||||
|
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 2.</exception>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Covar(int period)
|
||||||
|
{
|
||||||
|
if (period < MinimumPoints)
|
||||||
|
{
|
||||||
|
throw new ArgumentOutOfRangeException(nameof(period),
|
||||||
|
"Period must be greater than or equal to 2 for covariance calculation.");
|
||||||
|
}
|
||||||
|
Period = period;
|
||||||
|
WarmupPeriod = MinimumPoints;
|
||||||
|
_xValues = new CircularBuffer(period);
|
||||||
|
_yValues = new CircularBuffer(period);
|
||||||
|
Name = $"Covar(period={period})";
|
||||||
|
Init();
|
||||||
|
}
|
||||||
|
|
||||||
|
/// <param name="source">The data source object that publishes updates.</param>
|
||||||
|
/// <param name="period">The number of points to consider for covariance calculation.</param>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Covar(object source, int period) : this(period)
|
||||||
|
{
|
||||||
|
var pubEvent = source.GetType().GetEvent("Pub");
|
||||||
|
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public override void Init()
|
||||||
|
{
|
||||||
|
base.Init();
|
||||||
|
_xValues.Clear();
|
||||||
|
_yValues.Clear();
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
protected override void ManageState(bool isNew)
|
||||||
|
{
|
||||||
|
if (isNew)
|
||||||
|
{
|
||||||
|
_lastValidValue = Input.Value;
|
||||||
|
_index++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static double CalculateMean(ReadOnlySpan<double> values)
|
||||||
|
{
|
||||||
|
double sum = 0;
|
||||||
|
for (int i = 0; i < values.Length; i++)
|
||||||
|
{
|
||||||
|
sum += values[i];
|
||||||
|
}
|
||||||
|
return sum / values.Length;
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static double CalculateCovariance(ReadOnlySpan<double> xValues, ReadOnlySpan<double> yValues, double xMean, double yMean)
|
||||||
|
{
|
||||||
|
double covariance = 0;
|
||||||
|
for (int i = 0; i < xValues.Length; i++)
|
||||||
|
{
|
||||||
|
covariance += (xValues[i] - xMean) * (yValues[i] - yMean);
|
||||||
|
}
|
||||||
|
return covariance / xValues.Length;
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
protected override double Calculation()
|
||||||
|
{
|
||||||
|
ManageState(Input.IsNew);
|
||||||
|
|
||||||
|
_xValues.Add(Input.Value, Input.IsNew);
|
||||||
|
_yValues.Add(Input2.Value, Input.IsNew);
|
||||||
|
|
||||||
|
double covariance = 0;
|
||||||
|
if (_xValues.Count >= MinimumPoints && _yValues.Count >= MinimumPoints)
|
||||||
|
{
|
||||||
|
ReadOnlySpan<double> xValues = _xValues.GetSpan();
|
||||||
|
ReadOnlySpan<double> yValues = _yValues.GetSpan();
|
||||||
|
|
||||||
|
double xMean = CalculateMean(xValues);
|
||||||
|
double yMean = CalculateMean(yValues);
|
||||||
|
|
||||||
|
covariance = CalculateCovariance(xValues, yValues, xMean, yMean);
|
||||||
|
}
|
||||||
|
|
||||||
|
IsHot = _xValues.Count >= Period && _yValues.Count >= Period;
|
||||||
|
return covariance;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,182 @@
|
|||||||
|
using System.Runtime.CompilerServices;
|
||||||
|
namespace QuanTAlib;
|
||||||
|
|
||||||
|
/// <summary>
|
||||||
|
/// KENDALL: Kendall's Rank Correlation Coefficient (Tau)
|
||||||
|
/// A nonparametric measure that evaluates the degree of similarity between two sets
|
||||||
|
/// of rankings by analyzing concordant and discordant pairs. Unlike Spearman correlation,
|
||||||
|
/// Kendall's tau measures the ordinal association between two variables.
|
||||||
|
/// </summary>
|
||||||
|
/// <remarks>
|
||||||
|
/// The Kendall calculation process:
|
||||||
|
/// 1. Compares each pair of observations
|
||||||
|
/// 2. Counts concordant and discordant pairs
|
||||||
|
/// 3. Handles ties in both variables
|
||||||
|
///
|
||||||
|
/// Key characteristics:
|
||||||
|
/// - Measures ordinal association
|
||||||
|
/// - Range: -1 to +1
|
||||||
|
/// - Robust to outliers
|
||||||
|
/// - More intuitive probabilistic interpretation
|
||||||
|
/// - Less sensitive to error than Spearman
|
||||||
|
///
|
||||||
|
/// Formula:
|
||||||
|
/// τ = (nc - nd) / sqrt((n0 - n1)(n0 - n2))
|
||||||
|
/// where:
|
||||||
|
/// nc = number of concordant pairs
|
||||||
|
/// nd = number of discordant pairs
|
||||||
|
/// n0 = n(n-1)/2
|
||||||
|
/// n1 = sum(u(u-1)/2) for ties in x
|
||||||
|
/// n2 = sum(v(v-1)/2) for ties in y
|
||||||
|
///
|
||||||
|
/// Market Applications:
|
||||||
|
/// - Rank correlation analysis
|
||||||
|
/// - Portfolio diversification
|
||||||
|
/// - Risk assessment
|
||||||
|
/// - Market trend analysis
|
||||||
|
/// - Pattern recognition
|
||||||
|
///
|
||||||
|
/// Sources:
|
||||||
|
/// https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
|
||||||
|
/// "Rank Correlation Methods" - Maurice G. Kendall
|
||||||
|
///
|
||||||
|
/// Note: More robust to outliers and errors than other correlation measures
|
||||||
|
/// </remarks>
|
||||||
|
[SkipLocalsInit]
|
||||||
|
public sealed class Kendall : AbstractBase
|
||||||
|
{
|
||||||
|
private readonly int Period;
|
||||||
|
private readonly CircularBuffer _xValues;
|
||||||
|
private readonly CircularBuffer _yValues;
|
||||||
|
private const double Epsilon = 1e-10;
|
||||||
|
private const int MinimumPoints = 2;
|
||||||
|
|
||||||
|
/// <param name="period">The number of points to consider for Kendall correlation calculation.</param>
|
||||||
|
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 2.</exception>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Kendall(int period)
|
||||||
|
{
|
||||||
|
if (period < MinimumPoints)
|
||||||
|
{
|
||||||
|
throw new ArgumentOutOfRangeException(nameof(period),
|
||||||
|
"Period must be greater than or equal to 2 for Kendall correlation calculation.");
|
||||||
|
}
|
||||||
|
Period = period;
|
||||||
|
WarmupPeriod = MinimumPoints;
|
||||||
|
_xValues = new CircularBuffer(period);
|
||||||
|
_yValues = new CircularBuffer(period);
|
||||||
|
Name = $"Kendall(period={period})";
|
||||||
|
Init();
|
||||||
|
}
|
||||||
|
|
||||||
|
/// <param name="source">The data source object that publishes updates.</param>
|
||||||
|
/// <param name="period">The number of points to consider for Kendall correlation calculation.</param>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Kendall(object source, int period) : this(period)
|
||||||
|
{
|
||||||
|
var pubEvent = source.GetType().GetEvent("Pub");
|
||||||
|
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public override void Init()
|
||||||
|
{
|
||||||
|
base.Init();
|
||||||
|
_xValues.Clear();
|
||||||
|
_yValues.Clear();
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
protected override void ManageState(bool isNew)
|
||||||
|
{
|
||||||
|
if (isNew)
|
||||||
|
{
|
||||||
|
_lastValidValue = Input.Value;
|
||||||
|
_index++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static (int concordant, int discordant, int tiesX, int tiesY) CountPairs(ReadOnlySpan<double> x, ReadOnlySpan<double> y)
|
||||||
|
{
|
||||||
|
int n = x.Length;
|
||||||
|
int concordant = 0;
|
||||||
|
int discordant = 0;
|
||||||
|
int tiesX = 0;
|
||||||
|
int tiesY = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < n - 1; i++)
|
||||||
|
{
|
||||||
|
if (double.IsNaN(x[i]) || double.IsNaN(y[i])) continue;
|
||||||
|
|
||||||
|
for (int j = i + 1; j < n; j++)
|
||||||
|
{
|
||||||
|
if (double.IsNaN(x[j]) || double.IsNaN(y[j])) continue;
|
||||||
|
|
||||||
|
double xDiff = x[i] - x[j];
|
||||||
|
double yDiff = y[i] - y[j];
|
||||||
|
|
||||||
|
if (Math.Abs(xDiff) < Epsilon && Math.Abs(yDiff) < Epsilon)
|
||||||
|
{
|
||||||
|
tiesX++;
|
||||||
|
tiesY++;
|
||||||
|
}
|
||||||
|
else if (Math.Abs(xDiff) < Epsilon)
|
||||||
|
{
|
||||||
|
tiesX++;
|
||||||
|
}
|
||||||
|
else if (Math.Abs(yDiff) < Epsilon)
|
||||||
|
{
|
||||||
|
tiesY++;
|
||||||
|
}
|
||||||
|
else
|
||||||
|
{
|
||||||
|
int xSign = xDiff > 0 ? 1 : -1;
|
||||||
|
int ySign = yDiff > 0 ? 1 : -1;
|
||||||
|
if (xSign == ySign)
|
||||||
|
{
|
||||||
|
concordant++;
|
||||||
|
}
|
||||||
|
else
|
||||||
|
{
|
||||||
|
discordant++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return (concordant, discordant, tiesX, tiesY);
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
protected override double Calculation()
|
||||||
|
{
|
||||||
|
ManageState(Input.IsNew);
|
||||||
|
|
||||||
|
_xValues.Add(Input.Value, Input.IsNew);
|
||||||
|
_yValues.Add(Input2.Value, Input.IsNew);
|
||||||
|
|
||||||
|
double correlation = 0;
|
||||||
|
if (_xValues.Count >= MinimumPoints && _yValues.Count >= MinimumPoints)
|
||||||
|
{
|
||||||
|
ReadOnlySpan<double> xValues = _xValues.GetSpan();
|
||||||
|
ReadOnlySpan<double> yValues = _yValues.GetSpan();
|
||||||
|
|
||||||
|
var (concordant, discordant, tiesX, tiesY) = CountPairs(xValues, yValues);
|
||||||
|
|
||||||
|
int n = xValues.Length;
|
||||||
|
int n0 = (n * (n - 1)) / 2;
|
||||||
|
|
||||||
|
// Calculate denominator considering ties
|
||||||
|
double denominator = Math.Sqrt((n0 - tiesX) * (n0 - tiesY));
|
||||||
|
|
||||||
|
if (denominator > Epsilon)
|
||||||
|
{
|
||||||
|
correlation = (concordant - discordant) / denominator;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
IsHot = _xValues.Count >= Period && _yValues.Count >= Period;
|
||||||
|
return correlation;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,224 @@
|
|||||||
|
using System.Runtime.CompilerServices;
|
||||||
|
namespace QuanTAlib;
|
||||||
|
|
||||||
|
/// <summary>
|
||||||
|
/// SPEARMAN: Spearman's Rank Correlation Coefficient
|
||||||
|
/// A nonparametric measure of rank correlation that assesses the monotonic relationship
|
||||||
|
/// between two variables. Unlike Pearson correlation, Spearman correlation evaluates
|
||||||
|
/// the relationship based on ranked values rather than raw data.
|
||||||
|
/// </summary>
|
||||||
|
/// <remarks>
|
||||||
|
/// The Spearman calculation process:
|
||||||
|
/// 1. Ranks both sets of values
|
||||||
|
/// 2. Calculates correlation between ranks
|
||||||
|
/// 3. Handles ties by averaging ranks
|
||||||
|
///
|
||||||
|
/// Key characteristics:
|
||||||
|
/// - Resistant to outliers
|
||||||
|
/// - Detects monotonic relationships
|
||||||
|
/// - Range: -1 to +1
|
||||||
|
/// - Distribution-free measure
|
||||||
|
/// - Handles non-linear relationships
|
||||||
|
///
|
||||||
|
/// Formula:
|
||||||
|
/// ρ = Cov(rank(X), rank(Y)) / (σrank(X) * σrank(Y))
|
||||||
|
/// where:
|
||||||
|
/// X, Y = variables
|
||||||
|
/// rank() = ranking function
|
||||||
|
/// Cov = covariance
|
||||||
|
/// σ = standard deviation
|
||||||
|
///
|
||||||
|
/// Market Applications:
|
||||||
|
/// - Technical analysis
|
||||||
|
/// - Risk assessment
|
||||||
|
/// - Market correlation studies
|
||||||
|
/// - Trend analysis
|
||||||
|
/// - Pattern recognition
|
||||||
|
///
|
||||||
|
/// Sources:
|
||||||
|
/// https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
|
||||||
|
/// "Nonparametric Statistics for Non-Statisticians" - Gregory W. Corder
|
||||||
|
///
|
||||||
|
/// Note: More robust to outliers than Pearson correlation
|
||||||
|
/// </remarks>
|
||||||
|
[SkipLocalsInit]
|
||||||
|
public sealed class Spearman : AbstractBase
|
||||||
|
{
|
||||||
|
private readonly int Period;
|
||||||
|
private readonly CircularBuffer _xValues;
|
||||||
|
private readonly CircularBuffer _yValues;
|
||||||
|
private readonly CircularBuffer _xRanks;
|
||||||
|
private readonly CircularBuffer _yRanks;
|
||||||
|
private const double Epsilon = 1e-10;
|
||||||
|
private const int MinimumPoints = 2;
|
||||||
|
|
||||||
|
/// <param name="period">The number of points to consider for Spearman correlation calculation.</param>
|
||||||
|
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 2.</exception>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Spearman(int period)
|
||||||
|
{
|
||||||
|
if (period < MinimumPoints)
|
||||||
|
{
|
||||||
|
throw new ArgumentOutOfRangeException(nameof(period),
|
||||||
|
"Period must be greater than or equal to 2 for Spearman correlation calculation.");
|
||||||
|
}
|
||||||
|
Period = period;
|
||||||
|
WarmupPeriod = MinimumPoints;
|
||||||
|
_xValues = new CircularBuffer(period);
|
||||||
|
_yValues = new CircularBuffer(period);
|
||||||
|
_xRanks = new CircularBuffer(period);
|
||||||
|
_yRanks = new CircularBuffer(period);
|
||||||
|
Name = $"Spearman(period={period})";
|
||||||
|
Init();
|
||||||
|
}
|
||||||
|
|
||||||
|
/// <param name="source">The data source object that publishes updates.</param>
|
||||||
|
/// <param name="period">The number of points to consider for Spearman correlation calculation.</param>
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public Spearman(object source, int period) : this(period)
|
||||||
|
{
|
||||||
|
var pubEvent = source.GetType().GetEvent("Pub");
|
||||||
|
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
public override void Init()
|
||||||
|
{
|
||||||
|
base.Init();
|
||||||
|
_xValues.Clear();
|
||||||
|
_yValues.Clear();
|
||||||
|
_xRanks.Clear();
|
||||||
|
_yRanks.Clear();
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||||
|
protected override void ManageState(bool isNew)
|
||||||
|
{
|
||||||
|
if (isNew)
|
||||||
|
{
|
||||||
|
_lastValidValue = Input.Value;
|
||||||
|
_index++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static double[] CalculateRanks(ReadOnlySpan<double> values)
|
||||||
|
{
|
||||||
|
int n = values.Length;
|
||||||
|
var pairs = new (double value, int index)[n];
|
||||||
|
for (int i = 0; i < n; i++)
|
||||||
|
{
|
||||||
|
pairs[i] = double.IsNaN(values[i]) ? (double.NaN, i) : (values[i], i);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Sort non-NaN values
|
||||||
|
var validPairs = pairs.Where(p => !double.IsNaN(p.value)).OrderBy(p => p.value).ToArray();
|
||||||
|
var ranks = new double[n];
|
||||||
|
Array.Fill(ranks, double.NaN);
|
||||||
|
|
||||||
|
for (int i = 0; i < validPairs.Length;)
|
||||||
|
{
|
||||||
|
int j = i;
|
||||||
|
// Find ties
|
||||||
|
while (j < validPairs.Length - 1 && Math.Abs(validPairs[j].value - validPairs[j + 1].value) < Epsilon)
|
||||||
|
{
|
||||||
|
j++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Average rank for ties
|
||||||
|
double rank = (i + j) / 2.0 + 1;
|
||||||
|
for (int k = i; k <= j; k++)
|
||||||
|
{
|
||||||
|
ranks[validPairs[k].index] = rank;
|
||||||
|
}
|
||||||
|
i = j + 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
return ranks;
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static double CalculateCovariance(CircularBuffer xBuffer, CircularBuffer yBuffer, double xMean, double yMean)
|
||||||
|
{
|
||||||
|
var xSpan = xBuffer.GetSpan();
|
||||||
|
var ySpan = yBuffer.GetSpan();
|
||||||
|
double covariance = 0;
|
||||||
|
int count = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < xSpan.Length; i++)
|
||||||
|
{
|
||||||
|
if (!double.IsNaN(xSpan[i]) && !double.IsNaN(ySpan[i]))
|
||||||
|
{
|
||||||
|
covariance += (xSpan[i] - xMean) * (ySpan[i] - yMean);
|
||||||
|
count++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return count > 0 ? covariance / count : double.NaN;
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
private static double CalculateStandardDeviation(CircularBuffer buffer, double mean)
|
||||||
|
{
|
||||||
|
var span = buffer.GetSpan();
|
||||||
|
double sumSquaredDeviations = 0;
|
||||||
|
int count = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < span.Length; i++)
|
||||||
|
{
|
||||||
|
if (!double.IsNaN(span[i]))
|
||||||
|
{
|
||||||
|
double deviation = span[i] - mean;
|
||||||
|
sumSquaredDeviations += deviation * deviation;
|
||||||
|
count++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return count > 0 ? Math.Sqrt(sumSquaredDeviations / count) : double.NaN;
|
||||||
|
}
|
||||||
|
|
||||||
|
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
|
||||||
|
protected override double Calculation()
|
||||||
|
{
|
||||||
|
ManageState(Input.IsNew);
|
||||||
|
|
||||||
|
_xValues.Add(Input.Value, Input.IsNew);
|
||||||
|
_yValues.Add(Input2.Value, Input.IsNew);
|
||||||
|
|
||||||
|
double correlation = 0;
|
||||||
|
if (_xValues.Count >= MinimumPoints && _yValues.Count >= MinimumPoints)
|
||||||
|
{
|
||||||
|
// Convert values to ranks
|
||||||
|
var xRanks = CalculateRanks(_xValues.GetSpan());
|
||||||
|
var yRanks = CalculateRanks(_yValues.GetSpan());
|
||||||
|
|
||||||
|
// Store ranks in buffers for statistical calculations
|
||||||
|
_xRanks.Clear();
|
||||||
|
_yRanks.Clear();
|
||||||
|
for (int i = 0; i < xRanks.Length; i++)
|
||||||
|
{
|
||||||
|
_xRanks.Add(xRanks[i], true);
|
||||||
|
_yRanks.Add(yRanks[i], true);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Use CircularBuffer's optimized Average() method
|
||||||
|
double xMean = _xRanks.Average();
|
||||||
|
double yMean = _yRanks.Average();
|
||||||
|
|
||||||
|
if (!double.IsNaN(xMean) && !double.IsNaN(yMean))
|
||||||
|
{
|
||||||
|
double covariance = CalculateCovariance(_xRanks, _yRanks, xMean, yMean);
|
||||||
|
double xStdDev = CalculateStandardDeviation(_xRanks, xMean);
|
||||||
|
double yStdDev = CalculateStandardDeviation(_yRanks, yMean);
|
||||||
|
|
||||||
|
if (!double.IsNaN(covariance) && xStdDev > Epsilon && yStdDev > Epsilon)
|
||||||
|
{
|
||||||
|
correlation = covariance / (xStdDev * yStdDev);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
IsHot = _xValues.Count >= Period && _yValues.Count >= Period;
|
||||||
|
return correlation;
|
||||||
|
}
|
||||||
|
}
|
||||||
+25
-31
@@ -1,32 +1,26 @@
|
|||||||
# Statistics
|
# Statistics indicators
|
||||||
|
Done: 21, Todo: 2
|
||||||
|
|
||||||
Statistical functions and indicators for financial analysis.
|
✔️ BETA - Beta coefficient measuring volatility relative to market
|
||||||
|
✔️ CORR - Correlation coefficient between two series
|
||||||
## Implemented
|
✔️ COVAR - Covariance between two series
|
||||||
|
✔️ CURVATURE - Curvature of a time series
|
||||||
- [Beta](Beta.cs) - Beta coefficient measuring volatility relative to market
|
✔️ ENTROPY - Information entropy of a series
|
||||||
- [Corr](Corr.cs) - Correlation coefficient between two series
|
✔️ HURST - Hurst exponent for trend strength
|
||||||
- [Curvature](Curvature.cs) - Curvature of a time series
|
✔️ KENDALL - Kendall rank correlation
|
||||||
- [Entropy](Entropy.cs) - Information entropy of a series
|
✔️ KURTOSIS - Kurtosis measuring tail extremity
|
||||||
- [Hurst](Hurst.cs) - Hurst exponent for trend strength
|
✔️ MAX - Maximum value over period
|
||||||
- [Kurtosis](Kurtosis.cs) - Kurtosis measuring tail extremity
|
✔️ MEDIAN - Median value over period
|
||||||
- [Max](Max.cs) - Maximum value over period
|
✔️ MIN - Minimum value over period
|
||||||
- [Median](Median.cs) - Median value over period
|
✔️ MODE - Mode (most frequent value)
|
||||||
- [Min](Min.cs) - Minimum value over period
|
✔️ PERCENTILE - Percentile rank calculation
|
||||||
- [Mode](Mode.cs) - Mode (most frequent value)
|
✔️ SKEW - Skewness measuring distribution asymmetry
|
||||||
- [Percentile](Percentile.cs) - Percentile rank calculation
|
✔️ SLOPE - Linear regression slope
|
||||||
- [Skew](Skew.cs) - Skewness measuring distribution asymmetry
|
✔️ SPEARMAN - Spearman rank correlation
|
||||||
- [Slope](Slope.cs) - Linear regression slope
|
✔️ STDDEV - Standard deviation
|
||||||
- [Stddev](Stddev.cs) - Standard deviation
|
✔️ THEIL - Theil's U statistics for forecast accuracy
|
||||||
- [Theil](Theil.cs) - Theil's U statistics for forecast accuracy
|
✔️ TSF - Time series forecast
|
||||||
- [Tsf](Tsf.cs) - Time series forecast
|
✔️ VARIANCE - Statistical variance
|
||||||
- [Variance](Variance.cs) - Statistical variance
|
✔️ ZSCORE - Z-score standardization
|
||||||
- [Zscore](Zscore.cs) - Z-score standardization
|
COINTEGRATION - Test for cointegrated series
|
||||||
|
GRANGER - Granger causality test
|
||||||
## Planned
|
|
||||||
|
|
||||||
- Cointegration - Test for cointegrated series
|
|
||||||
- Granger - Granger causality test
|
|
||||||
- Jarque-Bera - Normality test
|
|
||||||
- Kendall - Kendall rank correlation
|
|
||||||
- Spearman - Spearman rank correlation
|
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
# Volatility indicators
|
# Volatility indicators
|
||||||
Done: 25, Todo: 10
|
Done: 31, Todo: 4
|
||||||
|
|
||||||
✔️ ADR - Average Daily Range
|
✔️ ADR - Average Daily Range
|
||||||
✔️ AP - Andrew's Pitchfork
|
✔️ AP - Andrew's Pitchfork
|
||||||
@@ -17,22 +17,22 @@ Done: 25, Todo: 10
|
|||||||
✔️ GKV - Garman-Klass Volatility
|
✔️ GKV - Garman-Klass Volatility
|
||||||
✔️ HLV - High-Low Volatility
|
✔️ HLV - High-Low Volatility
|
||||||
✔️ HV - Historical Volatility
|
✔️ HV - Historical Volatility
|
||||||
*ICH - Ichimoku Cloud (Conversion, Base, Leading Span A, Leading Span B, Lagging Span)
|
✔️ JVOLTY - Jurik Volatility (Jvolty, Upper band, Lower band)
|
||||||
✔️ *JVOLTY - Jurik Volatility (Jvolty, Upper band, Lower band)
|
|
||||||
*KC - Keltner Channels (Upper, Middle, Lower)
|
|
||||||
✔️ NATR - Normalized Average True Range
|
✔️ NATR - Normalized Average True Range
|
||||||
✔️ PCH - Price Channel Indicator
|
✔️ PCH - Price Channel Indicator
|
||||||
*PSAR - Parabolic Stop and Reverse (Value, Trend)
|
|
||||||
✔️ PV - Parkinson Volatility
|
✔️ PV - Parkinson Volatility
|
||||||
✔️ RSV - Rogers-Satchell Volatility
|
✔️ RSV - Rogers-Satchell Volatility
|
||||||
✔️ RV - Realized Volatility
|
✔️ RV - Realized Volatility
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✔️ RVI - Relative Volatility Index
|
✔️ RVI - Relative Volatility Index
|
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*STARC - Starc Bands (Upper, Middle, Lower)
|
|
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✔️ SV - Stochastic Volatility
|
✔️ SV - Stochastic Volatility
|
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✔️ TR - True Range
|
✔️ TR - True Range
|
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✔️ UI - Ulcer Index
|
✔️ UI - Ulcer Index
|
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✔️ *VC - Volatility Cone (Mean, Upper Bound, Lower Bound)
|
✔️ VC - Volatility Cone (Mean, Upper Bound, Lower Bound)
|
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✔️ VOV - Volatility of Volatility
|
✔️ VOV - Volatility of Volatility
|
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✔️ VR - Volatility Ratio
|
✔️ VR - Volatility Ratio
|
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✔️ *VS - Volatility Stop (Long Stop, Short Stop)
|
✔️ *VS - Volatility Stop (Long Stop, Short Stop)
|
||||||
✔️ YZV - Yang-Zhang Volatility
|
✔️ YZV - Yang-Zhang Volatility
|
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|
ICH - Ichimoku Cloud (Conversion, Base, Leading Span A, Leading Span B, Lagging Span)
|
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|
KC - Keltner Channels (Upper, Middle, Lower)
|
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|
PSAR - Parabolic Stop and Reverse (Value, Trend)
|
||||||
|
STARC - Starc Bands (Upper, Middle, Lower)
|
||||||
|
|||||||
Reference in New Issue
Block a user