From 63ae2c9ab2620ed930809c22634d3be04fc49899 Mon Sep 17 00:00:00 2001 From: Miha Kralj Date: Mon, 16 Feb 2026 21:26:44 -0800 Subject: [PATCH] Add TTM Scalper indicator implementation in C# and Pine Script; update Blma class for average calculation; remove missing indicators report and oscillator docs rewrite plans. --- .gitignore | 5 +- docs/indicators.md | 253 +++++-- docs/validation.md | 135 ++-- lib/oscillators/ultosc/Ultosc.cs | 12 +- lib/reversals/_index.md | 18 +- .../fractals/Fractals.Quantower.Tests.cs | 127 ++++ lib/reversals/fractals/Fractals.Quantower.cs | 52 ++ lib/reversals/fractals/Fractals.Tests.cs | 497 ++++++++++++++ .../fractals/Fractals.Validation.Tests.cs | 268 ++++++++ lib/reversals/fractals/Fractals.cs | 399 +++++++++++ lib/reversals/fractals/Fractals.md | 143 ++++ lib/reversals/pivot/Pivot.Quantower.Tests.cs | 124 ++++ lib/reversals/pivot/Pivot.Quantower.cs | 72 ++ lib/reversals/pivot/Pivot.Tests.cs | 560 ++++++++++++++++ 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lib/reversals/psar/Psar.Validation.Tests.cs | 188 ++++++ lib/reversals/psar/Psar.cs | 477 ++++++++++++++ lib/reversals/psar/Psar.md | 128 ++++ .../swings/Swings.Quantower.Tests.cs | 128 ++++ lib/reversals/swings/Swings.Quantower.cs | 55 ++ lib/reversals/swings/Swings.Tests.cs | 588 +++++++++++++++++ .../swings/Swings.Validation.Tests.cs | 266 ++++++++ lib/reversals/swings/Swings.cs | 471 +++++++++++++ lib/reversals/swings/Swings.md | 160 +++++ .../ttm_scalper/TtmScalper.Quantower.Tests.cs | 128 ++++ .../ttm_scalper/TtmScalper.Quantower.cs | 55 ++ lib/reversals/ttm_scalper/TtmScalper.Tests.cs | 572 ++++++++++++++++ .../TtmScalper.Validation.Tests.cs | 231 +++++++ lib/reversals/ttm_scalper/TtmScalper.cs | 447 +++++++++++++ lib/reversals/ttm_scalper/ttmscalper.pine | 40 ++ lib/trends_FIR/blma/Blma.cs | 4 +- plans/missing-indicators-report.md | 337 ---------- plans/oscillator-docs-rewrite.md | 106 --- 68 files changed, 16069 insertions(+), 587 deletions(-) create mode 100644 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create mode 100644 lib/reversals/swings/Swings.Quantower.cs create mode 100644 lib/reversals/swings/Swings.Tests.cs create mode 100644 lib/reversals/swings/Swings.Validation.Tests.cs create mode 100644 lib/reversals/swings/Swings.cs create mode 100644 lib/reversals/swings/Swings.md create mode 100644 lib/reversals/ttm_scalper/TtmScalper.Quantower.Tests.cs create mode 100644 lib/reversals/ttm_scalper/TtmScalper.Quantower.cs create mode 100644 lib/reversals/ttm_scalper/TtmScalper.Tests.cs create mode 100644 lib/reversals/ttm_scalper/TtmScalper.Validation.Tests.cs create mode 100644 lib/reversals/ttm_scalper/TtmScalper.cs create mode 100644 lib/reversals/ttm_scalper/ttmscalper.pine delete mode 100644 plans/missing-indicators-report.md delete mode 100644 plans/oscillator-docs-rewrite.md diff --git a/.gitignore b/.gitignore index ea7224e1..5a311ce7 100644 --- a/.gitignore +++ b/.gitignore @@ -60,4 +60,7 @@ docfx/ .github\instructions\codacy.instructions.md # Roo Code local config -.roo/ \ No newline at end of file +.roo/ + +# Internal planning docs (not shipped) +plans/ \ No newline at end of file diff --git a/docs/indicators.md b/docs/indicators.md index 0b1d8bbb..c2f434cb 100644 --- a/docs/indicators.md +++ b/docs/indicators.md @@ -9,18 +9,18 @@ QuanTAlib provides technical indicators organized into mathematical families. Un | [**Trends (FIR)**](../lib/trends_FIR/_index.md) | Trend direction via finite impulse response filters | SMA, WMA, ALMA, HMA, LSMA | Trend identification with predictable lag and finite memory. Output depends only on a fixed window of past prices. | | [**Trends (IIR)**](../lib/trends_IIR/_index.md) | Trend direction via infinite impulse response filters | EMA, DEMA, TEMA, JMA, KAMA, MAMA | Trend identification with recursive calculation and theoretically infinite memory. More responsive per unit of smoothness. | | [**Filters**](../lib/filters/_index.md) | Signal processing filters for noise reduction | Bessel, Butterworth, Super Smoother | Removing noise while preserving trend structure. Designed by engineers, borrowed by traders. | -| [**Oscillators**](../lib/oscillators/_index.md) | Cyclical movement around a baseline | RSI, MACD, AO, UltOsc | Identifying overbought/oversold conditions and potential reversals. Bounded indicators that oscillate. | +| [**Oscillators**](../lib/oscillators/_index.md) | Cyclical movement around a baseline | Stochastic, Fisher, UltOsc, Williams %R | Identifying overbought/oversold conditions and potential reversals. Bounded indicators that oscillate. | | [**Dynamics**](../lib/dynamics/_index.md) | Trend strength and structural changes | ADX, Aroon, SuperTrend, Chop | Determining market regime (trending vs ranging) and measuring trend conviction. | -| [**Momentum**](../lib/momentum/_index.md) | Speed and magnitude of price changes | Momentum, ROC, Velocity | Measuring acceleration or deceleration in price. First derivative territory. | -| [**Volatility**](../lib/volatility/_index.md) | Size and variability of price movements | ATR, StdDev, Bollinger Bands | Position sizing, stop-loss placement, regime identification. How much prices move matters as much as direction. | -| [**Volume**](../lib/volume/_index.md) | Trading activity and price-volume relationships | OBV, VWAP, A/D | Confirming price movements with participation. Volume validates or contradicts price action. | -| [**Channels**](../lib/channels/_index.md) | Price boundaries and range definitions | Donchian, Keltner, Bollinger | Breakout strategies and range-bound trading. Defining "normal" so abnormal becomes visible. | +| [**Momentum**](../lib/momentum/_index.md) | Speed and magnitude of price changes | ROC, RSI, MACD, CMO | Measuring acceleration or deceleration in price. First derivative territory. | +| [**Volatility**](../lib/volatility/_index.md) | Size and variability of price movements | ATR, StdDev, HV, YZV | Position sizing, stop-loss placement, regime identification. How much prices move matters as much as direction. | +| [**Volume**](../lib/volume/_index.md) | Trading activity and price-volume relationships | OBV, VWAP, MFI, CMF | Confirming price movements with participation. Volume validates or contradicts price action. | +| [**Channels**](../lib/channels/_index.md) | Price boundaries and range definitions | Bollinger, Keltner, Donchian | Breakout strategies and range-bound trading. Defining "normal" so abnormal becomes visible. | | [**Statistics**](../lib/statistics/_index.md) | Mathematical relationships between price series | Correlation, Covariance, Beta, Z-Score | Portfolio analysis, pairs trading, statistical arbitrage. Quantitative analysis beyond single instruments. | -| [**Numerics**](../lib/numerics/_index.md) | Mathematical transformations and signal processing | Convolution, Integration, Differentiation | Custom indicator development and advanced signal processing. Building blocks for novel indicators. | -| [**Errors**](../lib/errors/_index.md) | Measurement accuracy and model fit quality | MAE, RMSE, Residuals, R² | Model validation and forecast assessment. Quantifying wrongness before production quantifies losses. | -| [**Forecasts**](../lib/forecasts/_index.md) | Future price prediction and projection | Linear regression extrapolation, adaptive prediction | Projecting price based on historical patterns. Predictions that invite humility. | -| [**Cycles**](../lib/cycles/_index.md) | Periodic patterns and dominant frequencies | Hilbert Transform, Dominant Cycle | Identifying cyclical market behavior. Markets exhibit cycles; detecting them reliably remains hard. | -| [**Reversals**](../lib/reversals/_index.md) | Turning points and stop levels | Pivot Points, PSAR, Chande Kroll Stop | Identifying potential trend reversals, computing adaptive stops, and defining support/resistance. | +| [**Numerics**](../lib/numerics/_index.md) | Mathematical transformations and signal processing | Slope, Accel, Normalize, Sigmoid | Custom indicator development and advanced signal processing. Building blocks for novel indicators. | +| [**Errors**](../lib/errors/_index.md) | Measurement accuracy and model fit quality | MAE, RMSE, R², Huber | Model validation and forecast assessment. Quantifying wrongness before production quantifies losses. | +| [**Forecasts**](../lib/forecasts/_index.md) | Future price prediction and projection | AFIRMA | Projecting price based on historical patterns. Predictions that invite humility. | +| [**Cycles**](../lib/cycles/_index.md) | Periodic patterns and dominant frequencies | Hilbert Transform, EBSW, STC | Identifying cyclical market behavior. Markets exhibit cycles; detecting them reliably remains hard. | +| [**Reversals**](../lib/reversals/_index.md) | Turning points and stop levels | Pivot Points, PSAR, Chandelier, Swings | Identifying potential trend reversals, computing adaptive stops, and defining support/resistance. | ## Selection by Experience Level @@ -47,7 +47,7 @@ Finite Impulse Response filters. Output depends only on a fixed window of inputs | [**HAMMA**](../lib/trends_FIR/hamma/Hamma.md) | Hamming Weighted MA | Spectral analysis window | | [**HANMA**](../lib/trends_FIR/hanma/Hanma.md) | Hanning Weighted MA | Cosine-based window | | [**HMA**](../lib/trends_FIR/hma/Hma.md) | Hull MA | Reduced lag via WMA differencing | -| [**HWMA**](../lib/trends_FIR/hwma/Hwma.md) | Holt-Winters MA | Triple exponential smoothing | +| [**HWMA**](../lib/trends_FIR/hwma/Hwma.md) | Henderson Weighted MA | Henderson curve smoothing | | [**LSMA**](../lib/trends_FIR/lsma/Lsma.md) | Least Squares MA | Linear regression endpoint | | [**PWMA**](../lib/trends_FIR/pwma/Pwma.md) | Pascal Weighted MA | Binomial coefficient weights | | [**SGMA**](../lib/trends_FIR/sgma/Sgma.md) | Savitzky-Golay MA | Polynomial smoothing | @@ -71,18 +71,20 @@ Infinite Impulse Response filters. Output depends on current input and past outp | [**JMA**](../lib/trends_IIR/jma/Jma.md) | Jurik MA | Adaptive, low-lag, proprietary algorithm | | [**KAMA**](../lib/trends_IIR/kama/Kama.md) | Kaufman Adaptive MA | Efficiency ratio adaptation | | [**MAMA**](../lib/trends_IIR/mama/Mama.md) | MESA Adaptive MA | Homodyne discriminator based | -| [**MMA**](../lib/trends_IIR/mma/Mma.md) | Modified MA | Smoothed EMA variant | | [**MGDI**](../lib/trends_IIR/mgdi/Mgdi.md) | McGinley Dynamic | Market-speed tracking | +| [**MMA**](../lib/trends_IIR/mma/Mma.md) | Modified MA | Smoothed EMA variant | | [**QEMA**](../lib/trends_IIR/qema/Qema.md) | Quad Exponential MA | Four-stage exponential | -| [**RGMA**](../lib/trends_IIR/rgma/Rgma.md) | Recursive Gaussian MA | Gaussian approximation | | [**REMA**](../lib/trends_IIR/rema/Rema.md) | Regularized Exponential MA | Regularization for stability | +| [**RGMA**](../lib/trends_IIR/rgma/Rgma.md) | Recursive Gaussian MA | Gaussian approximation | | [**RMA**](../lib/trends_IIR/rma/Rma.md) | WildeR MA | Wilder's smoothing (1/n decay) | | [**T3**](../lib/trends_IIR/t3/T3.md) | Tillson T3 MA | Six-stage DEMA variant | | [**TEMA**](../lib/trends_IIR/tema/Tema.md) | Triple Exponential MA | Three-stage lag reduction | | [**VAMA**](../lib/trends_IIR/vama/Vama.md) | Volatility Adjusted MA | ATR-based adaptation | | [**VIDYA**](../lib/trends_IIR/vidya/Vidya.md) | Variable Index Dynamic | CMO-based adaptation | -| [**YZVAMA**](../lib/trends_IIR/yzvama/Yzvama.md) | Yang-Zhang Vol Adjusted | YZ volatility adaptation | +| [**YZVAMA**](../lib/trends_IIR/yzvama/Yzvama.md) | Yang-Zhang Vol Adjusted MA | YZ volatility adaptation | +| [**ZLDEMA**](../lib/trends_IIR/zldema/Zldema.md) | Zero-Lag Double Exponential MA | Momentum-compensated DEMA | | [**ZLEMA**](../lib/trends_IIR/zlema/Zlema.md) | Zero-Lag Exponential MA | Momentum-compensated EMA | +| [**ZLTEMA**](../lib/trends_IIR/zltema/Zltema.md) | Zero-Lag Triple Exponential MA | Momentum-compensated TEMA | ### Filters @@ -94,9 +96,20 @@ Signal processing filters adapted for financial time series. Designed to separat | [**BILATERAL**](../lib/filters/bilateral/Bilateral.md) | Bilateral Filter | Edge-preserving smoothing | | [**BPF**](../lib/filters/bpf/Bpf.md) | BandPass Filter | Frequency band isolation | | [**BUTTER**](../lib/filters/butter/Butter.md) | Butterworth Filter | Maximally flat passband | -| [**CHEBY1**](../lib/filters/cheby1/Cheby1.md) | Chebyshev Type I | Steeper rolloff with ripple | +| [**CHEBY1**](../lib/filters/cheby1/Cheby1.md) | Chebyshev Type I | Steeper rolloff with passband ripple | +| [**CHEBY2**](../lib/filters/cheby2/Cheby2.md) | Chebyshev Type II | Steeper rolloff with stopband ripple | +| [**ELLIPTIC**](../lib/filters/elliptic/Elliptic.md) | Elliptic (Cauer) Filter | Sharpest transition, both band ripple | +| [**GAUSS**](../lib/filters/gauss/Gauss.md) | Gaussian Filter | No overshoot, smooth response | +| [**HANN**](../lib/filters/hann/Hann.md) | Hann Filter | Raised cosine window filter | +| [**HP**](../lib/filters/hp/Hp.md) | Hodrick-Prescott Filter | Trend-cycle decomposition | +| [**HPF**](../lib/filters/hpf/Hpf.md) | High Pass Filter | Ehlers high-pass design | +| [**KALMAN**](../lib/filters/kalman/Kalman.md) | Kalman Filter | Optimal recursive estimation | +| [**LOESS**](../lib/filters/loess/Loess.md) | LOESS Smoothing | Local polynomial regression | +| [**NOTCH**](../lib/filters/notch/Notch.md) | Notch Filter | Single frequency rejection | +| [**SGF**](../lib/filters/sgf/Sgf.md) | Savitzky-Golay Filter | Polynomial least-squares fitting | | [**SSF**](../lib/filters/ssf/Ssf.md) | Super Smooth Filter | Ehlers two-pole design | | [**USF**](../lib/filters/usf/Usf.md) | Ultimate Smoother | Ehlers high-fidelity filter | +| [**WIENER**](../lib/filters/wiener/Wiener.md) | Wiener Filter | Minimum mean-square error denoising | ### Oscillators @@ -120,10 +133,8 @@ Bounded indicators that oscillate around a centerline or between fixed extremes. | [**STOCHF**](../lib/oscillators/stochf/Stochf.md) | Stochastic Fast | Unsmoothed Stochastic (%K/%D, SMA smoothing only) | | [**STOCHRSI**](../lib/oscillators/stochrsi/Stochrsi.md) | Stochastic RSI | Stochastic applied to RSI (%K/%D) | | [**TRIX**](../lib/oscillators/trix/Trix.md) | Triple Exponential Average | ROC of triple-smoothed EMA | -| [**MACD**](../lib/momentum/macd/Macd.md) | MACD | EMA crossover system | -| [**RSI**](../lib/momentum/rsi/Rsi.md) | Relative Strength Index | Bounded 0-100 momentum | -| [**ULTOSC**](../lib/oscillators/ultosc/Ultosc.md) | Ultimate Oscillator | Multi-timeframe weighted | | [**TTM_WAVE**](../lib/oscillators/ttm_wave/TtmWave.md) | TTM Wave | Fibonacci-period MACD composite (A/B/C waves) | +| [**ULTOSC**](../lib/oscillators/ultosc/Ultosc.md) | Ultimate Oscillator | Multi-timeframe weighted buying pressure | | [**WILLR**](../lib/oscillators/willr/Willr.md) | Williams %R | Inverse Stochastic (-100 to 0) | ### Dynamics @@ -134,12 +145,22 @@ Indicators measuring trend strength, regime, and directional movement quality. | :-------- | :-------- | :---- | | [**ADX**](../lib/dynamics/adx/Adx.md) | Average Directional Index | Trend strength 0-100 | | [**ADXR**](../lib/dynamics/adxr/Adxr.md) | ADX Rating | Smoothed ADX | +| [**ALLIGATOR**](../lib/dynamics/alligator/Alligator.md) | Williams Alligator | Three displaced SMAs for trend detection | | [**AMAT**](../lib/dynamics/amat/Amat.md) | Archer MA Trends | MA-based trend detection | | [**AROON**](../lib/dynamics/aroon/Aroon.md) | Aroon | High/low recency | | [**AROONOSC**](../lib/dynamics/aroonosc/AroonOsc.md) | Aroon Oscillator | Aroon Up minus Down | +| [**CHOP**](../lib/dynamics/chop/Chop.md) | Choppiness Index | ATR sum vs range; trending vs choppy | | [**DMX**](../lib/dynamics/dmx/Dmx.md) | Jurik DMX | Enhanced directional movement | +| [**DX**](../lib/dynamics/dx/Dx.md) | Directional Movement Index | Raw directional strength | +| [**HT_TRENDMODE**](../lib/dynamics/ht_trendmode/HtTrendmode.md) | Hilbert Transform Trend Mode | Cycle vs trend regime detection | +| [**ICHIMOKU**](../lib/dynamics/ichimoku/Ichimoku.md) | Ichimoku Cloud | Multi-component trend system | +| [**IMI**](../lib/dynamics/imi/Imi.md) | Intraday Momentum Index | Candlestick-based momentum | | [**IMPULSE**](../lib/dynamics/impulse/Impulse.md) | Elder Impulse System | EMA + MACD-H trend/momentum fusion | +| [**QSTICK**](../lib/dynamics/qstick/Qstick.md) | Qstick | Average close-open difference | | [**SUPER**](../lib/dynamics/super/Super.md) | SuperTrend | ATR-based trend bands | +| [**TTM_SQUEEZE**](../lib/dynamics/ttm_squeeze/TtmSqueeze.md) | TTM Squeeze | BB inside KC squeeze with momentum | +| [**TTM_TREND**](../lib/dynamics/ttm_trend/TtmTrend.md) | TTM Trend | Bar coloring by close vs midline | +| [**VORTEX**](../lib/dynamics/vortex/Vortex.md) | Vortex Indicator | Uptrend/downtrend movement comparison | ### Momentum @@ -148,11 +169,18 @@ Rate of change and velocity measurements. First derivatives of price. | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | | [**BOP**](../lib/momentum/bop/Bop.md) | Balance of Power | Close position in range | +| [**CCI**](../lib/momentum/cci/Cci.md) | Commodity Channel Index | Mean deviation normalized | | [**CFB**](../lib/momentum/cfb/Cfb.md) | Composite Fractal Behavior | Jurik fractal momentum | +| [**CMO**](../lib/momentum/cmo/Cmo.md) | Chande Momentum Oscillator | Up/down ratio oscillator | +| [**MACD**](../lib/momentum/macd/Macd.md) | Moving Average Convergence Divergence | EMA crossover system | +| [**MOM**](../lib/momentum/mom/Mom.md) | Momentum | Raw price difference over N periods | +| [**PMO**](../lib/momentum/pmo/Pmo.md) | Price Momentum Oscillator | Double-smoothed ROC | +| [**PPO**](../lib/momentum/ppo/Ppo.md) | Percentage Price Oscillator | Percentage EMA difference | +| [**PRS**](../lib/momentum/prs/Prs.md) | Price Relative Strength | Dual-input ratio comparison | | [**ROC**](../lib/momentum/roc/Roc.md) | Rate of Change | Absolute price change over N periods | | [**ROCP**](../lib/momentum/rocp/Rocp.md) | Rate of Change Percentage | Percentage price change over N periods | | [**ROCR**](../lib/momentum/rocr/Rocr.md) | Rate of Change Ratio | Price ratio over N periods | -| [**PRS**](../lib/momentum/prs/Prs.md) | Price Relative Strength | Dual-input ratio comparison | +| [**RSI**](../lib/momentum/rsi/Rsi.md) | Relative Strength Index | Bounded 0-100 momentum | | [**RSX**](../lib/momentum/rsx/Rsx.md) | Jurik RSX | Smoothed RSI variant | | [**TSI**](../lib/momentum/tsi/Tsi.md) | True Strength Index | Double-smoothed momentum oscillator | | [**VEL**](../lib/momentum/vel/Vel.md) | Jurik Velocity | Adaptive velocity | @@ -165,7 +193,30 @@ Measures of price variability and range. Essential for position sizing and stop | :-------- | :-------- | :---- | | [**ADR**](../lib/volatility/adr/Adr.md) | Average Daily Range | Simple range averaging | | [**ATR**](../lib/volatility/atr/Atr.md) | Average True Range | Gap-adjusted range | -| [**ATRP**](../lib/volatility/atrp/Atrp.md) | ATR Percent | Normalized ATR | +| [**ATRN**](../lib/volatility/atrn/Atrn.md) | ATR Normalized | ATR scaled to [0,1] | +| [**ATRP**](../lib/volatility/atrp/Atrp.md) | ATR Percent | Percentage-based ATR | +| [**BBW**](../lib/volatility/bbw/Bbw.md) | Bollinger Band Width | Band width as percentage of middle band | +| [**BBWN**](../lib/volatility/bbwn/Bbwn.md) | BB Width Normalized | Band width normalized to [0,1] | +| [**BBWP**](../lib/volatility/bbwp/Bbwp.md) | BB Width Percentile | Band width historical percentile | +| [**CCV**](../lib/volatility/ccv/Ccv.md) | Close-to-Close Volatility | Log-return standard deviation | +| [**CV**](../lib/volatility/cv/Cv.md) | Coefficient of Variation | StdDev / Mean ratio | +| [**CVI**](../lib/volatility/cvi/Cvi.md) | Chaikin Volatility | EMA change of H-L range | +| [**EWMA**](../lib/volatility/ewma/Ewma.md) | EWMA Volatility | Exponentially weighted variance | +| [**GKV**](../lib/volatility/gkv/Gkv.md) | Garman-Klass Volatility | OHLC-based efficiency estimator | +| [**HLV**](../lib/volatility/hlv/Hlv.md) | High-Low Volatility | Parkinson range-based estimator | +| [**HV**](../lib/volatility/hv/Hv.md) | Historical Volatility | Annualized log-return StdDev | +| [**JVOLTY**](../lib/volatility/jvolty/Jvolty.md) | Jurik Volatility | Adaptive volatility measure | +| [**JVOLTYN**](../lib/volatility/jvoltyn/Jvoltyn.md) | Jurik Volatility Normalized | Jurik volatility scaled to [0,100] | +| [**MASSI**](../lib/volatility/massi/Massi.md) | Mass Index | EMA ratio of H-L range | +| [**NATR**](../lib/volatility/natr/Natr.md) | Normalized ATR | ATR as percentage of close | +| [**RSV**](../lib/volatility/rsv/Rsv.md) | Rogers-Satchell Volatility | Drift-independent OHLC estimator | +| [**RV**](../lib/volatility/rv/Rv.md) | Realized Volatility | Sum of squared returns | +| [**RVI**](../lib/volatility/rvi/Rvi.md) | Relative Volatility Index | RSI applied to StdDev | +| [**TR**](../lib/volatility/tr/Tr.md) | True Range | Max(H-L, H-prevC, prevC-L) | +| [**UI**](../lib/volatility/ui/Ui.md) | Ulcer Index | Downside deviation from highs | +| [**VOV**](../lib/volatility/vov/Vov.md) | Volatility of Volatility | Second-order volatility | +| [**VR**](../lib/volatility/vr/Vr.md) | Volatility Ratio | ATR-relative true range | +| [**YZV**](../lib/volatility/yzv/Yzv.md) | Yang-Zhang Volatility | Optimal OHLC estimator | ### Volume @@ -173,13 +224,32 @@ Price-volume relationships and accumulation/distribution measurements. | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | -| [**ADL**](../lib/volume/adl/Adl.md) | Accumulation/Distribution | Volume-weighted close position | -| [**ADOSC**](../lib/volume/adosc/Adosc.md) | Chaikin A/D Oscillator | ADL momentum | +| [**ADL**](../lib/volume/adl/Adl.md) | Accumulation/Distribution Line | Volume-weighted close position | +| [**ADOSC**](../lib/volume/adosc/Adosc.md) | Chaikin A/D Oscillator | ADL momentum (fast EMA - slow EMA) | +| [**AOBV**](../lib/volume/aobv/Aobv.md) | Archer On-Balance Volume | OBV with signal line | +| [**CMF**](../lib/volume/cmf/Cmf.md) | Chaikin Money Flow | Volume-weighted close position over period | +| [**EFI**](../lib/volume/efi/Efi.md) | Elder's Force Index | Price change × volume | +| [**EOM**](../lib/volume/eom/Eom.md) | Ease of Movement | Price movement per unit volume | +| [**III**](../lib/volume/iii/Iii.md) | Intraday Intensity Index | Close position within H-L × volume | +| [**KVO**](../lib/volume/kvo/Kvo.md) | Klinger Volume Oscillator | Trend-volume force oscillator | +| [**MFI**](../lib/volume/mfi/Mfi.md) | Money Flow Index | Volume-weighted RSI | +| [**NVI**](../lib/volume/nvi/Nvi.md) | Negative Volume Index | Cumulative on low-volume days | +| [**OBV**](../lib/volume/obv/Obv.md) | On Balance Volume | Cumulative signed volume | +| [**PVD**](../lib/volume/pvd/Pvd.md) | Price Volume Divergence | Price-volume correlation divergence | +| [**PVI**](../lib/volume/pvi/Pvi.md) | Positive Volume Index | Cumulative on high-volume days | +| [**PVO**](../lib/volume/pvo/Pvo.md) | Percentage Volume Oscillator | Percentage volume MA difference | +| [**PVR**](../lib/volume/pvr/Pvr.md) | Price Volume Rank | Categorical price-volume classification | +| [**PVT**](../lib/volume/pvt/Pvt.md) | Price Volume Trend | ROC-weighted cumulative volume | +| [**TVI**](../lib/volume/tvi/Tvi.md) | Trade Volume Index | Tick-direction cumulative volume | | [**TWAP**](../lib/volume/twap/Twap.md) | Time Weighted Average Price | Time-equal-weighted price average | | [**VA**](../lib/volume/va/Va.md) | Volume Accumulation | Cumulative volume by close position | | [**VF**](../lib/volume/vf/Vf.md) | Volume Force | EMA-smoothed price-volume force | | [**VO**](../lib/volume/vo/Vo.md) | Volume Oscillator | Short vs long volume MA difference | | [**VROC**](../lib/volume/vroc/Vroc.md) | Volume Rate of Change | Volume change over lookback period | +| [**VWAD**](../lib/volume/vwad/Vwad.md) | Volume Weighted A/D | Close-position cumulative volume | +| [**VWAP**](../lib/volume/vwap/Vwap.md) | Volume Weighted Average Price | Price × volume / total volume | +| [**VWMA**](../lib/volume/vwma/Vwma.md) | Volume Weighted MA | Volume-weighted moving average | +| [**WAD**](../lib/volume/wad/Wad.md) | Williams A/D | True range-based accumulation | ### Channels @@ -189,17 +259,27 @@ Price envelope and boundary indicators for breakout and mean-reversion strategie | :-------- | :-------- | :---- | | [**ABBER**](../lib/channels/abber/abber.md) | Aberration Bands | Statistical deviation bands | | [**ACCBANDS**](../lib/channels/accbands/accbands.md) | Acceleration Bands | Volatility-adjusted envelope | +| [**APCHANNEL**](../lib/channels/apchannel/apchannel.md) | Andrews' Pitchfork | Three-line channel from pivot points | +| [**APZ**](../lib/channels/apz/apz.md) | Adaptive Price Zone | EMA-based volatility zone | +| [**ATRBANDS**](../lib/channels/atrbands/Atrbands.md) | ATR Bands | ATR-based envelope around price | +| [**BBANDS**](../lib/channels/bbands/Bbands.md) | Bollinger Bands | SMA ± StdDev bands | | [**DCHANNEL**](../lib/channels/dchannel/Dchannel.md) | Donchian Channels | Highest-high / lowest-low breakout bands | | [**DECAYCHANNEL**](../lib/channels/decaychannel/decaychannel.md) | Decay Min-Max Channel | Exponential decay toward midpoint | | [**FCB**](../lib/channels/fcb/fcb.md) | Fractal Chaos Bands | Williams fractal-based support/resistance | -| [**JBANDS**](../lib/channels/jbands/Jbands.md) | Jurik Adaptive Envelope Bands | Snap-to-extreme, decay-to-price volatility bands | +| [**JBANDS**](../lib/channels/jbands/Jbands.md) | Jurik Adaptive Bands | Snap-to-extreme, decay-to-price volatility bands | | [**KCHANNEL**](../lib/channels/kchannel/kchannel.md) | Keltner Channel | EMA with ATR bands; smoother than Bollinger | -| [**MAENV**](../lib/channels/maenv/maenv.md) | Moving Average Envelope | Fixed percentage bands around selectable MA type | -| [**MMCHANNEL**](../lib/channels/mmchannel/mmchannel.md) | Min-Max Channel | Rolling highest high / lowest low; O(1) monotonic deques | -| [**PCHANNEL**](../lib/channels/pchannel/pchannel.md) | Price Channel | Highest high / lowest low; identical to Donchian | -| [**REGCHANNEL**](../lib/channels/regchannel/regchannel.md) | Linear Regression Channel | Linear regression line with standard deviation bands | -| [**SDCHANNEL**](../lib/channels/sdchannel/sdchannel.md) | Standard Deviation Channel | Moving average with standard deviation bands | -| [**STARCHANNEL**](../lib/channels/starchannel/starchannel.md) | Stoller Average Range Channel | SMA with ATR bands; similar to Keltner but uses SMA | +| [**MAENV**](../lib/channels/maenv/maenv.md) | Moving Average Envelope | Fixed percentage bands around selectable MA | +| [**MMCHANNEL**](../lib/channels/mmchannel/mmchannel.md) | Min-Max Channel | Rolling highest high / lowest low | +| [**PCHANNEL**](../lib/channels/pchannel/pchannel.md) | Price Channel | Highest high / lowest low with midline | +| [**REGCHANNEL**](../lib/channels/regchannel/regchannel.md) | Regression Channel | Linear regression with StdDev bands | +| [**SDCHANNEL**](../lib/channels/sdchannel/sdchannel.md) | Standard Deviation Channel | MA with standard deviation bands | +| [**STARCHANNEL**](../lib/channels/starchannel/starchannel.md) | Stoller Average Range Channel | SMA with ATR bands | +| [**STBANDS**](../lib/channels/stbands/Stbands.md) | Super Trend Bands | ATR-based SuperTrend envelope | +| [**TTM_LRC**](../lib/channels/ttm_lrc/TtmLrc.md) | TTM Linear Regression Channel | John Carter's regression channel | +| [**UBANDS**](../lib/channels/ubands/Ubands.md) | Ultimate Bands | Ehlers bandpass-based bands | +| [**UCHANNEL**](../lib/channels/uchannel/Uchannel.md) | Ultimate Channel | Ehlers smoothed channel | +| [**VWAPBANDS**](../lib/channels/vwapbands/Vwapbands.md) | VWAP Bands | VWAP with StdDev bands | +| [**VWAPSD**](../lib/channels/vwapsd/Vwapsd.md) | VWAP StdDev Bands | VWAP with standard deviation envelopes | ### Statistics @@ -207,33 +287,36 @@ Mathematical and statistical computations on price series. | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | +| [**ACF**](../lib/statistics/acf/Acf.md) | Autocorrelation Function | Lagged self-correlation | +| [**BETA**](../lib/statistics/beta/Beta.md) | Beta Coefficient | Systematic risk measure | | [**BIAS**](../lib/statistics/bias/Bias.md) | Bias | Percentage deviation from SMA | -| [**COINTEGRATION**](../lib/statistics/cointegration/Cointegration.md) | Cointegration | Engle-Granger two-step method with ADF test | -| [**CORRELATION**](../lib/statistics/correlation/Correlation.md) | Pearson Correlation | Linear relationship between two series [-1, +1] | | [**CMA**](../lib/statistics/cma/Cma.md) | Cumulative Moving Average | Expanding window average | -| [**COVARIANCE**](../lib/statistics/covariance/Covariance.md) | Covariance | Joint variability | -| [**ENTROPY**](../lib/statistics/entropy/Entropy.md) | Shannon Entropy | Normalized information entropy via histogram binning | -| [**GEOMEAN**](../lib/statistics/geomean/Geomean.md) | Geometric Mean | Rolling geometric mean via log-sum approach | -| [**HARMEAN**](../lib/statistics/harmean/Harmean.md) | Harmonic Mean | Rolling harmonic mean via reciprocal-sum approach | -| [**HURST**](../lib/statistics/hurst/Hurst.md) | Hurst Exponent | Long-range dependence via Rescaled Range (R/S) analysis | -| [**IQR**](../lib/statistics/iqr/Iqr.md) | Interquartile Range | Robust dispersion measure (Q3 - Q1) | -| [**JB**](../lib/statistics/jb/Jb.md) | Jarque-Bera Test | Normality test combining skewness and kurtosis | -| [**KENDALL**](../lib/statistics/kendall/Kendall.md) | Kendall Tau-a | Rank-based ordinal association [-1, +1] | -| [**GRANGER**](../lib/statistics/granger/Granger.md) | Granger Causality | F-statistic testing if X helps predict Y | -| [**LINREG**](../lib/statistics/linreg/LinReg.md) | Linear Regression | Best-fit line | +| [**COINTEGRATION**](../lib/statistics/cointegration/Cointegration.md) | Cointegration | Engle-Granger two-step with ADF test | +| [**CORRELATION**](../lib/statistics/correlation/Correlation.md) | Pearson Correlation | Linear relationship [-1, +1] | +| [**COVARIANCE**](../lib/statistics/covariance/Covariance.md) | Covariance | Joint variability measure | +| [**ENTROPY**](../lib/statistics/entropy/Entropy.md) | Shannon Entropy | Information content via histogram binning | +| [**GEOMEAN**](../lib/statistics/geomean/Geomean.md) | Geometric Mean | Rolling geometric mean via log-sum | +| [**GRANGER**](../lib/statistics/granger/Granger.md) | Granger Causality | F-statistic testing if X predicts Y | +| [**HARMEAN**](../lib/statistics/harmean/Harmean.md) | Harmonic Mean | Rolling harmonic mean via reciprocal-sum | +| [**HURST**](../lib/statistics/hurst/Hurst.md) | Hurst Exponent | Long-range dependence via R/S analysis | +| [**IQR**](../lib/statistics/iqr/Iqr.md) | Interquartile Range | Robust dispersion (Q3 - Q1) | +| [**JB**](../lib/statistics/jb/Jb.md) | Jarque-Bera Test | Normality test (skewness + kurtosis) | +| [**KENDALL**](../lib/statistics/kendall/Kendall.md) | Kendall Tau-a | Rank-based ordinal association | +| [**KURTOSIS**](../lib/statistics/kurtosis/Kurtosis.md) | Kurtosis | Fourth-moment tail heaviness | +| [**LINREG**](../lib/statistics/linreg/LinReg.md) | Linear Regression | Best-fit line via least squares | | [**MEDIAN**](../lib/statistics/median/Median.md) | Rolling Median | 50th percentile | -| [**MODE**](../lib/statistics/mode/Mode.md) | Mode | Most frequent value in rolling window | -| [**KURTOSIS**](../lib/statistics/kurtosis/Kurtosis.md) | Kurtosis | Fourth-moment excess kurtosis (sample/population) | -| [**PERCENTILE**](../lib/statistics/percentile/Percentile.md) | Percentile | Value at given percentile via linear interpolation (PERCENTILE.INC) | -| [**QUANTILE**](../lib/statistics/quantile/Quantile.md) | Quantile | Value at given quantile (0–1) via linear interpolation | +| [**MODE**](../lib/statistics/mode/Mode.md) | Mode | Most frequent value in window | +| [**PACF**](../lib/statistics/pacf/Pacf.md) | Partial Autocorrelation | Direct correlation at lag k | +| [**PERCENTILE**](../lib/statistics/percentile/Percentile.md) | Percentile | Value at given percentile rank | +| [**QUANTILE**](../lib/statistics/quantile/Quantile.md) | Quantile | Value at given quantile (0-1) | | [**SKEW**](../lib/statistics/skew/Skew.md) | Skewness | Distribution asymmetry | -| [**SPEARMAN**](../lib/statistics/spearman/Spearman.md) | Spearman Rank Correlation | Pearson on ranks; monotonic association [-1, +1] | +| [**SPEARMAN**](../lib/statistics/spearman/Spearman.md) | Spearman Rank Correlation | Monotonic association [-1, +1] | | [**STDDEV**](../lib/statistics/stddev/StdDev.md) | Standard Deviation | Dispersion measure | | [**SUM**](../lib/statistics/sum/Sum.md) | Rolling Sum | Windowed sum | -| [**THEIL**](../lib/statistics/theil/Theil.md) | Theil T Index | Information-theoretic inequality/concentration measure | +| [**THEIL**](../lib/statistics/theil/Theil.md) | Theil T Index | Information-theoretic inequality | | [**VARIANCE**](../lib/statistics/variance/Variance.md) | Variance | Squared deviation | -| [**ZSCORE**](../lib/statistics/zscore/Zscore.md) | Z-Score | Population standard deviations from rolling mean | -| [**ZTEST**](../lib/statistics/ztest/Ztest.md) | Z-Test | One-sample t-statistic against hypothesized mean | +| [**ZSCORE**](../lib/statistics/zscore/Zscore.md) | Z-Score | Standard deviations from rolling mean | +| [**ZTEST**](../lib/statistics/ztest/Ztest.md) | Z-Test | One-sample t-statistic | ### Forecasts @@ -249,8 +332,20 @@ Periodic pattern detection and dominant frequency extraction. Markets exhibit cy | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | -| [**HT_SINE**](../lib/cycles/ht_sine/HtSine.md) | Hilbert Transform SineWave | Dominant cycle phase with 45° lead signal | -| [**SSFDSP**](../lib/cycles/ssfdsp/Ssfdsp.md) | SSF Detrended Synthetic Price | Dual Super Smoother Filter oscillator | +| [**CG**](../lib/cycles/cg/Cg.md) | Center of Gravity | Ehlers cycle measurement | +| [**DSP**](../lib/cycles/dsp/Dsp.md) | Detrended Synthetic Price | Cycle-isolated price component | +| [**EACP**](../lib/cycles/eacp/Eacp.md) | Autocorrelation Periodogram | Ehlers dominant cycle detection | +| [**EBSW**](../lib/cycles/ebsw/Ebsw.md) | Even Better Sinewave | Ehlers improved cycle indicator | +| [**HOMOD**](../lib/cycles/homod/Homod.md) | Homodyne Discriminator | Dominant cycle period tracking | +| [**HT_DCPERIOD**](../lib/cycles/ht_dcperiod/HtDcperiod.md) | HT Dominant Cycle Period | Hilbert Transform period estimation | +| [**HT_DCPHASE**](../lib/cycles/ht_dcphase/HtDcphase.md) | HT Dominant Cycle Phase | Hilbert Transform phase angle | +| [**HT_PHASOR**](../lib/cycles/ht_phasor/HtPhasor.md) | HT Phasor Components | In-phase and quadrature components | +| [**HT_SINE**](../lib/cycles/ht_sine/HtSine.md) | HT SineWave | Dominant cycle phase with lead signal | +| [**LUNAR**](../lib/cycles/lunar/Lunar.md) | Lunar Phase | Moon phase cycle | +| [**SINE**](../lib/cycles/sine/Sine.md) | Sine Wave | Periodic sine oscillation | +| [**SOLAR**](../lib/cycles/solar/Solar.md) | Solar Activity Cycle | Solar activity periodicity | +| [**SSFDSP**](../lib/cycles/ssfdsp/Ssfdsp.md) | SSF Detrended Synthetic Price | Dual Super Smoother oscillator | +| [**STC**](../lib/cycles/stc/Stc.md) | Schaff Trend Cycle | MACD-based cycle oscillator | ### Numerics @@ -258,20 +353,21 @@ Mathematical transformations and derivative indicators. Building blocks for anal | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | -| [**ACCEL**](../lib/numerics/accel/Accel.md) | Acceleration (2nd Derivative) | Change in slope; momentum | -| [**CHANGE**](../lib/numerics/change/Change.md) | Percentage Change | Relative price movement (current - past) / past | -| [**EXPTRANS**](../lib/numerics/exptrans/Exptrans.md) | Exponential Transform | e^x transform for log-space reversal | -| [**HIGHEST**](../lib/numerics/highest/Highest.md) | Rolling Maximum | O(1) amortized via monotonic deque | +| [**ACCEL**](../lib/numerics/accel/Accel.md) | Acceleration (2nd Derivative) | Change in slope | +| [**CHANGE**](../lib/numerics/change/Change.md) | Percentage Change | Relative price movement | +| [**EXPTRANS**](../lib/numerics/exptrans/Exptrans.md) | Exponential Transform | e^x for log-space reversal | +| [**HIGHEST**](../lib/numerics/highest/Highest.md) | Rolling Maximum | O(1) via monotonic deque | | [**JERK**](../lib/numerics/jerk/Jerk.md) | Jerk (3rd Derivative) | Change in acceleration | -| [**LINEARTRANS**](../lib/numerics/lineartrans/Lineartrans.md) | Linear Transform | y = ax + b scaling transformation | +| [**LINEARTRANS**](../lib/numerics/lineartrans/Lineartrans.md) | Linear Transform | y = ax + b scaling | | [**LOGTRANS**](../lib/numerics/logtrans/Logtrans.md) | Logarithmic Transform | Natural log for percentage analysis | -| [**LOWEST**](../lib/numerics/lowest/Lowest.md) | Rolling Minimum | O(1) amortized via monotonic deque | +| [**LOWEST**](../lib/numerics/lowest/Lowest.md) | Rolling Minimum | O(1) via monotonic deque | | [**MIDPOINT**](../lib/numerics/midpoint/Midpoint.md) | Rolling Midpoint | (Highest + Lowest) / 2 | | [**NORMALIZE**](../lib/numerics/normalize/Normalize.md) | Min-Max Normalization | Scale to [0,1] via rolling min/max | -| [**RELU**](../lib/numerics/relu/Relu.md) | Rectified Linear Unit | max(0, x); activation function | -| [**SIGMOID**](../lib/numerics/sigmoid/Sigmoid.md) | Logistic Function | 1/(1+e^-x); bounded [0,1] transform | +| [**RELU**](../lib/numerics/relu/Relu.md) | Rectified Linear Unit | max(0, x) activation | +| [**SIGMOID**](../lib/numerics/sigmoid/Sigmoid.md) | Logistic Function | 1/(1+e^-x) bounded [0,1] | | [**SLOPE**](../lib/numerics/slope/Slope.md) | Slope (1st Derivative) | Rate of change; velocity | -| [**SQRTTRANS**](../lib/numerics/sqrttrans/Sqrttrans.md) | Square Root Transform | √x; variance to standard deviation conversion | +| [**SQRTTRANS**](../lib/numerics/sqrttrans/Sqrttrans.md) | Square Root Transform | √x variance-to-StdDev conversion | +| [**STANDARDIZE**](../lib/numerics/standardize/Standardize.md) | Z-Score Normalization | (x - mean) / StdDev scaling | ### Errors @@ -279,7 +375,32 @@ Error metrics and loss functions for model evaluation, forecast assessment, and | Indicator | Full Name | Notes | | :-------- | :-------- | :---- | -| [**WRMSE**](../lib/errors/wrmse/Wrmse.md) | Weighted Root Mean Squared Error | Custom observation weighting for error emphasis | +| [**HUBER**](../lib/errors/huber/Huber.md) | Huber Loss | Quadratic for small errors, linear for large | +| [**LOGCOSH**](../lib/errors/logcosh/LogCosh.md) | Log-Cosh Loss | Smooth Huber approximation | +| [**MAAPE**](../lib/errors/maape/Maape.md) | Mean Arctangent APE | Bounded percentage error | +| [**MAE**](../lib/errors/mae/Mae.md) | Mean Absolute Error | Average absolute deviation | +| [**MAPD**](../lib/errors/mapd/Mapd.md) | Mean Absolute % Deviation | Percentage deviation from mean | +| [**MAPE**](../lib/errors/mape/Mape.md) | Mean Absolute % Error | Percentage prediction error | +| [**MASE**](../lib/errors/mase/Mase.md) | Mean Absolute Scaled Error | Scale-independent accuracy | +| [**MDAE**](../lib/errors/mdae/Mdae.md) | Median Absolute Error | Robust central error | +| [**MDAPE**](../lib/errors/mdape/Mdape.md) | Median Absolute % Error | Robust percentage error | +| [**ME**](../lib/errors/me/Me.md) | Mean Error | Bias direction indicator | +| [**MPE**](../lib/errors/mpe/Mpe.md) | Mean Percentage Error | Percentage bias measure | +| [**MRAE**](../lib/errors/mrae/Mrae.md) | Mean Relative Absolute Error | Benchmark-relative error | +| [**MSE**](../lib/errors/mse/Mse.md) | Mean Squared Error | Variance of residuals | +| [**MSLE**](../lib/errors/msle/Msle.md) | Mean Squared Log Error | Ratio-sensitive error | +| [**PSEUDOHUBER**](../lib/errors/pseudohuber/PseudoHuber.md) | Pseudo-Huber Loss | Differentiable Huber approximation | +| [**QUANTILELOSS**](../lib/errors/quantile/QuantileLoss.md) | Quantile Loss | Asymmetric pinball loss | +| [**RAE**](../lib/errors/rae/Rae.md) | Relative Absolute Error | MAE relative to baseline | +| [**RMSE**](../lib/errors/rmse/Rmse.md) | Root Mean Squared Error | Standard error magnitude | +| [**RMSLE**](../lib/errors/rmsle/Rmsle.md) | Root Mean Squared Log Error | Ratio-sensitive RMSE | +| [**RSE**](../lib/errors/rse/Rse.md) | Relative Squared Error | MSE relative to baseline | +| [**RSQUARED**](../lib/errors/rsquared/Rsquared.md) | R² (Coefficient of Determination) | Explained variance fraction | +| [**SMAPE**](../lib/errors/smape/Smape.md) | Symmetric MAPE | Symmetric percentage error | +| [**THEILU**](../lib/errors/theilu/TheilU.md) | Theil's U Statistic | Forecast accuracy relative to naive | +| [**TUKEY**](../lib/errors/tukey/TukeyBiweight.md) | Tukey Biweight Loss | Robust regression loss | +| [**WMAPE**](../lib/errors/wmape/Wmape.md) | Weighted MAPE | Volume-weighted percentage error | +| [**WRMSE**](../lib/errors/wrmse/Wrmse.md) | Weighted RMSE | Observation-weighted RMSE | ### Reversals @@ -289,3 +410,13 @@ Reversal indicators identify potential turning points, compute adaptive stop lev | :-------- | :-------- | :---- | | [**CHANDELIER**](../lib/reversals/chandelier/Chandelier.md) | Chandelier Exit | ATR-based trailing stops from HH/LL; dual ExitLong/ExitShort | | [**CKSTOP**](../lib/reversals/ckstop/Ckstop.md) | Chande Kroll Stop | ATR-based adaptive trailing stops; dual StopLong/StopShort levels | +| [**FRACTALS**](../lib/reversals/fractals/Fractals.md) | Williams Fractals | Five-bar pattern detecting local highs/lows; dual UpFractal/DownFractal | +| [**PIVOT**](../lib/reversals/pivot/Pivot.md) | Classic Pivot Points | Floor trader pivots: 7 levels (PP, R1-R3, S1-S3) from previous bar's HLC | +| [**PIVOTCAM**](../lib/reversals/pivotcam/Pivotcam.md) | Camarilla Pivot Points | Close-centric pivots: 9 levels (PP, R1-R4, S1-S4); R3/S3 mean-reversion zones | +| [**PIVOTDEM**](../lib/reversals/pivotdem/Pivotdem.md) | DeMark Pivot Points | Conditional pivots: 3 levels (PP, R1, S1); weights OHLC by bar direction | +| [**PIVOTEXT**](../lib/reversals/pivotext/Pivotext.md) | Extended Traditional Pivots | Extended pivots: 11 levels (PP, R1-R5, S1-S5); classic formula with R4/R5/S4/S5 | +| [**PIVOTFIB**](../lib/reversals/pivotfib/Pivotfib.md) | Fibonacci Pivot Points | Fibonacci pivots: 7 levels (PP, R1-R3, S1-S3); ratios 0.382/0.618/1.000 applied to range | +| [**PIVOTWOOD**](../lib/reversals/pivotwood/Pivotwood.md) | Woodie's Pivot Points | Close-weighted pivots: 7 levels (PP, R1-R3, S1-S3); PP = (H+L+2C)/4 biased toward close | +| [**PSAR**](../lib/reversals/psar/Psar.md) | Parabolic Stop And Reverse | Accelerating trailing stop; SAR dots flip on reversal; Welles Wilder (1978) | +| [**SWINGS**](../lib/reversals/swings/Swings.md) | Swing High/Low Detection | Configurable-lookback pattern detector; dual SwingHigh/SwingLow with persistent levels | +| [**TTM_SCALPER**](../lib/reversals/ttm_scalper/TtmScalper.md) | TTM Scalper Alert | 3-bar pivot high/low detection for scalping entries; John Carter | diff --git a/docs/validation.md b/docs/validation.md index 85793e5e..ef4eab4b 100644 --- a/docs/validation.md +++ b/docs/validation.md @@ -34,48 +34,48 @@ No external reference exists. Implementation verified through unit tests, edge c | :-------- | :-------- | :----: | :---: | :-----: | :----: | | **Aberration Bands** | [Abber](../lib/channels/abber/abber.md) | - | - | - | - | | **Absolute Price Oscillator** | [Apo](../lib/momentum/apo/apo.md) | ✔️ | ✔️ | - | ✔️ | -| **Acceleration Bands** | [AccBands](../lib/channels/accbands/accbands.md) | - | - | - | - | +| **Acceleration Bands** | [AccBands](../lib/channels/accbands/accbands.md) | ✔️ | - | - | ❔ | | **Acceleration Oscillator** | [Ac](../lib/oscillators/ac/Ac.md) | - | - | - | ❔ | | **Accumulation/Distribution Line** | [Adl](../lib/volume/adl/adl.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Accumulation/Distribution Oscillator** | [Adosc](../lib/volume/adosc/adosc.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Adaptive Price Zone** | [Apz](../lib/channels/apz/apz.md) | - | - | - | ❔ | -| **Andrews' Pitchfork** | Apchannel | - | - | - | - | +| **Andrews' Pitchfork** | Apchannel | - | - | ✔️ | - | | **Archer Moving Averages Trends** | [Amat](../lib/momentum/amat/Amat.md) | - | - | ✔️ | ✔️ | | **Archer On-Balance Volume** | [Aobv](../lib/volume/aobv/Aobv.md) | - | - | - | - | | **Arnaud Legoux Moving Average** | [Alma](../lib/trends/alma/alma.md) | - | - | ✔️ | ✔️ | | **Aroon** | [Aroon](../lib/momentum/aroon/aroon.md) | ✔️ | ✔️ | ✔️ | - | | **Aroon Oscillator** | [AroonOsc](../lib/momentum/aroonosc/AroonOsc.md) | ✔️ | ✔️ | ✔️ | - | -| **ATR Bands** | Atrbands | - | - | - | ❔ | +| **ATR Bands** | Atrbands | ✔️ | - | ✔️ | ❔ | | **Adaptive FIR Moving Average** | [Afirma](../lib/forecasts/afirma/Afirma.md) | - | - | - | - | | **Average Daily Range** | [Adr](../lib/volatility/adr/Adr.md) | - | - | - | - | | **Average Directional Index** | [Adx](../lib/momentum/adx/adx.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Average Directional Movement Rating** | [Adxr](../lib/momentum/adxr/Adxr.md) | ✔️ | ✔️ | - | - | | **Average True Range** | [Atr](../lib/volatility/atr/atr.md) | ✔️ | ✔️ | ✔️ | ✔️ | -| **Average True Range Normalized [0,1]** | [Atrn](../lib/volatility/atrn/Atrn.md) | - | - | - | - | +| **Average True Range Normalized [0,1]** | [Atrn](../lib/volatility/atrn/Atrn.md) | - | - | ✔️ | - | | **Average True Range Percent** | [Atrp](../lib/volatility/atrp/Atrp.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Awesome Oscillator** | [Ao](../lib/momentum/ao/ao.md) | - | ✔️ | ✔️ | ✔️ | | **Balance of Power** | [Bop](../lib/momentum/bop/Bop.md) | ✔️ | ✔️ | ✔️ | ✔️ | -| **Bollinger Bands** | [Bbands](../lib/channels/bbands/Bbands.md) | - | ✔️ | ✔️ | ✔️ | +| **Bollinger Bands** | [Bbands](../lib/channels/bbands/Bbands.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Bessel Filter** | [Bessel](../lib/trends/bessel/Bessel.md) | - | - | - | - | | **Bessel-Weighted MA** | [Bwma](../lib/trends_FIR/bwma/Bwma.md) | - | - | - | - | -| **Beta Coefficient** | [Beta](../lib/statistics/beta/Beta.md) | ✔️ | - | ✔️ | - | +| **Beta Coefficient** | [Beta](../lib/statistics/beta/Beta.md) | ❔ | - | ✔️ | - | | **Bias** | [Bias](../lib/statistics/bias/Bias.md) | - | - | - | - | | **Bilateral Filter** | [Bilateral](../lib/trends/bilateral/Bilateral.md) | - | - | - | - | | **Blackman Window MA** | [Blma](../lib/trends/blma/Blma.md) | - | - | - | - | -| **Bollinger %B** | [Bbb](../lib/oscillators/bbb/Bbb.md) | - | - | - | ❔ | -| **Bollinger Band Squeeze** | [Bbs](../lib/oscillators/bbs/Bbs.md) | - | - | - | ❔ | -| **Bollinger Band Width** | Bbw | - | - | - | ❔ | +| **Bollinger %B** | [Bbb](../lib/oscillators/bbb/Bbb.md) | - | - | ✔️ | ❔ | +| **Bollinger Band Squeeze** | [Bbs](../lib/oscillators/bbs/Bbs.md) | - | - | ✔️ | ❔ | +| **Bollinger Band Width** | Bbw | - | - | ✔️ | ❔ | | **Bollinger Band Width Normalized** | Bbwn | - | - | - | - | | **Bollinger Band Width Percentile** | Bbwp | - | - | - | - | | **Bollinger Bands** | Bbands | ✔️ | ✔️ | ✔️ | ❔ | | **Butterworth Filter** | [Butter](../lib/trends/butter/Butter.md) | - | - | - | ✔️ | -| **Camarilla Pivot Points** | Pivotcam | - | - | - | ❔ | -| **Chandelier Exit** | [Chandelier](../lib/reversals/chandelier/Chandelier.md) | - | ✔️ | - | - | +| **Camarilla Pivot Points** | [Pivotcam](../lib/reversals/pivotcam/Pivotcam.md) | - | - | - | ❔ | +| **Chandelier Exit** | [Chandelier](../lib/reversals/chandelier/Chandelier.md) | - | - | ✔️ | - | | **Chande Kroll Stop** | [Ckstop](../lib/reversals/ckstop/Ckstop.md) | - | - | - | - | | **Chaikin Money Flow** | Cmf | - | - | ✔️ | ❔ | -| **Chaikin Volatility** | [Cvi](../lib/volatility/cvi/Cvi.md) | - | ✔️ | - | ❔ | -| **Chande Forecast Oscillator** | [Cfo](../lib/oscillators/cfo/Cfo.md) | - | ✔️ | - | ❔ | -| **Chande Momentum Oscillator** | Cmo | ✔️ | ✔️ | ✔️ | ❔ | +| **Chaikin Volatility** | [Cvi](../lib/volatility/cvi/Cvi.md) | - | ❔ | - | ❔ | +| **Chande Forecast Oscillator** | [Cfo](../lib/oscillators/cfo/Cfo.md) | - | - | ✔️ | ❔ | +| **Chande Momentum Oscillator** | Cmo | - | ✔️ | ✔️ | ❔ | | **Chebyshev Type I Filter** | Cheby1 | - | - | - | - | | **Chebyshev Type II Filter** | Cheby2 | - | - | - | - | | **Choppiness Index** | Chop | - | - | ✔️ | ❔ | @@ -85,22 +85,22 @@ No external reference exists. Implementation verified through unit tests, edge c | **Composite Fractal Behavior** | [Cfb](../lib/momentum/cfb/cfb.md) | - | - | - | - | | **Conditional Volatility** | [Cv](../lib/volatility/cv/Cv.md) | - | - | - | - | | **Convolution Moving Average** | [Conv](../lib/trends/conv/conv.md) | ✔️ | ✔️ | ✔️ | ✔️ | -| **Correlation** | Correlation | ✔️ | - | ✔️ | - | +| **Correlation** | Correlation | - | - | ✔️ | - | | **Cumulative Moving Average** | [Cma](../lib/statistics/cma/Cma.md) | - | - | - | - | | **Decay Min-Max Channel** | [Decaychannel](../lib/channels/decaychannel/decaychannel.md) | - | - | - | - | -| **DeMark Pivot Points** | Pivotdem | - | - | - | ❔ | +| **DeMark Pivot Points** | [Pivotdem](../lib/reversals/pivotdem/Pivotdem.md) | - | - | - | ❔ | | **Detrended Price Oscillator** | [Dpo](../lib/oscillators/dpo/Dpo.md) | - | ⚠️ | - | ❔ | | **Detrended Synthetic Price** | Dsp | - | - | - | ❔ | | **Deviation-Scaled MA** | Dsma | - | - | - | ❔ | -| **Directional Movement Index** | Dx | ✔️ | ✔️ | - | - | +| **Directional Movement Index** | Dx | ✔️ | ✔️ | ✔️ | ✔️ | | **Directional Movement Index (Jurik)** | [Dmx](../lib/momentum/dmx/dmx.md) | - | - | - | - | | **Dirty Data Detection** | Dirty | - | - | - | - | | **Donchian Channels** | [Dchannel](../lib/channels/dchannel/Dchannel.md) | - | - | ✔️ | ❔ | | **Double Exponential Moving Average** | [Dema](../lib/trends/dema/dema.md) | ✔️ | ✔️ | ✔️ | ✔️ | -| **Double Weighted Moving Average** | [Dwma](../lib/trends/dwma/dwma.md) | - | - | - | - | -| **Ease of Movement** | [Eom](../lib/volume/eom/Eom.md) | - | - | - | - | +| **Double Weighted Moving Average** | [Dwma](../lib/trends/dwma/dwma.md) | ✔️ | ✔️ | ✔️ | - | +| **Ease of Movement** | [Eom](../lib/volume/eom/Eom.md) | - | ✔️ | - | - | | **Ehlers Autocorrelation Periodogram** | [Eacp](../lib/cycles/eacp/eacp.md) | - | - | - | - | -| **BandPass Filter** | [Bpf](../lib/filters/bpf/Bpf.md) | ✔️ | - | - | - | +| **BandPass Filter** | [Bpf](../lib/filters/bpf/Bpf.md) | - | - | - | - | | **Ehlers Center of Gravity** | Cg | - | - | - | ❔ | | **Ehlers Even Better Sinewave** | [Ebsw](../lib/cycles/ebsw/ebsw.md) | - | - | - | ❔ | | **Ehlers Fractal Adaptive MA** | [Frama](../lib/trends_IIR/frama/Frama.md) | - | - | - | ❔ | @@ -113,11 +113,11 @@ No external reference exists. Implementation verified through unit tests, edge c | **Elliptic (Cauer) Filter** | [Elliptic](../lib/filters/elliptic/Elliptic.md) | - | - | - | ❔ | | **Exponential Moving Average** | [Ema](../lib/trends/ema/ema.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Exponential Transformation** | Exptrans | - | - | - | - | -| **Exponential Weighted MA Volatility** | [Ewma](../lib/volatility/ewma/Ewma.md) | - | - | - | - | -| **Extended Traditional Pivots** | Pivotext | - | - | - | - | +| **Exponential Weighted MA Volatility** | [Ewma](../lib/volatility/ewma/Ewma.md) | - | - | - | ❔ | +| **Extended Traditional Pivots** | [Pivotext](../lib/reversals/pivotext/Pivotext.md) | - | - | - | - | | **Fibonacci Pivot Points** | Pivotfib | - | - | - | ❔ | -| **Fisher Transform** | [Fisher](../lib/oscillators/fisher/Fisher.md) | - | ✔️ | ✔️ | ❔ | -| **Force Index** | [Efi](../lib/volume/efi/Efi.md) | - | - | - | - | +| **Fisher Transform** | [Fisher](../lib/oscillators/fisher/Fisher.md) | - | ❔ | ❔ | ❔ | +| **Force Index** | [Efi](../lib/volume/efi/Efi.md) | - | - | ✔️ | ✔️ | | **Fractal Chaos Bands** | [Fcb](../lib/channels/fcb/fcb.md) | - | - | ✔️ | ❔ | | **Garman-Klass Volatility** | [Gkv](../lib/volatility/gkv/Gkv.md) | - | - | - | - | | **Gaussian Filter** | [Gauss](../lib/filters/gauss/Gauss.md) | - | - | - | ❔ | @@ -143,7 +143,7 @@ No external reference exists. Implementation verified through unit tests, edge c | **Huber Loss** | Huber | - | - | - | - | | **Hull Exponential MA** | [Hema](../lib/trends_IIR/hema/Hema.md) | - | - | - | - | | **Hull Moving Average** | [Hma](../lib/trends/hma/hma.md) | - | ✔️ | ✔️ | [⚠️](../lib/trends/hma/hma.md#external-library-discrepancies) | -| **Hurst Exponent** | Hurst | - | - | ✔️ | ❔ | +| **Hurst Exponent** | Hurst | - | - | - | ❔ | | **Ichimoku Cloud** | Ichimoku | - | - | ✔️ | ❔ | | **Impulse (Elder)** | [Impulse](../lib/dynamics/impulse/Impulse.md) | - | - | - | - | | **Inertia** | [Inertia](../lib/oscillators/inertia/Inertia.md) | - | - | - | ❔ | @@ -162,8 +162,8 @@ No external reference exists. Implementation verified through unit tests, edge c | **Kendall Rank Correlation** | [Kendall](../lib/statistics/kendall/Kendall.md) | - | - | - | - | | **Klinger Volume Oscillator** | [Kvo](../lib/volume/kvo/Kvo.md) | - | ✔️ | ✔️ | ❔ | | **Kurtosis** | [Kurtosis](../lib/statistics/kurtosis/Kurtosis.md) | - | - | - | [✔️](../lib/statistics/kurtosis/Kurtosis.md#validation) | -| **Least Squares Moving Average** | [Lsma](../lib/trends/lsma/lsma.md) | ✔️ | - | ✔️ | ❔ | -| **Linear Regression** | [LinReg](../lib/statistics/linreg/LinReg.md) | ✔️ | ✔️ | ✔️ | [⚠️](../lib/statistics/linreg/LinReg.md#validation) | +| **Least Squares Moving Average** | [Lsma](../lib/trends/lsma/lsma.md) | - | - | ✔️ | ❔ | +| **Linear Regression** | [LinReg](../lib/statistics/linreg/LinReg.md) | - | - | ✔️ | [⚠️](../lib/statistics/linreg/LinReg.md#validation) | | **Linear Transformation** | Lineartrans | - | - | - | - | | **Linear Trend MA** | Ltma | - | - | - | - | | **LOESS/LOWESS Smoothing** | [Loess](../lib/filters/loess/Loess.md) | - | - | - | - | @@ -173,7 +173,7 @@ No external reference exists. Implementation verified through unit tests, edge c | **Lunar Phase** | Lunar | - | - | - | - | | **Lowest value** | [Lowest](../lib/numerics/lowest/Lowest.md) | ✔️ | ✔️ | - | - | | **Lunar Phase** | Lunar | - | - | - | - | -| **Mass Index** | [Massi](../lib/volatility/massi/Massi.md) | - | ✔️ | - | ❔ | +| **Mass Index** | [Massi](../lib/volatility/massi/Massi.md) | - | - | - | ❔ | | **McGinley Dynamic** | [Mgdi](../lib/trends/mgdi/mgdi.md) | - | - | ✔️ | ✔️ | | **Mean Absolute Error** | Mae | - | - | - | - | | **Mean Absolute Percentage Difference** | Mapd | - | - | - | - | @@ -183,44 +183,48 @@ No external reference exists. Implementation verified through unit tests, edge c | **Mean Percentage Error** | Mpe | - | - | - | - | | **Mean Squared Error** | Mse | - | - | - | - | | **Mean Squared Logarithmic Error** | Msle | - | - | - | - | -| **MESA Adaptive Moving Average** | [Mama](../lib/trends/mama/mama.md) | ✔️ | - | ✔️ | ✔️ | +| **MESA Adaptive Moving Average** | [Mama](../lib/trends/mama/mama.md) | - | - | ✔️ | ✔️ | | **Midpoint** | [Midpoint](../lib/numerics/midpoint/Midpoint.md) | ✔️ | - | - | - | -| **Min-Max Channel** | [Mmchannel](../lib/channels/mmchannel/mmchannel.md) | ✔️ | ✔️ | ✔️ | - | +| **Min-Max Channel** | [Mmchannel](../lib/channels/mmchannel/mmchannel.md) | - | - | ✔️ | - | | **Min-Max Scaling (Normalization)** | [Normalize](../lib/numerics/normalize/Normalize.md) | - | - | - | - | | **Mode (Most Frequent)** | Mode | - | - | - | - | | **Modified MA** | [Mma](../lib/trends_IIR/mma/Mma.md) | - | - | - | - | -| **Momentum** | Mom | ✔️ | ✔️ | - | ❔ | +| **Momentum** | Mom | ✔️ | ✔️ | ✔️ | ❔ | | **Momentum change; 2nd derivative** | Accel | - | - | - | - | -| **Money Flow Index** | [Mfi](../lib/volume/mfi/Mfi.md) | ✔️ | ✔️ | ✔️ | ❔ | +| **Money Flow Index** | [Mfi](../lib/volume/mfi/Mfi.md) | - | - | ✔️ | ✔️ | | **Moon Phase** | Moon | - | - | - | - | -| **Moving Average Convergence/Divergence** | [Macd](../lib/momentum/macd/Macd.md) | ✔️ | ✔️ | ✔️ | ❔ | +| **Moving Average Convergence/Divergence** | [Macd](../lib/momentum/macd/Macd.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Moving Average Envelopes** | [Maenv](../lib/channels/maenv/maenv.md) | - | - | ✔️ | ❔ | | **Negative Volume Index** | [Nvi](../lib/volume/nvi/Nvi.md) | - | ✔️ | - | - | -| **Normalized Average True Range** | Natr | ✔️ | ✔️ | - | - | +| **Normalized Average True Range** | Natr | ✔️ | ✔️ | ✔️ | ✔️ | | **Normalized Shannon Entropy** | Entropy | - | - | - | - | | **Notch Filter** | [Notch](../lib/filters/notch/Notch.md) | - | - | - | - | | **On Balance Volume** | [Obv](../lib/volume/obv/Obv.md) | [⚠️](../lib/volume/obv/Obv.md#validation) | ✔️ | ✔️ | [⚠️](../lib/volume/obv/Obv.md#validation) | -| **Parabolic SAR** | Psar | ✔️ | ✔️ | ✔️ | ❔ | -| **Pascal Weighted Moving Average** | [Pwma](../lib/trends/pwma/pwma.md) | - | - | - | - | -| **Percentage Change** | [Change](../lib/numerics/change/Change.md) | ✔️ | - | - | - | -| **Percentage Price Oscillator** | Ppo | ✔️ | ✔️ | - | ❔ | -| **Percentage Volume Oscillator** | [Pvo](../lib/volume/pvo/Pvo.md) | - | - | ✔️ | ❔ | +| **Parabolic SAR** | [Psar](../lib/reversals/psar/Psar.md) | - | - | ✔️ | ❔ | +| **Pascal Weighted Moving Average** | [Pwma](../lib/trends/pwma/pwma.md) | - | - | - | ✔️ | +| **Percentage Change** | [Change](../lib/numerics/change/Change.md) | - | ✔️ | - | - | +| **Percentage Price Oscillator** | Ppo | ✔️ | ✔️ | - | ✔️ | +| **Percentage Volume Oscillator** | [Pvo](../lib/volume/pvo/Pvo.md) | - | - | - | ❔ | | **Percentile** | Percentile | - | - | - | - | -| **Pivot Points** | Pivot | - | - | ✔️ | ❔ | +| **Pivot Points** | [Pivot](../lib/reversals/pivot/Pivot.md) | - | - | - | ❔ | +| **Pivot Points (Camarilla)** | [Pivotcam](../lib/reversals/pivotcam/Pivotcam.md) | - | - | - | ❔ | +| **Pivot Points (DeMark)** | [Pivotdem](../lib/reversals/pivotdem/Pivotdem.md) | - | - | - | ❔ | +| **Pivot Points (Extended)** | [Pivotext](../lib/reversals/pivotext/Pivotext.md) | - | - | - | ❔ | +| **Pivot Points (Fibonacci)** | [Pivotfib](../lib/reversals/pivotfib/Pivotfib.md) | - | - | - | ❔ | | **Positive Volume Index** | [Pvi](../lib/volume/pvi/Pvi.md) | - | ✔️ | - | - | | **Pretty Good Oscillator** | [Pgo](../lib/oscillators/pgo/Pgo.md) | - | - | - | ❔ | -| **Price Channel** | [Pchannel](../lib/channels/pchannel/pchannel.md) | - | - | - | ✔️ | -| **Price Momentum Oscillator** | Pmo | - | - | ✔️ | ❔ | +| **Price Channel** | [Pchannel](../lib/channels/pchannel/pchannel.md) | - | - | ✔️ | - | +| **Price Momentum Oscillator** | Pmo | - | - | ✔️ | ✔️ | | **Price Relative Strength** | Prs | - | - | ✔️ | - | | **Price Volume Divergence** | [Pvd](../lib/volume/pvd/Pvd.md) | - | - | - | - | -| **Price Volume Rank** | [Pvr](../lib/volume/pvr/Pvr.md) | - | - | - | ✔️ | -| **Price Volume Trend** | [Pvt](../lib/volume/pvt/Pvt.md) | - | - | ✔️ | ✔️ | +| **Price Volume Rank** | [Pvr](../lib/volume/pvr/Pvr.md) | - | - | - | - | +| **Price Volume Trend** | [Pvt](../lib/volume/pvt/Pvt.md) | - | - | - | ✔️ | | **Qstick Indicator** | Qstick | - | - | - | ❔ | | **Quad Exponential MA** | [Qema](../lib/trends_IIR/qema/Qema.md) | - | - | - | - | | **Quantile** | Quantile | - | - | - | - | | **Rate of acceleration; 3rd derivative** | [Jerk](../lib/numerics/jerk/Jerk.md) | - | - | - | - | -| **Rate of Change** | [Roc](../lib/momentum/roc/Roc.md) | ✔️ | ✔️ | ✔️ | ❔ | -| **Rate of change; 1st derivative** | [Slope](../lib/statistics/linreg/LinReg.md) | ✔️ | ✔️ | ✔️ | ❔ | +| **Rate of Change** | [Roc](../lib/momentum/roc/Roc.md) | - | ✔️ | ✔️ | ❔ | +| **Rate of change; 1st derivative** | [Slope](../lib/statistics/linreg/LinReg.md) | - | - | ✔️ | ❔ | | **Rate of Change Percentage** | Rocp | ✔️ | - | - | - | | **Rate of Change Ratio** | Rocr | ✔️ | ✔️ | - | - | | **Realized Volatility** | [Rv](../lib/volatility/rv/Rv.md) | - | - | - | - | @@ -243,21 +247,21 @@ No external reference exists. Implementation verified through unit tests, edge c | **Schaff Trend Cycle** | [Stc](../lib/cycles/stc/Stc.md) | - | - | ✔️ | ❔ | | **Simple Moving Average** | [Sma](../lib/trends/sma/sma.md) | ✔️ | ✔️ | ✔️ | ✔️ | | **Sine-weighted MA** | [Sinema](../lib/trends_FIR/sinema/Sinema.md) | - | - | - | - | -| **Smoothed Moving Average** | [Rma](../lib/trends/rma/rma.md) | - | ✔️ | ✔️ | ✔️ | +| **Smoothed Moving Average** | [Rma](../lib/trends/rma/rma.md) | - | - | ✔️ | ✔️ | | **Solar Activity Cycle** | Solar | - | - | - | - | | **Spearman Rank Correlation** | Spearman | - | - | - | ❔ | | **Square Root Transformation** | [Sqrttrans](../lib/numerics/sqrttrans/Sqrttrans.md) | - | - | - | - | | **Standard Deviation Channel** | [Sdchannel](../lib/channels/sdchannel/sdchannel.md) | - | - | - | ❔ | | **Standardization (Z-score)** | Standardize | - | - | - | ❔ | | **Starc Bands** | Starc | - | - | - | - | -| **Stochastic Fast** | [Stochf](../lib/oscillators/stochf/Stochf.md) | ✔️ | - | - | ❔ | -| **Stochastic Momentum Index** | [Smi](../lib/oscillators/smi/Smi.md) | - | - | ✔️ | ❔ | +| **Stochastic Fast** | [Stochf](../lib/oscillators/stochf/Stochf.md) | ✔️ | - | ✔️ | ❔ | +| **Stochastic Momentum Index** | [Smi](../lib/oscillators/smi/Smi.md) | - | - | - | ❔ | | **Stochastic Oscillator** | [Stoch](../lib/oscillators/stoch/Stoch.md) | - | - | ✔️ | - | -| **Stochastic RSI** | [Stochrsi](../lib/oscillators/stochrsi/Stochrsi.md) | ✔️ | ✔️ | ✔️ | ❔ | -| **Stoller Average Range Channel** | [Starchannel](../lib/channels/starchannel/starchannel.md) | - | - | - | ❔ | +| **Stochastic RSI** | [Stochrsi](../lib/oscillators/stochrsi/Stochrsi.md) | ✔️ | - | ✔️ | ✔️ | +| **Stoller Average Range Channel** | [Starchannel](../lib/channels/starchannel/starchannel.md) | - | - | ✔️ | ❔ | | **Super Trend Bands** | [Stbands](../lib/channels/stbands/Stbands.md) | - | - | - | - | | **SuperTrend** | [Super](../lib/trends/super/super.md) | - | - | ✔️ | ❔ | -| **Swing High/Low Detection** | Swings | - | - | - | - | +| **Swing High/Low Detection** | [Swings](../lib/reversals/swings/Swings.md) | - | - | - | ❔ | | **Symmetric Mean Absolute Percentage Error** | Smape | - | - | - | - | | **T3 Moving Average** | [T3](../lib/trends/t3/t3.md) | ✔️ | - | ✔️ | ✔️ | | **Theil Index** | Theil | - | - | - | - | @@ -267,41 +271,42 @@ No external reference exists. Implementation verified through unit tests, edge c | **Triangular Moving Average** | [Trima](../lib/trends/trima/trima.md) | ✔️ | ✔️ | ✔️ | ❔ | | **Triple Exponential Average** | [Trix](../lib/oscillators/trix/Trix.md) | ✔️ | ✔️ | ✔️ | ❔ | | **Triple Exponential Moving Average** | [Tema](../lib/trends/tema/tema.md) | ✔️ | ✔️ | ✔️ | ❔ | -| **True Range** | Tr | ✔️ | ✔️ | ✔️ | - | -| **True Strength Index** | Tsi | - | - | ✔️ | ❔ | +| **True Range** | Tr | ✔️ | ✔️ | - | - | +| **True Strength Index** | Tsi | - | - | ✔️ | ✔️ | | **TTM Trend** | Ttm | - | - | - | - | +| **TTM Scalper Alert** | [TtmScalper](../lib/reversals/ttm_scalper/TtmScalper.md) | - | - | - | - | | **TTM Wave** | [TtmWave](../lib/oscillators/ttm_wave/TtmWave.md) | - | - | - | - | | **Two-Argument Arctangent** | Atan2 | - | - | - | - | -| **Ulcer Index** | Ui | - | - | ✔️ | ❔ | +| **Ulcer Index** | Ui | - | - | - | ❔ | | **Ultimate Bands (Ehlers)** | [Ubands](../lib/channels/ubands/Ubands.md) | - | - | - | - | | **Ultimate Channel** | [Uchannel](../lib/channels/uchannel/Uchannel.md) | - | - | - | - | | **Ultimate Oscillator** | [Ultosc](../lib/momentum/ultosc/Ultosc.md) | ✔️ | ✔️ | ✔️ | ✔️ | -| **Variable Index Dynamic Average** | [Vidya](../lib/trends/vidya/vidya.md) | - | ✔️ | - | ❔ | +| **Variable Index Dynamic Average** | [Vidya](../lib/trends/vidya/vidya.md) | - | - | - | ❔ | | **Velocity (Jurik)** | [Vel](../lib/momentum/vel/vel.md) | - | - | - | - | | **Volatility Adjusted Moving Average** | [Vama](../lib/trends_IIR/vama/Vama.md) | - | - | - | ❔ | | **Volatility of Volatility** | [Vov](../lib/volatility/vov/Vov.md) | - | - | - | - | | **Volatility Ratio** | [Vr](../lib/volatility/vr/Vr.md) | - | - | - | - | | **Volume Accumulation** | Va | - | - | - | ❔ | | **Volume Force** | Vf | - | - | - | - | -| **Volume Oscillator** | Vo | - | ✔️ | - | - | +| **Volume Oscillator** | Vo | - | - | - | - | | **Volume Rate of Change** | Vroc | - | - | - | - | | **Volume Weighted Accumulation/Distribution** | [Vwad](../lib/volume/vwad/Vwad.md) | - | - | - | - | -| **Volume Weighted Average Price** | [Vwap](../lib/volume/vwap/Vwap.md) | - | - | ✔️ | ✔️ | +| **Volume Weighted Average Price** | [Vwap](../lib/volume/vwap/Vwap.md) | - | - | - | - | | **Volume Weighted Moving Average** | [Vwma](../lib/volume/vwma/Vwma.md) | - | - | ✔️ | - | | **Vortex Indicator** | Vortex | - | - | ✔️ | ❔ | | **VWAP Bands** | [Vwapbands](../lib/channels/vwapbands/Vwapbands.md) | - | - | - | - | | **VWAP with Standard Deviation Bands** | [Vwapsd](../lib/channels/vwapsd/Vwapsd.md) | - | - | - | - | -| **Weighted Moving Average** | [Wma](../lib/trends/wma/wma.md) | ✔️ | ✔️ | ✔️ | ✔️ | +| **Weighted Moving Average** | [Wma](../lib/trends/wma/wma.md) | ✔️ | ✔️ | ✔️ | - | | **Wiener Filter** | Wiener | - | - | - | - | | **Williams %R** | [Willr](../lib/oscillators/willr/Willr.md) | ✔️ | ✔️ | ✔️ | ❔ | -| **Williams Accumulation/Distribution** | [Wad](../lib/volume/wad/Wad.md) | - | - | - | [⚠️](../lib/volume/wad/Wad.md#validation) | +| **Williams Accumulation/Distribution** | [Wad](../lib/volume/wad/Wad.md) | - | ✔️ | - | [⚠️](../lib/volume/wad/Wad.md#validation) | | **Williams Alligator** | Alligator | - | - | ✔️ | ❔ | -| **Williams Fractal** | Fractals | - | - | ✔️ | ❔ | -| **Woodie's Pivot Points** | Pivotwood | - | - | - | ❔ | +| **Williams Fractal** | [Fractals](../lib/reversals/fractals/Fractals.md) | - | - | ✔️ | ❔ | +| **Woodie's Pivot Points** | [Pivotwood](../lib/reversals/pivotwood/Pivotwood.md) | - | - | - | ❔ | | **Yang-Zhang Volatility** | Yzv | - | - | - | - | | **Yang-Zhang Volatility Adjusted MA** | [Yzvama](../lib/trends_IIR/yzvama/Yzvama.md) | - | - | - | - | | **Zero-Lag Double Exponential MA** | Zldema | - | - | - | - | -| **Zero-Lag Exponential Moving Average** | [Zlema](../lib/trends_IIR/zlema/Zlema.md) | - | ✔️ | - | ❔ | +| **Zero-Lag Exponential Moving Average** | [Zlema](../lib/trends_IIR/zlema/Zlema.md) | - | - | - | ❔ | | **Zero-Lag Triple Exponential MA** | Zltema | - | - | - | ❔ | | **ZigZag** | - | - | - | ✔️ | - | | **Z-score standardization** | Zscore | - | - | - | ✔️ Manual + Standardize cross-validation | @@ -314,8 +319,8 @@ No external reference exists. Implementation verified through unit tests, edge c | **Autocorrelation Function** | [Acf](../lib/statistics/acf/Acf.md) | - | - | - | - | | **Covariance** | [Covariance](../lib/statistics/covariance/Covariance.md) | - | - | - | - | | **Entropy (Shannon)** | [Entropy](../lib/statistics/entropy/Entropy.md) | - | - | - | - | -| **Geometric Mean** | [Geomean](../lib/statistics/geomean/Geomean.md) | ✔️ | - | - | - | -| **Harmonic Mean** | [Harmean](../lib/statistics/harmean/Harmean.md) | ✔️ | - | - | - | +| **Geometric Mean** | [Geomean](../lib/statistics/geomean/Geomean.md) | - | - | - | - | +| **Harmonic Mean** | [Harmean](../lib/statistics/harmean/Harmean.md) | - | - | - | - | | **Hurst Exponent** | [Hurst](../lib/statistics/hurst/Hurst.md) | - | - | - | - | | **Interquartile Range** | [Iqr](../lib/statistics/iqr/Iqr.md) | - | - | - | - | | **Granger Causality** | [Granger](../lib/statistics/granger/Granger.md) | - | - | - | - | diff --git a/lib/oscillators/ultosc/Ultosc.cs b/lib/oscillators/ultosc/Ultosc.cs index 4903b1df..f57ec7b9 100644 --- a/lib/oscillators/ultosc/Ultosc.cs +++ b/lib/oscillators/ultosc/Ultosc.cs @@ -195,12 +195,12 @@ public sealed class Ultosc : AbstractBase } // Calculate sums - double bpSum1 = _bp1.Sum(); - double bpSum2 = _bp2.Sum(); - double bpSum3 = _bp3.Sum(); - double trSum1 = _tr1.Sum(); - double trSum2 = _tr2.Sum(); - double trSum3 = _tr3.Sum(); + double bpSum1 = _bp1.Sum; + double bpSum2 = _bp2.Sum; + double bpSum3 = _bp3.Sum; + double trSum1 = _tr1.Sum; + double trSum2 = _tr2.Sum; + double trSum3 = _tr3.Sum; // Calculate averages (handle division by zero) const double epsilon = 1e-10; diff --git a/lib/reversals/_index.md b/lib/reversals/_index.md index 15c27fdc..eaeb26ef 100644 --- a/lib/reversals/_index.md +++ b/lib/reversals/_index.md @@ -8,13 +8,13 @@ Reversal indicators identify potential turning points where price may change dir | :--- | :--- | :--- | | [CHANDELIER](chandelier/Chandelier.md) | Chandelier Exit | ATR-based trailing stops hanging from HH/LL; dual ExitLong/ExitShort levels. | | [CKSTOP](ckstop/Ckstop.md) | Chande Kroll Stop | ATR-based adaptive trailing stops; dual StopLong/StopShort levels for trend detection. | -| FRACTALS | Williams Fractals | Five-bar pattern identifying local peaks/troughs; marks support/resistance levels. | -| PIVOT | Pivot Points (Classic) | Standard floor trader pivots with 7 levels (PP, R1-R3, S1-S3). | -| PIVOTCAM | Camarilla Pivot Points | Mean-reversion pivots with 9 levels; R3/S3 are key reversal zones. | -| PIVOTDEM | DeMark Pivot Points | Minimalist trend-following pivots with only 3 levels and conditional logic. | -| PIVOTEXT | Extended Traditional Pivots | Extended pivots with 11 levels (R1-R5, S1-S5) for volatile markets. | -| PIVOTFIB | Fibonacci Pivot Points | Fibonacci-ratio based pivots; Golden Ratio (61.8%) at R2/S2. | -| PIVOTWOOD | Woodie's Pivot Points | Weighted close pivots (2× close weight) for intraday trading. | -| PSAR | Parabolic Stop And Reverse | Trailing stop that accelerates with trend; SAR dots mark entry/exit signals. | -| SWINGS | Swing High/Low Detection | Identifies significant price reversals and swing points using configurable lookback. | +| [FRACTALS](fractals/Fractals.md) | Williams Fractals | Five-bar pattern identifying local peaks/troughs; marks support/resistance levels. | +| [PIVOT](pivot/Pivot.md) | Pivot Points (Classic) | Standard floor trader pivots with 7 levels (PP, R1-R3, S1-S3). | +| [PIVOTCAM](pivotcam/Pivotcam.md) | Camarilla Pivot Points | Mean-reversion pivots with 9 levels; R3/S3 are key reversal zones. | +| [PIVOTDEM](pivotdem/Pivotdem.md) | DeMark Pivot Points | Minimalist trend-following pivots with only 3 levels and conditional logic. | +| [PIVOTEXT](pivotext/Pivotext.md) | Extended Traditional Pivots | Extended pivots with 11 levels (R1-R5, S1-S5) for volatile markets. | +| [PIVOTFIB](pivotfib/Pivotfib.md) | Fibonacci Pivot Points | Fibonacci-ratio based pivots; Golden Ratio (61.8%) at R2/S2. | +| [PIVOTWOOD](pivotwood/Pivotwood.md) | Woodie's Pivot Points | Weighted close pivots (2× close weight) for intraday trading. | +| [PSAR](psar/Psar.md) | Parabolic Stop And Reverse | Trailing stop that accelerates with trend; SAR dots mark entry/exit signals. | +| [SWINGS](swings/Swings.md) | Swing High/Low Detection | Configurable-lookback pattern detector for swing highs/lows; dual SwingHigh/SwingLow. | | [TTM_SCALPER](ttm_scalper/TtmScalper.md) | TTM Scalper Alert | 3-bar pivot high/low detection for scalping entries. John Carter. | diff --git a/lib/reversals/fractals/Fractals.Quantower.Tests.cs b/lib/reversals/fractals/Fractals.Quantower.Tests.cs new file mode 100644 index 00000000..3be1cede --- /dev/null +++ b/lib/reversals/fractals/Fractals.Quantower.Tests.cs @@ -0,0 +1,127 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class FractalsIndicatorTests +{ + [Fact] + public void FractalsIndicator_Constructor_SetsDefaults() + { + var indicator = new FractalsIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("FRACTALS", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void FractalsIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new FractalsIndicator(); + + Assert.Equal(0, FractalsIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void FractalsIndicator_ShortName_IsFractals() + { + var indicator = new FractalsIndicator(); + indicator.Initialize(); + + Assert.Contains("FRACTALS", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void FractalsIndicator_SourceCodeLink_IsValid() + { + var indicator = new FractalsIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Fractals", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void FractalsIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new FractalsIndicator(); + + indicator.Initialize(); + + // After init, line series should exist (UpFractal + DownFractal) + Assert.Equal(2, indicator.LinesSeries.Count); + } + + [Fact] + public void FractalsIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new FractalsIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 20; i++) + { + // Create a pattern with varying highs/lows to generate fractals + double basePrice = 100 + (i % 5 == 2 ? 10 : 0); // spike every 5th bar at position 2 + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + double upFractal = indicator.LinesSeries[0].GetValue(0); + double downFractal = indicator.LinesSeries[1].GetValue(0); + + // Values should be set (either finite fractal or NaN=no fractal) + Assert.True(double.IsFinite(upFractal) || double.IsNaN(upFractal)); + Assert.True(double.IsFinite(downFractal) || double.IsNaN(downFractal)); + } + + [Fact] + public void FractalsIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new FractalsIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(10), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double upFractal = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(upFractal) || double.IsNaN(upFractal)); + } + + [Fact] + public void FractalsIndicator_TwoLineSeries_ArePresent() + { + var indicator = new FractalsIndicator(); + indicator.Initialize(); + + // UpFractal is index 0 (red), DownFractal is index 1 (green) + Assert.Equal(2, indicator.LinesSeries.Count); + Assert.Contains("Up", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("Down", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void FractalsIndicator_Description_IsSet() + { + var indicator = new FractalsIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("fractal", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/fractals/Fractals.Quantower.cs b/lib/reversals/fractals/Fractals.Quantower.cs new file mode 100644 index 00000000..25cecd82 --- /dev/null +++ b/lib/reversals/fractals/Fractals.Quantower.cs @@ -0,0 +1,52 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class FractalsIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Fractals _indicator = null!; + private readonly LineSeries _upFractalSeries; + private readonly LineSeries _downFractalSeries; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "FRACTALS"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/fractals/Fractals.cs"; + + public FractalsIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "FRACTALS - Williams Fractals"; + Description = "Five-bar pattern identifying local peaks (up fractals / resistance) and troughs (down fractals / support)."; + + _upFractalSeries = new LineSeries(name: "Up Fractal", color: Color.Red, width: 2, style: LineStyle.Dot); + _downFractalSeries = new LineSeries(name: "Down Fractal", color: Color.Green, width: 2, style: LineStyle.Dot); + + AddLineSeries(_upFractalSeries); + AddLineSeries(_downFractalSeries); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Fractals(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _upFractalSeries.SetValue(_indicator.UpFractal, _indicator.IsHot, ShowColdValues); + _downFractalSeries.SetValue(_indicator.DownFractal, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/fractals/Fractals.Tests.cs b/lib/reversals/fractals/Fractals.Tests.cs new file mode 100644 index 00000000..4b1d9649 --- /dev/null +++ b/lib/reversals/fractals/Fractals.Tests.cs @@ -0,0 +1,497 @@ +// FRACTALS Tests - Williams Fractals + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class FractalsConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var f = new Fractals(); + + Assert.Equal(5, f.WarmupPeriod); + Assert.Contains("Fractals", f.Name, StringComparison.Ordinal); + Assert.False(f.IsHot); + } + + [Fact] + public void Constructor_InitialState_NaN() + { + var f = new Fractals(); + + Assert.True(double.IsNaN(f.UpFractal)); + Assert.True(double.IsNaN(f.DownFractal)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class FractalsBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var f = new Fractals(); + // TBar(DateTime, open, high, low, close, volume) + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + TValue result = f.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var f = new Fractals(); + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + _ = f.Update(bar); + + Assert.True(double.IsFinite(f.Last.Value) || double.IsNaN(f.Last.Value)); + } + + [Fact] + public void Update_KnownUpFractal_Detected() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + // TBar(DateTime, open, high, low, close, volume) + // Pattern: bar[4] low, bar[3] medium, bar[2] HIGH peak, bar[1] medium, bar[0] low + // Bars fed in chronological order: bar[4] first, bar[0] last + _ = f.Update(new TBar(dt.AddMinutes(0), 97, 100, 95, 98, 1000), isNew: true); // bar[4]: high=100 + _ = f.Update(new TBar(dt.AddMinutes(1), 100, 103, 98, 101, 1000), isNew: true); // bar[3]: high=103 + _ = f.Update(new TBar(dt.AddMinutes(2), 104, 110, 92, 105, 1000), isNew: true); // bar[2]: high=110 (peak) + _ = f.Update(new TBar(dt.AddMinutes(3), 101, 104, 97, 102, 1000), isNew: true); // bar[1]: high=104 + _ = f.Update(new TBar(dt.AddMinutes(4), 98, 101, 96, 99, 1000), isNew: true); // bar[0]: high=101 + + // bar[2].High=110 > bar[0].High=101, bar[1].High=104, bar[3].High=103, bar[4].High=100 + Assert.Equal(110.0, f.UpFractal); + } + + [Fact] + public void Update_KnownDownFractal_Detected() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + // TBar(DateTime, open, high, low, close, volume) + // Pattern: bar[4] high lows, bar[3] medium, bar[2] LOW trough, bar[1] medium, bar[0] high lows + _ = f.Update(new TBar(dt.AddMinutes(0), 102, 105, 100, 103, 1000), isNew: true); // bar[4]: low=100 + _ = f.Update(new TBar(dt.AddMinutes(1), 100, 103, 98, 101, 1000), isNew: true); // bar[3]: low=98 + _ = f.Update(new TBar(dt.AddMinutes(2), 94, 102, 88, 95, 1000), isNew: true); // bar[2]: low=88 (trough) + _ = f.Update(new TBar(dt.AddMinutes(3), 100, 104, 97, 101, 1000), isNew: true); // bar[1]: low=97 + _ = f.Update(new TBar(dt.AddMinutes(4), 102, 106, 99, 103, 1000), isNew: true); // bar[0]: low=99 + + // bar[2].Low=88 < bar[0].Low=99, bar[1].Low=97, bar[3].Low=98, bar[4].Low=100 + Assert.Equal(88.0, f.DownFractal); + } + + [Fact] + public void Update_NoFractal_ReturnsNaN() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + // Monotone ascending - no fractal + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i * 5; + _ = f.Update(new TBar(dt.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000), isNew: true); + } + + Assert.True(double.IsNaN(f.UpFractal)); + } + + [Fact] + public void Name_ContainsFractals() + { + var f = new Fractals(); + Assert.Contains("Fractals", f.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class FractalsStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var f = new Fractals(); + + _ = f.Update(new TBar(DateTime.UtcNow, 100, 105, 95, 100, 1000), isNew: true); + var first = f.Last; + + _ = f.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 110, 100, 105, 1000), isNew: true); + var second = f.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + // Feed 4 bars + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + // New bar + _ = f.Update(new TBar(dt.AddMinutes(4), 98, 110, 85, 100, 1000), isNew: true); + + // Correct the bar (isNew=false with different values) + _ = f.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + + // Another correction with same values should produce same result + _ = f.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + var corrected1Up = f.UpFractal; + var corrected1Down = f.DownFractal; + + _ = f.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + var corrected2Up = f.UpFractal; + var corrected2Down = f.DownFractal; + + Assert.Equal(corrected1Up, corrected2Up); + Assert.Equal(corrected1Down, corrected2Down); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + _ = f.Update(new TBar(dt.AddMinutes(4), 105, 110, 90, 100, 1000), isNew: true); + + double[] upResults = new double[3]; + double[] downResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = f.Update(new TBar(dt.AddMinutes(4), 106, 112, 88, 102, 1000), isNew: false); + upResults[i] = f.UpFractal; + downResults[i] = f.DownFractal; + } + + Assert.Equal(upResults[0], upResults[1]); + Assert.Equal(upResults[1], upResults[2]); + Assert.Equal(downResults[0], downResults[1]); + Assert.Equal(downResults[1], downResults[2]); + } + + [Fact] + public void Reset_ClearsAllState() + { + var f = new Fractals(); + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(f.IsHot); + + f.Reset(); + + Assert.False(f.IsHot); + Assert.True(double.IsNaN(f.UpFractal)); + Assert.True(double.IsNaN(f.DownFractal)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class FractalsWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var f = new Fractals(); + + // Feed 4 bars -- should NOT be hot + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + Assert.False(f.IsHot, $"Should not be hot at bar {i}"); + } + + // Feed 5th bar -- should be hot + double p = 100.0 + 4; + _ = f.Update(new TBar(DateTime.UtcNow.AddMinutes(4), p, p + 2, p - 2, p + 1, 1000)); + Assert.True(f.IsHot, "Should be hot after 5 bars"); + } + + [Fact] + public void WarmupPeriod_Equals5() + { + var f = new Fractals(); + Assert.Equal(5, f.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class FractalsRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + // Feed valid bars + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(f.IsHot); + + // Feed NaN bar + _ = f.Update(new TBar(dt.AddMinutes(5), double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + // Should still be hot with valid fractal outputs (either NaN=no fractal or finite=fractal) + Assert.True(f.IsHot); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var f = new Fractals(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + _ = f.Update(new TBar(dt.AddMinutes(5), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0)); + + Assert.True(f.IsHot); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var f = new Fractals(); + + _ = f.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(f.Last.Value)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class FractalsConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Fractals(); + var streamUpResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamUpResults[i] = streaming.UpFractal; + } + + // Batch + var batchResults = Fractals.Batch(bars); + + int warmup = 4; // first 4 bars are NaN + for (int i = warmup; i < bars.Count; i++) + { + if (double.IsNaN(streamUpResults[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value)); + } + else + { + Assert.Equal(streamUpResults[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Fractals(); + var streamUpResults = new double[bars.Count]; + var streamDownResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamUpResults[i] = streaming.UpFractal; + streamDownResults[i] = streaming.DownFractal; + } + + // Span + var spanUp = new double[bars.Count]; + var spanDown = new double[bars.Count]; + Fractals.Batch(bars.HighValues, bars.LowValues, spanUp, spanDown); + + for (int i = 4; i < bars.Count; i++) + { + if (double.IsNaN(streamUpResults[i])) + { + Assert.True(double.IsNaN(spanUp[i]), $"Up fractal mismatch at {i}"); + } + else + { + Assert.Equal(streamUpResults[i], spanUp[i], precision: 10); + } + + if (double.IsNaN(streamDownResults[i])) + { + Assert.True(double.IsNaN(spanDown[i]), $"Down fractal mismatch at {i}"); + } + else + { + Assert.Equal(streamDownResults[i], spanDown[i], precision: 10); + } + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var f1 = new Fractals(); + var f2 = new Fractals(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double p = prices[i]; + _ = f1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), p, p, p, p, 0), isNew: true); + _ = f2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), p), isNew: true); + } + + Assert.Equal(f1.UpFractal, f2.UpFractal); + Assert.Equal(f1.DownFractal, f2.DownFractal); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class FractalsSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Fractals.Batch(new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Fractals.Batch(new double[10], new double[10], new double[5], new double[10])); + Assert.Equal("upOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_DownOutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Fractals.Batch(new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("downOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Fractals.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + Span.Empty, Span.Empty)); + Assert.Null(ex); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class FractalsEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var f = new Fractals(); + int fireCount = 0; + + f.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = f.Update(new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var f = new Fractals(); + int fireCount = 0; + + f.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = f.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class FractalsPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var f = new Fractals(); + f.Prime(bars); + + Assert.True(f.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var f = new Fractals(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => f.Prime(bars)); + Assert.Null(ex); + Assert.False(f.IsHot); + } +} diff --git a/lib/reversals/fractals/Fractals.Validation.Tests.cs b/lib/reversals/fractals/Fractals.Validation.Tests.cs new file mode 100644 index 00000000..16a7446a --- /dev/null +++ b/lib/reversals/fractals/Fractals.Validation.Tests.cs @@ -0,0 +1,268 @@ +// FRACTALS Validation Tests - Williams Fractals +// Cross-validated against Skender.Stock.Indicators GetFractal() +// +// Important alignment notes: +// - Skender reports fractal at the bar where the fractal occurs (bar[2] in our terms) +// - Our streaming indicator reports at the current bar (bar[0]) when detection happens +// - Therefore: our value at index i corresponds to Skender's value at index (i-2) +// - Skender naming: FractalBear = high point (resistance) = our UpFractal +// FractalBull = low point (support) = our DownFractal + +using Skender.Stock.Indicators; + +namespace QuanTAlib.Tests; + +public sealed class FractalsValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Cross-library: Skender UpFractal (= Skender FractalBear) ----------------- + + [Fact] + public void StreamingMatchesSkender_UpFractal() + { + var _data = new ValidationTestData(); + + // Skender: FractalBear = high point = our UpFractal + var skenderResults = _data.SkenderQuotes + .GetFractal() + .ToList(); + + // QuanTAlib streaming + var f = new Fractals(); + var ourUpValues = new double[_data.Bars.Count]; + for (int i = 0; i < _data.Bars.Count; i++) + { + _ = f.Update(_data.Bars[i], isNew: true); + ourUpValues[i] = f.UpFractal; + } + + // Compare with 2-bar offset: our value at i matches Skender at i-2 + int matched = 0; + for (int i = 4; i < _data.Bars.Count; i++) + { + int skenderIdx = i - 2; + if (skenderIdx < 0 || skenderIdx >= skenderResults.Count) + { + continue; + } + + decimal? skenderBear = skenderResults[skenderIdx].FractalBear; + bool skenderIsNull = !skenderBear.HasValue; + bool ourIsNaN = double.IsNaN(ourUpValues[i]); + + if (skenderIsNull && ourIsNaN) + { + matched++; + continue; + } + + if (!skenderIsNull && !ourIsNaN) + { + Assert.Equal((double)skenderBear!.Value, ourUpValues[i], precision: 6); + matched++; + } + } + + Assert.True(matched > 0, "Should have matched at least one warm value"); + _data.Dispose(); + } + + // -- Cross-library: Skender DownFractal (= Skender FractalBull) --------------- + + [Fact] + public void StreamingMatchesSkender_DownFractal() + { + var _data = new ValidationTestData(); + + // Skender: FractalBull = low point = our DownFractal + var skenderResults = _data.SkenderQuotes + .GetFractal() + .ToList(); + + // QuanTAlib streaming + var f = new Fractals(); + var ourDownValues = new double[_data.Bars.Count]; + for (int i = 0; i < _data.Bars.Count; i++) + { + _ = f.Update(_data.Bars[i], isNew: true); + ourDownValues[i] = f.DownFractal; + } + + // Compare with 2-bar offset: our value at i matches Skender at i-2 + int matched = 0; + for (int i = 4; i < _data.Bars.Count; i++) + { + int skenderIdx = i - 2; + if (skenderIdx < 0 || skenderIdx >= skenderResults.Count) + { + continue; + } + + decimal? skenderBull = skenderResults[skenderIdx].FractalBull; + bool skenderIsNull = !skenderBull.HasValue; + bool ourIsNaN = double.IsNaN(ourDownValues[i]); + + if (skenderIsNull && ourIsNaN) + { + matched++; + continue; + } + + if (!skenderIsNull && !ourIsNaN) + { + Assert.Equal((double)skenderBull!.Value, ourDownValues[i], precision: 6); + matched++; + } + } + + Assert.True(matched > 0, "Should have matched at least one warm value"); + _data.Dispose(); + } + + // -- Self-Consistency: Streaming == Batch -------------------------------------- + + [Fact] + public void StreamingMatchesBatch_UpFractal() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Fractals(); + var streamUp = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamUp[i] = streaming.UpFractal; + } + + // Batch + var batchResults = Fractals.Batch(bars); + + for (int i = 4; i < bars.Count; i++) + { + if (double.IsNaN(streamUp[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamUp[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span --------------------------------------- + + [Fact] + public void StreamingMatchesSpan_BothFractals() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Fractals(); + var streamUp = new double[bars.Count]; + var streamDown = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamUp[i] = streaming.UpFractal; + streamDown[i] = streaming.DownFractal; + } + + // Span + var spanUp = new double[bars.Count]; + var spanDown = new double[bars.Count]; + Fractals.Batch(bars.HighValues, bars.LowValues, spanUp, spanDown); + + for (int i = 4; i < bars.Count; i++) + { + if (double.IsNaN(streamUp[i])) + { + Assert.True(double.IsNaN(spanUp[i])); + } + else + { + Assert.Equal(streamUp[i], spanUp[i], precision: 10); + } + + if (double.IsNaN(streamDown[i])) + { + Assert.True(double.IsNaN(spanDown[i])); + } + else + { + Assert.Equal(streamDown[i], spanDown[i], precision: 10); + } + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var f1 = new Fractals(); + var f2 = new Fractals(); + + for (int i = 0; i < bars.Count; i++) + { + _ = f1.Update(bars[i], isNew: true); + _ = f2.Update(bars[i], isNew: true); + } + + Assert.Equal(f1.UpFractal, f2.UpFractal); + Assert.Equal(f1.DownFractal, f2.DownFractal); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Fractals.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- BatchDual Returns Both Fractals ------------------------------------------ + + [Fact] + public void BatchDual_ReturnsBothSeries() + { + var bars = CreateGbmBars(count: 100); + + var (upSeries, downSeries) = Fractals.BatchDual(bars); + + Assert.Equal(bars.Count, upSeries.Count); + Assert.Equal(bars.Count, downSeries.Count); + + // At least some fractals should be detected in 100 bars + bool hasUp = false; + bool hasDown = false; + for (int i = 0; i < upSeries.Count; i++) + { + if (double.IsFinite(upSeries[i].Value)) + { + hasUp = true; + } + if (double.IsFinite(downSeries[i].Value)) + { + hasDown = true; + } + } + + Assert.True(hasUp, "Should detect at least one up fractal in 100 bars"); + Assert.True(hasDown, "Should detect at least one down fractal in 100 bars"); + } +} diff --git a/lib/reversals/fractals/Fractals.cs b/lib/reversals/fractals/Fractals.cs new file mode 100644 index 00000000..cf9b365b --- /dev/null +++ b/lib/reversals/fractals/Fractals.cs @@ -0,0 +1,399 @@ +// FRACTALS: Williams Fractals +// Five-bar pattern identifying local highs (up fractals) and local lows (down fractals). +// Created by Larry Williams (1995, "Trading Chaos"). + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// FRACTALS: Williams Fractals +/// +/// +/// A retrospective 5-bar pattern detector. An up-fractal occurs when bar[2].High +/// is strictly greater than all four neighbors' highs. A down-fractal occurs when +/// bar[2].Low is strictly less than all four neighbors' lows. +/// +/// Calculation: +/// +/// UpFractal = high[2] > high[0] AND high[2] > high[1] AND high[2] > high[3] AND high[2] > high[4] +/// ? high[2] : NaN +/// DownFractal = low[2] < low[0] AND low[2] < low[1] AND low[2] < low[3] AND low[2] < low[4] +/// ? low[2] : NaN +/// +/// +/// Key characteristics: +/// - O(1) update via 5-element circular buffer (no deques needed) +/// - Outputs are naturally delayed by 2 bars (the fractal is at bar[2]) +/// - Dual output: UpFractal (bearish reversal / resistance) and DownFractal (bullish reversal / support) +/// - No configurable parameters -- fixed 5-bar pattern per Williams' definition +/// - WarmupPeriod = 5 (need exactly 5 bars to detect the first fractal) +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Fractals : ITValuePublisher +{ + private const int WindowSize = 5; + + // Circular buffers for highs and lows -- fixed 5 elements + private readonly double[] _hBuf; + private readonly double[] _lBuf; + + private int _count; + private long _index; + + [StructLayout(LayoutKind.Auto)] + private record struct State( + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Current up-fractal value (NaN if no up-fractal at current position). + public double UpFractal { get; private set; } + + /// Current down-fractal value (NaN if no down-fractal at current position). + public double DownFractal { get; private set; } + + /// Primary output value (UpFractal as TValue for overlay plotting). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= WindowSize; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Williams Fractals indicator. + /// + public Fractals() + { + _hBuf = new double[WindowSize]; + _lBuf = new double[WindowSize]; + + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN); + _ps = _s; + UpFractal = double.NaN; + DownFractal = double.NaN; + + Name = "Fractals"; + WarmupPeriod = WindowSize; + _barHandler = HandleBar; + } + + /// + /// Creates a Williams Fractals indicator chained to a TBarSeries source. + /// + public Fractals(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _index++; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs -- substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + UpFractal = double.NaN; + DownFractal = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Store in circular buffer + int bufIdx = (int)(_index % WindowSize); + _hBuf[bufIdx] = high; + _lBuf[bufIdx] = low; + + // Need at least 5 bars to evaluate a fractal + if (_count < WindowSize) + { + _s = s; + UpFractal = double.NaN; + DownFractal = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // The fractal candidate is at position [2] relative to current: + // Current bar = index 0 (newest), we look at bar[2] = 2 bars ago + // In circular buffer terms: + // bar[0] = bufIdx + // bar[1] = (bufIdx - 1 + 5) % 5 + // bar[2] = (bufIdx - 2 + 5) % 5 <- the candidate + // bar[3] = (bufIdx - 3 + 5) % 5 + // bar[4] = (bufIdx - 4 + 5) % 5 + + int i0 = bufIdx; + int i1 = (bufIdx + WindowSize - 1) % WindowSize; + int i2 = (bufIdx + WindowSize - 2) % WindowSize; // candidate + int i3 = (bufIdx + WindowSize - 3) % WindowSize; + int i4 = (bufIdx + WindowSize - 4) % WindowSize; + + double h2 = _hBuf[i2]; + double l2 = _lBuf[i2]; + + // Up fractal: high[2] > all four neighbors + UpFractal = (h2 > _hBuf[i0] && h2 > _hBuf[i1] && h2 > _hBuf[i3] && h2 > _hBuf[i4]) + ? h2 + : double.NaN; + + // Down fractal: low[2] < all four neighbors + DownFractal = (l2 < _lBuf[i0] && l2 < _lBuf[i1] && l2 < _lBuf[i3] && l2 < _lBuf[i4]) + ? l2 + : double.NaN; + + _s = s; + + Last = new TValue(input.Time, UpFractal); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(v), downBuf); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + Array.Clear(_hBuf); + Array.Clear(_lBuf); + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN); + _ps = _s; + UpFractal = double.NaN; + DownFractal = double.NaN; + Last = default; + } + + /// + /// Batch computation of Williams Fractals over span data. + /// Writes UpFractal values to and DownFractal values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + Span upOutput, + Span downOutput) + { + if (high.Length != low.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (upOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(upOutput)); + } + if (downOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(downOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // Fill first 4 bars with NaN (need 5 bars for first fractal) + int warmup = Math.Min(WindowSize - 1, len); + for (int i = 0; i < warmup; i++) + { + upOutput[i] = double.NaN; + downOutput[i] = double.NaN; + } + + // Evaluate fractals directly -- no streaming overhead needed + for (int i = WindowSize - 1; i < len; i++) + { + double h2 = high[i - 2]; + double l2 = low[i - 2]; + + upOutput[i] = (h2 > high[i] && h2 > high[i - 1] && h2 > high[i - 3] && h2 > high[i - 4]) + ? h2 + : double.NaN; + + downOutput[i] = (l2 < low[i] && l2 < low[i - 1] && l2 < low[i - 3] && l2 < low[i - 4]) + ? l2 + : double.NaN; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(v), downBuf); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation returning both UpFractal and DownFractal TSeries. + /// + public static (TSeries UpFractals, TSeries DownFractals) BatchDual(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return (new TSeries([], []), new TSeries([], [])); + } + + int len = source.Count; + var tUp = new List(len); + var vUp = new List(len); + var tDown = new List(len); + var vDown = new List(len); + + CollectionsMarshal.SetCount(tUp, len); + CollectionsMarshal.SetCount(vUp, len); + CollectionsMarshal.SetCount(tDown, len); + CollectionsMarshal.SetCount(vDown, len); + + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(vUp), CollectionsMarshal.AsSpan(vDown)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(tUp)); + source.Times.CopyTo(CollectionsMarshal.AsSpan(tDown)); + + return (new TSeries(tUp, vUp), new TSeries(tDown, vDown)); + } + + public static (TSeries Results, Fractals Indicator) Calculate(TBarSeries source) + { + var indicator = new Fractals(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/fractals/Fractals.md b/lib/reversals/fractals/Fractals.md new file mode 100644 index 00000000..bf13eac3 --- /dev/null +++ b/lib/reversals/fractals/Fractals.md @@ -0,0 +1,143 @@ +# FRACTALS: Williams Fractals + +> "Markets leave fingerprints at their turning points. Five bars is all it takes to read them." + +Williams Fractals detect local price extremes using a strict five-bar pattern: an Up Fractal marks a bar whose high exceeds the highs of the two bars before and after it; a Down Fractal marks a bar whose low undercuts the lows of the two bars before and after it. No parameters, no smoothing, no lag compensation. The pattern either exists or it does not. Developed by Bill Williams and published in *Trading Chaos* (1995). + +## Historical Context + +Bill Williams introduced fractals as part of his "Trading Chaos" methodology in the mid-1990s, drawing loosely on Benoit Mandelbrot's fractal geometry. The connection to actual mathematical fractals is tenuous at best. Mandelbrot's fractals describe self-similar structures across scales; Williams' fractals are fixed five-bar patterns. The naming was marketing, not mathematics. + +That said, the underlying observation is sound. Local extremes in price data correspond to temporary exhaustion of buying or selling pressure. A high that exceeds both its immediate predecessors and successors represents a point where bulls pushed price to a local maximum and then retreated. The five-bar window is the minimum viable detection size: two bars of context on each side of the pivot bar. + +Williams originally used fractals as entry signals within his Alligator trading system: buy above an Up Fractal, sell below a Down Fractal, but only when the Alligator's jaws/teeth/lips confirm the trend direction. In isolation, fractals produce many signals. Combined with trend filters, they become structural support/resistance markers. + +The indicator is closely related to Swing High/Low detection (which uses configurable lookback periods) and Fractal Chaos Bands (FCB, which draws upper/lower bands from the most recent fractal highs/lows). Where Swings offer flexibility via adjustable window size, Fractals commit to the five-bar pattern. Where FCB extends fractals into a channel overlay, Fractals provides the raw detection layer. + +Most implementations report the fractal on the center bar (bar[2] in a 0-indexed five-bar window). This creates an inherent two-bar reporting delay: you cannot confirm a fractal until two bars after the pivot bar completes. This QuanTAlib implementation reports the fractal value on the confirming bar (bar[0]), not the pivot bar, matching TradingView/PineScript convention. + +## Architecture and Physics + +The computation is a pure pattern match with no recursive state: + +### 1. Five-Bar Window + +The indicator maintains a five-element circular buffer for highs and a five-element circular buffer for lows. Each new bar shifts the window forward by one position. + +### 2. Up Fractal Detection + +An Up Fractal is detected when the center bar's high strictly exceeds all four neighbors: + +$$ \text{UpFractal}_t = \begin{cases} H_{t-2} & \text{if } H_{t-2} > H_{t-4} \text{ and } H_{t-2} > H_{t-3} \text{ and } H_{t-2} > H_{t-1} \text{ and } H_{t-2} > H_{t} \\ \text{NaN} & \text{otherwise} \end{cases} $$ + +Where $t$ is the current bar index and $H_{t-2}$ represents the high of the center (pivot) bar. + +### 3. Down Fractal Detection + +A Down Fractal is detected when the center bar's low is strictly less than all four neighbors: + +$$ \text{DownFractal}_t = \begin{cases} L_{t-2} & \text{if } L_{t-2} < L_{t-4} \text{ and } L_{t-2} < L_{t-3} \text{ and } L_{t-2} < L_{t-1} \text{ and } L_{t-2} < L_{t} \\ \text{NaN} & \text{otherwise} \end{cases} $$ + +### 4. Dual Output + +Both fractal values are available simultaneously. At any given bar, either, both, or neither fractal may be present. The primary output (`Last.Val`) defaults to `UpFractal` when present; the `DownFractal` is always accessible via the `DownFractal` property. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| UpFractal is not NaN | Local high identified two bars ago; potential resistance level | +| DownFractal is not NaN | Local low identified two bars ago; potential support level | +| Both present | Simultaneous peak and trough (possible inside bar patterns nearby) | +| Neither present | No five-bar pattern formed; trend continuation likely | +| Consecutive Up Fractals rising | Higher highs in local structure; bullish tendency | +| Consecutive Down Fractals rising | Higher lows in local structure; bullish tendency | + +## Mathematical Foundation + +### Parameters + +Williams Fractals has no configurable parameters. The five-bar window is fixed by definition. + +| Parameter | Value | Notes | +| :--- | :---: | :--- | +| Window size | 5 | Fixed; 2 bars before + pivot + 2 bars after | +| Comparison | Strict inequality | Pivot must strictly exceed (not equal) all neighbors | + +### Warmup Period + +$$ W = 5 $$ + +The indicator requires exactly 5 bars before producing valid output. Prior to that, both UpFractal and DownFractal output NaN. + +### Comparison to Configurable Swings + +Williams Fractals is equivalent to `Swings(period=2)` where the pivot bar must exceed exactly 2 bars on each side. Increasing the period to $n$ generalizes the pattern to $(2n+1)$-bar fractals, which is what the Swings indicator provides. The fixed five-bar pattern was chosen because it balances detection sensitivity against false positives in typical daily equity data. + +## Performance Profile + +### Implementation Design + +The implementation uses two five-element circular buffers (highs and lows) with index arithmetic. No sorting, no searching, no auxiliary data structures. The pattern check is four comparisons per fractal direction, evaluated only when the buffer is full. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(1) | Fixed 4 comparisons per direction; no loops | +| **Allocations** | 0 | Hot path is allocation-free; fixed-size buffers | +| **Warmup** | 5 bars | Minimum viable for the pattern | +| **Accuracy** | 10/10 | Exact match with Skender at precision 6 (decimal to double) | +| **Timeliness** | 5/10 | Inherent 2-bar reporting delay by definition | +| **Smoothness** | N/A | Binary signal; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks last-valid values for high, low, and close to handle NaN/Infinity input substitution. Bar correction via `isNew` flag enables same-timestamp rewrites without state corruption. + +### SIMD Applicability + +Not applicable. The five-bar window is too small (5 elements) to benefit from SIMD vectorization. The comparison logic is branchy by nature and cannot be meaningfully parallelized. The Batch span API processes multiple bars but each bar requires sequential buffer state. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | Matches streaming output | +| **Span** (`Batch(Span)`) | Passed | Matches streaming output | +| **Event** (`Pub` subscription) | Passed | Matches streaming output | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent | +| **Skender** | Passed | Matches via `GetFractal(2)` at precision 6 (decimal-to-double rounding) | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +Cross-validation with Skender.Stock.Indicators uses `GetFractal(windowSpan: 2)`. Skender names the outputs `FractalBear` (high-point fractal, our UpFractal) and `FractalBull` (low-point fractal, our DownFractal). Skender reports the fractal on the pivot bar itself; QuanTAlib reports on the confirming bar (2 bars later). Tolerance of $10^{-6}$ accounts for Skender's `decimal` to QuanTAlib's `double` conversion. + +## Common Pitfalls + +1. **Naming confusion with Skender.** Skender calls the high-point fractal `FractalBear` (because it signals a bearish turning point) and the low-point fractal `FractalBull` (bullish turning point). QuanTAlib uses `UpFractal` (high was up) and `DownFractal` (low was down). Same data, opposite naming convention. When cross-validating, map `UpFractal` to `FractalBear` and `DownFractal` to `FractalBull`. + +2. **Two-bar reporting offset.** QuanTAlib reports the fractal on the confirming bar (when all five bars of the pattern are available). Skender reports on the pivot bar itself (retroactively placing the value two bars back). When comparing arrays: `QuanTAlib[i]` corresponds to `Skender[i - 2]`. + +3. **Strict inequality is non-negotiable.** If the pivot bar's high equals a neighbor's high, no Up Fractal is detected. This is Williams' original definition and matches PineScript. Some implementations use `>=`, which produces more signals but deviates from the standard. + +4. **Most bars produce NaN.** In typical market data, fractals fire on roughly 15-25% of bars. The remaining 75-85% return NaN for both outputs. This is expected behavior, not a bug. + +5. **Not a standalone trading signal.** Williams designed fractals as a component of his Alligator system. Using fractals in isolation generates excessive signals. Pair with trend filters (Alligator, moving averages, ADX) to filter for signals aligned with the prevailing trend. + +6. **Decimal-to-double precision loss.** Skender returns `decimal?` values. Converting to `double` introduces rounding beyond the 15th significant digit. Validation tolerances of $10^{-6}$ accommodate this conversion. If you see differences only at the 7th decimal place, this is the cause. + +7. **Equal highs/lows in flat markets.** In low-volatility or range-bound conditions with many equal price levels, fractals become sparse. This is correct behavior: the strict inequality filter prevents false signals from price congestion zones. + +## References + +- Williams, B. M. (1995). *Trading Chaos: Applying Expert Techniques to Maximize Your Profits*. John Wiley and Sons. +- Williams, B. M. (2004). *Trading Chaos: Maximize Profits with Proven Technical Techniques* (2nd ed.). John Wiley and Sons. +- Mandelbrot, B. B. (1982). *The Fractal Geometry of Nature*. W. H. Freeman. +- TradingView PineScript Reference: [`ta.pivothigh()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivothigh), [`ta.pivotlow()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivotlow) +- Skender.Stock.Indicators: [`GetFractal()`](https://dotnet.stockindicators.dev/indicators/Fractal/) diff --git a/lib/reversals/pivot/Pivot.Quantower.Tests.cs b/lib/reversals/pivot/Pivot.Quantower.Tests.cs new file mode 100644 index 00000000..9dc0d15b --- /dev/null +++ b/lib/reversals/pivot/Pivot.Quantower.Tests.cs @@ -0,0 +1,124 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotIndicatorTests +{ + [Fact] + public void PivotIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOT", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotIndicator(); + + Assert.Equal(0, PivotIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotIndicator_ShortName_IsPivot() + { + var indicator = new PivotIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOT", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivot", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotIndicator(); + + indicator.Initialize(); + + // 7 line series: PP, R1, R2, R3, S1, S2, S3 + Assert.Equal(7, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotIndicator_SevenLineSeries_ArePresent() + { + var indicator = new PivotIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, R2=2, R3=3, S1=4, S2=5, S3=6 + Assert.Equal(7, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[4].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void PivotIndicator_Description_IsSet() + { + var indicator = new PivotIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("pivot", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivot/Pivot.Quantower.cs b/lib/reversals/pivot/Pivot.Quantower.cs new file mode 100644 index 00000000..b16ee460 --- /dev/null +++ b/lib/reversals/pivot/Pivot.Quantower.cs @@ -0,0 +1,72 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivot _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _r2Series; + private readonly LineSeries _r3Series; + private readonly LineSeries _s1Series; + private readonly LineSeries _s2Series; + private readonly LineSeries _s3Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOT"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivot/Pivot.cs"; + + public PivotIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOT - Classic Pivot Points"; + Description = "Floor trader pivot points: 7 support/resistance levels (PP, R1-R3, S1-S3) derived from previous bar's HLC."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1", color: Color.FromArgb(255, 128, 128), width: 1, style: LineStyle.Solid); + _r2Series = new LineSeries(name: "R2", color: Color.FromArgb(255, 80, 80), width: 1, style: LineStyle.Solid); + _r3Series = new LineSeries(name: "R3", color: Color.Red, width: 1, style: LineStyle.Dash); + _s1Series = new LineSeries(name: "S1", color: Color.FromArgb(128, 255, 128), width: 1, style: LineStyle.Solid); + _s2Series = new LineSeries(name: "S2", color: Color.FromArgb(80, 255, 80), width: 1, style: LineStyle.Solid); + _s3Series = new LineSeries(name: "S3", color: Color.Green, width: 1, style: LineStyle.Dash); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_r2Series); + AddLineSeries(_r3Series); + AddLineSeries(_s1Series); + AddLineSeries(_s2Series); + AddLineSeries(_s3Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivot(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _r2Series.SetValue(_indicator.R2, _indicator.IsHot, ShowColdValues); + _r3Series.SetValue(_indicator.R3, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + _s2Series.SetValue(_indicator.S2, _indicator.IsHot, ShowColdValues); + _s3Series.SetValue(_indicator.S3, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivot/Pivot.Tests.cs b/lib/reversals/pivot/Pivot.Tests.cs new file mode 100644 index 00000000..f664d9f9 --- /dev/null +++ b/lib/reversals/pivot/Pivot.Tests.cs @@ -0,0 +1,560 @@ +// PIVOT Tests - Classic Pivot Points (Floor Trader Pivots) + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class PivotConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var p = new Pivot(); + + Assert.Equal(2, p.WarmupPeriod); + Assert.Contains("Pivot", p.Name, StringComparison.Ordinal); + Assert.False(p.IsHot); + } + + [Fact] + public void Constructor_InitialState_AllNaN() + { + var p = new Pivot(); + + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class PivotBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var p = new Pivot(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + TValue result = p.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var p = new Pivot(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + _ = p.Update(bar); + + Assert.True(double.IsFinite(p.Last.Value) || double.IsNaN(p.Last.Value)); + } + + [Fact] + public void Update_KnownValues_CorrectPivotLevels() + { + // Given previous bar H=110, L=90, C=100 + // PP = (110+90+100)/3 = 100 + // R1 = 2*100-90 = 110, S1 = 2*100-110 = 90 + // R2 = 100+(110-90) = 120, S2 = 100-(110-90) = 80 + // R3 = 110+2*(100-90) = 130, S3 = 90-2*(110-100) = 70 + var p = new Pivot(); + var dt = DateTime.UtcNow; + + // First bar: stores HLC, no output yet + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + Assert.True(double.IsNaN(p.PP)); + + // Second bar: computes from first bar's HLC + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.Equal(100.0, p.PP, precision: 10); + Assert.Equal(110.0, p.R1, precision: 10); + Assert.Equal(90.0, p.S1, precision: 10); + Assert.Equal(120.0, p.R2, precision: 10); + Assert.Equal(80.0, p.S2, precision: 10); + Assert.Equal(130.0, p.R3, precision: 10); + Assert.Equal(70.0, p.S3, precision: 10); + } + + [Fact] + public void Update_SecondKnownValues_CorrectPivotLevels() + { + // Given previous bar H=120, L=100, C=115 + // PP = (120+100+115)/3 = 111.666... + // R1 = 2*111.666... - 100 = 123.333... + // S1 = 2*111.666... - 120 = 103.333... + // R2 = 111.666... + 20 = 131.666... + // S2 = 111.666... - 20 = 91.666... + // R3 = 120 + 2*(111.666...-100) = 143.333... + // S3 = 100 - 2*(120-111.666...) = 83.333... + var p = new Pivot(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 110, 120, 100, 115, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 115, 125, 105, 120, 1000), isNew: true); + + double expectedPP = (120.0 + 100.0 + 115.0) / 3.0; + Assert.Equal(expectedPP, p.PP, precision: 10); + Assert.Equal(2.0 * expectedPP - 100.0, p.R1, precision: 10); + Assert.Equal(2.0 * expectedPP - 120.0, p.S1, precision: 10); + Assert.Equal(expectedPP + 20.0, p.R2, precision: 10); + Assert.Equal(expectedPP - 20.0, p.S2, precision: 10); + Assert.Equal(120.0 + 2.0 * (expectedPP - 100.0), p.R3, precision: 10); + Assert.Equal(100.0 - 2.0 * (120.0 - expectedPP), p.S3, precision: 10); + } + + [Fact] + public void Update_LevelsHaveCorrectOrdering() + { + // For any normal bar, S3 < S2 < S1 < PP < R1 < R2 < R3 + var p = new Pivot(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.S3 < p.S2); + Assert.True(p.S2 < p.S1); + Assert.True(p.S1 < p.PP); + Assert.True(p.PP < p.R1); + Assert.True(p.R1 < p.R2); + Assert.True(p.R2 < p.R3); + } + + [Fact] + public void Name_ContainsPivot() + { + var p = new Pivot(); + Assert.Contains("Pivot", p.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class PivotStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var p = new Pivot(); + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000), isNew: true); + var first = p.Last; + + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + var second = p.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + // Two bars: first stores HLC, second computes + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + double ppBefore = p.PP; + + // Correct the second bar (isNew=false) + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 118, 92, 108, 1000), isNew: false); + + // PP should still be based on bar 0's HLC (H=110, L=90, C=100) + // since isNew=false doesn't change the "previous" bar + Assert.Equal(ppBefore, p.PP, precision: 10); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + double[] ppResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + ppResults[i] = p.PP; + } + + Assert.Equal(ppResults[0], ppResults[1]); + Assert.Equal(ppResults[1], ppResults[2]); + } + + [Fact] + public void IsNew_False_AllLevelsStable() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + double r1a = p.R1, s1a = p.S1, r2a = p.R2, s2a = p.S2, r3a = p.R3, s3a = p.S3; + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + Assert.Equal(r1a, p.R1); + Assert.Equal(s1a, p.S1); + Assert.Equal(r2a, p.R2); + Assert.Equal(s2a, p.S2); + Assert.Equal(r3a, p.R3); + Assert.Equal(s3a, p.S3); + } + + [Fact] + public void Reset_ClearsAllState() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(p.IsHot); + + p.Reset(); + + Assert.False(p.IsHot); + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class PivotWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var p = new Pivot(); + + // First bar - not hot + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + Assert.False(p.IsHot, "Should not be hot after 1 bar"); + + // Second bar - should be hot + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000)); + Assert.True(p.IsHot, "Should be hot after 2 bars"); + } + + [Fact] + public void WarmupPeriod_Equals2() + { + var p = new Pivot(); + Assert.Equal(2, p.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class PivotRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + // Feed valid bars + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.IsHot); + + // Feed NaN bar + _ = p.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + + // Should still be hot and produce valid pivots from last-valid values + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var p = new Pivot(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(2), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0), + isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var p = new Pivot(); + + _ = p.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(p.Last.Value)); + Assert.True(double.IsNaN(p.PP)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class PivotConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivot(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivot.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivot(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivot.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i]), $"PP mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 200); + + // Streaming + var streaming = new Pivot(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + + Pivot.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(bPP[i])); continue; } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var p1 = new Pivot(); + var p2 = new Pivot(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double pr = prices[i]; + _ = p1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), pr, pr, pr, pr, 0), isNew: true); + _ = p2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), pr), isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class PivotSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Pivot.Batch(new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivot.Batch(new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("ppOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Pivot.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, Span.Empty)); + Assert.Null(ex); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivot.BatchAll(new double[10], new double[10], new double[10], + new double[10], new double[5], new double[10], + new double[10], new double[10], new double[10], new double[10])); + Assert.Equal("r1Out", ex.ParamName); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class PivotEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var p = new Pivot(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var p = new Pivot(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class PivotPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var p = new Pivot(); + p.Prime(bars); + + Assert.True(p.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var p = new Pivot(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => p.Prime(bars)); + Assert.Null(ex); + Assert.False(p.IsHot); + } +} diff --git a/lib/reversals/pivot/Pivot.Validation.Tests.cs b/lib/reversals/pivot/Pivot.Validation.Tests.cs new file mode 100644 index 00000000..3bc89d7f --- /dev/null +++ b/lib/reversals/pivot/Pivot.Validation.Tests.cs @@ -0,0 +1,260 @@ +// PIVOT Validation Tests - Classic Pivot Points (Floor Trader Pivots) +// Self-consistency validation across all API modes. +// +// Note: Skender.Stock.Indicators ToPivotPoints() uses calendar-window periods +// (Day/Week/Month) which is conceptually different from our bar-to-bar implementation. +// Direct cross-validation is not applicable. TA-Lib, Tulip, and Ooples do not +// implement floor trader pivot points either. +// Validation focuses on mathematical correctness and mode consistency. + +namespace QuanTAlib.Tests; + +public sealed class PivotValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Mathematical Correctness ------------------------------------------------- + + [Fact] + public void MathCorrectness_PP_EqualsHLC_Over3() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivot(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) // Need previous bar + { + double prevH = bars[i - 1].High; + double prevL = bars[i - 1].Low; + double prevC = bars[i - 1].Close; + double expectedPP = (prevH + prevL + prevC) / 3.0; + + Assert.Equal(expectedPP, p.PP, precision: 10); + } + } + } + + [Fact] + public void MathCorrectness_AllLevels_MatchFormula() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivot(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + Assert.Equal(pp, p.PP, precision: 10); + Assert.Equal(2.0 * pp - pL, p.R1, precision: 10); + Assert.Equal(2.0 * pp - pH, p.S1, precision: 10); + Assert.Equal(pp + range, p.R2, precision: 10); + Assert.Equal(pp - range, p.S2, precision: 10); + Assert.Equal(pH + 2.0 * (pp - pL), p.R3, precision: 10); + Assert.Equal(pL - 2.0 * (pH - pp), p.S3, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Batch -------------------------------------- + + [Fact] + public void StreamingMatchesBatch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivot(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivot.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), + $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span --------------------------------------- + + [Fact] + public void StreamingMatchesSpan_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivot(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivot.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i])); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == BatchAll (all 7 levels) -------------------- + + [Fact] + public void StreamingMatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 300); + + // Streaming + var streaming = new Pivot(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + + Pivot.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(bPP[i])); + continue; + } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var p1 = new Pivot(); + var p2 = new Pivot(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p1.Update(bars[i], isNew: true); + _ = p2.Update(bars[i], isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + Assert.Equal(p1.R2, p2.R2); + Assert.Equal(p1.S2, p2.S2); + Assert.Equal(p1.R3, p2.R3); + Assert.Equal(p1.S3, p2.S3); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Pivot.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- Level Ordering Invariant -------------------------------------------------- + + [Fact] + public void AllBars_LevelsOrdered_S3_S2_S1_PP_R1_R2_R3() + { + var bars = CreateGbmBars(count: 200); + var p = new Pivot(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (p.IsHot) + { + Assert.True(p.S3 <= p.S2, $"S3 > S2 at bar {i}"); + Assert.True(p.S2 <= p.S1, $"S2 > S1 at bar {i}"); + Assert.True(p.S1 <= p.PP, $"S1 > PP at bar {i}"); + Assert.True(p.PP <= p.R1, $"PP > R1 at bar {i}"); + Assert.True(p.R1 <= p.R2, $"R1 > R2 at bar {i}"); + Assert.True(p.R2 <= p.R3, $"R2 > R3 at bar {i}"); + } + } + } +} diff --git a/lib/reversals/pivot/Pivot.cs b/lib/reversals/pivot/Pivot.cs new file mode 100644 index 00000000..979d4076 --- /dev/null +++ b/lib/reversals/pivot/Pivot.cs @@ -0,0 +1,424 @@ +// PIVOT: Classic Pivot Points (Floor Trader Pivots) +// Calculates 7 support/resistance levels from previous bar's HLC. +// Standard floor trader formula used since the 1930s. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOT: Classic Pivot Points (Floor Trader Pivots) +/// +/// +/// Computes 7 horizontal support/resistance levels from the previous bar's +/// high, low, and close. The central pivot point (PP) is the arithmetic mean +/// of HLC; resistance (R1-R3) and support (S1-S3) levels are derived from +/// PP and the prior bar's range. +/// +/// Calculation (using previous bar's H, L, C): +/// +/// PP = (H + L + C) / 3 +/// R1 = 2 * PP - L S1 = 2 * PP - H +/// R2 = PP + (H - L) S2 = PP - (H - L) +/// R3 = H + 2 * (PP - L) S3 = L - 2 * (H - PP) +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's HLC +/// - 7 outputs: PP, R1, R2, R3, S1, S2, S3 +/// - WarmupPeriod = 2 (need previous bar's HLC) +/// - No configurable parameters +/// - Levels remain constant until a new bar arrives +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivot : ITValuePublisher +{ + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: (prevH + prevL + prevC) / 3 + public double PP { get; private set; } + + /// Resistance 1: 2 * PP - prevL + public double R1 { get; private set; } + + /// Resistance 2: PP + (prevH - prevL) + public double R2 { get; private set; } + + /// Resistance 3: prevH + 2 * (PP - prevL) + public double R3 { get; private set; } + + /// Support 1: 2 * PP - prevH + public double S1 { get; private set; } + + /// Support 2: PP - (prevH - prevL) + public double S2 { get; private set; } + + /// Support 3: prevL - 2 * (prevH - PP) + public double S3 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Classic Pivot Points indicator. + /// + public Pivot() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + + Name = "Pivot"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates a Classic Pivot Points indicator chained to a TBarSeries source. + /// + public Pivot(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store HLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute pivot levels from PREVIOUS bar's HLC + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + PP = pp; + R1 = Math.FusedMultiplyAdd(2.0, pp, -pL); // 2*pp - pL + S1 = Math.FusedMultiplyAdd(2.0, pp, -pH); // 2*pp - pH + R2 = pp + range; // pp + (pH - pL) + S2 = pp - range; // pp - (pH - pL) + R3 = Math.FusedMultiplyAdd(2.0, pp - pL, pH); // pH + 2*(pp - pL) + S3 = Math.FusedMultiplyAdd(-2.0, pH - pp, pL); // pL - 2*(pH - pp) + + // Store current bar's HLC as "previous" for next bar + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + } + + /// + /// Batch computation of Classic Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute from previous bar's HLC + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + ppOutput[i] = (pH + pL + pC) / 3.0; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 7 Classic Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out, + Span r2Out, + Span s2Out, + Span r3Out, + Span s3Out) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + if (r2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r2Out)); } + if (s2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s2Out)); } + if (r3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r3Out)); } + if (s3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s3Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + r2Out[0] = double.NaN; + s2Out[0] = double.NaN; + r3Out[0] = double.NaN; + s3Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + ppOut[i] = pp; + r1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pL); + s1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pH); + r2Out[i] = pp + range; + s2Out[i] = pp - range; + r3Out[i] = Math.FusedMultiplyAdd(2.0, pp - pL, pH); + s3Out[i] = Math.FusedMultiplyAdd(-2.0, pH - pp, pL); + } + } + + public static (TSeries Results, Pivot Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivot(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivot/Pivot.md b/lib/reversals/pivot/Pivot.md new file mode 100644 index 00000000..bae45b11 --- /dev/null +++ b/lib/reversals/pivot/Pivot.md @@ -0,0 +1,173 @@ +# PIVOT: Classic Pivot Points (Floor Trader Pivots) + +> "The floor traders had it figured out before the quants arrived. Three numbers from yesterday's bar, seven levels for today. No optimization, no curve fitting, no excuses." + +Classic Pivot Points calculate seven horizontal support and resistance levels from the previous bar's high, low, and close. The central pivot point (PP) is the arithmetic mean of HLC; three resistance levels (R1-R3) and three support levels (S1-S3) are derived from PP and the prior bar's range. The formula has been in continuous use since the 1930s among floor traders at commodity exchanges. Zero parameters, zero lag, zero ambiguity. + +## Historical Context + +Floor traders at the Chicago Board of Trade developed pivot points as a pre-session planning tool. Before electronic markets, traders needed levels they could calculate by hand during the commute to work. The HLC average was the simplest possible summary of the prior session; the support and resistance levels followed from elementary arithmetic on the range. + +The method spread through oral tradition among pit traders for decades before appearing in print. Neil Weintraub documented the technique in *Tricks of the Floor Trader* (1996), and John Person expanded on it in *A Complete Guide to Technical Trading Tactics* (2004). By then, pivot points were already embedded in virtually every trading terminal. + +The beauty of the formula is its universality. Unlike moving averages (which require choosing a period), Bollinger Bands (which require choosing a standard deviation multiplier), or Fibonacci retracements (which require choosing swing points), pivot points have no parameters. Every trader using the same prior bar's HLC computes identical levels. This made them natural Schelling points: self-fulfilling prophecies where enough participants watched the same numbers to create genuine support and resistance. + +Several variants emerged over the decades: Woodie (weighting close double), Camarilla (using range fractions), DeMark (conditional on open-close relationship), and Fibonacci (applying golden ratios to range). This implementation covers the original "Standard" or "Floor Trader" formulation only. The variants differ in the derivation of R/S levels but share the core concept of previous-bar HLC as input. + +## Architecture and Physics + +### 1. Previous Bar's HLC + +The indicator stores the high ($H$), low ($L$), and close ($C$) of the most recently completed bar. On each new bar, these stored values become the basis for computing the current bar's pivot levels, and the new bar's HLC replaces the stored values for the next computation. + +### 2. Central Pivot Point (PP) + +$$PP = \frac{H_{prev} + L_{prev} + C_{prev}}{3}$$ + +The arithmetic mean of the previous bar's high, low, and close. This represents the "fair value" or equilibrium price implied by the prior period's trading range. + +### 3. Support and Resistance Levels + +First-level support and resistance reflect the previous range from PP: + +$$R_1 = 2 \cdot PP - L_{prev}$$ + +$$S_1 = 2 \cdot PP - H_{prev}$$ + +Second-level support and resistance add the full range: + +$$R_2 = PP + (H_{prev} - L_{prev})$$ + +$$S_2 = PP - (H_{prev} - L_{prev})$$ + +Third-level support and resistance extend from the extremes: + +$$R_3 = H_{prev} + 2 \cdot (PP - L_{prev})$$ + +$$S_3 = L_{prev} - 2 \cdot (H_{prev} - PP)$$ + +### 4. Level Ordering Invariant + +For any bar where $H_{prev} > L_{prev}$ (non-degenerate range): + +$$S_3 < S_2 < S_1 < PP < R_1 < R_2 < R_3$$ + +When $H_{prev} = L_{prev}$ (zero range), all seven levels collapse to a single value equal to the close. + +### 5. Seven Outputs + +All seven levels are computed simultaneously and remain constant until a new bar arrives. The primary output (`Last.Val`) returns PP; individual properties expose all seven levels. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| Price above PP | Bullish bias for current bar | +| Price below PP | Bearish bias for current bar | +| Price tests R1 | First resistance; potential reversal or breakout level | +| Price tests S1 | First support; potential bounce or breakdown level | +| Price reaches R3/S3 | Extended move; third-level tests rare, indicate strong momentum | +| All levels cluster tightly | Low volatility prior bar; expect range expansion | +| Wide level spacing | High volatility prior bar; wider intraday range expected | + +## Mathematical Foundation + +### Parameters + +Classic Pivot Points has no configurable parameters. The formula is fixed by definition. + +| Parameter | Value | Notes | +| :--- | :---: | :--- | +| Inputs | H, L, C | Previous bar's high, low, close | +| Outputs | 7 | PP, R1, R2, R3, S1, S2, S3 | +| Parameters | 0 | No tuning required | + +### Warmup Period + +$$W = 2$$ + +The indicator requires 2 bars: the first bar provides HLC for storage; the second bar triggers computation from the stored values. Prior to warmup completion, all outputs are NaN. + +### Derivation Notes + +The R1/S1 formulas can be rewritten to show the geometric relationship: + +$$R_1 = PP + (PP - L_{prev}) \quad \text{(PP reflected above its distance to the low)}$$ + +$$S_1 = PP - (H_{prev} - PP) \quad \text{(PP reflected below its distance to the high)}$$ + +R2/S2 add the full range to/from PP. R3/S3 extend beyond the previous extremes by the distance from PP to the opposite extreme. + +## Performance Profile + +### Implementation Design + +Pure arithmetic with no loops, no buffers, no auxiliary data structures. Each `Update` call performs 3 divisions (via the single division in PP), 6 multiplications/additions, and 3 comparisons for NaN validation. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(1) | Fixed arithmetic; no iteration | +| **Allocations** | 0 | Hot path is allocation-free | +| **Warmup** | 2 bars | Minimum possible | +| **Accuracy** | 10/10 | Exact arithmetic; no approximation | +| **Timeliness** | 10/10 | No lag; levels available immediately on new bar | +| **Smoothness** | N/A | Discrete levels; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks previous bar's HLC and last-valid values for NaN/Infinity input substitution. Bar correction via `isNew` flag enables same-timestamp rewrites without state corruption. + +### SIMD Applicability + +Not applicable for streaming (single bar computation). The `BatchAll` span API processes multiple bars but the per-bar computation is too simple (7 arithmetic operations) to benefit from vectorization overhead. The `Batch` span API for PP-only output could theoretically use SIMD but the division-heavy computation and small operation count make the benefit negligible. + +### FMA Usage + +The implementation uses `Math.FusedMultiplyAdd` for R1, S1, R3, and S3 computations, providing both a minor precision benefit (single rounding instead of two) and potential performance benefit on hardware with FMA support. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | PP values match streaming | +| **Span** (`Batch(Span)`) | Passed | PP values match streaming | +| **BatchAll** (`BatchAll(Span)`) | Passed | All 7 levels match streaming | +| **Event** (`Pub` subscription) | Passed | Fires on every update | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent; level ordering invariant holds | +| **Skender** | N/A | Uses calendar-window periods (Day/Week/Month); conceptually different | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +Skender.Stock.Indicators provides `ToPivotPoints()` which computes pivot levels over calendar windows (daily, weekly, monthly). This is a fundamentally different granularity from our bar-to-bar implementation. Skender summarizes an entire period's HLC into one set of pivots for the next period; our implementation uses each individual bar's HLC for the subsequent bar's levels. Both approaches are valid floor trader pivot calculations at different time scales. Direct numerical comparison is not meaningful. + +Mathematical correctness is validated by computing expected values from the formula for each bar and comparing against the indicator output at precision 10. + +## Common Pitfalls + +1. **First bar returns NaN.** The indicator needs the previous bar's HLC to compute pivots. The first bar stores HLC but produces no output. This is correct behavior, not a bug. `WarmupPeriod = 2`. + +2. **Levels are constant within a bar.** Pivot levels do not change as the current bar's price moves. They change only when a new bar starts (providing new "previous" HLC). Multiple `isNew=false` corrections on the current bar do not alter the pivot levels because they are derived from the already-stored previous bar. + +3. **Skender comparison is not applicable.** Skender.Stock.Indicators uses calendar-period windows (Day/Week/Month). Our implementation is bar-to-bar. Comparing numbers directly will produce mismatches that are not errors. + +4. **Zero-range bars collapse all levels.** When $H_{prev} = L_{prev}$ (a doji or single-print bar), all seven levels equal the close. This is mathematically correct but may surprise users expecting spread levels. + +5. **PP is not the midpoint of High and Low.** PP includes the close, weighting it equally with high and low. For bars where close is near the high, PP shifts upward; near the low, PP shifts downward. This is intentional and reflects the market's closing sentiment. + +6. **TValue input uses price as all four OHLC fields.** When updating with `TValue` instead of `TBar`, the single price value is used for open, high, low, and close. This means $range = 0$ and all levels collapse to the price. Use `TBar` input for meaningful pivot calculations. + +7. **NaN/Infinity inputs use last-valid substitution.** If any of H, L, C is NaN or Infinity, the last valid value for that field is substituted. This prevents NaN propagation but may produce stale levels. Monitor data quality upstream. + +## References + +- Weintraub, N. (1996). *Tricks of the Floor Trader*. McGraw-Hill. +- Person, J. L. (2004). *A Complete Guide to Technical Trading Tactics: How to Profit Using Pivot Points, Candlesticks & Other Indicators*. John Wiley and Sons. +- Wikipedia: [Pivot point (technical analysis)](https://en.wikipedia.org/wiki/Pivot_point_(technical_analysis)) +- TradingView PineScript Reference: [`ta.pivothigh()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivothigh), [`ta.pivotlow()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivotlow) diff --git a/lib/reversals/pivotcam/Pivotcam.Quantower.Tests.cs b/lib/reversals/pivotcam/Pivotcam.Quantower.Tests.cs new file mode 100644 index 00000000..6eded56b --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.Quantower.Tests.cs @@ -0,0 +1,126 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotcamIndicatorTests +{ + [Fact] + public void PivotcamIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotcamIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOTCAM", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotcamIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotcamIndicator(); + + Assert.Equal(0, PivotcamIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotcamIndicator_ShortName_IsPivotcam() + { + var indicator = new PivotcamIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOTCAM", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotcamIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotcamIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivotcam", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotcamIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotcamIndicator(); + + indicator.Initialize(); + + // 9 line series: PP, R1, R2, R3, R4, S1, S2, S3, S4 + Assert.Equal(9, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotcamIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotcamIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotcamIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotcamIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotcamIndicator_NineLineSeries_ArePresent() + { + var indicator = new PivotcamIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, R2=2, R3=3, R4=4, S1=5, S2=6, S3=7, S4=8 + Assert.Equal(9, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R4", indicator.LinesSeries[4].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[5].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S4", indicator.LinesSeries[8].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void PivotcamIndicator_Description_IsSet() + { + var indicator = new PivotcamIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("Camarilla", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivotcam/Pivotcam.Quantower.cs b/lib/reversals/pivotcam/Pivotcam.Quantower.cs new file mode 100644 index 00000000..99a4c244 --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.Quantower.cs @@ -0,0 +1,80 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotcamIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivotcam _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _r2Series; + private readonly LineSeries _r3Series; + private readonly LineSeries _r4Series; + private readonly LineSeries _s1Series; + private readonly LineSeries _s2Series; + private readonly LineSeries _s3Series; + private readonly LineSeries _s4Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOTCAM"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivotcam/Pivotcam.cs"; + + public PivotcamIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOTCAM - Camarilla Pivot Points"; + Description = "Camarilla pivot points: 9 support/resistance levels (PP, R1-R4, S1-S4) derived from previous bar's HLC."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1", color: Color.FromArgb(255, 160, 160), width: 1, style: LineStyle.Solid); + _r2Series = new LineSeries(name: "R2", color: Color.FromArgb(255, 128, 128), width: 1, style: LineStyle.Solid); + _r3Series = new LineSeries(name: "R3", color: Color.FromArgb(255, 80, 80), width: 1, style: LineStyle.Solid); + _r4Series = new LineSeries(name: "R4", color: Color.Red, width: 1, style: LineStyle.Dash); + _s1Series = new LineSeries(name: "S1", color: Color.FromArgb(160, 255, 160), width: 1, style: LineStyle.Solid); + _s2Series = new LineSeries(name: "S2", color: Color.FromArgb(128, 255, 128), width: 1, style: LineStyle.Solid); + _s3Series = new LineSeries(name: "S3", color: Color.FromArgb(80, 255, 80), width: 1, style: LineStyle.Solid); + _s4Series = new LineSeries(name: "S4", color: Color.Green, width: 1, style: LineStyle.Dash); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_r2Series); + AddLineSeries(_r3Series); + AddLineSeries(_r4Series); + AddLineSeries(_s1Series); + AddLineSeries(_s2Series); + AddLineSeries(_s3Series); + AddLineSeries(_s4Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivotcam(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _r2Series.SetValue(_indicator.R2, _indicator.IsHot, ShowColdValues); + _r3Series.SetValue(_indicator.R3, _indicator.IsHot, ShowColdValues); + _r4Series.SetValue(_indicator.R4, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + _s2Series.SetValue(_indicator.S2, _indicator.IsHot, ShowColdValues); + _s3Series.SetValue(_indicator.S3, _indicator.IsHot, ShowColdValues); + _s4Series.SetValue(_indicator.S4, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivotcam/Pivotcam.Tests.cs b/lib/reversals/pivotcam/Pivotcam.Tests.cs new file mode 100644 index 00000000..4608230d --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.Tests.cs @@ -0,0 +1,582 @@ +// PIVOTCAM Tests - Camarilla Pivot Points + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class PivotcamConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var p = new Pivotcam(); + + Assert.Equal(2, p.WarmupPeriod); + Assert.Contains("Pivotcam", p.Name, StringComparison.Ordinal); + Assert.False(p.IsHot); + } + + [Fact] + public void Constructor_InitialState_AllNaN() + { + var p = new Pivotcam(); + + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.R4)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + Assert.True(double.IsNaN(p.S4)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class PivotcamBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var p = new Pivotcam(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + TValue result = p.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var p = new Pivotcam(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + _ = p.Update(bar); + + Assert.True(double.IsFinite(p.Last.Value) || double.IsNaN(p.Last.Value)); + } + + [Fact] + public void Update_KnownValues_CorrectPivotLevels() + { + // Given previous bar H=110, L=90, C=100, range=20 + // PP = (110+90+100)/3 = 100 + // R1 = 100 + 20*1.0833/12 ≈ 101.8055 + // S1 = 100 - 20*1.0833/12 ≈ 98.1945 + // R2 = 100 + 20*1.1666/12 ≈ 101.9443 + // S2 = 100 - 20*1.1666/12 ≈ 98.0557 + // R3 = 100 + 20*1.25/12 ≈ 102.0833 + // S3 = 100 - 20*1.25/12 ≈ 97.9167 + // R4 = 100 + 20*1.5/12 = 102.5 + // S4 = 100 - 20*1.5/12 = 97.5 + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + // First bar: stores HLC, no output yet + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + Assert.True(double.IsNaN(p.PP)); + + // Second bar: computes from first bar's HLC + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + double range = 20.0; + double pC = 100.0; + Assert.Equal(100.0, p.PP, precision: 10); + Assert.Equal(pC + range * 1.0833 / 12.0, p.R1, precision: 4); + Assert.Equal(pC - range * 1.0833 / 12.0, p.S1, precision: 4); + Assert.Equal(pC + range * 1.1666 / 12.0, p.R2, precision: 4); + Assert.Equal(pC - range * 1.1666 / 12.0, p.S2, precision: 4); + Assert.Equal(pC + range * 1.25 / 12.0, p.R3, precision: 4); + Assert.Equal(pC - range * 1.25 / 12.0, p.S3, precision: 4); + Assert.Equal(pC + range * 1.5 / 12.0, p.R4, precision: 4); + Assert.Equal(pC - range * 1.5 / 12.0, p.S4, precision: 4); + } + + [Fact] + public void Update_SecondKnownValues_CorrectPivotLevels() + { + // Given previous bar H=120, L=100, C=115, range=20 + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 110, 120, 100, 115, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 115, 125, 105, 120, 1000), isNew: true); + + double expectedPP = (120.0 + 100.0 + 115.0) / 3.0; + double range = 20.0; + double pC = 115.0; + + Assert.Equal(expectedPP, p.PP, precision: 10); + Assert.Equal(pC + range * 1.0833 / 12.0, p.R1, precision: 4); + Assert.Equal(pC - range * 1.0833 / 12.0, p.S1, precision: 4); + Assert.Equal(pC + range * 1.1666 / 12.0, p.R2, precision: 4); + Assert.Equal(pC - range * 1.1666 / 12.0, p.S2, precision: 4); + Assert.Equal(pC + range * 1.25 / 12.0, p.R3, precision: 4); + Assert.Equal(pC - range * 1.25 / 12.0, p.S3, precision: 4); + Assert.Equal(pC + range * 1.5 / 12.0, p.R4, precision: 4); + Assert.Equal(pC - range * 1.5 / 12.0, p.S4, precision: 4); + } + + [Fact] + public void Update_LevelsHaveCorrectOrdering() + { + // For any normal bar: S4 < S3 < S2 < S1 < PP < R1 < R2 < R3 < R4 + // (when close is near the range center, PP may be above or below close, + // but S/R levels are always ordered by their multiplier magnitude) + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.S4 < p.S3); + Assert.True(p.S3 < p.S2); + Assert.True(p.S2 < p.S1); + Assert.True(p.R1 < p.R2); + Assert.True(p.R2 < p.R3); + Assert.True(p.R3 < p.R4); + } + + [Fact] + public void Name_ContainsPivotcam() + { + var p = new Pivotcam(); + Assert.Contains("Pivotcam", p.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class PivotcamStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var p = new Pivotcam(); + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000), isNew: true); + var first = p.Last; + + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + var second = p.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + double ppBefore = p.PP; + + // Correct the second bar (isNew=false) + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 118, 92, 108, 1000), isNew: false); + + // PP should still be based on bar 0's HLC (H=110, L=90, C=100) + Assert.Equal(ppBefore, p.PP, precision: 10); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + double[] ppResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + ppResults[i] = p.PP; + } + + Assert.Equal(ppResults[0], ppResults[1]); + Assert.Equal(ppResults[1], ppResults[2]); + } + + [Fact] + public void IsNew_False_AllLevelsStable() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + double r1a = p.R1, s1a = p.S1, r2a = p.R2, s2a = p.S2; + double r3a = p.R3, s3a = p.S3, r4a = p.R4, s4a = p.S4; + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + Assert.Equal(r1a, p.R1); + Assert.Equal(s1a, p.S1); + Assert.Equal(r2a, p.R2); + Assert.Equal(s2a, p.S2); + Assert.Equal(r3a, p.R3); + Assert.Equal(s3a, p.S3); + Assert.Equal(r4a, p.R4); + Assert.Equal(s4a, p.S4); + } + + [Fact] + public void Reset_ClearsAllState() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(p.IsHot); + + p.Reset(); + + Assert.False(p.IsHot); + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.R4)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + Assert.True(double.IsNaN(p.S4)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class PivotcamWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var p = new Pivotcam(); + + // First bar - not hot + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + Assert.False(p.IsHot, "Should not be hot after 1 bar"); + + // Second bar - should be hot + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000)); + Assert.True(p.IsHot, "Should be hot after 2 bars"); + } + + [Fact] + public void WarmupPeriod_Equals2() + { + var p = new Pivotcam(); + Assert.Equal(2, p.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class PivotcamRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + // Feed valid bars + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.IsHot); + + // Feed NaN bar + _ = p.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var p = new Pivotcam(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(2), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0), + isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var p = new Pivotcam(); + + _ = p.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(p.Last.Value)); + Assert.True(double.IsNaN(p.PP)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class PivotcamConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotcam(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotcam.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotcam(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotcam.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i]), $"PP mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 200); + + // Streaming + var streaming = new Pivotcam(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + var sR4 = new double[bars.Count]; + var sS4 = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + sR4[i] = streaming.R4; + sS4[i] = streaming.S4; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + var bR4 = new double[bars.Count]; + var bS4 = new double[bars.Count]; + + Pivotcam.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3, bR4, bS4); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(bPP[i])); continue; } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + Assert.Equal(sR4[i], bR4[i], precision: 10); + Assert.Equal(sS4[i], bS4[i], precision: 10); + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var p1 = new Pivotcam(); + var p2 = new Pivotcam(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double pr = prices[i]; + _ = p1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), pr, pr, pr, pr, 0), isNew: true); + _ = p2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), pr), isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class PivotcamSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Pivotcam.Batch(new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotcam.Batch(new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("ppOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Pivotcam.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, Span.Empty)); + Assert.Null(ex); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotcam.BatchAll(new double[10], new double[10], new double[10], + new double[10], new double[5], new double[10], + new double[10], new double[10], new double[10], + new double[10], new double[10], new double[10])); + Assert.Equal("r1Out", ex.ParamName); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class PivotcamEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var p = new Pivotcam(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var p = new Pivotcam(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class PivotcamPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var p = new Pivotcam(); + p.Prime(bars); + + Assert.True(p.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var p = new Pivotcam(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => p.Prime(bars)); + Assert.Null(ex); + Assert.False(p.IsHot); + } +} diff --git a/lib/reversals/pivotcam/Pivotcam.Validation.Tests.cs b/lib/reversals/pivotcam/Pivotcam.Validation.Tests.cs new file mode 100644 index 00000000..0358dbf8 --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.Validation.Tests.cs @@ -0,0 +1,274 @@ +// PIVOTCAM Validation Tests - Camarilla Pivot Points +// Self-consistency validation across all API modes. +// +// Note: No external library (Skender, TA-Lib, Tulip, Ooples) implements +// Camarilla Pivot Points. Validation focuses on mathematical correctness +// and mode consistency. + +namespace QuanTAlib.Tests; + +public sealed class PivotcamValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Mathematical Correctness ------------------------------------------------- + + [Fact] + public void MathCorrectness_PP_EqualsHLC_Over3() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotcam(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double prevH = bars[i - 1].High; + double prevL = bars[i - 1].Low; + double prevC = bars[i - 1].Close; + double expectedPP = (prevH + prevL + prevC) / 3.0; + + Assert.Equal(expectedPP, p.PP, precision: 10); + } + } + } + + [Fact] + public void MathCorrectness_AllLevels_MatchCamarillaFormula() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotcam(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + Assert.Equal(pp, p.PP, precision: 10); + Assert.Equal(pC + range * 1.0833 / 12.0, p.R1, precision: 4); + Assert.Equal(pC - range * 1.0833 / 12.0, p.S1, precision: 4); + Assert.Equal(pC + range * 1.1666 / 12.0, p.R2, precision: 4); + Assert.Equal(pC - range * 1.1666 / 12.0, p.S2, precision: 4); + Assert.Equal(pC + range * 1.25 / 12.0, p.R3, precision: 4); + Assert.Equal(pC - range * 1.25 / 12.0, p.S3, precision: 4); + Assert.Equal(pC + range * 1.5 / 12.0, p.R4, precision: 4); + Assert.Equal(pC - range * 1.5 / 12.0, p.S4, precision: 4); + } + } + } + + // -- Self-Consistency: Streaming == Batch -------------------------------------- + + [Fact] + public void StreamingMatchesBatch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotcam(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotcam.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), + $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span --------------------------------------- + + [Fact] + public void StreamingMatchesSpan_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotcam(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotcam.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i])); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == BatchAll (all 9 levels) -------------------- + + [Fact] + public void StreamingMatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 300); + + // Streaming + var streaming = new Pivotcam(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + var sR4 = new double[bars.Count]; + var sS4 = new double[bars.Count]; + + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + sR4[i] = streaming.R4; + sS4[i] = streaming.S4; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + var bR4 = new double[bars.Count]; + var bS4 = new double[bars.Count]; + + Pivotcam.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3, bR4, bS4); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(bPP[i])); + continue; + } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + Assert.Equal(sR4[i], bR4[i], precision: 10); + Assert.Equal(sS4[i], bS4[i], precision: 10); + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var p1 = new Pivotcam(); + var p2 = new Pivotcam(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p1.Update(bars[i], isNew: true); + _ = p2.Update(bars[i], isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + Assert.Equal(p1.R2, p2.R2); + Assert.Equal(p1.S2, p2.S2); + Assert.Equal(p1.R3, p2.R3); + Assert.Equal(p1.S3, p2.S3); + Assert.Equal(p1.R4, p2.R4); + Assert.Equal(p1.S4, p2.S4); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Pivotcam.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- Level Ordering Invariant -------------------------------------------------- + + [Fact] + public void AllBars_SupportResistanceLevelsOrdered() + { + // Camarilla: S4 < S3 < S2 < S1 < Close-based < R1 < R2 < R3 < R4 + // Note: PP is based on HLC/3 and may be above or below close, + // but resistance levels are always ordered R1 < R2 < R3 < R4 + // and support levels are always ordered S4 < S3 < S2 < S1 + var bars = CreateGbmBars(count: 200); + var p = new Pivotcam(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (p.IsHot) + { + Assert.True(p.S4 <= p.S3, $"S4 > S3 at bar {i}"); + Assert.True(p.S3 <= p.S2, $"S3 > S2 at bar {i}"); + Assert.True(p.S2 <= p.S1, $"S2 > S1 at bar {i}"); + Assert.True(p.R1 <= p.R2, $"R1 > R2 at bar {i}"); + Assert.True(p.R2 <= p.R3, $"R2 > R3 at bar {i}"); + Assert.True(p.R3 <= p.R4, $"R3 > R4 at bar {i}"); + } + } + } +} diff --git a/lib/reversals/pivotcam/Pivotcam.cs b/lib/reversals/pivotcam/Pivotcam.cs new file mode 100644 index 00000000..34989645 --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.cs @@ -0,0 +1,443 @@ +// PIVOTCAM: Camarilla Pivot Points +// Calculates 9 support/resistance levels from previous bar's HLC. +// Close-centric formula with range-fraction multipliers. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOTCAM: Camarilla Pivot Points +/// +/// +/// Computes 9 horizontal support/resistance levels from the previous bar's +/// high, low, and close. The central pivot point (PP) is the arithmetic mean +/// of HLC; resistance and support levels are derived from the close plus/minus +/// fractions of the previous range using the Camarilla equation. +/// +/// Calculation (using previous bar's H, L, C): +/// +/// PP = (H + L + C) / 3 +/// R1 = C + range × 1.0833 / 12 S1 = C − range × 1.0833 / 12 +/// R2 = C + range × 1.1666 / 12 S2 = C − range × 1.1666 / 12 +/// R3 = C + range × 1.2500 / 12 S3 = C − range × 1.2500 / 12 +/// R4 = C + range × 1.5000 / 12 S4 = C − range × 1.5000 / 12 +/// where range = H − L +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's HLC +/// - 9 outputs: PP, R1, R2, R3, R4, S1, S2, S3, S4 +/// - WarmupPeriod = 2 (need previous bar's HLC) +/// - No configurable parameters +/// - Close-centric: levels radiate symmetrically from close, not PP +/// - R3/S3 are the primary mean-reversion levels +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivotcam : ITValuePublisher +{ + // Camarilla multiplier constants: numerator / 12.0 + private const double C1 = 1.0833 / 12.0; // ≈ 0.090275 + private const double C2 = 1.1666 / 12.0; // ≈ 0.097217 + private const double C3 = 1.2500 / 12.0; // ≈ 0.104167 + private const double C4 = 1.5000 / 12.0; // = 0.125 + + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: (prevH + prevL + prevC) / 3 + public double PP { get; private set; } + + /// Resistance 1: prevC + range × 1.0833 / 12 + public double R1 { get; private set; } + + /// Resistance 2: prevC + range × 1.1666 / 12 + public double R2 { get; private set; } + + /// Resistance 3: prevC + range × 1.2500 / 12 + public double R3 { get; private set; } + + /// Resistance 4: prevC + range × 1.5000 / 12 + public double R4 { get; private set; } + + /// Support 1: prevC − range × 1.0833 / 12 + public double S1 { get; private set; } + + /// Support 2: prevC − range × 1.1666 / 12 + public double S2 { get; private set; } + + /// Support 3: prevC − range × 1.2500 / 12 + public double S3 { get; private set; } + + /// Support 4: prevC − range × 1.5000 / 12 + public double S4 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Camarilla Pivot Points indicator. + /// + public Pivotcam() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + SetAllNaN(); + + Name = "Pivotcam"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates a Camarilla Pivot Points indicator chained to a TBarSeries source. + /// + public Pivotcam(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store HLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute Camarilla pivot levels from PREVIOUS bar's HLC + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + double range = pH - pL; + + PP = (pH + pL + pC) / 3.0; + R1 = Math.FusedMultiplyAdd(range, C1, pC); // pC + range * C1 + S1 = Math.FusedMultiplyAdd(-range, C1, pC); // pC - range * C1 + R2 = Math.FusedMultiplyAdd(range, C2, pC); // pC + range * C2 + S2 = Math.FusedMultiplyAdd(-range, C2, pC); // pC - range * C2 + R3 = Math.FusedMultiplyAdd(range, C3, pC); // pC + range * C3 + S3 = Math.FusedMultiplyAdd(-range, C3, pC); // pC - range * C3 + R4 = Math.FusedMultiplyAdd(range, C4, pC); // pC + range * C4 + S4 = Math.FusedMultiplyAdd(-range, C4, pC); // pC - range * C4 + + // Store current bar's HLC as "previous" for next bar + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + R4 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + S4 = double.NaN; + } + + /// + /// Batch computation of Camarilla Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute from previous bar's HLC + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + ppOutput[i] = (pH + pL + pC) / 3.0; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 9 Camarilla Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out, + Span r2Out, + Span s2Out, + Span r3Out, + Span s3Out, + Span r4Out, + Span s4Out) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + if (r2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r2Out)); } + if (s2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s2Out)); } + if (r3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r3Out)); } + if (s3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s3Out)); } + if (r4Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r4Out)); } + if (s4Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s4Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + r2Out[0] = double.NaN; + s2Out[0] = double.NaN; + r3Out[0] = double.NaN; + s3Out[0] = double.NaN; + r4Out[0] = double.NaN; + s4Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double range = pH - pL; + + ppOut[i] = (pH + pL + pC) / 3.0; + r1Out[i] = Math.FusedMultiplyAdd(range, C1, pC); + s1Out[i] = Math.FusedMultiplyAdd(-range, C1, pC); + r2Out[i] = Math.FusedMultiplyAdd(range, C2, pC); + s2Out[i] = Math.FusedMultiplyAdd(-range, C2, pC); + r3Out[i] = Math.FusedMultiplyAdd(range, C3, pC); + s3Out[i] = Math.FusedMultiplyAdd(-range, C3, pC); + r4Out[i] = Math.FusedMultiplyAdd(range, C4, pC); + s4Out[i] = Math.FusedMultiplyAdd(-range, C4, pC); + } + } + + public static (TSeries Results, Pivotcam Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivotcam(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivotcam/Pivotcam.md b/lib/reversals/pivotcam/Pivotcam.md new file mode 100644 index 00000000..62f027ff --- /dev/null +++ b/lib/reversals/pivotcam/Pivotcam.md @@ -0,0 +1,182 @@ +# PIVOTCAM: Camarilla Pivot Points + +> "The Camarilla trader does not care where the market opens. The trader cares how far price strays from yesterday's close, and whether it returns." + +Camarilla Pivot Points calculate nine horizontal support and resistance levels from the previous bar's high, low, and close. Unlike classic floor trader pivots that radiate from the PP midpoint, Camarilla levels radiate symmetrically from the previous close using fixed fractions of the prior range. The R3/S3 levels serve as the primary mean-reversion zone; breakouts beyond R4/S4 signal trend continuation. Developed by Nick Scott in 1989 using bond market data, the equation was originally distributed as a shareware Excel plugin. + +## Historical Context + +Nick Scott developed the Camarilla Equation in 1989 while trading bonds. The name references the "Camarilla" (a group of secret advisors), reflecting Scott's belief that institutional traders used similar range-fraction calculations internally. The formula was originally sold as a $50 Excel plug-in, one of the earliest examples of retail algorithmic trading tools. + +The key insight behind Camarilla differs from classic pivots in a fundamental way. Classic pivots treat the prior bar's PP (mean of HLC) as the center of gravity. Camarilla treats the prior close as the center, reasoning that the close represents the market's final consensus. Support and resistance levels are then computed as fixed fractions of the prior range added to or subtracted from the close. + +The specific multiplier constants (1.0833/12, 1.1666/12, 1.25/12, 1.5/12) were derived empirically from bond market data. They produce levels that are tighter than classic pivots, making them more suited to mean-reversion strategies. The R3/S3 levels correspond roughly to the boundaries where intraday price tends to reverse; R4/S4 mark breakout thresholds. + +Classic pivot variants (Woodie, DeMark, Fibonacci) all derive levels from the PP center. Camarilla stands alone in using the close as the anchor point, which makes it inherently different from all other pivot formulations. This close-centric design means Camarilla levels shift when the close changes even if the range stays constant, while classic pivots shift when the range midpoint changes. + +## Architecture and Physics + +### 1. Previous Bar's HLC + +The indicator stores the high ($H$), low ($L$), and close ($C$) of the most recently completed bar. On each new bar, these stored values become the basis for computing the current bar's pivot levels, and the new bar's HLC replaces them for the next computation. + +### 2. Central Pivot Point (PP) + +$$PP = \frac{H_{prev} + L_{prev} + C_{prev}}{3}$$ + +The arithmetic mean of the previous bar's HLC. Identical to classic pivot PP. Included for reference and compatibility, though Camarilla levels do not derive from PP. + +### 3. Range + +$$range = H_{prev} - L_{prev}$$ + +The previous bar's trading range, used as the scaling factor for all support and resistance levels. + +### 4. Camarilla Multiplier Constants + +| Level | Numerator | Divisor | Effective Multiplier | +| :--- | :---: | :---: | :---: | +| R1 / S1 | 1.0833 | 12 | 0.090275 | +| R2 / S2 | 1.1666 | 12 | 0.097217 | +| R3 / S3 | 1.2500 | 12 | 0.104167 | +| R4 / S4 | 1.5000 | 12 | 0.125000 | + +### 5. Resistance Levels + +$$R_1 = C_{prev} + range \times \frac{1.0833}{12}$$ + +$$R_2 = C_{prev} + range \times \frac{1.1666}{12}$$ + +$$R_3 = C_{prev} + range \times \frac{1.2500}{12}$$ + +$$R_4 = C_{prev} + range \times \frac{1.5000}{12}$$ + +### 6. Support Levels + +$$S_1 = C_{prev} - range \times \frac{1.0833}{12}$$ + +$$S_2 = C_{prev} - range \times \frac{1.1666}{12}$$ + +$$S_3 = C_{prev} - range \times \frac{1.2500}{12}$$ + +$$S_4 = C_{prev} - range \times \frac{1.5000}{12}$$ + +### 7. Level Ordering Invariant + +For any bar where $H_{prev} > L_{prev}$ (non-degenerate range): + +$$S_4 < S_3 < S_2 < S_1 < C_{prev} < R_1 < R_2 < R_3 < R_4$$ + +Note that PP may be above or below $C_{prev}$ depending on whether the close was nearer the high or low. The support and resistance levels are always ordered by their multiplier magnitude. + +### 8. Nine Outputs + +All nine levels are computed simultaneously and remain constant until a new bar arrives. The primary output (`Last.Val`) returns PP; individual properties expose all nine levels. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| Price between S1 and R1 | Normal range; no signal | +| Price tests R3 from below | Mean-reversion short entry zone | +| Price tests S3 from above | Mean-reversion long entry zone | +| Price breaks above R4 | Bullish breakout; trend continuation | +| Price breaks below S4 | Bearish breakdown; trend continuation | +| R3/S3 rejected | High-probability reversal setup | +| Levels cluster tightly | Low volatility prior bar; expect range expansion | + +## Mathematical Foundation + +### Parameters + +Camarilla Pivot Points has no configurable parameters. The formula constants are fixed by definition. + +| Parameter | Value | Notes | +| :--- | :---: | :--- | +| Inputs | H, L, C | Previous bar's high, low, close | +| Outputs | 9 | PP, R1, R2, R3, R4, S1, S2, S3, S4 | +| Parameters | 0 | No tuning required | + +### Warmup Period + +$$W = 2$$ + +The indicator requires 2 bars: the first bar provides HLC for storage; the second bar triggers computation from the stored values. Prior to warmup completion, all outputs are NaN. + +### Derivation Notes + +The Camarilla multipliers are not derived from mathematical first principles. They are empirical constants fitted to bond market data by Nick Scott. The progression (1.0833, 1.1666, 1.25, 1.5) divided by 12 creates four concentric bands around the close. The spacing between levels is not uniform: the gap between R3/S3 and R4/S4 is wider than between R1/S1 and R2/S2, creating a natural "breakout zone" at the extremes. + +All levels use `Math.FusedMultiplyAdd` for the `close + range * constant` computation, providing single-rounding precision. + +## Performance Profile + +### Implementation Design + +Pure arithmetic with no loops, no buffers, no auxiliary data structures. Each `Update` call performs 1 division (PP), 8 FMA operations, and 3 comparisons for NaN validation. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(1) | Fixed arithmetic; no iteration | +| **Allocations** | 0 | Hot path is allocation-free | +| **Warmup** | 2 bars | Minimum possible | +| **Accuracy** | 10/10 | Exact arithmetic via FMA; no approximation | +| **Timeliness** | 10/10 | No lag; levels available immediately on new bar | +| **Smoothness** | N/A | Discrete levels; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks previous bar's HLC and last-valid values for NaN/Infinity input substitution. Bar correction via `isNew` flag enables same-timestamp rewrites without state corruption. + +### SIMD Applicability + +Not applicable for streaming (single bar computation). The `BatchAll` span API processes multiple bars but the per-bar computation is too simple (9 arithmetic operations) to benefit from vectorization overhead. + +### FMA Usage + +The implementation uses `Math.FusedMultiplyAdd` for all eight R/S level computations (R1-R4, S1-S4), providing both precision benefit (single rounding instead of two) and potential performance benefit on hardware with FMA support. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | PP values match streaming | +| **Span** (`Batch(Span)`) | Passed | PP values match streaming | +| **BatchAll** (`BatchAll(Span)`) | Passed | All 9 levels match streaming | +| **Event** (`Pub` subscription) | Passed | Fires on every update | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent; level ordering invariant holds | +| **Skender** | N/A | Does not implement Camarilla variant | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +Mathematical correctness is validated by computing expected values from the Camarilla formula for each bar and comparing against the indicator output. + +## Common Pitfalls + +1. **First bar returns NaN.** The indicator needs the previous bar's HLC to compute pivots. The first bar stores HLC but produces no output. This is correct behavior, not a bug. `WarmupPeriod = 2`. + +2. **Levels radiate from close, not PP.** Unlike classic pivots where R/S levels are derived from PP, Camarilla levels are offsets from the previous close. PP is provided for reference only. Do not expect R1 = f(PP) as in classic pivots. + +3. **R3/S3 are the primary trading levels.** The Camarilla system treats R3/S3 as mean-reversion entry zones and R4/S4 as breakout confirmation. R1/S1 and R2/S2 are intermediate levels with less trading significance in the original system. + +4. **Zero-range bars collapse all levels to the close.** When $H_{prev} = L_{prev}$ (doji or single-print bar), all eight R/S levels equal the close, and PP equals the close. This is mathematically correct. + +5. **PP may be above R1 or below S1.** Because R/S levels radiate from the close but PP is based on HLC/3, unusual close positions can cause PP to fall outside the S1-R1 range. This is not a bug; it reflects the different anchoring of classic PP vs. Camarilla levels. + +6. **TValue input collapses range to zero.** When updating with `TValue` instead of `TBar`, all OHLC fields equal the single price, producing zero range and all levels equal to that price. Use `TBar` input for meaningful pivot calculations. + +7. **Multiplier constants are empirical, not mathematical.** The 1.0833/12, 1.1666/12, 1.25/12, 1.5/12 values are fitted constants from bond market data. They have no derivation from probability theory or signal processing. Their effectiveness depends on market microstructure alignment. + +## References + +- Scott, N. (1989). *The Camarilla Equation*. Originally distributed as Excel shareware. +- Person, J. L. (2004). *A Complete Guide to Technical Trading Tactics: How to Profit Using Pivot Points, Candlesticks & Other Indicators*. John Wiley and Sons. +- Wikipedia: [Pivot point (technical analysis)](https://en.wikipedia.org/wiki/Pivot_point_(technical_analysis)) +- TradingView: [Camarilla Pivot Points](https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/) diff --git a/lib/reversals/pivotdem/Pivotdem.Quantower.Tests.cs b/lib/reversals/pivotdem/Pivotdem.Quantower.Tests.cs new file mode 100644 index 00000000..b9d73265 --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.Quantower.Tests.cs @@ -0,0 +1,124 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotdemIndicatorTests +{ + [Fact] + public void PivotdemIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotdemIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOTDEM", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotdemIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotdemIndicator(); + + Assert.Equal(0, PivotdemIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotdemIndicator_ShortName_IsPivotdem() + { + var indicator = new PivotdemIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOTDEM", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotdemIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotdemIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivotdem", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotdemIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotdemIndicator(); + + indicator.Initialize(); + + // 3 line series: PP, R1, S1 + Assert.Equal(3, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotdemIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotdemIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotdemIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotdemIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotdemIndicator_ThreeLineSeries_ArePresent() + { + var indicator = new PivotdemIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, S1=2 — DeMark only produces 3 levels + Assert.Equal(3, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[2].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void PivotdemIndicator_Description_IsSet() + { + var indicator = new PivotdemIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("pivot", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivotdem/Pivotdem.Quantower.cs b/lib/reversals/pivotdem/Pivotdem.Quantower.cs new file mode 100644 index 00000000..9805e9ba --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.Quantower.cs @@ -0,0 +1,56 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotdemIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivotdem _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _s1Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOTDEM"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivotdem/Pivotdem.cs"; + + public PivotdemIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOTDEM - DeMark Pivot Points"; + Description = "DeMark pivot points: 3 support/resistance levels (PP, R1, S1) with conditional logic based on Open vs Close."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1", color: Color.FromArgb(255, 128, 128), width: 1, style: LineStyle.Solid); + _s1Series = new LineSeries(name: "S1", color: Color.FromArgb(128, 255, 128), width: 1, style: LineStyle.Solid); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_s1Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivotdem(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivotdem/Pivotdem.Tests.cs b/lib/reversals/pivotdem/Pivotdem.Tests.cs new file mode 100644 index 00000000..808dc664 --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.Tests.cs @@ -0,0 +1,533 @@ +// PIVOTDEM Tests - DeMark Pivot Points + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class PivotdemConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var p = new Pivotdem(); + + Assert.Equal(2, p.WarmupPeriod); + Assert.Contains("Pivotdem", p.Name, StringComparison.Ordinal); + Assert.False(p.IsHot); + } + + [Fact] + public void Constructor_InitialState_AllNaN() + { + var p = new Pivotdem(); + + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.S1)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class PivotdemBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var p = new Pivotdem(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + TValue result = p.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var p = new Pivotdem(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + _ = p.Update(bar); + + Assert.True(double.IsFinite(p.Last.Value) || double.IsNaN(p.Last.Value)); + } + + [Fact] + public void Update_KnownValues_Bullish_CorrectLevels() + { + // Previous bar: O=100, H=110, L=90, C=105 => C>O (bullish) + // x = 2*H + L + C = 220 + 90 + 105 = 415 + // PP = 415/4 = 103.75 + // R1 = 415/2 - L = 207.5 - 90 = 117.5 + // S1 = 415/2 - H = 207.5 - 110 = 97.5 + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + // First bar: stores OHLC, no output yet + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + Assert.True(double.IsNaN(p.PP)); + + // Second bar: computes from first bar's OHLC + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 108, 1000), isNew: true); + + Assert.Equal(103.75, p.PP, precision: 10); + Assert.Equal(117.5, p.R1, precision: 10); + Assert.Equal(97.5, p.S1, precision: 10); + } + + [Fact] + public void Update_KnownValues_Bearish_CorrectLevels() + { + // Previous bar: O=105, H=110, L=90, C=100 => C C==O (doji) + // x = H + L + 2*C = 110 + 90 + 200 = 400 + // PP = 400/4 = 100 + // R1 = 400/2 - L = 200 - 90 = 110 + // S1 = 400/2 - H = 200 - 110 = 90 + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 100, 115, 95, 102, 1000), isNew: true); + + Assert.Equal(100.0, p.PP, precision: 10); + Assert.Equal(110.0, p.R1, precision: 10); + Assert.Equal(90.0, p.S1, precision: 10); + } + + [Fact] + public void Update_LevelsHaveCorrectOrdering() + { + // For a normal bar with H > L, S1 < PP < R1 + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.S1 < p.PP); + Assert.True(p.PP < p.R1); + } + + [Fact] + public void Name_ContainsPivotdem() + { + var p = new Pivotdem(); + Assert.Contains("Pivotdem", p.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class PivotdemStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var p = new Pivotdem(); + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000), isNew: true); + var first = p.Last; + + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + var second = p.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + // Two bars: first stores OHLC, second computes + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + double ppBefore = p.PP; + + // Correct the second bar (isNew=false) + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 118, 92, 108, 1000), isNew: false); + + // PP should still be based on bar 0's OHLC + Assert.Equal(ppBefore, p.PP, precision: 10); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + double[] ppResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + ppResults[i] = p.PP; + } + + Assert.Equal(ppResults[0], ppResults[1]); + Assert.Equal(ppResults[1], ppResults[2]); + } + + [Fact] + public void IsNew_False_AllLevelsStable() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + double r1a = p.R1, s1a = p.S1; + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + Assert.Equal(r1a, p.R1); + Assert.Equal(s1a, p.S1); + } + + [Fact] + public void Reset_ClearsAllState() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(p.IsHot); + + p.Reset(); + + Assert.False(p.IsHot); + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.S1)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class PivotdemWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var p = new Pivotdem(); + + // First bar - not hot + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + Assert.False(p.IsHot, "Should not be hot after 1 bar"); + + // Second bar - should be hot + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000)); + Assert.True(p.IsHot, "Should be hot after 2 bars"); + } + + [Fact] + public void WarmupPeriod_Equals2() + { + var p = new Pivotdem(); + Assert.Equal(2, p.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class PivotdemRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + // Feed valid bars + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.IsHot); + + // Feed NaN bar + _ = p.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + + // Should still be hot and produce valid pivots from last-valid values + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var p = new Pivotdem(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 105, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(2), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0), + isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var p = new Pivotdem(); + + _ = p.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(p.Last.Value)); + Assert.True(double.IsNaN(p.PP)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class PivotdemConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotdem(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotdem.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotdem(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotdem.Batch(bars.OpenValues, bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i]), $"PP mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 200); + + // Streaming + var streaming = new Pivotdem(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + + Pivotdem.BatchAll(bars.OpenValues, bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(bPP[i])); continue; } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var p1 = new Pivotdem(); + var p2 = new Pivotdem(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double pr = prices[i]; + _ = p1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), pr, pr, pr, pr, 0), isNew: true); + _ = p2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), pr), isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class PivotdemSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Pivotdem.Batch(new double[10], new double[5], new double[10], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotdem.Batch(new double[10], new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("ppOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Pivotdem.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, ReadOnlySpan.Empty, Span.Empty)); + Assert.Null(ex); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotdem.BatchAll(new double[10], new double[10], new double[10], new double[10], + new double[10], new double[5], new double[10])); + Assert.Equal("r1Out", ex.ParamName); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class PivotdemEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var p = new Pivotdem(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var p = new Pivotdem(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class PivotdemPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var p = new Pivotdem(); + p.Prime(bars); + + Assert.True(p.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var p = new Pivotdem(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => p.Prime(bars)); + Assert.Null(ex); + Assert.False(p.IsHot); + } +} diff --git a/lib/reversals/pivotdem/Pivotdem.Validation.Tests.cs b/lib/reversals/pivotdem/Pivotdem.Validation.Tests.cs new file mode 100644 index 00000000..4bc2dd0e --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.Validation.Tests.cs @@ -0,0 +1,241 @@ +// PIVOTDEM Validation Tests - DeMark Pivot Points +// Self-consistency validation across all API modes. +// +// Note: No external libraries (Skender, TA-Lib, Tulip, Ooples) implement +// DeMark pivot points. Validation focuses on mathematical correctness, +// conditional logic verification, and mode consistency. + +namespace QuanTAlib.Tests; + +public sealed class PivotdemValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Mathematical Correctness ------------------------------------------------- + + [Fact] + public void MathCorrectness_PP_MatchesConditionalFormula() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotdem(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) // Need previous bar + { + double pO = bars[i - 1].Open; + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + + double x; + if (pC < pO) { x = pH + 2.0 * pL + pC; } + else if (pC > pO) { x = 2.0 * pH + pL + pC; } + else { x = pH + pL + 2.0 * pC; } + + double expectedPP = x * 0.25; + Assert.Equal(expectedPP, p.PP, precision: 10); + } + } + } + + [Fact] + public void MathCorrectness_AllLevels_MatchFormula() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotdem(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double pO = bars[i - 1].Open; + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + + double x; + if (pC < pO) { x = pH + 2.0 * pL + pC; } + else if (pC > pO) { x = 2.0 * pH + pL + pC; } + else { x = pH + pL + 2.0 * pC; } + + double halfX = x * 0.5; + Assert.Equal(x * 0.25, p.PP, precision: 10); + Assert.Equal(halfX - pL, p.R1, precision: 10); + Assert.Equal(halfX - pH, p.S1, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Batch -------------------------------------- + + [Fact] + public void StreamingMatchesBatch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotdem(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotdem.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), + $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span --------------------------------------- + + [Fact] + public void StreamingMatchesSpan_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotdem(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotdem.Batch(bars.OpenValues, bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i])); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == BatchAll (all 3 levels) -------------------- + + [Fact] + public void StreamingMatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 300); + + // Streaming + var streaming = new Pivotdem(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + + Pivotdem.BatchAll(bars.OpenValues, bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(bPP[i])); + continue; + } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var p1 = new Pivotdem(); + var p2 = new Pivotdem(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p1.Update(bars[i], isNew: true); + _ = p2.Update(bars[i], isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Pivotdem.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- Level Ordering Invariant -------------------------------------------------- + + [Fact] + public void AllBars_LevelsOrdered_S1_PP_R1() + { + var bars = CreateGbmBars(count: 200); + var p = new Pivotdem(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (p.IsHot) + { + Assert.True(p.S1 <= p.PP, $"S1 > PP at bar {i}"); + Assert.True(p.PP <= p.R1, $"PP > R1 at bar {i}"); + } + } + } +} diff --git a/lib/reversals/pivotdem/Pivotdem.cs b/lib/reversals/pivotdem/Pivotdem.cs new file mode 100644 index 00000000..b5d69561 --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.cs @@ -0,0 +1,423 @@ +// PIVOTDEM: DeMark Pivot Points +// Calculates 3 support/resistance levels from previous bar's OHLC. +// Uses conditional logic based on Open vs Close relationship. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOTDEM: DeMark Pivot Points +/// +/// +/// Computes 3 horizontal support/resistance levels from the previous bar's +/// open, high, low, and close. The key innovation is the conditional calculation +/// of the intermediate value X, which varies depending on the relationship +/// between open and close, weighting different price components accordingly. +/// +/// Calculation (using previous bar's O, H, L, C): +/// +/// If C < O: X = H + 2L + C +/// If C > O: X = 2H + L + C +/// If C == O: X = H + L + 2C +/// +/// PP = X / 4 +/// R1 = X / 2 − L +/// S1 = X / 2 − H +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's OHLC +/// - 3 outputs: PP, R1, S1 (minimalist) +/// - WarmupPeriod = 2 (need previous bar's OHLC) +/// - No configurable parameters +/// - Conditional weighting: bearish bars weight Low, bullish bars weight High +/// - Only pivot variant that uses Open in the calculation +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivotdem : ITValuePublisher +{ + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevOpen, + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidOpen, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: X / 4 (conditionally weighted) + public double PP { get; private set; } + + /// Resistance 1: X / 2 − prevLow + public double R1 { get; private set; } + + /// Support 1: X / 2 − prevHigh + public double S1 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a DeMark Pivot Points indicator. + /// + public Pivotdem() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, + double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + SetAllNaN(); + + Name = "Pivotdem"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates a DeMark Pivot Points indicator chained to a TBarSeries source. + /// + public Pivotdem(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double open = input.Open; + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(open)) { s.LastValidOpen = open; } + else { open = s.LastValidOpen; } + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(open) || double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store OHLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevOpen = open; + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute DeMark pivot levels from PREVIOUS bar's OHLC + double pO = s.PrevOpen; + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + // Conditional X calculation + double x; + if (pC < pO) + { + x = pH + 2.0 * pL + pC; // Bearish: weight Low + } + else if (pC > pO) + { + x = 2.0 * pH + pL + pC; // Bullish: weight High + } + else + { + x = pH + pL + 2.0 * pC; // Doji: weight Close + } + + double halfX = x * 0.5; + PP = x * 0.25; + R1 = halfX - pL; + S1 = halfX - pH; + + // Store current bar's OHLC as "previous" for next bar + s.PrevOpen = open; + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.OpenValues, source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, + double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + S1 = double.NaN; + } + + /// + /// Batch computation of DeMark Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan open, + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (open.Length != high.Length || high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute from previous bar's OHLC + for (int i = 1; i < len; i++) + { + double pO = open[i - 1]; + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double x; + if (pC < pO) { x = pH + 2.0 * pL + pC; } + else if (pC > pO) { x = 2.0 * pH + pL + pC; } + else { x = pH + pL + 2.0 * pC; } + + ppOutput[i] = x * 0.25; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.OpenValues, source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 3 DeMark Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan open, + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out) + { + if (open.Length != high.Length || high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pO = open[i - 1]; + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double x; + if (pC < pO) { x = pH + 2.0 * pL + pC; } + else if (pC > pO) { x = 2.0 * pH + pL + pC; } + else { x = pH + pL + 2.0 * pC; } + + double halfX = x * 0.5; + ppOut[i] = x * 0.25; + r1Out[i] = halfX - pL; + s1Out[i] = halfX - pH; + } + } + + public static (TSeries Results, Pivotdem Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivotdem(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivotdem/Pivotdem.md b/lib/reversals/pivotdem/Pivotdem.md new file mode 100644 index 00000000..e985d347 --- /dev/null +++ b/lib/reversals/pivotdem/Pivotdem.md @@ -0,0 +1,167 @@ +# PIVOTDEM: DeMark Pivot Points + +> "Most pivot formulas treat every bar the same. DeMark looked at the open-close relationship and asked: why would a bearish bar predict the same levels as a bullish one?" + +DeMark Pivot Points calculate three horizontal support and resistance levels from the previous bar's open, high, low, and close. The defining characteristic is a conditional intermediate value X that changes its weighting depending on whether the prior bar closed below, above, or equal to its open. Bearish bars weight the low; bullish bars weight the high; doji bars weight the close. Three levels (PP, R1, S1) emerge from this single conditional calculation. The only pivot variant that uses the open price. + +## Historical Context + +Tom DeMark introduced his pivot point variant as part of a broader system of conditional indicators published in *The New Science of Technical Analysis* (1994) and *New Market Timing Techniques* (1997). Where floor trader pivots summarize the prior bar with an equal-weight HLC average, DeMark argued that the relationship between open and close carries directional information that should influence the levels. + +The logic is straightforward: if the bar closed below the open (bearish), the low was more "tested" and should carry more weight. If the bar closed above the open (bullish), the high was more relevant. If open equals close (a doji), the close itself — representing the equilibrium point where neither bulls nor bears won — gets the extra weight. + +This conditional approach differs from all other pivot variants (Classic, Woodie, Camarilla, Fibonacci) which apply the same formula regardless of bar direction. DeMark's innovation was treating the prior bar as a signal, not just a data source. + +The tradeoff is minimalism: DeMark produces only 3 levels (PP, R1, S1) compared to the Classic formula's 7 or Camarilla's 9. What you lose in level density you gain in directional sensitivity. The formula adapts to bar structure rather than imposing a fixed geometry. + +## Architecture and Physics + +### 1. Previous Bar's OHLC + +The indicator stores the open ($O$), high ($H$), low ($L$), and close ($C$) of the most recently completed bar. On each new bar, these stored values become the basis for computing the current bar's pivot levels. This is the only pivot variant that requires the open. + +### 2. Conditional Intermediate Value X + +The core innovation is the conditional calculation of $X$: + +$$X = \begin{cases} H_{prev} + 2 \cdot L_{prev} + C_{prev} & \text{if } C_{prev} < O_{prev} \text{ (bearish)} \\ 2 \cdot H_{prev} + L_{prev} + C_{prev} & \text{if } C_{prev} > O_{prev} \text{ (bullish)} \\ H_{prev} + L_{prev} + 2 \cdot C_{prev} & \text{if } C_{prev} = O_{prev} \text{ (doji)} \end{cases}$$ + +Each case sums four price components but doubles one of them: + +- **Bearish bar** doubles the low (the level that absorbed selling pressure) +- **Bullish bar** doubles the high (the level that absorbed buying pressure) +- **Doji bar** doubles the close (the neutral equilibrium) + +### 3. Pivot Levels + +From the intermediate value $X$: + +$$PP = \frac{X}{4}$$ + +$$R_1 = \frac{X}{2} - L_{prev}$$ + +$$S_1 = \frac{X}{2} - H_{prev}$$ + +### 4. Level Ordering Invariant + +For any bar where $H_{prev} > L_{prev}$ (non-degenerate range): + +$$S_1 < PP < R_1$$ + +This holds regardless of the bar direction, since $R_1 - PP = PP - S_1 = \frac{H_{prev} - L_{prev}}{4}$ is always positive for non-zero range. The range between R1 and S1 equals $\frac{H_{prev} - L_{prev}}{2}$, exactly half the prior bar's range. + +### 5. Three Outputs + +All three levels are computed simultaneously and remain constant until a new bar arrives. The primary output (`Last.Val`) returns PP; individual properties expose PP, R1, and S1. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| Price above PP | Bullish bias; prior bar direction influences level placement | +| Price below PP | Bearish bias for current bar | +| Price tests R1 | Resistance; level is higher after bullish prior bar (H weighted) | +| Price tests S1 | Support; level is lower after bearish prior bar (L weighted) | +| Bearish prior bar | Levels shift downward (low weighted), implying defensive positioning | +| Bullish prior bar | Levels shift upward (high weighted), implying aggressive positioning | +| Doji prior bar | Levels center on close (neutral), tightest level spacing | + +## Mathematical Foundation + +### Parameters + +DeMark Pivot Points has no configurable parameters. The conditional formula is fixed by definition. + +| Parameter | Value | Notes | +| :--- | :---: | :--- | +| Inputs | O, H, L, C | Previous bar's open, high, low, close | +| Outputs | 3 | PP, R1, S1 | +| Parameters | 0 | No tuning required | + +### Warmup Period + +$$W = 2$$ + +The indicator requires 2 bars: the first bar provides OHLC for storage; the second bar triggers computation from the stored values. Prior to warmup completion, all outputs are NaN. + +### Derivation Notes + +The R1 and S1 formulas can be rewritten to show their relationship to PP: + +$$R_1 = PP + \frac{H_{prev} - L_{prev}}{4}$$ + +$$S_1 = PP - \frac{H_{prev} - L_{prev}}{4}$$ + +This means R1 and S1 are always equidistant from PP, separated by one-quarter of the prior bar's range on each side. The conditional logic affects where PP itself sits (closer to the low for bearish bars, closer to the high for bullish), but the R1-PP and PP-S1 distances are always identical. + +## Performance Profile + +### Implementation Design + +Pure arithmetic with no loops, no buffers, no auxiliary data structures. Each `Update` call performs one conditional branch, 3 multiplications, 3 additions/subtractions, and 4 comparisons for NaN validation. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(1) | Fixed arithmetic; one branch | +| **Allocations** | 0 | Hot path is allocation-free | +| **Warmup** | 2 bars | Minimum possible | +| **Accuracy** | 10/10 | Exact arithmetic; no approximation | +| **Timeliness** | 10/10 | No lag; levels available immediately on new bar | +| **Smoothness** | N/A | Discrete levels; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks previous bar's OHLC and last-valid values for NaN/Infinity input substitution. Bar correction via `isNew` flag enables same-timestamp rewrites without state corruption. + +### SIMD Applicability + +Not applicable for streaming (single bar computation). The `BatchAll` span API could theoretically vectorize but the conditional branch per bar prevents efficient SIMD. The arithmetic is too simple (3 operations after the branch) to justify vectorization overhead. + +### FMA Usage + +Not used. The per-level computation (`x * 0.5 - L` or `x * 0.25`) involves only one multiply and one subtract, which does not form an `a*b + c` pattern that benefits from FMA. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | PP values match streaming | +| **Span** (`Batch(Span)`) | Passed | PP values match streaming | +| **BatchAll** (`BatchAll(Span)`) | Passed | All 3 levels match streaming | +| **Event** (`Pub` subscription) | Passed | Fires on every update | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent; level ordering invariant holds | +| **Skender** | N/A | Uses calendar-window periods; conceptually different | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +No external libraries implement bar-to-bar DeMark pivot points. Mathematical correctness is validated by computing expected values from the conditional formula for each bar and comparing against the indicator output at precision 10. + +## Common Pitfalls + +1. **First bar returns NaN.** The indicator needs the previous bar's OHLC to compute pivots. The first bar stores OHLC but produces no output. This is correct behavior. `WarmupPeriod = 2`. + +2. **Open price is required.** Unlike Classic Pivot Points (which use only HLC), DeMark requires the open to determine which branch of the conditional to take. Using `TValue` input (which sets all four OHLC fields to the same price) always triggers the doji branch ($C = O$). Use `TBar` input for meaningful DeMark calculations. + +3. **Levels are constant within a bar.** Pivot levels do not change as the current bar's price moves. They change only when a new bar starts. Multiple `isNew=false` corrections on the current bar do not alter the pivot levels. + +4. **Only 3 levels, not 7.** DeMark produces PP, R1, and S1 only. There are no R2/R3/S2/S3 levels. If you need more levels, use Classic Pivot Points or Camarilla. + +5. **Floating-point equality for doji detection.** The doji branch triggers when `Close == Open` exactly. In practice with real market data, exact equality is rare. The bearish and bullish branches handle the vast majority of bars. The doji branch matters most for synthetic data or instruments with minimum tick sizes that create frequent doji bars. + +6. **NaN/Infinity inputs use last-valid substitution.** If any of O, H, L, C is NaN or Infinity, the last valid value for that field is substituted. This prevents NaN propagation but may produce stale levels. + +7. **Level spacing is always half the prior range.** R1 minus S1 always equals $(H_{prev} - L_{prev}) / 2$, regardless of bar direction. The conditional logic shifts PP up or down but does not change the R1-S1 width. Low-range prior bars produce tightly clustered levels. + +## References + +- DeMark, T. R. (1994). *The New Science of Technical Analysis*. John Wiley and Sons. +- DeMark, T. R. (1997). *New Market Timing Techniques: Innovative Studies in Market Rhythm and Price Exhaustion*. John Wiley and Sons. +- Person, J. L. (2004). *A Complete Guide to Technical Trading Tactics*. John Wiley and Sons. +- Wikipedia: [Pivot point (technical analysis)](https://en.wikipedia.org/wiki/Pivot_point_(technical_analysis)) diff --git a/lib/reversals/pivotext/Pivotext.Quantower.Tests.cs b/lib/reversals/pivotext/Pivotext.Quantower.Tests.cs new file mode 100644 index 00000000..c49e5494 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.Quantower.Tests.cs @@ -0,0 +1,126 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotextIndicatorTests +{ + [Fact] + public void PivotextIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotextIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOTEXT", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotextIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotextIndicator(); + + Assert.Equal(0, PivotextIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotextIndicator_ShortName_IsPivotext() + { + var indicator = new PivotextIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOTEXT", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotextIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotextIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivotext", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotextIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotextIndicator(); + + indicator.Initialize(); + + // 11 line series: PP, R1, R2, R3, R4, R5, S1, S2, S3, S4, S5 + Assert.Equal(11, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotextIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotextIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotextIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotextIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotextIndicator_ElevenLineSeries_ArePresent() + { + var indicator = new PivotextIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, R2=2, R3=3, R4=4, R5=5, S1=6, S2=7, S3=8, S4=9, S5=10 + Assert.Equal(11, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R5", indicator.LinesSeries[5].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[6].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S5", indicator.LinesSeries[10].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void PivotextIndicator_Description_IsSet() + { + var indicator = new PivotextIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("Extended", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivotext/Pivotext.Quantower.cs b/lib/reversals/pivotext/Pivotext.Quantower.cs new file mode 100644 index 00000000..f9734114 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.Quantower.cs @@ -0,0 +1,88 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotextIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivotext _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _r2Series; + private readonly LineSeries _r3Series; + private readonly LineSeries _r4Series; + private readonly LineSeries _r5Series; + private readonly LineSeries _s1Series; + private readonly LineSeries _s2Series; + private readonly LineSeries _s3Series; + private readonly LineSeries _s4Series; + private readonly LineSeries _s5Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOTEXT"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivotext/Pivotext.cs"; + + public PivotextIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOTEXT - Extended Traditional Pivot Points"; + Description = "Extended traditional pivot points: 11 support/resistance levels (PP, R1-R5, S1-S5) derived from previous bar's HLC."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1", color: Color.FromArgb(255, 180, 180), width: 1, style: LineStyle.Solid); + _r2Series = new LineSeries(name: "R2", color: Color.FromArgb(255, 140, 140), width: 1, style: LineStyle.Solid); + _r3Series = new LineSeries(name: "R3", color: Color.FromArgb(255, 100, 100), width: 1, style: LineStyle.Solid); + _r4Series = new LineSeries(name: "R4", color: Color.FromArgb(255, 60, 60), width: 1, style: LineStyle.Dash); + _r5Series = new LineSeries(name: "R5", color: Color.Red, width: 1, style: LineStyle.Dash); + _s1Series = new LineSeries(name: "S1", color: Color.FromArgb(180, 255, 180), width: 1, style: LineStyle.Solid); + _s2Series = new LineSeries(name: "S2", color: Color.FromArgb(140, 255, 140), width: 1, style: LineStyle.Solid); + _s3Series = new LineSeries(name: "S3", color: Color.FromArgb(100, 255, 100), width: 1, style: LineStyle.Solid); + _s4Series = new LineSeries(name: "S4", color: Color.FromArgb(60, 255, 60), width: 1, style: LineStyle.Dash); + _s5Series = new LineSeries(name: "S5", color: Color.Green, width: 1, style: LineStyle.Dash); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_r2Series); + AddLineSeries(_r3Series); + AddLineSeries(_r4Series); + AddLineSeries(_r5Series); + AddLineSeries(_s1Series); + AddLineSeries(_s2Series); + AddLineSeries(_s3Series); + AddLineSeries(_s4Series); + AddLineSeries(_s5Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivotext(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _r2Series.SetValue(_indicator.R2, _indicator.IsHot, ShowColdValues); + _r3Series.SetValue(_indicator.R3, _indicator.IsHot, ShowColdValues); + _r4Series.SetValue(_indicator.R4, _indicator.IsHot, ShowColdValues); + _r5Series.SetValue(_indicator.R5, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + _s2Series.SetValue(_indicator.S2, _indicator.IsHot, ShowColdValues); + _s3Series.SetValue(_indicator.S3, _indicator.IsHot, ShowColdValues); + _s4Series.SetValue(_indicator.S4, _indicator.IsHot, ShowColdValues); + _s5Series.SetValue(_indicator.S5, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivotext/Pivotext.Tests.cs b/lib/reversals/pivotext/Pivotext.Tests.cs new file mode 100644 index 00000000..1c64a0f8 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.Tests.cs @@ -0,0 +1,617 @@ +// PIVOTEXT Tests - Extended Traditional Pivot Points + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class PivotextConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var p = new Pivotext(); + + Assert.Equal(2, p.WarmupPeriod); + Assert.Contains("Pivotext", p.Name, StringComparison.Ordinal); + Assert.False(p.IsHot); + } + + [Fact] + public void Constructor_InitialState_AllNaN() + { + var p = new Pivotext(); + + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.R4)); + Assert.True(double.IsNaN(p.R5)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + Assert.True(double.IsNaN(p.S4)); + Assert.True(double.IsNaN(p.S5)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class PivotextBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var p = new Pivotext(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + TValue result = p.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var p = new Pivotext(); + var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000); + + _ = p.Update(bar); + + Assert.True(double.IsFinite(p.Last.Value) || double.IsNaN(p.Last.Value)); + } + + [Fact] + public void Update_KnownValues_CorrectPivotLevels() + { + // Given previous bar H=110, L=90, C=100, range=20 + // PP = (110+90+100)/3 = 100 + // ppMinusL = 100-90 = 10, hMinusPP = 110-100 = 10 + // R1 = 2*100 - 90 = 110 + // S1 = 2*100 - 110 = 90 + // R2 = 100 + 20 = 120 + // S2 = 100 - 20 = 80 + // R3 = 110 + 2*10 = 130 + // S3 = 90 - 2*10 = 70 + // R4 = 110 + 3*10 = 140 + // S4 = 90 - 3*10 = 60 + // R5 = 110 + 4*10 = 150 + // S5 = 90 - 4*10 = 50 + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + // First bar: stores HLC, no output yet + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + Assert.True(double.IsNaN(p.PP)); + + // Second bar: computes from first bar's HLC + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.Equal(100.0, p.PP, precision: 10); + Assert.Equal(110.0, p.R1, precision: 10); + Assert.Equal(90.0, p.S1, precision: 10); + Assert.Equal(120.0, p.R2, precision: 10); + Assert.Equal(80.0, p.S2, precision: 10); + Assert.Equal(130.0, p.R3, precision: 10); + Assert.Equal(70.0, p.S3, precision: 10); + Assert.Equal(140.0, p.R4, precision: 10); + Assert.Equal(60.0, p.S4, precision: 10); + Assert.Equal(150.0, p.R5, precision: 10); + Assert.Equal(50.0, p.S5, precision: 10); + } + + [Fact] + public void Update_SecondKnownValues_CorrectPivotLevels() + { + // Given previous bar H=120, L=100, C=115, range=20 + // PP = (120+100+115)/3 = 111.6667 + // ppMinusL = 111.6667-100 = 11.6667, hMinusPP = 120-111.6667 = 8.3333 + // R1 = 2*111.6667 - 100 = 123.3333 + // S1 = 2*111.6667 - 120 = 103.3333 + // R2 = 111.6667 + 20 = 131.6667 + // S2 = 111.6667 - 20 = 91.6667 + // R3 = 120 + 2*11.6667 = 143.3333 + // S3 = 100 - 2*8.3333 = 83.3333 + // R4 = 120 + 3*11.6667 = 155.0 + // S4 = 100 - 3*8.3333 = 75.0 + // R5 = 120 + 4*11.6667 = 166.6667 + // S5 = 100 - 4*8.3333 = 66.6667 + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 110, 120, 100, 115, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 115, 125, 105, 120, 1000), isNew: true); + + double expectedPP = (120.0 + 100.0 + 115.0) / 3.0; + double ppMinusL = expectedPP - 100.0; + double hMinusPP = 120.0 - expectedPP; + + Assert.Equal(expectedPP, p.PP, precision: 10); + Assert.Equal(2.0 * expectedPP - 100.0, p.R1, precision: 10); + Assert.Equal(2.0 * expectedPP - 120.0, p.S1, precision: 10); + Assert.Equal(expectedPP + 20.0, p.R2, precision: 10); + Assert.Equal(expectedPP - 20.0, p.S2, precision: 10); + Assert.Equal(120.0 + 2.0 * ppMinusL, p.R3, precision: 10); + Assert.Equal(100.0 - 2.0 * hMinusPP, p.S3, precision: 10); + Assert.Equal(120.0 + 3.0 * ppMinusL, p.R4, precision: 10); + Assert.Equal(100.0 - 3.0 * hMinusPP, p.S4, precision: 10); + Assert.Equal(120.0 + 4.0 * ppMinusL, p.R5, precision: 10); + Assert.Equal(100.0 - 4.0 * hMinusPP, p.S5, precision: 10); + } + + [Fact] + public void Update_LevelsHaveCorrectOrdering() + { + // For any normal bar: S5 < S4 < S3 < S2 < S1 < PP < R1 < R2 < R3 < R4 < R5 + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.S5 < p.S4); + Assert.True(p.S4 < p.S3); + Assert.True(p.S3 < p.S2); + Assert.True(p.S2 < p.S1); + Assert.True(p.S1 < p.PP); + Assert.True(p.PP < p.R1); + Assert.True(p.R1 < p.R2); + Assert.True(p.R2 < p.R3); + Assert.True(p.R3 < p.R4); + Assert.True(p.R4 < p.R5); + } + + [Fact] + public void Name_ContainsPivotext() + { + var p = new Pivotext(); + Assert.Contains("Pivotext", p.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class PivotextStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var p = new Pivotext(); + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000), isNew: true); + var first = p.Last; + + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + var second = p.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + double ppBefore = p.PP; + + // Correct the second bar (isNew=false) + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 118, 92, 108, 1000), isNew: false); + + // PP should still be based on bar 0's HLC (H=110, L=90, C=100) + Assert.Equal(ppBefore, p.PP, precision: 10); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + double[] ppResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + ppResults[i] = p.PP; + } + + Assert.Equal(ppResults[0], ppResults[1]); + Assert.Equal(ppResults[1], ppResults[2]); + } + + [Fact] + public void IsNew_False_AllLevelsStable() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + double r1a = p.R1, s1a = p.S1, r2a = p.R2, s2a = p.S2; + double r3a = p.R3, s3a = p.S3, r4a = p.R4, s4a = p.S4; + double r5a = p.R5, s5a = p.S5; + + _ = p.Update(new TBar(dt.AddMinutes(1), 108, 120, 88, 110, 1000), isNew: false); + Assert.Equal(r1a, p.R1); + Assert.Equal(s1a, p.S1); + Assert.Equal(r2a, p.R2); + Assert.Equal(s2a, p.S2); + Assert.Equal(r3a, p.R3); + Assert.Equal(s3a, p.S3); + Assert.Equal(r4a, p.R4); + Assert.Equal(s4a, p.S4); + Assert.Equal(r5a, p.R5); + Assert.Equal(s5a, p.S5); + } + + [Fact] + public void Reset_ClearsAllState() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(p.IsHot); + + p.Reset(); + + Assert.False(p.IsHot); + Assert.True(double.IsNaN(p.PP)); + Assert.True(double.IsNaN(p.R1)); + Assert.True(double.IsNaN(p.R2)); + Assert.True(double.IsNaN(p.R3)); + Assert.True(double.IsNaN(p.R4)); + Assert.True(double.IsNaN(p.R5)); + Assert.True(double.IsNaN(p.S1)); + Assert.True(double.IsNaN(p.S2)); + Assert.True(double.IsNaN(p.S3)); + Assert.True(double.IsNaN(p.S4)); + Assert.True(double.IsNaN(p.S5)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class PivotextWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var p = new Pivotext(); + + // First bar - not hot + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + Assert.False(p.IsHot, "Should not be hot after 1 bar"); + + // Second bar - should be hot + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 95, 105, 1000)); + Assert.True(p.IsHot, "Should be hot after 2 bars"); + } + + [Fact] + public void WarmupPeriod_Equals2() + { + var p = new Pivotext(); + Assert.Equal(2, p.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class PivotextRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + // Feed valid bars + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + Assert.True(p.IsHot); + + // Feed NaN bar + _ = p.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var p = new Pivotext(); + var dt = DateTime.UtcNow; + + _ = p.Update(new TBar(dt, 100, 110, 90, 100, 1000), isNew: true); + _ = p.Update(new TBar(dt.AddMinutes(1), 105, 115, 95, 105, 1000), isNew: true); + + _ = p.Update(new TBar(dt.AddMinutes(2), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0), + isNew: true); + + Assert.True(p.IsHot); + Assert.True(double.IsFinite(p.PP)); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var p = new Pivotext(); + + _ = p.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(p.Last.Value)); + Assert.True(double.IsNaN(p.PP)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class PivotextConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotext(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotext.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotext(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotext.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i]), $"PP mismatch at {i}"); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 200); + + // Streaming + var streaming = new Pivotext(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + var sR4 = new double[bars.Count]; + var sS4 = new double[bars.Count]; + var sR5 = new double[bars.Count]; + var sS5 = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + sR4[i] = streaming.R4; + sS4[i] = streaming.S4; + sR5[i] = streaming.R5; + sS5[i] = streaming.S5; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + var bR4 = new double[bars.Count]; + var bS4 = new double[bars.Count]; + var bR5 = new double[bars.Count]; + var bS5 = new double[bars.Count]; + + Pivotext.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3, bR4, bS4, bR5, bS5); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(bPP[i])); continue; } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + Assert.Equal(sR4[i], bR4[i], precision: 10); + Assert.Equal(sS4[i], bS4[i], precision: 10); + Assert.Equal(sR5[i], bR5[i], precision: 10); + Assert.Equal(sS5[i], bS5[i], precision: 10); + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var p1 = new Pivotext(); + var p2 = new Pivotext(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double pr = prices[i]; + _ = p1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), pr, pr, pr, pr, 0), isNew: true); + _ = p2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), pr), isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class PivotextSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Pivotext.Batch(new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotext.Batch(new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("ppOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Pivotext.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, Span.Empty)); + Assert.Null(ex); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Pivotext.BatchAll(new double[10], new double[10], new double[10], + new double[10], new double[5], new double[10], + new double[10], new double[10], new double[10], + new double[10], new double[10], new double[10], + new double[10], new double[10])); + Assert.Equal("r1Out", ex.ParamName); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class PivotextEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var p = new Pivotext(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = p.Update(new TBar(DateTime.UtcNow, 100, 110, 90, 100, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var p = new Pivotext(); + int fireCount = 0; + + p.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = p.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class PivotextPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var p = new Pivotext(); + p.Prime(bars); + + Assert.True(p.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var p = new Pivotext(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => p.Prime(bars)); + Assert.Null(ex); + Assert.False(p.IsHot); + } +} diff --git a/lib/reversals/pivotext/Pivotext.Validation.Tests.cs b/lib/reversals/pivotext/Pivotext.Validation.Tests.cs new file mode 100644 index 00000000..85be4110 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.Validation.Tests.cs @@ -0,0 +1,288 @@ +// PIVOTEXT Validation Tests - Extended Traditional Pivot Points +// Self-consistency validation across all API modes. +// +// Note: No external library (Skender, TA-Lib, Tulip, Ooples) implements +// Extended Traditional Pivot Points with R4/R5/S4/S5. Validation focuses +// on mathematical correctness and mode consistency. + +namespace QuanTAlib.Tests; + +public sealed class PivotextValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Mathematical Correctness ------------------------------------------------- + + [Fact] + public void MathCorrectness_PP_EqualsHLC_Over3() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotext(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double prevH = bars[i - 1].High; + double prevL = bars[i - 1].Low; + double prevC = bars[i - 1].Close; + double expectedPP = (prevH + prevL + prevC) / 3.0; + + Assert.Equal(expectedPP, p.PP, precision: 10); + } + } + } + + [Fact] + public void MathCorrectness_AllLevels_MatchExtendedFormula() + { + var bars = CreateGbmBars(count: 100); + var p = new Pivotext(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (i >= 1) + { + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + double ppMinusL = pp - pL; + double hMinusPP = pH - pp; + + Assert.Equal(pp, p.PP, precision: 10); + Assert.Equal(2.0 * pp - pL, p.R1, precision: 10); + Assert.Equal(2.0 * pp - pH, p.S1, precision: 10); + Assert.Equal(pp + range, p.R2, precision: 10); + Assert.Equal(pp - range, p.S2, precision: 10); + Assert.Equal(pH + 2.0 * ppMinusL, p.R3, precision: 10); + Assert.Equal(pL - 2.0 * hMinusPP, p.S3, precision: 10); + Assert.Equal(pH + 3.0 * ppMinusL, p.R4, precision: 10); + Assert.Equal(pL - 3.0 * hMinusPP, p.S4, precision: 10); + Assert.Equal(pH + 4.0 * ppMinusL, p.R5, precision: 10); + Assert.Equal(pL - 4.0 * hMinusPP, p.S5, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Batch -------------------------------------- + + [Fact] + public void StreamingMatchesBatch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotext(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Batch + var batchResults = Pivotext.Batch(bars); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), + $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamPP[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span --------------------------------------- + + [Fact] + public void StreamingMatchesSpan_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Pivotext(); + var streamPP = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamPP[i] = streaming.PP; + } + + // Span + var spanPP = new double[bars.Count]; + Pivotext.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanPP); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(spanPP[i])); + } + else + { + Assert.Equal(streamPP[i], spanPP[i], precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == BatchAll (all 11 levels) ------------------- + + [Fact] + public void StreamingMatchesBatchAll_AllLevels() + { + var bars = CreateGbmBars(count: 300); + + // Streaming + var streaming = new Pivotext(); + var sPP = new double[bars.Count]; + var sR1 = new double[bars.Count]; + var sS1 = new double[bars.Count]; + var sR2 = new double[bars.Count]; + var sS2 = new double[bars.Count]; + var sR3 = new double[bars.Count]; + var sS3 = new double[bars.Count]; + var sR4 = new double[bars.Count]; + var sS4 = new double[bars.Count]; + var sR5 = new double[bars.Count]; + var sS5 = new double[bars.Count]; + + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + sPP[i] = streaming.PP; + sR1[i] = streaming.R1; + sS1[i] = streaming.S1; + sR2[i] = streaming.R2; + sS2[i] = streaming.S2; + sR3[i] = streaming.R3; + sS3[i] = streaming.S3; + sR4[i] = streaming.R4; + sS4[i] = streaming.S4; + sR5[i] = streaming.R5; + sS5[i] = streaming.S5; + } + + // BatchAll + var bPP = new double[bars.Count]; + var bR1 = new double[bars.Count]; + var bS1 = new double[bars.Count]; + var bR2 = new double[bars.Count]; + var bS2 = new double[bars.Count]; + var bR3 = new double[bars.Count]; + var bS3 = new double[bars.Count]; + var bR4 = new double[bars.Count]; + var bS4 = new double[bars.Count]; + var bR5 = new double[bars.Count]; + var bS5 = new double[bars.Count]; + + Pivotext.BatchAll(bars.HighValues, bars.LowValues, bars.CloseValues, + bPP, bR1, bS1, bR2, bS2, bR3, bS3, bR4, bS4, bR5, bS5); + + for (int i = 1; i < bars.Count; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(bPP[i])); + continue; + } + + Assert.Equal(sPP[i], bPP[i], precision: 10); + Assert.Equal(sR1[i], bR1[i], precision: 10); + Assert.Equal(sS1[i], bS1[i], precision: 10); + Assert.Equal(sR2[i], bR2[i], precision: 10); + Assert.Equal(sS2[i], bS2[i], precision: 10); + Assert.Equal(sR3[i], bR3[i], precision: 10); + Assert.Equal(sS3[i], bS3[i], precision: 10); + Assert.Equal(sR4[i], bR4[i], precision: 10); + Assert.Equal(sS4[i], bS4[i], precision: 10); + Assert.Equal(sR5[i], bR5[i], precision: 10); + Assert.Equal(sS5[i], bS5[i], precision: 10); + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var p1 = new Pivotext(); + var p2 = new Pivotext(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p1.Update(bars[i], isNew: true); + _ = p2.Update(bars[i], isNew: true); + } + + Assert.Equal(p1.PP, p2.PP); + Assert.Equal(p1.R1, p2.R1); + Assert.Equal(p1.S1, p2.S1); + Assert.Equal(p1.R2, p2.R2); + Assert.Equal(p1.S2, p2.S2); + Assert.Equal(p1.R3, p2.R3); + Assert.Equal(p1.S3, p2.S3); + Assert.Equal(p1.R4, p2.R4); + Assert.Equal(p1.S4, p2.S4); + Assert.Equal(p1.R5, p2.R5); + Assert.Equal(p1.S5, p2.S5); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Pivotext.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- Level Ordering Invariant -------------------------------------------------- + + [Fact] + public void AllBars_SupportResistanceLevelsOrdered() + { + // Extended: S5 < S4 < S3 < S2 < S1 < PP < R1 < R2 < R3 < R4 < R5 + // (when close equals midpoint of range, PP lies at center) + var bars = CreateGbmBars(count: 200); + var p = new Pivotext(); + + for (int i = 0; i < bars.Count; i++) + { + _ = p.Update(bars[i], isNew: true); + + if (p.IsHot) + { + Assert.True(p.S5 <= p.S4, $"S5 > S4 at bar {i}"); + Assert.True(p.S4 <= p.S3, $"S4 > S3 at bar {i}"); + Assert.True(p.S3 <= p.S2, $"S3 > S2 at bar {i}"); + Assert.True(p.S2 <= p.S1, $"S2 > S1 at bar {i}"); + Assert.True(p.R1 <= p.R2, $"R1 > R2 at bar {i}"); + Assert.True(p.R2 <= p.R3, $"R2 > R3 at bar {i}"); + Assert.True(p.R3 <= p.R4, $"R3 > R4 at bar {i}"); + Assert.True(p.R4 <= p.R5, $"R4 > R5 at bar {i}"); + } + } + } +} diff --git a/lib/reversals/pivotext/Pivotext.cs b/lib/reversals/pivotext/Pivotext.cs new file mode 100644 index 00000000..6a3b2e80 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.cs @@ -0,0 +1,462 @@ +// PIVOTEXT: Extended Traditional Pivot Points +// Calculates 11 support/resistance levels from previous bar's HLC. +// Classic floor trader formula extended with R4/R5 and S4/S5 levels. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOTEXT: Extended Traditional Pivot Points +/// +/// +/// Computes 11 horizontal support/resistance levels from the previous bar's +/// high, low, and close. The central pivot point (PP) is the arithmetic mean +/// of HLC; resistance (R1-R5) and support (S1-S5) levels are derived from +/// PP and the prior bar's range. R1-R3/S1-S3 are identical to classic pivots; +/// R4/R5 and S4/S5 extend the range further for extreme move scenarios. +/// +/// Calculation (using previous bar's H, L, C): +/// +/// PP = (H + L + C) / 3 +/// R1 = 2 * PP - L S1 = 2 * PP - H +/// R2 = PP + (H - L) S2 = PP - (H - L) +/// R3 = H + 2 * (PP - L) S3 = L - 2 * (H - PP) +/// R4 = H + 3 * (PP - L) S4 = L - 3 * (H - PP) +/// R5 = H + 4 * (PP - L) S5 = L - 4 * (H - PP) +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's HLC +/// - 11 outputs: PP, R1, R2, R3, R4, R5, S1, S2, S3, S4, S5 +/// - WarmupPeriod = 2 (need previous bar's HLC) +/// - No configurable parameters +/// - Levels remain constant until a new bar arrives +/// - R4/R5 and S4/S5 provide extreme support/resistance for gap scenarios +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivotext : ITValuePublisher +{ + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: (prevH + prevL + prevC) / 3 + public double PP { get; private set; } + + /// Resistance 1: 2 * PP - prevL + public double R1 { get; private set; } + + /// Resistance 2: PP + (prevH - prevL) + public double R2 { get; private set; } + + /// Resistance 3: prevH + 2 * (PP - prevL) + public double R3 { get; private set; } + + /// Resistance 4: prevH + 3 * (PP - prevL) + public double R4 { get; private set; } + + /// Resistance 5: prevH + 4 * (PP - prevL) + public double R5 { get; private set; } + + /// Support 1: 2 * PP - prevH + public double S1 { get; private set; } + + /// Support 2: PP - (prevH - prevL) + public double S2 { get; private set; } + + /// Support 3: prevL - 2 * (prevH - PP) + public double S3 { get; private set; } + + /// Support 4: prevL - 3 * (prevH - PP) + public double S4 { get; private set; } + + /// Support 5: prevL - 4 * (prevH - PP) + public double S5 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates an Extended Traditional Pivot Points indicator. + /// + public Pivotext() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + SetAllNaN(); + + Name = "Pivotext"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates an Extended Traditional Pivot Points indicator chained to a TBarSeries source. + /// + public Pivotext(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store HLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute extended pivot levels from PREVIOUS bar's HLC + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + double ppMinusL = pp - pL; // used for R3, R4, R5 + double hMinusPP = pH - pp; // used for S3, S4, S5 + + PP = pp; + R1 = Math.FusedMultiplyAdd(2.0, pp, -pL); // 2*pp - pL + S1 = Math.FusedMultiplyAdd(2.0, pp, -pH); // 2*pp - pH + R2 = pp + range; // pp + (pH - pL) + S2 = pp - range; // pp - (pH - pL) + R3 = Math.FusedMultiplyAdd(2.0, ppMinusL, pH); // pH + 2*(pp - pL) + S3 = Math.FusedMultiplyAdd(-2.0, hMinusPP, pL); // pL - 2*(pH - pp) + R4 = Math.FusedMultiplyAdd(3.0, ppMinusL, pH); // pH + 3*(pp - pL) + S4 = Math.FusedMultiplyAdd(-3.0, hMinusPP, pL); // pL - 3*(pH - pp) + R5 = Math.FusedMultiplyAdd(4.0, ppMinusL, pH); // pH + 4*(pp - pL) + S5 = Math.FusedMultiplyAdd(-4.0, hMinusPP, pL); // pL - 4*(pH - pp) + + // Store current bar's HLC as "previous" for next bar + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + R4 = double.NaN; + R5 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + S4 = double.NaN; + S5 = double.NaN; + } + + /// + /// Batch computation of Extended Traditional Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute from previous bar's HLC + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + ppOutput[i] = (pH + pL + pC) / 3.0; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 11 Extended Traditional Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out, + Span r2Out, + Span s2Out, + Span r3Out, + Span s3Out, + Span r4Out, + Span s4Out, + Span r5Out, + Span s5Out) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + if (r2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r2Out)); } + if (s2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s2Out)); } + if (r3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r3Out)); } + if (s3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s3Out)); } + if (r4Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r4Out)); } + if (s4Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s4Out)); } + if (r5Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r5Out)); } + if (s5Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s5Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + r2Out[0] = double.NaN; + s2Out[0] = double.NaN; + r3Out[0] = double.NaN; + s3Out[0] = double.NaN; + r4Out[0] = double.NaN; + s4Out[0] = double.NaN; + r5Out[0] = double.NaN; + s5Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + double ppMinusL = pp - pL; + double hMinusPP = pH - pp; + + ppOut[i] = pp; + r1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pL); + s1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pH); + r2Out[i] = pp + range; + s2Out[i] = pp - range; + r3Out[i] = Math.FusedMultiplyAdd(2.0, ppMinusL, pH); + s3Out[i] = Math.FusedMultiplyAdd(-2.0, hMinusPP, pL); + r4Out[i] = Math.FusedMultiplyAdd(3.0, ppMinusL, pH); + s4Out[i] = Math.FusedMultiplyAdd(-3.0, hMinusPP, pL); + r5Out[i] = Math.FusedMultiplyAdd(4.0, ppMinusL, pH); + s5Out[i] = Math.FusedMultiplyAdd(-4.0, hMinusPP, pL); + } + } + + public static (TSeries Results, Pivotext Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivotext(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivotext/Pivotext.md b/lib/reversals/pivotext/Pivotext.md new file mode 100644 index 00000000..006bd5e1 --- /dev/null +++ b/lib/reversals/pivotext/Pivotext.md @@ -0,0 +1,176 @@ +# PIVOTEXT: Extended Traditional Pivot Points + +> "Classic pivots tell you where the crowd expects the market to pause. Extended pivots tell you where the crowd starts to panic." + +Extended Traditional Pivot Points calculate eleven horizontal support and resistance levels from the previous bar's high, low, and close. The core levels (PP, R1-R3, S1-S3) are identical to classic floor trader pivots. The extension adds R4/R5 and S4/S5 levels that project further beyond the prior bar's range, covering extreme move scenarios such as gap opens, news-driven spikes, and trend continuation through multiple prior-range increments. The formula is pure arithmetic with zero parameters. + +## Historical Context + +Floor trader pivots date to the 1930s when pit traders computed PP = (H + L + C) / 3 and derived three symmetric support/resistance levels from it. The formula was simple enough to compute by hand before the market open, making it one of the earliest systematic approaches to intraday level identification. + +Classic pivots (R1-R3, S1-S3) cover the range from roughly 1x to 2x the prior bar's range projected from the high or low. In practice, large gap opens or momentum-driven moves routinely exceed R3/S3. Traders discovered they needed additional levels to bracket these extreme scenarios without switching to entirely different frameworks (Fibonacci extensions, measured moves, etc.). + +The extended formula simply continues the same arithmetic progression. R3 uses a 2x multiplier on (PP - L) added to H; R4 uses 3x; R5 uses 4x. The symmetry holds for support levels. This mechanical extension preserves the simplicity of the original system while providing reference levels for moves that exceed the "normal" 1-3 range pivots. + +Unlike Fibonacci pivots, Camarilla pivots, or DeMark pivots, the extended traditional formula makes no claim about specific retracement ratios or market microstructure. The levels are pure geometric projections of the prior range. Their value lies in consensus: enough traders watch these levels that they become self-reinforcing reference points. + +## Architecture and Physics + +### 1. Previous Bar's HLC + +The indicator stores the high ($H$), low ($L$), and close ($C$) of the most recently completed bar. On each new bar, these stored values become the basis for computing the current bar's pivot levels, and the new bar's HLC replaces them for the next computation. + +### 2. Central Pivot Point (PP) + +$$PP = \frac{H_{prev} + L_{prev} + C_{prev}}{3}$$ + +The arithmetic mean of the previous bar's HLC. This is the gravitational center of the level system. All other levels derive from PP and the prior range. + +### 3. Range and Intermediate Values + +$$range = H_{prev} - L_{prev}$$ + +$$ppMinusL = PP - L_{prev}$$ + +$$hMinusPP = H_{prev} - PP$$ + +The range scales the distance between levels. The asymmetric terms $ppMinusL$ and $hMinusPP$ determine how far resistance extends above H and support extends below L. + +### 4. Classic Levels (R1-R3, S1-S3) + +$$R_1 = 2 \cdot PP - L_{prev} \qquad S_1 = 2 \cdot PP - H_{prev}$$ + +$$R_2 = PP + range \qquad S_2 = PP - range$$ + +$$R_3 = H_{prev} + 2 \cdot (PP - L_{prev}) \qquad S_3 = L_{prev} - 2 \cdot (H_{prev} - PP)$$ + +### 5. Extended Levels (R4-R5, S4-S5) + +$$R_4 = H_{prev} + 3 \cdot (PP - L_{prev}) \qquad S_4 = L_{prev} - 3 \cdot (H_{prev} - PP)$$ + +$$R_5 = H_{prev} + 4 \cdot (PP - L_{prev}) \qquad S_5 = L_{prev} - 4 \cdot (H_{prev} - PP)$$ + +The progression is arithmetic: each successive level adds one more $ppMinusL$ (resistance) or $hMinusPP$ (support) increment. + +### 6. Level Ordering Invariant + +For any bar where $H_{prev} > L_{prev}$ (non-degenerate range): + +$$S_5 < S_4 < S_3 < S_2 < S_1 \leq PP \leq R_1 < R_2 < R_3 < R_4 < R_5$$ + +When the close is exactly at the midpoint of the range, $PP = S_1 = R_1$ (all collapse to the midpoint), and the S/R levels fan out symmetrically. In general, the close's position within the range determines the asymmetry between resistance and support spacing. + +### 7. Eleven Outputs + +All eleven levels are computed simultaneously and remain constant until a new bar arrives. The primary output (`Last.Val`) returns PP; individual properties expose all eleven levels. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| Price between S1 and R1 | Normal range; no directional bias | +| Price tests R2 from below | First extension test; watch for rejection | +| Price tests S2 from above | First support extension; potential bounce | +| Price reaches R3/S3 | Classic extreme; high-probability reversal zone | +| Price breaks R4/S4 | Significant momentum; extended trend likely | +| Price reaches R5/S5 | Rare extreme; potential exhaustion or blow-off | +| Levels cluster tightly | Prior bar had low range; expect volatility expansion | +| Wide level spacing | Prior bar was volatile; levels may be less precise | + +## Mathematical Foundation + +### Parameters + +Extended Traditional Pivot Points has no configurable parameters. The formula is fixed by definition. + +| Parameter | Value | Notes | +| :--- | :---: | :--- | +| Inputs | H, L, C | Previous bar's high, low, close | +| Outputs | 11 | PP, R1, R2, R3, R4, R5, S1, S2, S3, S4, S5 | +| Parameters | 0 | No tuning required | + +### Warmup Period + +$$W = 2$$ + +The indicator requires 2 bars: the first bar provides HLC for storage; the second bar triggers computation from the stored values. Prior to warmup completion, all outputs are NaN. + +### Derivation Notes + +The classic pivot formula is not derived from statistical theory. It is an empirical heuristic that became standardized through widespread adoption. The extension to R4/R5 and S4/S5 follows the same arithmetic progression pattern already established by R3/S3. No new constants or empirical fitting are introduced. + +R3 and S3 use a coefficient of 2 on the asymmetric terms. R4/S4 use 3. R5/S5 use 4. The progression could continue indefinitely, but levels beyond R5/S5 are rarely referenced in practice. + +All R/S level computations use `Math.FusedMultiplyAdd` for the `multiplier * offset + base` pattern, providing single-rounding precision. + +## Performance Profile + +### Implementation Design + +Pure arithmetic with no loops, no buffers, no auxiliary data structures. Each `Update` call performs 1 division (PP), 1 subtraction (range), 2 subtractions (ppMinusL, hMinusPP), 2 additions (R2, S2), and 8 FMA operations (R1, S1, R3-R5, S3-S5), plus 3 comparisons for NaN validation. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(1) | Fixed arithmetic; no iteration | +| **Allocations** | 0 | Hot path is allocation-free | +| **Warmup** | 2 bars | Minimum possible | +| **Accuracy** | 10/10 | Exact arithmetic via FMA; no approximation | +| **Timeliness** | 10/10 | No lag; levels available immediately on new bar | +| **Smoothness** | N/A | Discrete levels; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks previous bar's HLC and last-valid values for NaN/Infinity input substitution. Bar correction via `isNew` flag enables same-timestamp rewrites without state corruption. + +### SIMD Applicability + +Not applicable for streaming (single bar computation). The `BatchAll` span API processes multiple bars but the per-bar computation (13 arithmetic operations) is too simple to benefit from vectorization overhead. + +### FMA Usage + +The implementation uses `Math.FusedMultiplyAdd` for eight of the eleven R/S level computations. R1 and S1 use `FMA(2, pp, -pL)` and `FMA(2, pp, -pH)`. R3-R5 and S3-S5 use FMA with the precomputed `ppMinusL` and `hMinusPP` intermediate values. R2 and S2 are simple additions/subtractions that do not benefit from FMA. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | PP values match streaming | +| **Span** (`Batch(Span)`) | Passed | PP values match streaming | +| **BatchAll** (`BatchAll(Span)`) | Passed | All 11 levels match streaming | +| **Event** (`Pub` subscription) | Passed | Fires on every update | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent; level ordering invariant holds | +| **Skender** | N/A | Does not implement extended traditional variant | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +Mathematical correctness is validated by computing expected values from the extended formula for each bar and comparing against the indicator output at precision 10. + +## Common Pitfalls + +1. **First bar returns NaN.** The indicator needs the previous bar's HLC to compute pivots. The first bar stores HLC but produces no output. This is correct behavior, not a bug. `WarmupPeriod = 2`. + +2. **R1-R3/S1-S3 are identical to classic PIVOT.** The extended indicator adds R4/R5/S4/S5 but does not modify the classic levels. If you only need 7 levels, use the `Pivot` class instead to avoid computing unused outputs. + +3. **R4/R5 and S4/S5 project far from the current range.** These levels represent 3x and 4x prior-range extensions. For low-volatility instruments, they may be so distant as to be meaningless. For high-volatility instruments or gap scenarios, they provide the only pre-computed reference levels. + +4. **Level spacing is asymmetric when close is not at the range midpoint.** When the close is near the high, resistance levels are spaced more tightly than support levels, and vice versa. This is by design: the formula reflects where the close sits within the prior range. + +5. **Zero-range bars collapse all levels to a single price.** When $H_{prev} = L_{prev}$ (doji or single-print bar), all eleven levels equal the prior close, and PP equals the prior close. This is mathematically correct but provides no useful levels. + +6. **TValue input collapses range to zero.** When updating with `TValue` instead of `TBar`, all OHLC fields equal the single price, producing zero range and all levels equal to that price. Use `TBar` input for meaningful pivot calculations. + +7. **R5/S5 are rarely reached.** In typical market conditions, price reaching R5 or S5 represents approximately a 4x prior-range move. This occurs during panic selling, short squeezes, or major news events. Do not expect these levels to act as regular support/resistance. + +## References + +- Person, J. L. (2004). *A Complete Guide to Technical Trading Tactics: How to Profit Using Pivot Points, Candlesticks & Other Indicators*. John Wiley and Sons. +- Wikipedia: [Pivot point (technical analysis)](https://en.wikipedia.org/wiki/Pivot_point_(technical_analysis)) +- TradingView: [Pivot Points Standard](https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/) +- Nison, S. (2001). *Japanese Candlestick Charting Techniques*. Prentice Hall Press. (Discussion of floor trader pivot methodology.) diff --git a/lib/reversals/pivotfib/Pivotfib.Quantower.Tests.cs b/lib/reversals/pivotfib/Pivotfib.Quantower.Tests.cs new file mode 100644 index 00000000..88707e86 --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.Quantower.Tests.cs @@ -0,0 +1,127 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotfibIndicatorTests +{ + [Fact] + public void PivotfibIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotfibIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOTFIB", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotfibIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotfibIndicator(); + + Assert.Equal(0, PivotfibIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotfibIndicator_ShortName_IsPivotfib() + { + var indicator = new PivotfibIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOTFIB", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotfibIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotfibIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivotfib", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotfibIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotfibIndicator(); + + indicator.Initialize(); + + // 7 line series: PP, R1, R2, R3, S1, S2, S3 + Assert.Equal(7, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotfibIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotfibIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotfibIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotfibIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotfibIndicator_SevenLineSeries_ArePresent() + { + var indicator = new PivotfibIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, R2=2, R3=3, S1=4, S2=5, S3=6 + Assert.Equal(7, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[4].Name, StringComparison.OrdinalIgnoreCase); + // Verify Fibonacci ratios in series names + Assert.Contains("38.2", indicator.LinesSeries[1].Name, StringComparison.Ordinal); + Assert.Contains("61.8", indicator.LinesSeries[2].Name, StringComparison.Ordinal); + } + + [Fact] + public void PivotfibIndicator_Description_IsSet() + { + var indicator = new PivotfibIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("Fibonacci", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivotfib/Pivotfib.Quantower.cs b/lib/reversals/pivotfib/Pivotfib.Quantower.cs new file mode 100644 index 00000000..79da4698 --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.Quantower.cs @@ -0,0 +1,72 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotfibIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivotfib _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _r2Series; + private readonly LineSeries _r3Series; + private readonly LineSeries _s1Series; + private readonly LineSeries _s2Series; + private readonly LineSeries _s3Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOTFIB"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivotfib/Pivotfib.cs"; + + public PivotfibIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOTFIB - Fibonacci Pivot Points"; + Description = "Fibonacci pivot points: 7 support/resistance levels (PP, R1-R3, S1-S3) using Fibonacci ratios (0.382, 0.618, 1.000) applied to previous bar's range."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1 (38.2%)", color: Color.FromArgb(255, 200, 200), width: 1, style: LineStyle.Solid); + _r2Series = new LineSeries(name: "R2 (61.8%)", color: Color.FromArgb(255, 150, 150), width: 1, style: LineStyle.Solid); + _r3Series = new LineSeries(name: "R3 (100%)", color: Color.FromArgb(255, 100, 100), width: 1, style: LineStyle.Dash); + _s1Series = new LineSeries(name: "S1 (38.2%)", color: Color.FromArgb(200, 255, 200), width: 1, style: LineStyle.Solid); + _s2Series = new LineSeries(name: "S2 (61.8%)", color: Color.FromArgb(150, 255, 150), width: 1, style: LineStyle.Solid); + _s3Series = new LineSeries(name: "S3 (100%)", color: Color.FromArgb(100, 255, 100), width: 1, style: LineStyle.Dash); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_r2Series); + AddLineSeries(_r3Series); + AddLineSeries(_s1Series); + AddLineSeries(_s2Series); + AddLineSeries(_s3Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivotfib(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _r2Series.SetValue(_indicator.R2, _indicator.IsHot, ShowColdValues); + _r3Series.SetValue(_indicator.R3, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + _s2Series.SetValue(_indicator.S2, _indicator.IsHot, ShowColdValues); + _s3Series.SetValue(_indicator.S3, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivotfib/Pivotfib.Tests.cs b/lib/reversals/pivotfib/Pivotfib.Tests.cs new file mode 100644 index 00000000..2b356c4a --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.Tests.cs @@ -0,0 +1,554 @@ +// PIVOTFIB Tests - Fibonacci Pivot Points + +namespace QuanTAlib.Tests; + +// ── A) Constructor Tests ──────────────────────────────────────────── +public sealed class PivotfibConstructorTests +{ + [Fact] + public void DefaultConstructor_SetsExpectedDefaults() + { + var ind = new Pivotfib(); + Assert.Equal("Pivotfib", ind.Name); + Assert.Equal(2, ind.WarmupPeriod); + Assert.False(ind.IsHot); + Assert.True(double.IsNaN(ind.PP)); + } + + [Fact] + public void SourceConstructor_PrimesFromSource() + { + var bars = new TBarSeries(); + var dt = DateTime.UtcNow; + bars.Add(new TBar(dt, 110, 110, 90, 100, 1000)); + bars.Add(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000)); + + var ind = new Pivotfib(bars); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} + +// ── B) Basic Calculation Tests ────────────────────────────────────── +public sealed class PivotfibBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var ind = new Pivotfib(); + var bar = new TBar(DateTime.UtcNow, 110, 110, 90, 100, 1000); + TValue result = ind.Update(bar); + Assert.IsType(result); + } + + [Fact] + public void Last_IsAccessible() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + Assert.True(ind.IsHot); + Assert.True(double.IsFinite(ind.Last.Value)); + } + + [Fact] + public void KnownValues_FibonacciLevels() + { + // H=110, L=90, C=100 → PP=100, range=20 + // R1=100+0.382*20=107.64, S1=100-0.382*20=92.36 + // R2=100+0.618*20=112.36, S2=100-0.618*20=87.64 + // R3=100+1.000*20=120, S3=100-1.000*20=80 + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + Assert.Equal(100.0, ind.PP, 10); + Assert.Equal(107.64, ind.R1, 10); + Assert.Equal(92.36, ind.S1, 10); + Assert.Equal(112.36, ind.R2, 10); + Assert.Equal(87.64, ind.S2, 10); + Assert.Equal(120.0, ind.R3, 10); + Assert.Equal(80.0, ind.S3, 10); + } + + [Fact] + public void LevelOrdering_S3_LessThan_S2_LessThan_S1_LessThan_PP_LessThan_R1_LessThan_R2_LessThan_R3() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + Assert.True(ind.S3 < ind.S2); + Assert.True(ind.S2 < ind.S1); + Assert.True(ind.S1 < ind.PP); + Assert.True(ind.PP < ind.R1); + Assert.True(ind.R1 < ind.R2); + Assert.True(ind.R2 < ind.R3); + } + + [Fact] + public void FibonacciRatios_AreCorrect() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + double range = 110.0 - 90.0; // 20 + double pp = ind.PP; + + // Verify Fibonacci ratios + Assert.Equal(0.382, (ind.R1 - pp) / range, 10); + Assert.Equal(0.618, (ind.R2 - pp) / range, 10); + Assert.Equal(1.000, (ind.R3 - pp) / range, 10); + Assert.Equal(0.382, (pp - ind.S1) / range, 10); + Assert.Equal(0.618, (pp - ind.S2) / range, 10); + Assert.Equal(1.000, (pp - ind.S3) / range, 10); + } + + [Fact] + public void Name_ReturnsExpectedString() + { + var ind = new Pivotfib(); + Assert.Equal("Pivotfib", ind.Name); + } +} + +// ── C) State + Bar Correction Tests ───────────────────────────────── +public sealed class PivotfibStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double pp1 = ind.PP; + + ind.Update(new TBar(dt.AddMinutes(2), 120, 120, 100, 110, 1000), isNew: true); + double pp2 = ind.PP; + + Assert.NotEqual(pp1, pp2); + } + + [Fact] + public void IsNew_False_RewritesCurrentBar() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double pp1 = ind.PP; + + // Correction: rewrite the same bar + ind.Update(new TBar(dt.AddMinutes(1), 120, 120, 100, 110, 1000), isNew: false); + double pp2 = ind.PP; + + // PP should be unchanged (still based on previous bar H=110,L=90,C=100) + Assert.Equal(pp1, pp2, 10); + } + + [Fact] + public void IterativeCorrections_RestoreState() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double ppBefore = ind.PP; + + // Apply multiple corrections + ind.Update(new TBar(dt.AddMinutes(1), 200, 200, 50, 125, 1000), isNew: false); + ind.Update(new TBar(dt.AddMinutes(1), 300, 300, 10, 155, 1000), isNew: false); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: false); + + Assert.Equal(ppBefore, ind.PP, 10); + } + + [Fact] + public void Reset_ClearsAllState() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + Assert.True(ind.IsHot); + + ind.Reset(); + Assert.False(ind.IsHot); + Assert.True(double.IsNaN(ind.PP)); + Assert.True(double.IsNaN(ind.R1)); + Assert.True(double.IsNaN(ind.S1)); + } + + [Fact] + public void Reset_ThenReplay_MatchesOriginal() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + var bar1 = new TBar(dt, 110, 110, 90, 100, 1000); + var bar2 = new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000); + var bar3 = new TBar(dt.AddMinutes(2), 120, 120, 100, 110, 1000); + + ind.Update(bar1, isNew: true); + ind.Update(bar2, isNew: true); + ind.Update(bar3, isNew: true); + double ppOriginal = ind.PP; + double r1Original = ind.R1; + double s1Original = ind.S1; + + ind.Reset(); + ind.Update(bar1, isNew: true); + ind.Update(bar2, isNew: true); + ind.Update(bar3, isNew: true); + + Assert.Equal(ppOriginal, ind.PP, 10); + Assert.Equal(r1Original, ind.R1, 10); + Assert.Equal(s1Original, ind.S1, 10); + } +} + +// ── D) Warmup / Convergence Tests ─────────────────────────────────── +public sealed class PivotfibWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmupPeriod() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + Assert.False(ind.IsHot); + + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + Assert.True(ind.IsHot); + } + + [Fact] + public void WarmupPeriod_IsTwo() + { + var ind = new Pivotfib(); + Assert.Equal(2, ind.WarmupPeriod); + } +} + +// ── E) Robustness Tests ───────────────────────────────────────────── +public sealed class PivotfibRobustnessTests +{ + [Fact] + public void NaN_UsesLastValidValue() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + // Feed NaN bar - should use last valid values and still produce valid PP + ind.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + Assert.False(double.IsNaN(ind.PP)); + } + + [Fact] + public void Infinity_UsesLastValidValue() + { + var ind = new Pivotfib(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + ind.Update(new TBar(dt.AddMinutes(2), double.PositiveInfinity, double.PositiveInfinity, + double.NegativeInfinity, double.PositiveInfinity, 0), isNew: true); + Assert.False(double.IsNaN(ind.PP)); + Assert.True(double.IsFinite(ind.PP)); + } + + [Fact] + public void BatchNaN_Safe() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var ind = new Pivotfib(); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + } + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} + +// ── F) Consistency Tests ──────────────────────────────────────────── +public sealed class PivotfibConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_Matches_Batch() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotfib(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamResults.Add(ind.PP); + } + + // Batch + var batchResult = Pivotfib.Batch(bars); + + Assert.Equal(bars.Count, batchResult.Count); + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(batchResult[i].Value)) + { + continue; + } + Assert.Equal(streamResults[i], batchResult[i].Value, 10); + } + } + + [Fact] + public void Streaming_Matches_Span() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotfib(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamResults.Add(ind.PP); + } + + // Span + int len = bars.Count; + var ppOut = new double[len]; + Pivotfib.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, ppOut); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(ppOut[i])) + { + continue; + } + Assert.Equal(streamResults[i], ppOut[i], 10); + } + } + + [Fact] + public void Streaming_Matches_BatchAll() + { + var bars = CreateGbmBars(); + + // Streaming - collect all 7 levels + var ind = new Pivotfib(); + var sPP = new List(bars.Count); + var sR1 = new List(bars.Count); + var sS1 = new List(bars.Count); + var sR2 = new List(bars.Count); + var sS2 = new List(bars.Count); + var sR3 = new List(bars.Count); + var sS3 = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + sPP.Add(ind.PP); + sR1.Add(ind.R1); + sS1.Add(ind.S1); + sR2.Add(ind.R2); + sS2.Add(ind.S2); + sR3.Add(ind.R3); + sS3.Add(ind.S3); + } + + // BatchAll + int len = bars.Count; + var ppOut = new double[len]; + var r1Out = new double[len]; + var s1Out = new double[len]; + var r2Out = new double[len]; + var s2Out = new double[len]; + var r3Out = new double[len]; + var s3Out = new double[len]; + + Pivotfib.BatchAll( + bars.HighValues, bars.LowValues, bars.CloseValues, + ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(ppOut[i])); continue; } + Assert.Equal(sPP[i], ppOut[i], 10); + Assert.Equal(sR1[i], r1Out[i], 10); + Assert.Equal(sS1[i], s1Out[i], 10); + Assert.Equal(sR2[i], r2Out[i], 10); + Assert.Equal(sS2[i], s2Out[i], 10); + Assert.Equal(sR3[i], r3Out[i], 10); + Assert.Equal(sS3[i], s3Out[i], 10); + } + } + + [Fact] + public void EventBased_MatchesStreaming() + { + var bars = CreateGbmBars(); + + // Streaming + var ind1 = new Pivotfib(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind1.Update(bars[i], isNew: true); + streamResults.Add(ind1.PP); + } + + // Event-based via Update(TBarSeries) + var ind2 = new Pivotfib(); + var batchTSeries = ind2.Update(bars); + + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(batchTSeries[i].Value)) + { + continue; + } + Assert.Equal(streamResults[i], batchTSeries[i].Value, 10); + } + } +} + +// ── G) Span API Tests ─────────────────────────────────────────────── +public sealed class PivotfibSpanTests +{ + [Fact] + public void Batch_MismatchedInputLengths_Throws() + { + var high = new double[10]; + var low = new double[9]; + var close = new double[10]; + var output = new double[10]; + Assert.Throws(() => Pivotfib.Batch(high, low, close, output)); + } + + [Fact] + public void Batch_OutputTooShort_Throws() + { + var high = new double[10]; + var low = new double[10]; + var close = new double[10]; + var output = new double[5]; + Assert.Throws(() => Pivotfib.Batch(high, low, close, output)); + } + + [Fact] + public void BatchAll_MismatchedInputLengths_Throws() + { + var high = new double[10]; + var low = new double[9]; + var close = new double[10]; + var pp = new double[10]; + var r1 = new double[10]; + var s1 = new double[10]; + var r2 = new double[10]; + var s2 = new double[10]; + var r3 = new double[10]; + var s3 = new double[10]; + Assert.Throws(() => Pivotfib.BatchAll(high, low, close, pp, r1, s1, r2, s2, r3, s3)); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var high = new double[10]; + var low = new double[10]; + var close = new double[10]; + var pp = new double[5]; + var r1 = new double[10]; + var s1 = new double[10]; + var r2 = new double[10]; + var s2 = new double[10]; + var r3 = new double[10]; + var s3 = new double[10]; + Assert.Throws(() => Pivotfib.BatchAll(high, low, close, pp, r1, s1, r2, s2, r3, s3)); + } +} + +// ── H) Event / Chainability Tests ─────────────────────────────────── +public sealed class PivotfibEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var ind = new Pivotfib(); + int fireCount = 0; + ind.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + Assert.Equal(2, fireCount); + } + + [Fact] + public void EventChaining_Works() + { + var bars = new TBarSeries(); + var ind = new Pivotfib(bars); + + var receivedValues = new List(); + ind.Pub += (object? _, in TValueEventArgs e) => { receivedValues.Add(e.Value.Value); }; + + var dt = DateTime.UtcNow; + bars.Add(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + bars.Add(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + Assert.True(receivedValues.Count >= 2); + } +} + +// ── I) Prime Tests ────────────────────────────────────────────────── +public sealed class PivotfibPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var ind = new Pivotfib(); + ind.Prime(bars); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } + + [Fact] + public void Prime_ReadOnlySpan_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var values = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + values[i] = bars[i].Close; + } + + var ind = new Pivotfib(); + ind.Prime(values); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} diff --git a/lib/reversals/pivotfib/Pivotfib.Validation.Tests.cs b/lib/reversals/pivotfib/Pivotfib.Validation.Tests.cs new file mode 100644 index 00000000..2d198aab --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.Validation.Tests.cs @@ -0,0 +1,248 @@ +// PIVOTFIB Validation Tests - Fibonacci Pivot Points +// Self-consistency validation: math correctness, streaming==batch, streaming==span, +// streaming==batchAll, determinism, Calculate, level ordering. +// No external library implements Fibonacci Pivot Points with bar-to-bar granularity. + +using System.Runtime.InteropServices; + +namespace QuanTAlib.Tests; + +public sealed class PivotfibValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // ── Math correctness ──────────────────────────────────────────── + [Fact] + public void MathCorrectness_FibonacciFormula() + { + var bars = CreateGbmBars(); + var ind = new Pivotfib(); + + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + + if (i < 1) { continue; } + + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + + double expectedPP = (pH + pL + pC) / 3.0; + double range = pH - pL; + + Assert.Equal(expectedPP, ind.PP, 10); + Assert.Equal(expectedPP + 0.382 * range, ind.R1, 10); + Assert.Equal(expectedPP - 0.382 * range, ind.S1, 10); + Assert.Equal(expectedPP + 0.618 * range, ind.R2, 10); + Assert.Equal(expectedPP - 0.618 * range, ind.S2, 10); + Assert.Equal(expectedPP + range, ind.R3, 10); + Assert.Equal(expectedPP - range, ind.S3, 10); + } + } + + // ── Streaming == Batch ────────────────────────────────────────── + [Fact] + public void Streaming_Matches_Batch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotfib(); + var streamPP = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamPP.Add(ind.PP); + } + + // Batch + var batchResult = Pivotfib.Batch(bars); + + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResult[i].Value)); + continue; + } + Assert.Equal(streamPP[i], batchResult[i].Value, 10); + } + } + + // ── Streaming == Span ─────────────────────────────────────────── + [Fact] + public void Streaming_Matches_Span_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotfib(); + var streamPP = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamPP.Add(ind.PP); + } + + // Span + int len = bars.Count; + var ppOut = new double[len]; + Pivotfib.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, ppOut); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(ppOut[i])); + continue; + } + Assert.Equal(streamPP[i], ppOut[i], 10); + } + } + + // ── Streaming == BatchAll (all 7 levels) ──────────────────────── + [Fact] + public void Streaming_Matches_BatchAll_AllLevels() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotfib(); + var sPP = new List(bars.Count); + var sR1 = new List(bars.Count); + var sS1 = new List(bars.Count); + var sR2 = new List(bars.Count); + var sS2 = new List(bars.Count); + var sR3 = new List(bars.Count); + var sS3 = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + sPP.Add(ind.PP); + sR1.Add(ind.R1); + sS1.Add(ind.S1); + sR2.Add(ind.R2); + sS2.Add(ind.S2); + sR3.Add(ind.R3); + sS3.Add(ind.S3); + } + + // BatchAll + int len = bars.Count; + var ppOut = new double[len]; + var r1Out = new double[len]; + var s1Out = new double[len]; + var r2Out = new double[len]; + var s2Out = new double[len]; + var r3Out = new double[len]; + var s3Out = new double[len]; + + Pivotfib.BatchAll( + bars.HighValues, bars.LowValues, bars.CloseValues, + ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(ppOut[i])); + continue; + } + Assert.Equal(sPP[i], ppOut[i], 10); + Assert.Equal(sR1[i], r1Out[i], 10); + Assert.Equal(sS1[i], s1Out[i], 10); + Assert.Equal(sR2[i], r2Out[i], 10); + Assert.Equal(sS2[i], s2Out[i], 10); + Assert.Equal(sR3[i], r3Out[i], 10); + Assert.Equal(sS3[i], s3Out[i], 10); + } + } + + // ── Determinism ───────────────────────────────────────────────── + [Fact] + public void Determinism_TwoRuns_IdenticalResults() + { + var bars = CreateGbmBars(); + + var ind1 = new Pivotfib(); + var ind2 = new Pivotfib(); + + for (int i = 0; i < bars.Count; i++) + { + ind1.Update(bars[i], isNew: true); + ind2.Update(bars[i], isNew: true); + } + + Assert.Equal(ind1.PP, ind2.PP, 15); + Assert.Equal(ind1.R1, ind2.R1, 15); + Assert.Equal(ind1.S1, ind2.S1, 15); + Assert.Equal(ind1.R2, ind2.R2, 15); + Assert.Equal(ind1.S2, ind2.S2, 15); + Assert.Equal(ind1.R3, ind2.R3, 15); + Assert.Equal(ind1.S3, ind2.S3, 15); + } + + // ── Calculate factory ─────────────────────────────────────────── + [Fact] + public void Calculate_ReturnsValidResults() + { + var bars = CreateGbmBars(); + var (results, indicator) = Pivotfib.Calculate(bars); + + Assert.NotNull(results); + Assert.NotNull(indicator); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // ── Level Ordering ────────────────────────────────────────────── + [Fact] + public void LevelOrdering_S3_LessThan_S2_LessThan_S1_LessThan_PP_LessThan_R1_LessThan_R2_LessThan_R3() + { + var bars = CreateGbmBars(); + var ind = new Pivotfib(); + + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + + if (!ind.IsHot) { continue; } + + // For Fibonacci pivots with positive range, strict ordering holds + if (bars[i - 1].High > bars[i - 1].Low) + { + Assert.True(ind.S3 < ind.S2, $"S3 ({ind.S3}) should be < S2 ({ind.S2}) at bar {i}"); + Assert.True(ind.S2 < ind.S1, $"S2 ({ind.S2}) should be < S1 ({ind.S1}) at bar {i}"); + Assert.True(ind.S1 < ind.PP, $"S1 ({ind.S1}) should be < PP ({ind.PP}) at bar {i}"); + Assert.True(ind.PP < ind.R1, $"PP ({ind.PP}) should be < R1 ({ind.R1}) at bar {i}"); + Assert.True(ind.R1 < ind.R2, $"R1 ({ind.R1}) should be < R2 ({ind.R2}) at bar {i}"); + Assert.True(ind.R2 < ind.R3, $"R2 ({ind.R2}) should be < R3 ({ind.R3}) at bar {i}"); + } + } + } + + // ── Symmetry ──────────────────────────────────────────────────── + [Fact] + public void Symmetry_DistancesAboveAndBelowPP_AreEqual() + { + var bars = CreateGbmBars(); + var ind = new Pivotfib(); + + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + + if (!ind.IsHot) { continue; } + + // Fibonacci pivots are symmetric: R_n - PP == PP - S_n + Assert.Equal(ind.R1 - ind.PP, ind.PP - ind.S1, 10); + Assert.Equal(ind.R2 - ind.PP, ind.PP - ind.S2, 10); + Assert.Equal(ind.R3 - ind.PP, ind.PP - ind.S3, 10); + } + } +} diff --git a/lib/reversals/pivotfib/Pivotfib.cs b/lib/reversals/pivotfib/Pivotfib.cs new file mode 100644 index 00000000..f78a1a2e --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.cs @@ -0,0 +1,425 @@ +// PIVOTFIB: Fibonacci Pivot Points +// Calculates 7 support/resistance levels using Fibonacci ratios applied to previous bar's HLC range. +// Fibonacci retracement levels (38.2%, 61.8%, 100%) centered on the pivot point. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOTFIB: Fibonacci Pivot Points +/// +/// +/// Computes 7 horizontal support/resistance levels from the previous bar's +/// high, low, and close using Fibonacci ratios. The central pivot point (PP) +/// is the arithmetic mean of HLC; resistance and support levels are derived +/// by adding/subtracting Fibonacci proportions of the prior bar's range. +/// +/// Calculation (using previous bar's H, L, C): +/// +/// PP = (H + L + C) / 3 +/// range = H - L +/// R1 = PP + 0.382 * range S1 = PP - 0.382 * range +/// R2 = PP + 0.618 * range S2 = PP - 0.618 * range +/// R3 = PP + 1.000 * range S3 = PP - 1.000 * range +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's HLC +/// - 7 outputs: PP, R1, R2, R3, S1, S2, S3 +/// - WarmupPeriod = 2 (need previous bar's HLC) +/// - No configurable parameters +/// - Levels remain constant until a new bar arrives +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivotfib : ITValuePublisher +{ + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: (prevH + prevL + prevC) / 3 + public double PP { get; private set; } + + /// Resistance 1: PP + 0.382 * range + public double R1 { get; private set; } + + /// Resistance 2: PP + 0.618 * range + public double R2 { get; private set; } + + /// Resistance 3: PP + 1.000 * range + public double R3 { get; private set; } + + /// Support 1: PP - 0.382 * range + public double S1 { get; private set; } + + /// Support 2: PP - 0.618 * range + public double S2 { get; private set; } + + /// Support 3: PP - 1.000 * range + public double S3 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Fibonacci Pivot Points indicator. + /// + public Pivotfib() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + + Name = "Pivotfib"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates a Fibonacci Pivot Points indicator chained to a TBarSeries source. + /// + public Pivotfib(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store HLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute pivot levels from PREVIOUS bar's HLC + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + PP = pp; + R1 = Math.FusedMultiplyAdd(0.382, range, pp); // pp + 0.382 * range + S1 = Math.FusedMultiplyAdd(-0.382, range, pp); // pp - 0.382 * range + R2 = Math.FusedMultiplyAdd(0.618, range, pp); // pp + 0.618 * range + S2 = Math.FusedMultiplyAdd(-0.618, range, pp); // pp - 0.618 * range + R3 = pp + range; // pp + 1.000 * range + S3 = pp - range; // pp - 1.000 * range + + // Store current bar's HLC as "previous" for next bar + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + } + + /// + /// Batch computation of Fibonacci Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute from previous bar's HLC + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + ppOutput[i] = (pH + pL + pC) / 3.0; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 7 Fibonacci Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out, + Span r2Out, + Span s2Out, + Span r3Out, + Span s3Out) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + if (r2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r2Out)); } + if (s2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s2Out)); } + if (r3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r3Out)); } + if (s3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s3Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + r2Out[0] = double.NaN; + s2Out[0] = double.NaN; + r3Out[0] = double.NaN; + s3Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double pp = (pH + pL + pC) / 3.0; + double range = pH - pL; + + ppOut[i] = pp; + r1Out[i] = Math.FusedMultiplyAdd(0.382, range, pp); + s1Out[i] = Math.FusedMultiplyAdd(-0.382, range, pp); + r2Out[i] = Math.FusedMultiplyAdd(0.618, range, pp); + s2Out[i] = Math.FusedMultiplyAdd(-0.618, range, pp); + r3Out[i] = pp + range; + s3Out[i] = pp - range; + } + } + + public static (TSeries Results, Pivotfib Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivotfib(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivotfib/Pivotfib.md b/lib/reversals/pivotfib/Pivotfib.md new file mode 100644 index 00000000..6db651d8 --- /dev/null +++ b/lib/reversals/pivotfib/Pivotfib.md @@ -0,0 +1,72 @@ +# PIVOTFIB: Fibonacci Pivot Points + +## Overview +Fibonacci Pivot Points apply Fibonacci retracement ratios (38.2%, 61.8%, 100%) to the standard pivot point formula. The central pivot (PP) uses the classic HLC/3 calculation, while support and resistance levels are derived by adding or subtracting Fibonacci proportions of the previous bar's trading range. + +## Origin and Sources +- **Concept**: Adaptation of Leonardo Fibonacci's ratios (derived from the Fibonacci sequence) to traditional pivot point analysis +- **Foundation**: Standard pivot points combined with Fibonacci retracement levels (0.382, 0.618, 1.000) + +## Formula + +Using previous bar's High (H), Low (L), Close (C): + +``` +PP = (H + L + C) / 3 +range = H - L + +R1 = PP + 0.382 × range S1 = PP - 0.382 × range +R2 = PP + 0.618 × range S2 = PP - 0.618 × range +R3 = PP + 1.000 × range S3 = PP - 1.000 × range +``` + +### Known Values Example +For H = 110, L = 90, C = 100: +- PP = 100.0, range = 20 +- R1 = 107.64, S1 = 92.36 +- R2 = 112.36, S2 = 87.64 +- R3 = 120.00, S3 = 80.00 + +## Key Properties +- **Symmetry**: R_n - PP = PP - S_n for all levels +- **Level ordering**: S3 < S2 < S1 < PP < R1 < R2 < R3 (when range > 0) +- **Fibonacci ratios**: Distances from PP are proportional to 0.382, 0.618, and 1.000 of the range +- **Golden ratio relationship**: 0.618 ≈ φ - 1, where φ = (1 + √5) / 2; 0.382 = 1 - 0.618 + +## Usage +```csharp +// Streaming +var fib = new Pivotfib(); +var result = fib.Update(bar); +double pp = fib.PP; +double r1 = fib.R1; // 38.2% resistance +double r2 = fib.R2; // 61.8% resistance +double r3 = fib.R3; // 100% resistance +double s1 = fib.S1; // 38.2% support +double s2 = fib.S2; // 61.8% support +double s3 = fib.S3; // 100% support + +// Batch +var results = Pivotfib.Batch(bars); + +// All 7 levels at once +Pivotfib.BatchAll(high, low, close, ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); +``` + +## Comparison with Other Pivot Variants + +| Variant | R/S Formula | Levels | Ratios Used | +|---------|------------|--------|-------------| +| **PIVOT** (Classic) | Arithmetic from PP | 7 | 1×, 2× range | +| **PIVOTFIB** | Fibonacci × range | 7 | 0.382, 0.618, 1.000 | +| **PIVOTCAM** (Camarilla) | Close ± ratio × range | 9 | 1.1/12 series | +| **PIVOTEXT** (Extended) | Arithmetic extended | 11 | 1×–4× range | +| **PIVOTDEM** (DeMark) | Conditional X/4 | 3 | Direction-based | + +## Implementation Details +- **WarmupPeriod**: 2 bars (need previous bar's HLC) +- **Parameters**: None +- **Outputs**: 7 (PP, R1, R2, R3, S1, S2, S3) +- **Input**: TBar (OHLCV) +- **Complexity**: O(1) per bar +- **Uses FMA**: `Math.FusedMultiplyAdd` for R1/S1/R2/S2 computations diff --git a/lib/reversals/pivotwood/Pivotwood.Quantower.Tests.cs b/lib/reversals/pivotwood/Pivotwood.Quantower.Tests.cs new file mode 100644 index 00000000..4289c36c --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.Quantower.Tests.cs @@ -0,0 +1,124 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PivotwoodIndicatorTests +{ + [Fact] + public void PivotwoodIndicator_Constructor_SetsDefaults() + { + var indicator = new PivotwoodIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.Contains("PIVOTWOOD", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PivotwoodIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PivotwoodIndicator(); + + Assert.Equal(0, PivotwoodIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PivotwoodIndicator_ShortName_IsPivotwood() + { + var indicator = new PivotwoodIndicator(); + indicator.Initialize(); + + Assert.Contains("PIVOTWOOD", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PivotwoodIndicator_SourceCodeLink_IsValid() + { + var indicator = new PivotwoodIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Pivotwood", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PivotwoodIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PivotwoodIndicator(); + + indicator.Initialize(); + + // 7 line series: PP, R1, R2, R3, S1, S2, S3 + Assert.Equal(7, indicator.LinesSeries.Count); + } + + [Fact] + public void PivotwoodIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PivotwoodIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + double basePrice = 100 + i * 2; + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // PP is index 0 + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotwoodIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PivotwoodIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 5; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(5), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pp = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pp) || double.IsNaN(pp)); + } + + [Fact] + public void PivotwoodIndicator_SevenLineSeries_ArePresent() + { + var indicator = new PivotwoodIndicator(); + indicator.Initialize(); + + // PP=0, R1=1, R2=2, R3=3, S1=4, S2=5, S3=6 + Assert.Equal(7, indicator.LinesSeries.Count); + Assert.Contains("PP", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("R1", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("S1", indicator.LinesSeries[4].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void PivotwoodIndicator_Description_IsSet() + { + var indicator = new PivotwoodIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("Woodie", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/pivotwood/Pivotwood.Quantower.cs b/lib/reversals/pivotwood/Pivotwood.Quantower.cs new file mode 100644 index 00000000..6f8acc8b --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.Quantower.cs @@ -0,0 +1,72 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PivotwoodIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Pivotwood _indicator = null!; + private readonly LineSeries _ppSeries; + private readonly LineSeries _r1Series; + private readonly LineSeries _r2Series; + private readonly LineSeries _r3Series; + private readonly LineSeries _s1Series; + private readonly LineSeries _s2Series; + private readonly LineSeries _s3Series; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "PIVOTWOOD"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/pivotwood/Pivotwood.cs"; + + public PivotwoodIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PIVOTWOOD - Woodie's Pivot Points"; + Description = "Woodie's pivot points: 7 support/resistance levels (PP, R1-R3, S1-S3) with close weighted twice in the PP calculation, biasing toward recent price action."; + + _ppSeries = new LineSeries(name: "PP", color: Color.Yellow, width: 2, style: LineStyle.Solid); + _r1Series = new LineSeries(name: "R1", color: Color.FromArgb(255, 200, 200), width: 1, style: LineStyle.Solid); + _r2Series = new LineSeries(name: "R2", color: Color.FromArgb(255, 150, 150), width: 1, style: LineStyle.Solid); + _r3Series = new LineSeries(name: "R3", color: Color.FromArgb(255, 100, 100), width: 1, style: LineStyle.Dash); + _s1Series = new LineSeries(name: "S1", color: Color.FromArgb(200, 255, 200), width: 1, style: LineStyle.Solid); + _s2Series = new LineSeries(name: "S2", color: Color.FromArgb(150, 255, 150), width: 1, style: LineStyle.Solid); + _s3Series = new LineSeries(name: "S3", color: Color.FromArgb(100, 255, 100), width: 1, style: LineStyle.Dash); + + AddLineSeries(_ppSeries); + AddLineSeries(_r1Series); + AddLineSeries(_r2Series); + AddLineSeries(_r3Series); + AddLineSeries(_s1Series); + AddLineSeries(_s2Series); + AddLineSeries(_s3Series); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Pivotwood(); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _ppSeries.SetValue(_indicator.PP, _indicator.IsHot, ShowColdValues); + _r1Series.SetValue(_indicator.R1, _indicator.IsHot, ShowColdValues); + _r2Series.SetValue(_indicator.R2, _indicator.IsHot, ShowColdValues); + _r3Series.SetValue(_indicator.R3, _indicator.IsHot, ShowColdValues); + _s1Series.SetValue(_indicator.S1, _indicator.IsHot, ShowColdValues); + _s2Series.SetValue(_indicator.S2, _indicator.IsHot, ShowColdValues); + _s3Series.SetValue(_indicator.S3, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/pivotwood/Pivotwood.Tests.cs b/lib/reversals/pivotwood/Pivotwood.Tests.cs new file mode 100644 index 00000000..ab0d63dc --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.Tests.cs @@ -0,0 +1,548 @@ +// PIVOTWOOD Tests - Woodie's Pivot Points + +namespace QuanTAlib.Tests; + +// ── A) Constructor Tests ──────────────────────────────────────────── +public sealed class PivotwoodConstructorTests +{ + [Fact] + public void DefaultConstructor_SetsExpectedDefaults() + { + var ind = new Pivotwood(); + Assert.Equal("Pivotwood", ind.Name); + Assert.Equal(2, ind.WarmupPeriod); + Assert.False(ind.IsHot); + Assert.True(double.IsNaN(ind.PP)); + } + + [Fact] + public void SourceConstructor_PrimesFromSource() + { + var bars = new TBarSeries(); + var dt = DateTime.UtcNow; + bars.Add(new TBar(dt, 110, 110, 90, 100, 1000)); + bars.Add(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000)); + + var ind = new Pivotwood(bars); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} + +// ── B) Basic Calculation Tests ────────────────────────────────────── +public sealed class PivotwoodBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var ind = new Pivotwood(); + var bar = new TBar(DateTime.UtcNow, 110, 110, 90, 100, 1000); + TValue result = ind.Update(bar); + Assert.IsType(result); + } + + [Fact] + public void Last_IsAccessible() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + Assert.True(ind.IsHot); + Assert.True(double.IsFinite(ind.Last.Value)); + } + + [Fact] + public void KnownValues_WoodieLevels() + { + // H=110, L=90, C=100 → PP=(110+90+200)/4=100, range=20 + // R1=2*100-90=110, S1=2*100-110=90 + // R2=100+20=120, S2=100-20=80 + // R3=110+2*(100-90)=130, S3=90-2*(110-100)=70 + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + Assert.Equal(100.0, ind.PP, 10); + Assert.Equal(110.0, ind.R1, 10); + Assert.Equal(90.0, ind.S1, 10); + Assert.Equal(120.0, ind.R2, 10); + Assert.Equal(80.0, ind.S2, 10); + Assert.Equal(130.0, ind.R3, 10); + Assert.Equal(70.0, ind.S3, 10); + } + + [Fact] + public void LevelOrdering_S3_LessThan_S2_LessThan_S1_LessThan_PP_LessThan_R1_LessThan_R2_LessThan_R3() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + Assert.True(ind.S3 < ind.S2); + Assert.True(ind.S2 < ind.S1); + Assert.True(ind.S1 < ind.PP); + Assert.True(ind.PP < ind.R1); + Assert.True(ind.R1 < ind.R2); + Assert.True(ind.R2 < ind.R3); + } + + [Fact] + public void WoodieFormula_CloseWeightedTwice() + { + // Verify PP = (H + L + 2C) / 4 (NOT (H+L+C)/3) + // H=120, L=80, C=110 → PP=(120+80+220)/4 = 420/4 = 105 + // Classic PP would be (120+80+110)/3 = 103.33 + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 120, 120, 80, 110, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 105, 105, 95, 100, 1000), isNew: true); + + Assert.Equal(105.0, ind.PP, 10); + } + + [Fact] + public void Name_ReturnsExpectedString() + { + var ind = new Pivotwood(); + Assert.Equal("Pivotwood", ind.Name); + } +} + +// ── C) State + Bar Correction Tests ───────────────────────────────── +public sealed class PivotwoodStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double pp1 = ind.PP; + + ind.Update(new TBar(dt.AddMinutes(2), 120, 120, 100, 110, 1000), isNew: true); + double pp2 = ind.PP; + + Assert.NotEqual(pp1, pp2); + } + + [Fact] + public void IsNew_False_RewritesCurrentBar() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double pp1 = ind.PP; + + // Correction: rewrite the same bar + ind.Update(new TBar(dt.AddMinutes(1), 120, 120, 100, 110, 1000), isNew: false); + double pp2 = ind.PP; + + // PP should be unchanged (still based on previous bar H=110,L=90,C=100) + Assert.Equal(pp1, pp2, 10); + } + + [Fact] + public void IterativeCorrections_RestoreState() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + double ppBefore = ind.PP; + + // Apply multiple corrections + ind.Update(new TBar(dt.AddMinutes(1), 200, 200, 50, 125, 1000), isNew: false); + ind.Update(new TBar(dt.AddMinutes(1), 300, 300, 10, 155, 1000), isNew: false); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: false); + + Assert.Equal(ppBefore, ind.PP, 10); + } + + [Fact] + public void Reset_ClearsAllState() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + Assert.True(ind.IsHot); + + ind.Reset(); + Assert.False(ind.IsHot); + Assert.True(double.IsNaN(ind.PP)); + Assert.True(double.IsNaN(ind.R1)); + Assert.True(double.IsNaN(ind.S1)); + } + + [Fact] + public void Reset_ThenReplay_MatchesOriginal() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + var bar1 = new TBar(dt, 110, 110, 90, 100, 1000); + var bar2 = new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000); + var bar3 = new TBar(dt.AddMinutes(2), 120, 120, 100, 110, 1000); + + ind.Update(bar1, isNew: true); + ind.Update(bar2, isNew: true); + ind.Update(bar3, isNew: true); + double ppOriginal = ind.PP; + double r1Original = ind.R1; + double s1Original = ind.S1; + + ind.Reset(); + ind.Update(bar1, isNew: true); + ind.Update(bar2, isNew: true); + ind.Update(bar3, isNew: true); + + Assert.Equal(ppOriginal, ind.PP, 10); + Assert.Equal(r1Original, ind.R1, 10); + Assert.Equal(s1Original, ind.S1, 10); + } +} + +// ── D) Warmup / Convergence Tests ─────────────────────────────────── +public sealed class PivotwoodWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmupPeriod() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + Assert.False(ind.IsHot); + + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + Assert.True(ind.IsHot); + } + + [Fact] + public void WarmupPeriod_IsTwo() + { + var ind = new Pivotwood(); + Assert.Equal(2, ind.WarmupPeriod); + } +} + +// ── E) Robustness Tests ───────────────────────────────────────────── +public sealed class PivotwoodRobustnessTests +{ + [Fact] + public void NaN_UsesLastValidValue() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + // Feed NaN bar - should use last valid values and still produce valid PP + ind.Update(new TBar(dt.AddMinutes(2), double.NaN, double.NaN, double.NaN, double.NaN, 0), isNew: true); + Assert.False(double.IsNaN(ind.PP)); + } + + [Fact] + public void Infinity_UsesLastValidValue() + { + var ind = new Pivotwood(); + var dt = DateTime.UtcNow; + + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + ind.Update(new TBar(dt.AddMinutes(2), double.PositiveInfinity, double.PositiveInfinity, + double.NegativeInfinity, double.PositiveInfinity, 0), isNew: true); + Assert.False(double.IsNaN(ind.PP)); + Assert.True(double.IsFinite(ind.PP)); + } + + [Fact] + public void BatchNaN_Safe() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var ind = new Pivotwood(); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + } + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} + +// ── F) Consistency Tests ──────────────────────────────────────────── +public sealed class PivotwoodConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_Matches_Batch() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotwood(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamResults.Add(ind.PP); + } + + // Batch + var batchResult = Pivotwood.Batch(bars); + + Assert.Equal(bars.Count, batchResult.Count); + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(batchResult[i].Value)) + { + continue; + } + Assert.Equal(streamResults[i], batchResult[i].Value, 10); + } + } + + [Fact] + public void Streaming_Matches_Span() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotwood(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamResults.Add(ind.PP); + } + + // Span + int len = bars.Count; + var ppOut = new double[len]; + Pivotwood.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, ppOut); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(ppOut[i])) + { + continue; + } + Assert.Equal(streamResults[i], ppOut[i], 10); + } + } + + [Fact] + public void Streaming_Matches_BatchAll() + { + var bars = CreateGbmBars(); + + // Streaming - collect all 7 levels + var ind = new Pivotwood(); + var sPP = new List(bars.Count); + var sR1 = new List(bars.Count); + var sS1 = new List(bars.Count); + var sR2 = new List(bars.Count); + var sS2 = new List(bars.Count); + var sR3 = new List(bars.Count); + var sS3 = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + sPP.Add(ind.PP); + sR1.Add(ind.R1); + sS1.Add(ind.S1); + sR2.Add(ind.R2); + sS2.Add(ind.S2); + sR3.Add(ind.R3); + sS3.Add(ind.S3); + } + + // BatchAll + int len = bars.Count; + var ppOut = new double[len]; + var r1Out = new double[len]; + var s1Out = new double[len]; + var r2Out = new double[len]; + var s2Out = new double[len]; + var r3Out = new double[len]; + var s3Out = new double[len]; + + Pivotwood.BatchAll( + bars.HighValues, bars.LowValues, bars.CloseValues, + ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(sPP[i])) { Assert.True(double.IsNaN(ppOut[i])); continue; } + Assert.Equal(sPP[i], ppOut[i], 10); + Assert.Equal(sR1[i], r1Out[i], 10); + Assert.Equal(sS1[i], s1Out[i], 10); + Assert.Equal(sR2[i], r2Out[i], 10); + Assert.Equal(sS2[i], s2Out[i], 10); + Assert.Equal(sR3[i], r3Out[i], 10); + Assert.Equal(sS3[i], s3Out[i], 10); + } + } + + [Fact] + public void EventBased_MatchesStreaming() + { + var bars = CreateGbmBars(); + + // Streaming + var ind1 = new Pivotwood(); + var streamResults = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind1.Update(bars[i], isNew: true); + streamResults.Add(ind1.PP); + } + + // Event-based via Update(TBarSeries) + var ind2 = new Pivotwood(); + var batchTSeries = ind2.Update(bars); + + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamResults[i]) && double.IsNaN(batchTSeries[i].Value)) + { + continue; + } + Assert.Equal(streamResults[i], batchTSeries[i].Value, 10); + } + } +} + +// ── G) Span API Tests ─────────────────────────────────────────────── +public sealed class PivotwoodSpanTests +{ + [Fact] + public void Batch_MismatchedInputLengths_Throws() + { + var high = new double[10]; + var low = new double[9]; + var close = new double[10]; + var output = new double[10]; + Assert.Throws(() => Pivotwood.Batch(high, low, close, output)); + } + + [Fact] + public void Batch_OutputTooShort_Throws() + { + var high = new double[10]; + var low = new double[10]; + var close = new double[10]; + var output = new double[5]; + Assert.Throws(() => Pivotwood.Batch(high, low, close, output)); + } + + [Fact] + public void BatchAll_MismatchedInputLengths_Throws() + { + var high = new double[10]; + var low = new double[9]; + var close = new double[10]; + var pp = new double[10]; + var r1 = new double[10]; + var s1 = new double[10]; + var r2 = new double[10]; + var s2 = new double[10]; + var r3 = new double[10]; + var s3 = new double[10]; + Assert.Throws(() => Pivotwood.BatchAll(high, low, close, pp, r1, s1, r2, s2, r3, s3)); + } + + [Fact] + public void BatchAll_OutputTooShort_Throws() + { + var high = new double[10]; + var low = new double[10]; + var close = new double[10]; + var pp = new double[5]; + var r1 = new double[10]; + var s1 = new double[10]; + var r2 = new double[10]; + var s2 = new double[10]; + var r3 = new double[10]; + var s3 = new double[10]; + Assert.Throws(() => Pivotwood.BatchAll(high, low, close, pp, r1, s1, r2, s2, r3, s3)); + } +} + +// ── H) Event / Chainability Tests ─────────────────────────────────── +public sealed class PivotwoodEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var ind = new Pivotwood(); + int fireCount = 0; + ind.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + var dt = DateTime.UtcNow; + ind.Update(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + ind.Update(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + Assert.Equal(2, fireCount); + } + + [Fact] + public void EventChaining_Works() + { + var bars = new TBarSeries(); + var ind = new Pivotwood(bars); + + var receivedValues = new List(); + ind.Pub += (object? _, in TValueEventArgs e) => { receivedValues.Add(e.Value.Value); }; + + var dt = DateTime.UtcNow; + bars.Add(new TBar(dt, 110, 110, 90, 100, 1000), isNew: true); + bars.Add(new TBar(dt.AddMinutes(1), 115, 115, 95, 105, 1000), isNew: true); + + Assert.True(receivedValues.Count >= 2); + } +} + +// ── I) Prime Tests ────────────────────────────────────────────────── +public sealed class PivotwoodPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var ind = new Pivotwood(); + ind.Prime(bars); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } + + [Fact] + public void Prime_ReadOnlySpan_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + var values = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + values[i] = bars[i].Close; + } + + var ind = new Pivotwood(); + ind.Prime(values); + Assert.True(ind.IsHot); + Assert.False(double.IsNaN(ind.PP)); + } +} diff --git a/lib/reversals/pivotwood/Pivotwood.Validation.Tests.cs b/lib/reversals/pivotwood/Pivotwood.Validation.Tests.cs new file mode 100644 index 00000000..4935b809 --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.Validation.Tests.cs @@ -0,0 +1,256 @@ +// PIVOTWOOD Validation Tests - Woodie's Pivot Points +// Self-consistency validation: math correctness, streaming==batch, streaming==span, +// streaming==batchAll, determinism, Calculate, level ordering. +// No external library implements Woodie's Pivot Points with bar-to-bar granularity. + +using System.Runtime.InteropServices; + +namespace QuanTAlib.Tests; + +public sealed class PivotwoodValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // ── Math correctness ──────────────────────────────────────────── + [Fact] + public void MathCorrectness_WoodieFormula() + { + var bars = CreateGbmBars(); + var ind = new Pivotwood(); + + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + + if (i < 1) { continue; } + + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + + double expectedPP = (pH + pL + 2.0 * pC) / 4.0; + double range = pH - pL; + + Assert.Equal(expectedPP, ind.PP, 10); + Assert.Equal(2.0 * expectedPP - pL, ind.R1, 10); + Assert.Equal(2.0 * expectedPP - pH, ind.S1, 10); + Assert.Equal(expectedPP + range, ind.R2, 10); + Assert.Equal(expectedPP - range, ind.S2, 10); + Assert.Equal(pH + 2.0 * (expectedPP - pL), ind.R3, 10); + Assert.Equal(pL - 2.0 * (pH - expectedPP), ind.S3, 10); + } + } + + // ── Streaming == Batch ────────────────────────────────────────── + [Fact] + public void Streaming_Matches_Batch_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotwood(); + var streamPP = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamPP.Add(ind.PP); + } + + // Batch + var batchResult = Pivotwood.Batch(bars); + + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(batchResult[i].Value)); + continue; + } + Assert.Equal(streamPP[i], batchResult[i].Value, 10); + } + } + + // ── Streaming == Span ─────────────────────────────────────────── + [Fact] + public void Streaming_Matches_Span_PP() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotwood(); + var streamPP = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + streamPP.Add(ind.PP); + } + + // Span + int len = bars.Count; + var ppOut = new double[len]; + Pivotwood.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, ppOut); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(streamPP[i])) + { + Assert.True(double.IsNaN(ppOut[i])); + continue; + } + Assert.Equal(streamPP[i], ppOut[i], 10); + } + } + + // ── Streaming == BatchAll (all 7 levels) ──────────────────────── + [Fact] + public void Streaming_Matches_BatchAll_AllLevels() + { + var bars = CreateGbmBars(); + + // Streaming + var ind = new Pivotwood(); + var sPP = new List(bars.Count); + var sR1 = new List(bars.Count); + var sS1 = new List(bars.Count); + var sR2 = new List(bars.Count); + var sS2 = new List(bars.Count); + var sR3 = new List(bars.Count); + var sS3 = new List(bars.Count); + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + sPP.Add(ind.PP); + sR1.Add(ind.R1); + sS1.Add(ind.S1); + sR2.Add(ind.R2); + sS2.Add(ind.S2); + sR3.Add(ind.R3); + sS3.Add(ind.S3); + } + + // BatchAll + int len = bars.Count; + var ppOut = new double[len]; + var r1Out = new double[len]; + var s1Out = new double[len]; + var r2Out = new double[len]; + var s2Out = new double[len]; + var r3Out = new double[len]; + var s3Out = new double[len]; + + Pivotwood.BatchAll( + bars.HighValues, bars.LowValues, bars.CloseValues, + ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); + + for (int i = 0; i < len; i++) + { + if (double.IsNaN(sPP[i])) + { + Assert.True(double.IsNaN(ppOut[i])); + continue; + } + Assert.Equal(sPP[i], ppOut[i], 10); + Assert.Equal(sR1[i], r1Out[i], 10); + Assert.Equal(sS1[i], s1Out[i], 10); + Assert.Equal(sR2[i], r2Out[i], 10); + Assert.Equal(sS2[i], s2Out[i], 10); + Assert.Equal(sR3[i], r3Out[i], 10); + Assert.Equal(sS3[i], s3Out[i], 10); + } + } + + // ── Determinism ───────────────────────────────────────────────── + [Fact] + public void Determinism_TwoRuns_IdenticalResults() + { + var bars = CreateGbmBars(); + + var ind1 = new Pivotwood(); + var ind2 = new Pivotwood(); + + for (int i = 0; i < bars.Count; i++) + { + ind1.Update(bars[i], isNew: true); + ind2.Update(bars[i], isNew: true); + } + + Assert.Equal(ind1.PP, ind2.PP, 15); + Assert.Equal(ind1.R1, ind2.R1, 15); + Assert.Equal(ind1.S1, ind2.S1, 15); + Assert.Equal(ind1.R2, ind2.R2, 15); + Assert.Equal(ind1.S2, ind2.S2, 15); + Assert.Equal(ind1.R3, ind2.R3, 15); + Assert.Equal(ind1.S3, ind2.S3, 15); + } + + // ── Calculate factory ─────────────────────────────────────────── + [Fact] + public void Calculate_ReturnsValidResults() + { + var bars = CreateGbmBars(); + var (results, indicator) = Pivotwood.Calculate(bars); + + Assert.NotNull(results); + Assert.NotNull(indicator); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // ── Level Ordering ────────────────────────────────────────────── + [Fact] + public void LevelOrdering_S3_LessThan_S2_LessThan_S1_LessThan_PP_LessThan_R1_LessThan_R2_LessThan_R3() + { + var bars = CreateGbmBars(); + var ind = new Pivotwood(); + + for (int i = 0; i < bars.Count; i++) + { + ind.Update(bars[i], isNew: true); + + if (!ind.IsHot) { continue; } + + // For Woodie's pivots with positive range, strict ordering holds + if (bars[i - 1].High > bars[i - 1].Low) + { + Assert.True(ind.S3 < ind.S2, $"S3 ({ind.S3}) should be < S2 ({ind.S2}) at bar {i}"); + Assert.True(ind.S2 < ind.S1, $"S2 ({ind.S2}) should be < S1 ({ind.S1}) at bar {i}"); + Assert.True(ind.S1 < ind.PP, $"S1 ({ind.S1}) should be < PP ({ind.PP}) at bar {i}"); + Assert.True(ind.PP < ind.R1, $"PP ({ind.PP}) should be < R1 ({ind.R1}) at bar {i}"); + Assert.True(ind.R1 < ind.R2, $"R1 ({ind.R1}) should be < R2 ({ind.R2}) at bar {i}"); + Assert.True(ind.R2 < ind.R3, $"R2 ({ind.R2}) should be < R3 ({ind.R3}) at bar {i}"); + } + } + } + + // ── Close-weight bias ─────────────────────────────────────────── + [Fact] + public void CloseWeightBias_PP_DiffersFromClassicPivot() + { + var bars = CreateGbmBars(); + var woodie = new Pivotwood(); + var classic = new Pivot(); + + for (int i = 0; i < bars.Count; i++) + { + woodie.Update(bars[i], isNew: true); + classic.Update(bars[i], isNew: true); + + if (!woodie.IsHot) { continue; } + + double pH = bars[i - 1].High; + double pL = bars[i - 1].Low; + double pC = bars[i - 1].Close; + + // When close != (H+L)/2, Woodie and Classic PP differ + double midpoint = (pH + pL) * 0.5; + if (Math.Abs(pC - midpoint) > 1e-10) + { + Assert.NotEqual(woodie.PP, classic.PP); + } + } + } +} diff --git a/lib/reversals/pivotwood/Pivotwood.cs b/lib/reversals/pivotwood/Pivotwood.cs new file mode 100644 index 00000000..363eac64 --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.cs @@ -0,0 +1,426 @@ +// PIVOTWOOD: Woodie's Pivot Points +// Calculates 7 support/resistance levels from previous bar's HLC with close weighted 2×. +// Woodie's formula weights the closing price twice, biasing the pivot toward recent action. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// PIVOTWOOD: Woodie's Pivot Points +/// +/// +/// Computes 7 horizontal support/resistance levels from the previous bar's +/// high, low, and close. Unlike classic pivots where PP = (H+L+C)/3, +/// Woodie's formula weights the close price twice: PP = (H+L+2C)/4. +/// This biases the pivot toward the most recent closing price, giving +/// more weight to where the market settled. +/// +/// Calculation (using previous bar's H, L, C): +/// +/// PP = (H + L + 2*C) / 4 (weighted close) +/// R1 = 2*PP - L S1 = 2*PP - H +/// R2 = PP + (H - L) S2 = PP - (H - L) +/// R3 = H + 2*(PP - L) S3 = L - 2*(H - PP) +/// +/// +/// Key characteristics: +/// - O(1) computation: pure arithmetic from previous bar's HLC +/// - 7 outputs: PP, R1, R2, R3, S1, S2, S3 +/// - WarmupPeriod = 2 (need previous bar's HLC) +/// - No configurable parameters +/// - Close-weighted PP biases toward recent price action +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Pivotwood : ITValuePublisher +{ + [StructLayout(LayoutKind.Auto)] + private record struct State( + double PrevHigh, + double PrevLow, + double PrevClose, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + private int _count; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Central Pivot Point: (prevH + prevL + 2*prevC) / 4 + public double PP { get; private set; } + + /// Resistance 1: 2*PP - prevL + public double R1 { get; private set; } + + /// Resistance 2: PP + range + public double R2 { get; private set; } + + /// Resistance 3: prevH + 2*(PP - prevL) + public double R3 { get; private set; } + + /// Support 1: 2*PP - prevH + public double S1 { get; private set; } + + /// Support 2: PP - range + public double S2 { get; private set; } + + /// Support 3: prevL - 2*(prevH - PP) + public double S3 { get; private set; } + + /// Primary output value (PP as TValue). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 2; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Woodie's Pivot Points indicator. + /// + public Pivotwood() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + + Name = "Pivotwood"; + WarmupPeriod = 2; + _barHandler = HandleBar; + } + + /// + /// Creates a Woodie's Pivot Points indicator chained to a TBarSeries source. + /// + public Pivotwood(TBarSeries source) + : this() + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs - substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // First bar: store HLC but cannot compute pivots yet (no previous bar) + if (_count < 2) + { + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + SetAllNaN(); + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Compute pivot levels from PREVIOUS bar's HLC + double pH = s.PrevHigh; + double pL = s.PrevLow; + double pC = s.PrevClose; + + // Woodie's PP: weight close 2× → (H + L + 2C) / 4 + double pp = (pH + pL + Math.FusedMultiplyAdd(2.0, pC, 0.0)) * 0.25; + double range = pH - pL; + + PP = pp; + R1 = Math.FusedMultiplyAdd(2.0, pp, -pL); // 2*PP - L + S1 = Math.FusedMultiplyAdd(2.0, pp, -pH); // 2*PP - H + R2 = pp + range; // PP + range + S2 = pp - range; // PP - range + R3 = Math.FusedMultiplyAdd(2.0, pp - pL, pH); // H + 2*(PP - L) + S3 = Math.FusedMultiplyAdd(-2.0, pH - pp, pL); // L - 2*(H - PP) + + // Store current bar's HLC as "previous" for next bar + s.PrevHigh = high; + s.PrevLow = low; + s.PrevClose = close; + _s = s; + + Last = new TValue(input.Time, PP); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SetAllNaN(); + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void SetAllNaN() + { + PP = double.NaN; + R1 = double.NaN; + R2 = double.NaN; + R3 = double.NaN; + S1 = double.NaN; + S2 = double.NaN; + S3 = double.NaN; + } + + /// + /// Batch computation of Woodie's Pivot Points over span data. + /// Writes PP values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOutput) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (ppOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(ppOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOutput[0] = double.NaN; + + // Remaining bars: compute Woodie's PP from previous bar's HLC + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + ppOutput[i] = (pH + pL + Math.FusedMultiplyAdd(2.0, pC, 0.0)) * 0.25; + } + } + + public static TSeries Batch(TBarSeries source) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v)); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation of all 7 Woodie's Pivot Point levels over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void BatchAll( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span ppOut, + Span r1Out, + Span s1Out, + Span r2Out, + Span s2Out, + Span r3Out, + Span s3Out) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + + int len = high.Length; + + if (ppOut.Length < len) { throw new ArgumentException("Output span too short.", nameof(ppOut)); } + if (r1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r1Out)); } + if (s1Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s1Out)); } + if (r2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r2Out)); } + if (s2Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s2Out)); } + if (r3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(r3Out)); } + if (s3Out.Length < len) { throw new ArgumentException("Output span too short.", nameof(s3Out)); } + + if (len == 0) + { + return; + } + + // First bar: no previous data + ppOut[0] = double.NaN; + r1Out[0] = double.NaN; + s1Out[0] = double.NaN; + r2Out[0] = double.NaN; + s2Out[0] = double.NaN; + r3Out[0] = double.NaN; + s3Out[0] = double.NaN; + + for (int i = 1; i < len; i++) + { + double pH = high[i - 1]; + double pL = low[i - 1]; + double pC = close[i - 1]; + + double pp = (pH + pL + Math.FusedMultiplyAdd(2.0, pC, 0.0)) * 0.25; + double range = pH - pL; + + ppOut[i] = pp; + r1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pL); + s1Out[i] = Math.FusedMultiplyAdd(2.0, pp, -pH); + r2Out[i] = pp + range; + s2Out[i] = pp - range; + r3Out[i] = Math.FusedMultiplyAdd(2.0, pp - pL, pH); + s3Out[i] = Math.FusedMultiplyAdd(-2.0, pH - pp, pL); + } + } + + public static (TSeries Results, Pivotwood Indicator) Calculate(TBarSeries source) + { + var indicator = new Pivotwood(); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/pivotwood/Pivotwood.md b/lib/reversals/pivotwood/Pivotwood.md new file mode 100644 index 00000000..c1eaec9a --- /dev/null +++ b/lib/reversals/pivotwood/Pivotwood.md @@ -0,0 +1,91 @@ +# PIVOTWOOD: Woodie's Pivot Points + +## Overview + +Woodie's Pivot Points weight the closing price twice in the pivot calculation, biasing the central pivot toward where the market actually settled rather than treating high, low, and close equally. This close-weighted approach gives more emphasis to recent price action, making the pivot levels more responsive to the prior bar's close. + +## Origin and Sources + +- **Creator**: Ken Wood (Woodie), active trader and educator +- **Foundation**: Modification of classic floor-trader pivot points with double-weighted close +- **Philosophy**: The close is the most important price of the bar because it represents consensus; weighting it twice reflects this belief + +## Formula + +Using previous bar's High (H), Low (L), Close (C): + +``` +PP = (H + L + 2×C) / 4 +range = H - L + +R1 = 2×PP - L S1 = 2×PP - H +R2 = PP + range S2 = PP - range +R3 = H + 2×(PP - L) S3 = L - 2×(H - PP) +``` + +### Known Values Example + +For H = 110, L = 90, C = 100: + +- PP = (110 + 90 + 200) / 4 = 100.0, range = 20 +- R1 = 200 - 90 = 110.0, S1 = 200 - 110 = 90.0 +- R2 = 100 + 20 = 120.0, S2 = 100 - 20 = 80.0 +- R3 = 110 + 2×10 = 130.0, S3 = 90 - 2×10 = 70.0 + +### Close-Weight Effect + +When close differs from the midpoint of the range, Woodie's PP shifts toward the close: + +- Classic PP (H=120, L=80, C=110): (120+80+110)/3 = 103.33 +- Woodie PP (H=120, L=80, C=110): (120+80+220)/4 = 105.00 + +The 1.67-point difference biases all derived levels toward the close. + +## Key Properties + +- **Close bias**: PP shifts toward close when close != (H+L)/2 +- **Level ordering**: S3 < S2 < S1 < PP < R1 < R2 < R3 (when range > 0) +- **R2/S2 symmetry**: R2 - PP = PP - S2 = range (always symmetric) +- **R1/S1 asymmetry**: R1 - PP = PP - L, PP - S1 = H - PP (asymmetric unless close = midpoint) +- **R3/S3**: Widest levels, incorporating both the previous high/low and the pivot distance + +## Usage + +```csharp +// Streaming +var wood = new Pivotwood(); +var result = wood.Update(bar); +double pp = wood.PP; +double r1 = wood.R1; // First resistance +double r2 = wood.R2; // Second resistance +double r3 = wood.R3; // Third resistance +double s1 = wood.S1; // First support +double s2 = wood.S2; // Second support +double s3 = wood.S3; // Third support + +// Batch +var results = Pivotwood.Batch(bars); + +// All 7 levels at once +Pivotwood.BatchAll(high, low, close, ppOut, r1Out, s1Out, r2Out, s2Out, r3Out, s3Out); +``` + +## Comparison with Other Pivot Variants + +| Variant | PP Formula | R/S Formula | Levels | Key Difference | +|---------|-----------|------------|--------|----------------| +| **PIVOT** (Classic) | (H+L+C)/3 | Arithmetic from PP | 7 | Equal HLC weight | +| **PIVOTWOOD** | (H+L+2C)/4 | Mixed arithmetic | 7 | Close weighted 2x | +| **PIVOTFIB** | (H+L+C)/3 | Fibonacci x range | 7 | 0.382, 0.618, 1.000 ratios | +| **PIVOTCAM** (Camarilla) | (H+L+C)/3 | Close +/- ratio x range | 9 | 1.1/12 series | +| **PIVOTEXT** (Extended) | (H+L+C)/3 | Arithmetic extended | 11 | 1x-4x range | +| **PIVOTDEM** (DeMark) | Conditional X/4 | X/2 based | 3 | Direction-based | + +## Implementation Details + +- **WarmupPeriod**: 2 bars (need previous bar's HLC) +- **Parameters**: None +- **Outputs**: 7 (PP, R1, R2, R3, S1, S2, S3) +- **Input**: TBar (OHLCV) +- **Complexity**: O(1) per bar +- **Uses FMA**: `Math.FusedMultiplyAdd` for R1, S1, R3, S3 computations diff --git a/lib/reversals/psar/Psar.Quantower.Tests.cs b/lib/reversals/psar/Psar.Quantower.Tests.cs new file mode 100644 index 00000000..f580243a --- /dev/null +++ b/lib/reversals/psar/Psar.Quantower.Tests.cs @@ -0,0 +1,133 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class PsarIndicatorTests +{ + [Fact] + public void PsarIndicator_Constructor_SetsDefaults() + { + var indicator = new PsarIndicator(); + + Assert.Equal(0.02, indicator.AfStart); + Assert.Equal(0.02, indicator.AfIncrement); + Assert.Equal(0.20, indicator.AfMax); + Assert.True(indicator.ShowColdValues); + Assert.Contains("PSAR", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void PsarIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new PsarIndicator(); + + Assert.Equal(0, PsarIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void PsarIndicator_ShortName_IncludesParameters() + { + var indicator = new PsarIndicator { AfStart = 0.02, AfMax = 0.20 }; + indicator.Initialize(); + + Assert.Contains("PSAR", indicator.ShortName, StringComparison.Ordinal); + Assert.Contains("0.02", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void PsarIndicator_SourceCodeLink_IsValid() + { + var indicator = new PsarIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Psar", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void PsarIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new PsarIndicator(); + + indicator.Initialize(); + + // After init, line series should exist (SAR only) + Assert.Single(indicator.LinesSeries); + } + + [Fact] + public void PsarIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new PsarIndicator { AfStart = 0.02, AfIncrement = 0.02, AfMax = 0.20 }; + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 20; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + double sar = indicator.LinesSeries[0].GetValue(0); + + Assert.True(double.IsFinite(sar)); + } + + [Fact] + public void PsarIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new PsarIndicator { AfStart = 0.02, AfIncrement = 0.02, AfMax = 0.20 }; + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(10), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double sar = indicator.LinesSeries[0].GetValue(0); + + Assert.True(double.IsFinite(sar)); + } + + [Fact] + public void PsarIndicator_SingleLineSeries_IsPresent() + { + var indicator = new PsarIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 20; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + Assert.Single(indicator.LinesSeries); + Assert.True(double.IsFinite(indicator.LinesSeries[0].GetValue(0))); + } + + [Fact] + public void PsarIndicator_Description_IsSet() + { + var indicator = new PsarIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("stop", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/psar/Psar.Quantower.cs b/lib/reversals/psar/Psar.Quantower.cs new file mode 100644 index 00000000..648cf97d --- /dev/null +++ b/lib/reversals/psar/Psar.Quantower.cs @@ -0,0 +1,57 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class PsarIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Start AF", sortIndex: 0, 0.001, 1.0, 0.001, 3)] + public double AfStart { get; set; } = 0.02; + + [InputParameter("AF Increment", sortIndex: 1, 0.001, 1.0, 0.001, 3)] + public double AfIncrement { get; set; } = 0.02; + + [InputParameter("Max AF", sortIndex: 2, 0.001, 1.0, 0.01, 2)] + public double AfMax { get; set; } = 0.20; + + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Psar _indicator = null!; + private readonly LineSeries _sarSeries; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => $"PSAR({AfStart:F2},{AfIncrement:F2},{AfMax:F2})"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/psar/Psar.cs"; + + public PsarIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "PSAR - Parabolic Stop And Reverse"; + Description = "Trend-following trailing stop indicator. SAR accelerates toward price as trend progresses, flipping on reversal."; + + _sarSeries = new LineSeries(name: "SAR", color: Color.DodgerBlue, width: 2, style: LineStyle.Dot); + + AddLineSeries(_sarSeries); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Psar(AfStart, AfIncrement, AfMax); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _sarSeries.SetValue(_indicator.Sar, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/psar/Psar.Tests.cs b/lib/reversals/psar/Psar.Tests.cs new file mode 100644 index 00000000..c31be50c --- /dev/null +++ b/lib/reversals/psar/Psar.Tests.cs @@ -0,0 +1,560 @@ +// PSAR Tests - Parabolic Stop And Reverse + +namespace QuanTAlib.Tests; + +// ── A) Constructor Validation ──────────────────────────────────────────── +public sealed class PsarConstructorTests +{ + [Fact] + public void Constructor_ZeroAfStart_Throws() + { + var ex = Assert.Throws(() => new Psar(afStart: 0)); + Assert.Equal("afStart", ex.ParamName); + } + + [Fact] + public void Constructor_NegativeAfStart_Throws() + { + var ex = Assert.Throws(() => new Psar(afStart: -0.01)); + Assert.Equal("afStart", ex.ParamName); + } + + [Fact] + public void Constructor_ZeroAfIncrement_Throws() + { + var ex = Assert.Throws(() => new Psar(afIncrement: 0)); + Assert.Equal("afIncrement", ex.ParamName); + } + + [Fact] + public void Constructor_NegativeAfIncrement_Throws() + { + var ex = Assert.Throws(() => new Psar(afIncrement: -0.01)); + Assert.Equal("afIncrement", ex.ParamName); + } + + [Fact] + public void Constructor_AfMaxEqualAfStart_Throws() + { + var ex = Assert.Throws(() => new Psar(afStart: 0.02, afMax: 0.02)); + Assert.Equal("afMax", ex.ParamName); + } + + [Fact] + public void Constructor_AfMaxLessThanAfStart_Throws() + { + var ex = Assert.Throws(() => new Psar(afStart: 0.10, afMax: 0.05)); + Assert.Equal("afStart", ex.ParamName); + } + + [Fact] + public void Constructor_ValidDefaults_SetsProperties() + { + var psar = new Psar(); + + Assert.Equal(0.02, psar.AfStart); + Assert.Equal(0.02, psar.AfIncrement); + Assert.Equal(0.20, psar.AfMax); + Assert.Equal(1, psar.WarmupPeriod); + Assert.Contains("Psar", psar.Name, StringComparison.Ordinal); + } + + [Fact] + public void Constructor_CustomParams_SetsProperties() + { + var psar = new Psar(afStart: 0.01, afIncrement: 0.01, afMax: 0.10); + + Assert.Equal(0.01, psar.AfStart); + Assert.Equal(0.01, psar.AfIncrement); + Assert.Equal(0.10, psar.AfMax); + } +} + +// ── B) Basic Calculation ───────────────────────────────────────────────── +public sealed class PsarBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var psar = new Psar(); + var bar = new TBar(DateTime.UtcNow, 100, 95, 98, 97, 1000); + + TValue result = psar.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var psar = new Psar(); + var bar = new TBar(DateTime.UtcNow, 100, 95, 98, 97, 1000); + + _ = psar.Update(bar); + + Assert.True(double.IsFinite(psar.Last.Value) || double.IsNaN(psar.Last.Value)); + } + + [Fact] + public void Update_Sar_IsAccessible() + { + var psar = new Psar(); + + // Feed enough bars + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(i), + price + 2, price - 2, price + 1, price, 1000)); + } + + Assert.True(double.IsFinite(psar.Sar)); + } + + [Fact] + public void Name_ContainsParameters() + { + var psar = new Psar(afStart: 0.01, afIncrement: 0.02, afMax: 0.10); + + Assert.Contains("0.01", psar.Name, StringComparison.Ordinal); + Assert.Contains("0.10", psar.Name, StringComparison.Ordinal); + } + + [Fact] + public void FirstBar_Uptrend_SarEqualsLow() + { + var psar = new Psar(); + // Close(105) > Open(95) → long mode → SAR = low(90) + _ = psar.Update(new TBar(DateTime.UtcNow, 95, 110, 90, 105, 1000)); + + Assert.Equal(90.0, psar.Sar); + Assert.True(psar.IsLong); + } + + [Fact] + public void FirstBar_Downtrend_SarEqualsHigh() + { + var psar = new Psar(); + // Close(90) < Open(105) → short mode → SAR = high(110) + _ = psar.Update(new TBar(DateTime.UtcNow, 105, 110, 85, 90, 1000)); + + Assert.Equal(110.0, psar.Sar); + Assert.False(psar.IsLong); + } + + [Fact] + public void Sar_BelowPrice_InUptrend() + { + var psar = new Psar(); + + // Steady uptrend - SAR should trail below + for (int i = 0; i < 20; i++) + { + double price = 100.0 + i * 2; + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(i), + price + 1, price - 1, price + 0.5, price, 1000)); + } + + double lastClose = 100.0 + 19 * 2; + Assert.True(psar.Sar < lastClose, "SAR should be below price in uptrend"); + Assert.True(psar.IsLong, "Should be in long mode during uptrend"); + } + + [Fact] + public void Sar_AbovePrice_InDowntrend() + { + var psar = new Psar(); + + // Steady downtrend - SAR should trail above + for (int i = 0; i < 20; i++) + { + double price = 200.0 - i * 2; + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(i), + price + 1, price - 1, price + 0.5, price, 1000)); + } + + double lastClose = 200.0 - 19 * 2; + Assert.True(psar.Sar > lastClose, "SAR should be above price in downtrend"); + Assert.False(psar.IsLong, "Should be in short mode during downtrend"); + } + + [Fact] + public void IsHot_TrueAfterFirstBar() + { + var psar = new Psar(); + + Assert.False(psar.IsHot); + + _ = psar.Update(new TBar(DateTime.UtcNow, 100, 95, 98, 97, 1000)); + + Assert.True(psar.IsHot); + } +} + +// ── C) State + Bar Correction ──────────────────────────────────────────── +public sealed class PsarStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var psar = new Psar(); + + _ = psar.Update(new TBar(DateTime.UtcNow, 105, 95, 100, 100, 1000), isNew: true); + var first = psar.Last; + + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 110, 100, 105, 105, 1000), isNew: true); + var second = psar.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var psar = new Psar(); + var dt = DateTime.UtcNow; + + // Feed some bars to warm up + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(dt.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000), isNew: true); + } + + // New bar + _ = psar.Update(new TBar(dt.AddMinutes(5), 110, 105, 108, 107, 1000), isNew: true); + + // Correct the bar (isNew=false with different values) + _ = psar.Update(new TBar(dt.AddMinutes(5), 111, 104, 109, 108, 1000), isNew: false); + + // Another correction should produce same result + _ = psar.Update(new TBar(dt.AddMinutes(5), 111, 104, 109, 108, 1000), isNew: false); + var corrected1 = psar.Sar; + + _ = psar.Update(new TBar(dt.AddMinutes(5), 111, 104, 109, 108, 1000), isNew: false); + var corrected2 = psar.Sar; + + Assert.Equal(corrected1, corrected2); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var psar = new Psar(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(dt.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000), isNew: true); + } + + // Add new bar then correct 3 times + _ = psar.Update(new TBar(dt.AddMinutes(5), 110, 100, 108, 105, 1000), isNew: true); + + double[] results = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = psar.Update(new TBar(dt.AddMinutes(5), 112, 101, 110, 107, 1000), isNew: false); + results[i] = psar.Sar; + } + + Assert.Equal(results[0], results[1]); + Assert.Equal(results[1], results[2]); + } + + [Fact] + public void Reset_ClearsAllState() + { + var psar = new Psar(); + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000)); + } + + Assert.True(psar.IsHot); + + psar.Reset(); + + Assert.False(psar.IsHot); + Assert.True(double.IsNaN(psar.Sar)); + } +} + +// ── D) Warmup / Convergence ────────────────────────────────────────────── +public sealed class PsarWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterFirstBar() + { + var psar = new Psar(); + + Assert.False(psar.IsHot); + + _ = psar.Update(new TBar(DateTime.UtcNow, 100, 95, 98, 97, 1000)); + + Assert.True(psar.IsHot); + } + + [Fact] + public void WarmupPeriod_EqualsOne() + { + var psar = new Psar(); + + Assert.Equal(1, psar.WarmupPeriod); + } +} + +// ── E) Robustness ──────────────────────────────────────────────────────── +public sealed class PsarRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var psar = new Psar(); + var dt = DateTime.UtcNow; + + // Feed valid bars + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(dt.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000)); + } + + // Feed NaN bar + _ = psar.Update(new TBar(dt.AddMinutes(5), double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsFinite(psar.Sar)); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var psar = new Psar(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(dt.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000)); + } + + _ = psar.Update(new TBar(dt.AddMinutes(5), + double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, double.PositiveInfinity, 0)); + + Assert.True(double.IsFinite(psar.Sar)); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var psar = new Psar(); + + _ = psar.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(psar.Last.Value)); + } +} + +// ── F) Consistency ─────────────────────────────────────────────────────── +public sealed class PsarConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Psar(); + var streamResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamResults[i] = streaming.Sar; + } + + // Batch + var batchResults = Psar.Batch(bars); + + for (int i = 0; i < bars.Count; i++) + { + Assert.Equal(streamResults[i], batchResults[i].Value, precision: 10); + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var ch1 = new Psar(); + var ch2 = new Psar(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double p = prices[i]; + // TBar with equal OHLC + _ = ch1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), p, p, p, p, 0), isNew: true); + // TValue + _ = ch2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), p), isNew: true); + } + + Assert.Equal(ch1.Sar, ch2.Sar); + } + + [Fact] + public void Reversal_DetectedOnPriceCrossover() + { + var psar = new Psar(); + var dt = DateTime.UtcNow; + + // Start in uptrend + _ = psar.Update(new TBar(dt, 100, 90, 95, 105, 1000), isNew: true); + Assert.True(psar.IsLong); + + // Continue uptrend + for (int i = 1; i <= 5; i++) + { + double price = 105 + i * 2; + _ = psar.Update(new TBar(dt.AddMinutes(i), + price + 1, price - 1, price + 0.5, price, 1000), isNew: true); + } + Assert.True(psar.IsLong); + + // Sharp reversal — price drops below SAR + double sarBeforeReversal = psar.Sar; + _ = psar.Update(new TBar(dt.AddMinutes(10), + sarBeforeReversal - 5, sarBeforeReversal - 20, + sarBeforeReversal - 18, sarBeforeReversal - 15, 1000), isNew: true); + + Assert.False(psar.IsLong, "Should reverse to short after price crosses below SAR"); + } + + [Fact] + public void Update_TSeries_MatchesStreaming() + { + var bars = CreateGbmBars(100); + + // Streaming + var streaming = new Psar(); + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + } + double streamLast = streaming.Sar; + + // TSeries batch + var batch = new Psar(); + _ = batch.Update(bars); + + Assert.Equal(streamLast, batch.Sar, precision: 10); + } +} + +// ── G) Span API Tests ──────────────────────────────────────────────────── +public sealed class PsarSpanTests +{ + [Fact] + public void Batch_Span_InvalidAfStart_Throws() + { + var ex = Assert.Throws(() => + Psar.Batch(new double[10], new double[10], new double[10], new double[10], new double[10], afStart: 0)); + Assert.Equal("afStart", ex.ParamName); + } + + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Psar.Batch(new double[10], new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Psar.Batch(new double[10], new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("output", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var output = Array.Empty(); + var ex = Record.Exception(() => + Psar.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, ReadOnlySpan.Empty, output.AsSpan())); + Assert.Null(ex); + } +} + +// ── H) Event / Chainability ────────────────────────────────────────────── +public sealed class PsarEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var psar = new Psar(); + int fireCount = 0; + + psar.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = psar.Update(new TBar(DateTime.UtcNow, 100, 95, 98, 97, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var psar = new Psar(); + int fireCount = 0; + + psar.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = psar.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price + 2, price - 2, price + 1, price, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// ── I) Prime Tests ─────────────────────────────────────────────────────── +public sealed class PsarPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var psar = new Psar(); + psar.Prime(bars); + + Assert.True(psar.IsHot); + Assert.True(double.IsFinite(psar.Sar)); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var psar = new Psar(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => psar.Prime(bars)); + Assert.Null(ex); + Assert.False(psar.IsHot); + } +} diff --git a/lib/reversals/psar/Psar.Validation.Tests.cs b/lib/reversals/psar/Psar.Validation.Tests.cs new file mode 100644 index 00000000..253c9194 --- /dev/null +++ b/lib/reversals/psar/Psar.Validation.Tests.cs @@ -0,0 +1,188 @@ +// PSAR Validation Tests - Parabolic Stop And Reverse +// Cross-validated against Skender.Stock.Indicators GetParabolicSar() + +using Skender.Stock.Indicators; + +namespace QuanTAlib.Tests; + +public sealed class PsarValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // ── Cross-library: Skender ─────────────────────────────────────────── + + [Fact] + public void StreamingMatchesSkender() + { + var _data = new ValidationTestData(); + + // Skender: GetParabolicSar(accelerationStep, maxAccelerationFactor, initialFactor) + var skenderResults = _data.SkenderQuotes + .GetParabolicSar(0.02, 0.2, 0.02) + .ToList(); + + // QuanTAlib streaming + var psar = new Psar(afStart: 0.02, afIncrement: 0.02, afMax: 0.20); + var ourValues = new double[_data.Bars.Count]; + for (int i = 0; i < _data.Bars.Count; i++) + { + _ = psar.Update(_data.Bars[i], isNew: true); + ourValues[i] = psar.Sar; + } + + // Compare warm values (skip first bar where SAR is initialization) + int matched = 0; + for (int i = 2; i < skenderResults.Count && i < _data.Bars.Count; i++) + { + if (skenderResults[i].Sar.HasValue && double.IsFinite(ourValues[i])) + { + Assert.Equal( + skenderResults[i].Sar!.Value, + ourValues[i], + precision: 6); + matched++; + } + } + + Assert.True(matched > 0, "Should have matched at least one warm value"); + _data.Dispose(); + } + + // ── Self-Consistency: Streaming == Batch ────────────────────────────── + + [Fact] + public void StreamingMatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Psar(); + var streamValues = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamValues[i] = streaming.Sar; + } + + // Batch + var batchResults = Psar.Batch(bars); + + for (int i = 0; i < bars.Count; i++) + { + Assert.Equal(streamValues[i], batchResults[i].Value, precision: 10); + } + } + + // ── Self-Consistency: Streaming == Span ─────────────────────────────── + + [Fact] + public void StreamingMatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Psar(); + var streamValues = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamValues[i] = streaming.Sar; + } + + // Span + var spanOutput = new double[bars.Count]; + Psar.Batch(bars.OpenValues, bars.HighValues, bars.LowValues, bars.CloseValues, spanOutput); + + for (int i = 0; i < bars.Count; i++) + { + Assert.Equal(streamValues[i], spanOutput[i], precision: 10); + } + } + + // ── AF Sensitivity ─────────────────────────────────────────────────── + + [Fact] + public void HigherAfStart_TighterTrailingStop() + { + var bars = CreateGbmBars(count: 100); + + var slow = new Psar(afStart: 0.01, afIncrement: 0.01, afMax: 0.20); + var fast = new Psar(afStart: 0.10, afIncrement: 0.05, afMax: 0.50); + + for (int i = 0; i < bars.Count; i++) + { + _ = slow.Update(bars[i], isNew: true); + _ = fast.Update(bars[i], isNew: true); + } + + // Higher AF = more responsive = SAR closer to price + // Just verify both produce finite output (direction depends on data) + Assert.True(double.IsFinite(slow.Sar)); + Assert.True(double.IsFinite(fast.Sar)); + } + + // ── Determinism ────────────────────────────────────────────────────── + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var psar1 = new Psar(); + var psar2 = new Psar(); + + for (int i = 0; i < bars.Count; i++) + { + _ = psar1.Update(bars[i], isNew: true); + _ = psar2.Update(bars[i], isNew: true); + } + + Assert.Equal(psar1.Sar, psar2.Sar); + } + + // ── Calculate Returns Valid Indicator ───────────────────────────────── + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Psar.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + Assert.True(double.IsFinite(indicator.Sar)); + } + + // ── Reversal Count Is Reasonable ───────────────────────────────────── + + [Fact] + public void ReversalCount_IsReasonable() + { + var bars = CreateGbmBars(count: 500); + var psar = new Psar(); + + int reversals = 0; + bool prevIsLong = true; + + for (int i = 0; i < bars.Count; i++) + { + _ = psar.Update(bars[i], isNew: true); + + if (i > 0 && psar.IsLong != prevIsLong) + { + reversals++; + } + prevIsLong = psar.IsLong; + } + + // In 500 bars of GBM data, expect several reversals but not every bar + Assert.True(reversals > 5, $"Expected > 5 reversals, got {reversals}"); + Assert.True(reversals < 250, $"Expected < 250 reversals, got {reversals}"); + } +} diff --git a/lib/reversals/psar/Psar.cs b/lib/reversals/psar/Psar.cs new file mode 100644 index 00000000..235cf38b --- /dev/null +++ b/lib/reversals/psar/Psar.cs @@ -0,0 +1,477 @@ +// PSAR: Parabolic Stop And Reverse (Wilder, 1978) +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +// skipcq: CS-W1028 - Intentional sealed class with no inheritance +// skipcq: CS-R1140 - State machine requires sequential long/short logic; splitting fragments state transitions + +namespace QuanTAlib; + +/// +/// PSAR: Parabolic Stop And Reverse +/// +/// +/// Trend-following overlay indicator developed by J. Welles Wilder Jr. (1978). +/// Produces a trailing stop that accelerates toward price as the trend progresses. +/// +/// Calculation: +/// +/// Bar 0: isLong = close > open; SAR = isLong ? low : high; EP = isLong ? high : low; AF = afStart +/// Bar 1+: newSAR = SAR + AF * (EP - SAR) +/// Long: clamp newSAR ≤ min(low[1], low[2]); if low < newSAR → reverse +/// Short: clamp newSAR ≥ max(high[1], high[2]); if high > newSAR → reverse +/// On new EP: AF = min(AF + afIncrement, afMax) +/// On reversal: SAR = EP; EP = new extreme; AF = afStart; flip direction +/// +/// +/// Key characteristics: +/// - O(1) per-bar state machine with long/short mode transitions +/// - Acceleration factor ramps from afStart to afMax as trend strengthens +/// - SAR clamped to prior 2 bars' extremes to prevent crossover artifacts +/// - Default parameters: afStart=0.02, afIncrement=0.02, afMax=0.20 (Wilder's originals) +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Psar : ITValuePublisher +{ + private const double DefaultAfStart = 0.02; + private const double DefaultAfIncrement = 0.02; + private const double DefaultAfMax = 0.20; + + private readonly double _afStart; + private readonly double _afIncrement; + private readonly double _afMax; + + private int _count; + + [StructLayout(LayoutKind.Auto)] + private record struct State( + bool IsLong, + double Sar, + double Ep, + double Af, + double Prev1High, + double Prev1Low, + double Prev2High, + double Prev2Low, + double LastValidOpen, + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Initial acceleration factor. + public double AfStart => _afStart; + + /// Acceleration factor increment per new extreme. + public double AfIncrement => _afIncrement; + + /// Maximum acceleration factor. + public double AfMax => _afMax; + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Current SAR value (the stop level). + public double Sar { get; private set; } + + /// True when the PSAR is in long (uptrend) mode. + public bool IsLong => _s.IsLong; + + /// Primary output value (SAR as TValue for overlay plotting). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= 1; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Parabolic SAR indicator. + /// + /// Initial acceleration factor (default 0.02). + /// AF increment per new extreme (default 0.02). + /// Maximum acceleration factor (default 0.20). + public Psar(double afStart = DefaultAfStart, double afIncrement = DefaultAfIncrement, double afMax = DefaultAfMax) + { + if (afStart <= 0) + { + throw new ArgumentException("Start AF must be > 0.", nameof(afStart)); + } + if (afIncrement <= 0) + { + throw new ArgumentException("AF increment must be > 0.", nameof(afIncrement)); + } + if (afStart > afMax) + { + throw new ArgumentException("Start AF must be <= Max AF.", nameof(afStart)); + } + if (afMax <= afStart) + { + throw new ArgumentException("Max AF must be > Start AF.", nameof(afMax)); + } + + _afStart = afStart; + _afIncrement = afIncrement; + _afMax = afMax; + + _count = 0; + _s = new State( + IsLong: true, + Sar: double.NaN, + Ep: double.NaN, + Af: afStart, + Prev1High: double.NaN, + Prev1Low: double.NaN, + Prev2High: double.NaN, + Prev2Low: double.NaN, + LastValidOpen: double.NaN, + LastValidHigh: double.NaN, + LastValidLow: double.NaN, + LastValidClose: double.NaN); + _ps = _s; + + Name = $"Psar({afStart:F2},{afIncrement:F2},{afMax:F2})"; + WarmupPeriod = 1; + _barHandler = HandleBar; + } + + /// + /// Creates a Parabolic SAR chained to a TBarSeries source. + /// + public Psar(TBarSeries source, double afStart = DefaultAfStart, double afIncrement = DefaultAfIncrement, double afMax = DefaultAfMax) + : this(afStart, afIncrement, afMax) + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs — substitute last-valid on NaN/Infinity + double open = input.Open; + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(open)) { s.LastValidOpen = open; } + else { open = s.LastValidOpen; } + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(open) || double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + double sarResult; + + if (_count == 1) + { + // Bar 0: Initialize direction from close vs open + s.IsLong = close > open; + s.Sar = s.IsLong ? low : high; + s.Ep = s.IsLong ? high : low; + s.Af = _afStart; + s.Prev1High = high; + s.Prev1Low = low; + s.Prev2High = high; + s.Prev2Low = low; + sarResult = s.Sar; + } + else + { + // Compute new SAR: sar + af * (ep - sar) → FMA: af*ep + sar*(1-af) + double newSar = Math.FusedMultiplyAdd(s.Af, s.Ep - s.Sar, s.Sar); + + if (s.IsLong) + { + // Clamp SAR to be at or below prior lows + newSar = Math.Min(newSar, s.Prev1Low); + if (_count > 2) + { + newSar = Math.Min(newSar, s.Prev2Low); + } + + // Check for reversal: price crosses below SAR + if (low < newSar) + { + // Reverse to short + s.IsLong = false; + newSar = s.Ep; + s.Ep = low; + s.Af = _afStart; + } + else + { + // Check for new extreme point + if (high > s.Ep) + { + s.Ep = high; + s.Af = Math.Min(s.Af + _afIncrement, _afMax); + } + } + } + else + { + // Short mode: clamp SAR to be at or above prior highs + newSar = Math.Max(newSar, s.Prev1High); + if (_count > 2) + { + newSar = Math.Max(newSar, s.Prev2High); + } + + // Check for reversal: price crosses above SAR + if (high > newSar) + { + // Reverse to long + s.IsLong = true; + newSar = s.Ep; + s.Ep = high; + s.Af = _afStart; + } + else + { + // Check for new extreme point + if (low < s.Ep) + { + s.Ep = low; + s.Af = Math.Min(s.Af + _afIncrement, _afMax); + } + } + } + + s.Sar = newSar; + sarResult = newSar; + + // Shift prior bar tracking + if (isNew) + { + s.Prev2High = s.Prev1High; + s.Prev2Low = s.Prev1Low; + s.Prev1High = high; + s.Prev1Low = low; + } + else + { + // Bar correction: update current bar's values + s.Prev1High = high; + s.Prev1Low = low; + } + } + + Sar = sarResult; + _s = s; + + Last = new TValue(input.Time, sarResult); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.OpenValues, source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v), _afStart, _afIncrement, _afMax); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + _count = 0; + _s = new State( + IsLong: true, + Sar: double.NaN, + Ep: double.NaN, + Af: _afStart, + Prev1High: double.NaN, + Prev1Low: double.NaN, + Prev2High: double.NaN, + Prev2Low: double.NaN, + LastValidOpen: double.NaN, + LastValidHigh: double.NaN, + LastValidLow: double.NaN, + LastValidClose: double.NaN); + _ps = _s; + Sar = double.NaN; + Last = default; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan open, + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span output, + double afStart = DefaultAfStart, + double afIncrement = DefaultAfIncrement, + double afMax = DefaultAfMax) + { + if (afStart <= 0 || afStart > afMax) + { + throw new ArgumentException("Start AF must be > 0 and <= Max AF.", nameof(afStart)); + } + if (afIncrement <= 0) + { + throw new ArgumentException("AF increment must be > 0.", nameof(afIncrement)); + } + if (afMax <= afStart) + { + throw new ArgumentException("Max AF must be > Start AF.", nameof(afMax)); + } + if (high.Length != low.Length || high.Length != close.Length || high.Length != open.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (output.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(output)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // Compute via streaming instance for correctness (state machine prevents SIMD) + var indicator = new Psar(afStart, afIncrement, afMax); + + long baseTime = DateTime.UtcNow.Ticks; + for (int i = 0; i < len; i++) + { + _ = indicator.Update( + new TBar(baseTime + i, open[i], high[i], low[i], close[i], 0), + isNew: true); + output[i] = indicator.Sar; + } + } + + public static TSeries Batch(TBarSeries source, double afStart = DefaultAfStart, double afIncrement = DefaultAfIncrement, double afMax = DefaultAfMax) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + Batch(source.OpenValues, source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v), afStart, afIncrement, afMax); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static (TSeries Results, Psar Indicator) Calculate( + TBarSeries source, double afStart = DefaultAfStart, double afIncrement = DefaultAfIncrement, double afMax = DefaultAfMax) + { + var indicator = new Psar(afStart, afIncrement, afMax); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/psar/Psar.md b/lib/reversals/psar/Psar.md new file mode 100644 index 00000000..db438553 --- /dev/null +++ b/lib/reversals/psar/Psar.md @@ -0,0 +1,128 @@ +# PSAR: Parabolic Stop And Reverse + +> "The trend is your friend until the end when it bends." — Ed Seykota + +## Introduction + +The Parabolic Stop And Reverse (PSAR) is a trend-following overlay indicator created by J. Welles Wilder Jr. in 1978. It produces a trailing stop level that accelerates toward price as the trend extends, then flips to the opposite side when price crosses the stop. The acceleration mechanism is the key differentiator: SAR starts slow and tightens progressively, creating the characteristic parabolic curve that gives the indicator its name. Default parameters (0.02 start, 0.02 increment, 0.20 maximum) produce approximately 10–30 reversals per 500 bars on typical equity data. + +## Historical Context + +Wilder introduced PSAR alongside RSI, ATR, and ADX in *New Concepts in Technical Trading Systems* (1978). Unlike fixed-percentage trailing stops, PSAR uses an acceleration factor (AF) that increases each time price makes a new extreme in the trend direction, creating time-dependent tightening. This was novel for 1978: most trailing stops were static. The parabolic shape emerges because SAR converges on price at an accelerating rate, mathematically similar to a particle under constant acceleration. Most implementations today follow Wilder's original specification with minor variations in initialization logic (first-bar handling). + +## Architecture and Physics + +### 1. State Machine + +PSAR operates as a two-state machine: **Long** (uptrend) and **Short** (downtrend). Each state tracks three variables: + +- **SAR**: Current stop level +- **EP** (Extreme Point): Highest high in long mode, lowest low in short mode +- **AF** (Acceleration Factor): Ramps from `afStart` to `afMax` in `afIncrement` steps + +### 2. SAR Update Rule + +$$\text{SAR}_{t} = \text{SAR}_{t-1} + \text{AF} \times (\text{EP} - \text{SAR}_{t-1})$$ + +This is an exponential chase: SAR moves toward EP at a rate proportional to the gap, with AF controlling the speed. As AF increases, SAR accelerates toward the extreme point. + +### 3. SAR Clamping + +In long mode, SAR is clamped to be at or below the minimum of the prior two bars' lows: + +$$\text{SAR}_{t} = \min(\text{SAR}_{t}, \text{Low}_{t-1}, \text{Low}_{t-2})$$ + +In short mode, SAR is clamped to be at or above the maximum of the prior two bars' highs: + +$$\text{SAR}_{t} = \max(\text{SAR}_{t}, \text{High}_{t-1}, \text{High}_{t-2})$$ + +### 4. Reversal Detection + +- **Long → Short**: When $\text{Low}_t < \text{SAR}_t$, reverse. Set SAR = EP, EP = Low, AF = afStart. +- **Short → Long**: When $\text{High}_t > \text{SAR}_t$, reverse. Set SAR = EP, EP = High, AF = afStart. + +### 5. EP/AF Update (No Reversal) + +If no reversal occurs and price makes a new extreme: + +- Long: if $\text{High}_t > \text{EP}$, then EP = High, AF = min(AF + afIncrement, afMax) +- Short: if $\text{Low}_t < \text{EP}$, then EP = Low, AF = min(AF + afIncrement, afMax) + +## Mathematical Foundation + +The SAR update equation is a first-order IIR filter with time-varying coefficient: + +$$y_t = y_{t-1} + \alpha_t (x^* - y_{t-1})$$ + +where $y_t$ = SAR, $x^*$ = EP (target), and $\alpha_t$ = AF (time-varying). This is equivalent to exponential smoothing toward a moving target, where the smoothing constant increases over time. + +The acceleration factor progression: + +$$\text{AF}_t = \min(\text{AF}_{\text{start}} + n \times \text{AF}_{\text{increment}}, \text{AF}_{\text{max}})$$ + +where $n$ is the number of new extreme points observed since the last reversal. The maximum number of acceleration steps is: + +$$n_{\max} = \left\lfloor \frac{\text{AF}_{\max} - \text{AF}_{\text{start}}}{\text{AF}_{\text{increment}}} \right\rfloor = \left\lfloor \frac{0.20 - 0.02}{0.02} \right\rfloor = 9$$ + +At AF = 0.20 (maximum), SAR covers 20% of the EP-SAR gap per bar. + +### Parameter Mapping + +| Parameter | Default | Effect | +|-----------|---------|--------| +| afStart | 0.02 | Initial tracking speed. Lower = slower start. | +| afIncrement | 0.02 | How fast AF ramps. Lower = slower acceleration. | +| afMax | 0.20 | Terminal tracking speed. Higher = tighter final stop. | + +## Performance Profile + +| Operation | Complexity | Notes | +|-----------|-----------|-------| +| Update (streaming) | O(1) | State machine: constant work per bar | +| Batch (span) | O(n) | Sequential state machine (no SIMD possible) | +| Memory | O(1) | Fixed state: 12 doubles + 1 bool | +| Warmup | 1 bar | First bar initializes direction | + +### SIMD Analysis + +PSAR cannot be vectorized. The state machine has data-dependent branches (reversal detection) and sequential dependencies (SAR depends on prior SAR). The Batch API delegates to streaming for correctness. + +### Quality Metrics (1–10 Scale) + +| Metric | Score | Rationale | +|--------|-------|-----------| +| Trend detection | 7 | Good in strong trends; whipsaws in ranges | +| Responsiveness | 8 | Acceleration factor provides adaptive speed | +| False signals | 5 | Prone to whipsaws in sideways markets | +| Simplicity | 9 | Three intuitive parameters | +| Universality | 8 | Works on any timeframe and asset class | + +## Validation + +| Library | Match | Tolerance | Notes | +|---------|-------|-----------|-------| +| Skender | ✅ | 1e-8 | `GetParabolicSar(0.02, 0.02, 0.2)` | +| TA-Lib | ✅ | 1e-8 | `Core.Sar(highs, lows, 0.02, 0.2)` | +| Self | ✅ | 1e-10 | Streaming == Batch == Span | + +Note: Different libraries may vary on first-bar initialization (close > open vs. first-bar direction). QuanTAlib follows Wilder's original: direction from close vs. open on bar 0. + +## Common Pitfalls + +1. **Whipsaw in ranges**: PSAR reverses on every price crossover. In tight ranges, this produces rapid alternation. Mitigation: combine with ADX filter (only follow PSAR when ADX > 25). Impact: 30–50% of signals may be false in ranging markets. + +2. **AF sensitivity**: Setting afStart too high (e.g., 0.10) makes SAR track price so tightly that minor retracements trigger reversals. Setting afMax too low (e.g., 0.05) makes SAR lag badly in strong trends. + +3. **Initialization ambiguity**: Different implementations handle bar 0 differently (some use first 5 bars to determine initial direction). QuanTAlib uses Wilder's original close > open test. This may cause initial-bar divergence from other libraries. + +4. **Bar correction with state machine**: The isNew=false rollback must restore the complete state machine (isLong, SAR, EP, AF, prev bars). Missing any field corrupts the trailing stop. + +5. **No SIMD path**: The sequential state machine with data-dependent branches prevents vectorization. Batch API is O(n) sequential, not O(n/vector_width). + +6. **SAR clamping requires history**: The clamp to prior-2-bars' extremes means bars 1–2 have limited clamping. This is by design (Wilder's specification) but can produce slightly different values than implementations that don't clamp on early bars. + +## References + +- Wilder, J. W. Jr. (1978). *New Concepts in Technical Trading Systems*. Trend Research. ISBN 978-0894590276. +- Kaufman, P. J. (2013). *Trading Systems and Methods*, 5th ed. Wiley. Chapter on Parabolic Time/Price System. +- StockCharts.com. "Parabolic SAR." ChartSchool Technical Indicators. diff --git a/lib/reversals/swings/Swings.Quantower.Tests.cs b/lib/reversals/swings/Swings.Quantower.Tests.cs new file mode 100644 index 00000000..d0ab4f77 --- /dev/null +++ b/lib/reversals/swings/Swings.Quantower.Tests.cs @@ -0,0 +1,128 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class SwingsIndicatorTests +{ + [Fact] + public void SwingsIndicator_Constructor_SetsDefaults() + { + var indicator = new SwingsIndicator(); + + Assert.Equal(5, indicator.Lookback); + Assert.True(indicator.ShowColdValues); + Assert.Contains("SWINGS", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void SwingsIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new SwingsIndicator(); + + Assert.Equal(0, SwingsIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void SwingsIndicator_ShortName_IsSwings() + { + var indicator = new SwingsIndicator(); + indicator.Initialize(); + + Assert.Contains("SWINGS", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void SwingsIndicator_SourceCodeLink_IsValid() + { + var indicator = new SwingsIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("Swings", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void SwingsIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new SwingsIndicator(); + + indicator.Initialize(); + + // After init, line series should exist (SwingHigh + SwingLow) + Assert.Equal(2, indicator.LinesSeries.Count); + } + + [Fact] + public void SwingsIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new SwingsIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 20; i++) + { + // Create a pattern with varying highs/lows to generate swings + double basePrice = 100 + (i % 5 == 2 ? 10 : 0); + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + double swingHigh = indicator.LinesSeries[0].GetValue(0); + double swingLow = indicator.LinesSeries[1].GetValue(0); + + // Values should be set (either finite swing or NaN=no swing) + Assert.True(double.IsFinite(swingHigh) || double.IsNaN(swingHigh)); + Assert.True(double.IsFinite(swingLow) || double.IsNaN(swingLow)); + } + + [Fact] + public void SwingsIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new SwingsIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 15; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(15), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double swingHigh = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(swingHigh) || double.IsNaN(swingHigh)); + } + + [Fact] + public void SwingsIndicator_TwoLineSeries_ArePresent() + { + var indicator = new SwingsIndicator(); + indicator.Initialize(); + + // SwingHigh is index 0 (red), SwingLow is index 1 (green) + Assert.Equal(2, indicator.LinesSeries.Count); + Assert.Contains("High", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("Low", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void SwingsIndicator_Description_IsSet() + { + var indicator = new SwingsIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("swing", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/swings/Swings.Quantower.cs b/lib/reversals/swings/Swings.Quantower.cs new file mode 100644 index 00000000..3e1a388b --- /dev/null +++ b/lib/reversals/swings/Swings.Quantower.cs @@ -0,0 +1,55 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class SwingsIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Lookback", sortIndex: 10, minimum: 1, maximum: 100, increment: 1)] + public int Lookback { get; set; } = 5; + + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private Swings _indicator = null!; + private readonly LineSeries _swingHighSeries; + private readonly LineSeries _swingLowSeries; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "SWINGS"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/swings/Swings.cs"; + + public SwingsIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "SWINGS - Swing High/Low Detection"; + Description = "Configurable-lookback pattern detector for swing highs (resistance) and swing lows (support)."; + + _swingHighSeries = new LineSeries(name: "Swing High", color: Color.Red, width: 2, style: LineStyle.Dot); + _swingLowSeries = new LineSeries(name: "Swing Low", color: Color.Green, width: 2, style: LineStyle.Dot); + + AddLineSeries(_swingHighSeries); + AddLineSeries(_swingLowSeries); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new Swings(Lookback); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _swingHighSeries.SetValue(_indicator.SwingHigh, _indicator.IsHot, ShowColdValues); + _swingLowSeries.SetValue(_indicator.SwingLow, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/swings/Swings.Tests.cs b/lib/reversals/swings/Swings.Tests.cs new file mode 100644 index 00000000..5a095d53 --- /dev/null +++ b/lib/reversals/swings/Swings.Tests.cs @@ -0,0 +1,588 @@ +// SWINGS Tests - Swing High/Low Detection + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class SwingsConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var sw = new Swings(); + + Assert.Equal(11, sw.WarmupPeriod); + Assert.Equal(5, sw.Lookback); + Assert.Contains("Swings", sw.Name, StringComparison.Ordinal); + Assert.False(sw.IsHot); + } + + [Fact] + public void Constructor_CustomLookback_SetsProperties() + { + var sw = new Swings(lookback: 3); + + Assert.Equal(7, sw.WarmupPeriod); // 2*3+1 + Assert.Equal(3, sw.Lookback); + Assert.Contains("Swings(3)", sw.Name, StringComparison.Ordinal); + } + + [Fact] + public void Constructor_InvalidLookback_Throws() + { + var ex = Assert.Throws(() => new Swings(lookback: 0)); + Assert.Equal("lookback", ex.ParamName); + } + + [Fact] + public void Constructor_InitialState_NaN() + { + var sw = new Swings(); + + Assert.True(double.IsNaN(sw.SwingHigh)); + Assert.True(double.IsNaN(sw.SwingLow)); + Assert.True(double.IsNaN(sw.LastSwingHigh)); + Assert.True(double.IsNaN(sw.LastSwingLow)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class SwingsBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var sw = new Swings(lookback: 2); + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + TValue result = sw.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var sw = new Swings(lookback: 2); + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + _ = sw.Update(bar); + + Assert.True(double.IsFinite(sw.Last.Value) || double.IsNaN(sw.Last.Value)); + } + + [Fact] + public void Update_KnownSwingHigh_Detected() + { + // lookback=2, windowSize=5 + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Pattern: center bar (bar[2]) has highest high + _ = sw.Update(new TBar(dt.AddMinutes(0), 97, 100, 95, 98, 1000), isNew: true); // high=100 + _ = sw.Update(new TBar(dt.AddMinutes(1), 100, 103, 98, 101, 1000), isNew: true); // high=103 + _ = sw.Update(new TBar(dt.AddMinutes(2), 104, 110, 92, 105, 1000), isNew: true); // high=110 (peak) + _ = sw.Update(new TBar(dt.AddMinutes(3), 101, 104, 97, 102, 1000), isNew: true); // high=104 + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 101, 96, 99, 1000), isNew: true); // high=101 + + // center high=110 > {100, 103, 104, 101} + Assert.Equal(110.0, sw.SwingHigh); + } + + [Fact] + public void Update_KnownSwingLow_Detected() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Pattern: center bar (bar[2]) has lowest low + _ = sw.Update(new TBar(dt.AddMinutes(0), 102, 105, 100, 103, 1000), isNew: true); // low=100 + _ = sw.Update(new TBar(dt.AddMinutes(1), 100, 103, 98, 101, 1000), isNew: true); // low=98 + _ = sw.Update(new TBar(dt.AddMinutes(2), 94, 102, 88, 95, 1000), isNew: true); // low=88 (trough) + _ = sw.Update(new TBar(dt.AddMinutes(3), 100, 104, 97, 101, 1000), isNew: true); // low=97 + _ = sw.Update(new TBar(dt.AddMinutes(4), 102, 106, 99, 103, 1000), isNew: true); // low=99 + + // center low=88 < {100, 98, 97, 99} + Assert.Equal(88.0, sw.SwingLow); + } + + [Fact] + public void Update_NoSwing_ReturnsNaN() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Monotone ascending - no swing high or low + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i * 5; + _ = sw.Update(new TBar(dt.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000), isNew: true); + } + + Assert.True(double.IsNaN(sw.SwingHigh)); + } + + [Fact] + public void LastSwingHigh_PersistsAcrossBars() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Create a swing high + _ = sw.Update(new TBar(dt.AddMinutes(0), 97, 100, 95, 98, 1000), isNew: true); + _ = sw.Update(new TBar(dt.AddMinutes(1), 100, 103, 98, 101, 1000), isNew: true); + _ = sw.Update(new TBar(dt.AddMinutes(2), 104, 110, 92, 105, 1000), isNew: true); + _ = sw.Update(new TBar(dt.AddMinutes(3), 101, 104, 97, 102, 1000), isNew: true); + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 101, 96, 99, 1000), isNew: true); + + Assert.Equal(110.0, sw.LastSwingHigh); + + // Feed more bars without a new swing high + _ = sw.Update(new TBar(dt.AddMinutes(5), 99, 102, 97, 100, 1000), isNew: true); + _ = sw.Update(new TBar(dt.AddMinutes(6), 100, 103, 98, 101, 1000), isNew: true); + + // LastSwingHigh should persist + Assert.Equal(110.0, sw.LastSwingHigh); + } + + [Fact] + public void Name_ContainsSwings() + { + var sw = new Swings(); + Assert.Contains("Swings", sw.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class SwingsStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var sw = new Swings(lookback: 2); + + _ = sw.Update(new TBar(DateTime.UtcNow, 100, 105, 95, 100, 1000), isNew: true); + var first = sw.Last; + + _ = sw.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 110, 100, 105, 1000), isNew: true); + var second = sw.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Feed 4 bars + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + // New bar + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 110, 85, 100, 1000), isNew: true); + + // Correct the bar + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + + // Another correction with same values should produce same result + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + var corrected1High = sw.SwingHigh; + var corrected1Low = sw.SwingLow; + + _ = sw.Update(new TBar(dt.AddMinutes(4), 98, 111, 84, 100, 1000), isNew: false); + var corrected2High = sw.SwingHigh; + var corrected2Low = sw.SwingLow; + + Assert.Equal(corrected1High, corrected2High); + Assert.Equal(corrected1Low, corrected2Low); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + _ = sw.Update(new TBar(dt.AddMinutes(4), 105, 110, 90, 100, 1000), isNew: true); + + double[] highResults = new double[3]; + double[] lowResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = sw.Update(new TBar(dt.AddMinutes(4), 106, 112, 88, 102, 1000), isNew: false); + highResults[i] = sw.SwingHigh; + lowResults[i] = sw.SwingLow; + } + + Assert.Equal(highResults[0], highResults[1]); + Assert.Equal(highResults[1], highResults[2]); + Assert.Equal(lowResults[0], lowResults[1]); + Assert.Equal(lowResults[1], lowResults[2]); + } + + [Fact] + public void Reset_ClearsAllState() + { + var sw = new Swings(lookback: 2); + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(sw.IsHot); + + sw.Reset(); + + Assert.False(sw.IsHot); + Assert.True(double.IsNaN(sw.SwingHigh)); + Assert.True(double.IsNaN(sw.SwingLow)); + Assert.True(double.IsNaN(sw.LastSwingHigh)); + Assert.True(double.IsNaN(sw.LastSwingLow)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class SwingsWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var sw = new Swings(lookback: 2); // windowSize = 5 + + // Feed 4 bars -- should NOT be hot + for (int i = 0; i < 4; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + Assert.False(sw.IsHot, $"Should not be hot at bar {i}"); + } + + // Feed 5th bar -- should be hot + double p = 100.0 + 4; + _ = sw.Update(new TBar(DateTime.UtcNow.AddMinutes(4), p, p + 2, p - 2, p + 1, 1000)); + Assert.True(sw.IsHot, "Should be hot after 5 bars (windowSize)"); + } + + [Fact] + public void WarmupPeriod_EqualsWindowSize() + { + var sw = new Swings(lookback: 3); + Assert.Equal(7, sw.WarmupPeriod); // 2*3+1 + } + + [Fact] + public void WarmupPeriod_DefaultLookback5_Equals11() + { + var sw = new Swings(); + Assert.Equal(11, sw.WarmupPeriod); // 2*5+1 + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class SwingsRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + // Feed valid bars + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(sw.IsHot); + + // Feed NaN bar + _ = sw.Update(new TBar(dt.AddMinutes(5), double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + // Should still be hot + Assert.True(sw.IsHot); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var sw = new Swings(lookback: 2); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + _ = sw.Update(new TBar(dt.AddMinutes(5), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0)); + + Assert.True(sw.IsHot); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var sw = new Swings(lookback: 2); + + _ = sw.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(sw.Last.Value)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class SwingsConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Swings(lookback: 3); + var streamHighResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHighResults[i] = streaming.SwingHigh; + } + + // Batch + var batchResults = Swings.Batch(bars, lookback: 3); + + int warmup = 6; // windowSize - 1 = 2*3+1 - 1 = 6 + for (int i = warmup; i < bars.Count; i++) + { + if (double.IsNaN(streamHighResults[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}: stream=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamHighResults[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Swings(lookback: 3); + var streamHighResults = new double[bars.Count]; + var streamLowResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHighResults[i] = streaming.SwingHigh; + streamLowResults[i] = streaming.SwingLow; + } + + // Span + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + Swings.Batch(bars.HighValues, bars.LowValues, spanHigh, spanLow, lookback: 3); + + int warmup = 6; + for (int i = warmup; i < bars.Count; i++) + { + if (double.IsNaN(streamHighResults[i])) + { + Assert.True(double.IsNaN(spanHigh[i]), $"SwingHigh mismatch at {i}"); + } + else + { + Assert.Equal(streamHighResults[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLowResults[i])) + { + Assert.True(double.IsNaN(spanLow[i]), $"SwingLow mismatch at {i}"); + } + else + { + Assert.Equal(streamLowResults[i], spanLow[i], precision: 10); + } + } + } + + [Fact] + public void BatchDual_MatchesSpan() + { + var bars = CreateGbmBars(); + + var (swingHighs, swingLows) = Swings.BatchDual(bars, lookback: 3); + + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + Swings.Batch(bars.HighValues, bars.LowValues, spanHigh, spanLow, lookback: 3); + + for (int i = 0; i < bars.Count; i++) + { + if (double.IsNaN(spanHigh[i])) + { + Assert.True(double.IsNaN(swingHighs[i].Value), $"SwingHigh mismatch at {i}"); + } + else + { + Assert.Equal(spanHigh[i], swingHighs[i].Value, precision: 10); + } + + if (double.IsNaN(spanLow[i])) + { + Assert.True(double.IsNaN(swingLows[i].Value), $"SwingLow mismatch at {i}"); + } + else + { + Assert.Equal(spanLow[i], swingLows[i].Value, precision: 10); + } + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var sw1 = new Swings(lookback: 2); + var sw2 = new Swings(lookback: 2); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double p = prices[i]; + _ = sw1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), p, p, p, p, 0), isNew: true); + _ = sw2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), p), isNew: true); + } + + Assert.Equal(sw1.SwingHigh, sw2.SwingHigh); + Assert.Equal(sw1.SwingLow, sw2.SwingLow); + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class SwingsSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + Swings.Batch(new double[10], new double[5], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_OutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Swings.Batch(new double[10], new double[10], new double[5], new double[10])); + Assert.Equal("highOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_LowOutputTooShort_Throws() + { + var ex = Assert.Throws(() => + Swings.Batch(new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("lowOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_InvalidLookback_Throws() + { + var ex = Assert.Throws(() => + Swings.Batch(new double[10], new double[10], new double[10], new double[10], lookback: 0)); + Assert.Equal("lookback", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + Swings.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + Span.Empty, Span.Empty)); + Assert.Null(ex); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class SwingsEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var sw = new Swings(lookback: 2); + int fireCount = 0; + + sw.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = sw.Update(new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var sw = new Swings(lookback: 2); + int fireCount = 0; + + sw.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = sw.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class SwingsPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var sw = new Swings(lookback: 3); + sw.Prime(bars); + + Assert.True(sw.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var sw = new Swings(lookback: 2); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => sw.Prime(bars)); + Assert.Null(ex); + Assert.False(sw.IsHot); + } +} diff --git a/lib/reversals/swings/Swings.Validation.Tests.cs b/lib/reversals/swings/Swings.Validation.Tests.cs new file mode 100644 index 00000000..767f8f4c --- /dev/null +++ b/lib/reversals/swings/Swings.Validation.Tests.cs @@ -0,0 +1,266 @@ +// SWINGS Validation Tests - Swing High/Low Detection +// No external library validation available (Skender/TA-Lib/Tulip/Ooples don't have configurable swings) +// Tests focus on mathematical correctness and self-consistency + +namespace QuanTAlib.Tests; + +public sealed class SwingsValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Self-Consistency: Streaming == Batch (SwingHigh) -------------------------- + + [Fact] + public void StreamingMatchesBatch_SwingHigh() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Swings(lookback: 3); + var streamHigh = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHigh[i] = streaming.SwingHigh; + } + + // Batch + var batchResults = Swings.Batch(bars, lookback: 3); + + int warmup = 6; // windowSize - 1 + for (int i = warmup; i < bars.Count; i++) + { + if (double.IsNaN(streamHigh[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamHigh[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span (Both outputs) ----------------------- + + [Fact] + public void StreamingMatchesSpan_BothSwings() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new Swings(lookback: 3); + var streamHigh = new double[bars.Count]; + var streamLow = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHigh[i] = streaming.SwingHigh; + streamLow[i] = streaming.SwingLow; + } + + // Span + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + Swings.Batch(bars.HighValues, bars.LowValues, spanHigh, spanLow, lookback: 3); + + for (int i = 6; i < bars.Count; i++) + { + if (double.IsNaN(streamHigh[i])) + { + Assert.True(double.IsNaN(spanHigh[i])); + } + else + { + Assert.Equal(streamHigh[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLow[i])) + { + Assert.True(double.IsNaN(spanLow[i])); + } + else + { + Assert.Equal(streamLow[i], spanLow[i], precision: 10); + } + } + } + + // -- Mathematical correctness: swing highs are actual local maxima ------------- + + [Fact] + public void SwingHigh_IsActualLocalMaximum() + { + var bars = CreateGbmBars(count: 200, seed: 77); + + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + int lookback = 3; + Swings.Batch(bars.HighValues, bars.LowValues, spanHigh, spanLow, lookback); + + int windowSize = 2 * lookback + 1; + for (int i = windowSize - 1; i < bars.Count; i++) + { + if (double.IsNaN(spanHigh[i])) + { + continue; + } + + // The swing high value should be the center bar's high + int center = i - lookback; + double centerHigh = bars.HighValues[center]; + Assert.Equal(centerHigh, spanHigh[i], precision: 10); + + // Verify it's strictly greater than all neighbors + for (int j = center - lookback; j <= center + lookback; j++) + { + if (j != center) + { + Assert.True(centerHigh > bars.HighValues[j], + $"Bar {center} high={centerHigh} should be > bar {j} high={bars.HighValues[j]}"); + } + } + } + } + + // -- Mathematical correctness: swing lows are actual local minima -------------- + + [Fact] + public void SwingLow_IsActualLocalMinimum() + { + var bars = CreateGbmBars(count: 200, seed: 77); + + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + int lookback = 3; + Swings.Batch(bars.HighValues, bars.LowValues, spanHigh, spanLow, lookback); + + int windowSize = 2 * lookback + 1; + for (int i = windowSize - 1; i < bars.Count; i++) + { + if (double.IsNaN(spanLow[i])) + { + continue; + } + + // The swing low value should be the center bar's low + int center = i - lookback; + double centerLow = bars.LowValues[center]; + Assert.Equal(centerLow, spanLow[i], precision: 10); + + // Verify it's strictly less than all neighbors + for (int j = center - lookback; j <= center + lookback; j++) + { + if (j != center) + { + Assert.True(centerLow < bars.LowValues[j], + $"Bar {center} low={centerLow} should be < bar {j} low={bars.LowValues[j]}"); + } + } + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var sw1 = new Swings(lookback: 4); + var sw2 = new Swings(lookback: 4); + + for (int i = 0; i < bars.Count; i++) + { + _ = sw1.Update(bars[i], isNew: true); + _ = sw2.Update(bars[i], isNew: true); + } + + Assert.Equal(sw1.SwingHigh, sw2.SwingHigh); + Assert.Equal(sw1.SwingLow, sw2.SwingLow); + Assert.Equal(sw1.LastSwingHigh, sw2.LastSwingHigh); + Assert.Equal(sw1.LastSwingLow, sw2.LastSwingLow); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = Swings.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- BatchDual Returns Both Swings ------------------------------------------- + + [Fact] + public void BatchDual_ReturnsBothSeries() + { + var bars = CreateGbmBars(count: 100); + + var (swingHighs, swingLows) = Swings.BatchDual(bars, lookback: 3); + + Assert.Equal(bars.Count, swingHighs.Count); + Assert.Equal(bars.Count, swingLows.Count); + + // At least some swings should be detected in 100 bars + bool hasHigh = false; + bool hasLow = false; + for (int i = 0; i < swingHighs.Count; i++) + { + if (double.IsFinite(swingHighs[i].Value)) + { + hasHigh = true; + } + if (double.IsFinite(swingLows[i].Value)) + { + hasLow = true; + } + } + + Assert.True(hasHigh, "Should detect at least one swing high in 100 bars"); + Assert.True(hasLow, "Should detect at least one swing low in 100 bars"); + } + + // -- Different lookback periods produce different results ---------------------- + + [Fact] + public void DifferentLookbacks_ProduceDifferentResults() + { + var bars = CreateGbmBars(count: 200, seed: 55); + + var sw2 = new Swings(lookback: 2); + var sw5 = new Swings(lookback: 5); + + int swingCount2 = 0; + int swingCount5 = 0; + + for (int i = 0; i < bars.Count; i++) + { + _ = sw2.Update(bars[i], isNew: true); + _ = sw5.Update(bars[i], isNew: true); + + if (double.IsFinite(sw2.SwingHigh)) + { + swingCount2++; + } + if (double.IsFinite(sw5.SwingHigh)) + { + swingCount5++; + } + } + + // Larger lookback should generally yield fewer swings + Assert.True(swingCount2 > swingCount5 || swingCount5 == 0, + $"lookback=2 detected {swingCount2} swings, lookback=5 detected {swingCount5} — shorter lookback should detect more"); + } +} diff --git a/lib/reversals/swings/Swings.cs b/lib/reversals/swings/Swings.cs new file mode 100644 index 00000000..879921a2 --- /dev/null +++ b/lib/reversals/swings/Swings.cs @@ -0,0 +1,471 @@ +// SWINGS: Swing High/Low Detection +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// SWINGS: Swing High/Low Detection +/// +/// +/// A configurable-lookback pattern detector for swing highs and swing lows. +/// A swing high occurs when the center bar's high is strictly greater than all +/// neighbors' highs within the lookback window on each side. A swing low occurs +/// when the center bar's low is strictly less than all neighbors' lows. +/// +/// Calculation: +/// +/// windowSize = 2 * lookback + 1 +/// center = lookback (index into the window) +/// +/// SwingHigh = high[center] > ALL high[i] for i in [0..windowSize) where i != center +/// ? high[center] : NaN +/// SwingLow = low[center] < ALL low[i] for i in [0..windowSize) where i != center +/// ? low[center] : NaN +/// +/// +/// Key characteristics: +/// - O(1) update via circular buffer (no deques needed) +/// - Outputs are delayed by lookback bars (the swing point is at center) +/// - Dual output: SwingHigh (resistance) and SwingLow (support) +/// - Persistent LastSwingHigh / LastSwingLow hold most recent swing level +/// - WarmupPeriod = 2 * lookback + 1 +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class Swings : ITValuePublisher +{ + private const int DefaultLookback = 5; + + private readonly int _lookback; + private readonly int _windowSize; + + // Circular buffers for highs and lows + private readonly double[] _hBuf; + private readonly double[] _lBuf; + + private int _count; + private long _index; + + [StructLayout(LayoutKind.Auto)] + private record struct State( + double LastValidHigh, + double LastValidLow, + double LastValidClose, + double LastSwingHigh, + double LastSwingLow); + + private State _s; + private State _ps; + + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// The lookback period on each side of the center bar. + public int Lookback => _lookback; + + /// Current swing high value (NaN if no swing high at current position). + public double SwingHigh { get; private set; } + + /// Current swing low value (NaN if no swing low at current position). + public double SwingLow { get; private set; } + + /// Most recent confirmed swing high level (persists until next swing high). + public double LastSwingHigh => _s.LastSwingHigh; + + /// Most recent confirmed swing low level (persists until next swing low). + public double LastSwingLow => _s.LastSwingLow; + + /// Primary output value (SwingHigh as TValue for overlay plotting). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= _windowSize; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a Swing High/Low detector with the specified lookback period. + /// + /// Number of bars on each side to confirm a swing point (default: 5). + public Swings(int lookback = DefaultLookback) + { + if (lookback < 1) + { + throw new ArgumentException("Lookback must be >= 1.", nameof(lookback)); + } + + _lookback = lookback; + _windowSize = 2 * lookback + 1; + + _hBuf = new double[_windowSize]; + _lBuf = new double[_windowSize]; + + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SwingHigh = double.NaN; + SwingLow = double.NaN; + + Name = $"Swings({lookback})"; + WarmupPeriod = _windowSize; + _barHandler = HandleBar; + } + + /// + /// Creates a Swing High/Low detector chained to a TBarSeries source. + /// + public Swings(TBarSeries source, int lookback = DefaultLookback) + : this(lookback) + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _index++; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs -- substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + SwingHigh = double.NaN; + SwingLow = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Store in circular buffer + int bufIdx = (int)(_index % _windowSize); + _hBuf[bufIdx] = high; + _lBuf[bufIdx] = low; + + // Need at least windowSize bars to evaluate a swing + if (_count < _windowSize) + { + _s = s; + SwingHigh = double.NaN; + SwingLow = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // The swing candidate is at center = lookback bars ago + // In circular buffer: + // bar[0] = bufIdx (newest) + // bar[lookback] = (bufIdx - lookback + windowSize) % windowSize <- the candidate + int centerIdx = (bufIdx + _windowSize - _lookback) % _windowSize; + double centerHigh = _hBuf[centerIdx]; + double centerLow = _lBuf[centerIdx]; + + bool isSwingHigh = true; + bool isSwingLow = true; + + for (int i = 0; i < _windowSize; i++) + { + if (i == centerIdx) + { + continue; + } + + if (_hBuf[i] >= centerHigh) + { + isSwingHigh = false; + } + + if (_lBuf[i] <= centerLow) + { + isSwingLow = false; + } + + if (!isSwingHigh && !isSwingLow) + { + break; + } + } + + SwingHigh = isSwingHigh ? centerHigh : double.NaN; + SwingLow = isSwingLow ? centerLow : double.NaN; + + // Update persistent last-swing levels + if (isSwingHigh) + { + s.LastSwingHigh = centerHigh; + } + + if (isSwingLow) + { + s.LastSwingLow = centerLow; + } + + _s = s; + + Last = new TValue(input.Time, SwingHigh); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(v), downBuf, _lookback); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + Array.Clear(_hBuf); + Array.Clear(_lBuf); + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); + _ps = _s; + SwingHigh = double.NaN; + SwingLow = double.NaN; + Last = default; + } + + /// + /// Batch computation of Swing High/Low over span data. + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + Span highOutput, + Span lowOutput, + int lookback = DefaultLookback) + { + if (lookback < 1) + { + throw new ArgumentException("Lookback must be >= 1.", nameof(lookback)); + } + if (high.Length != low.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (highOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(highOutput)); + } + if (lowOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(lowOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + int windowSize = 2 * lookback + 1; + + // Fill warmup bars with NaN + int warmup = Math.Min(windowSize - 1, len); + for (int i = 0; i < warmup; i++) + { + highOutput[i] = double.NaN; + lowOutput[i] = double.NaN; + } + + // Evaluate swings directly — center is at index [i - lookback] + for (int i = windowSize - 1; i < len; i++) + { + int center = i - lookback; + double centerHigh = high[center]; + double centerLow = low[center]; + + bool isSwingHigh = true; + bool isSwingLow = true; + + for (int j = center - lookback; j <= center + lookback; j++) + { + if (j == center) + { + continue; + } + + if (high[j] >= centerHigh) + { + isSwingHigh = false; + } + + if (low[j] <= centerLow) + { + isSwingLow = false; + } + + if (!isSwingHigh && !isSwingLow) + { + break; + } + } + + highOutput[i] = isSwingHigh ? centerHigh : double.NaN; + lowOutput[i] = isSwingLow ? centerLow : double.NaN; + } + } + + public static TSeries Batch(TBarSeries source, int lookback = DefaultLookback) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(v), downBuf, lookback); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation returning both SwingHigh and SwingLow TSeries. + /// + public static (TSeries SwingHighs, TSeries SwingLows) BatchDual(TBarSeries source, int lookback = DefaultLookback) + { + if (source == null || source.Count == 0) + { + return (new TSeries([], []), new TSeries([], [])); + } + + int len = source.Count; + var tUp = new List(len); + var vUp = new List(len); + var tDown = new List(len); + var vDown = new List(len); + + CollectionsMarshal.SetCount(tUp, len); + CollectionsMarshal.SetCount(vUp, len); + CollectionsMarshal.SetCount(tDown, len); + CollectionsMarshal.SetCount(vDown, len); + + Batch(source.HighValues, source.LowValues, + CollectionsMarshal.AsSpan(vUp), CollectionsMarshal.AsSpan(vDown), lookback); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(tUp)); + source.Times.CopyTo(CollectionsMarshal.AsSpan(tDown)); + + return (new TSeries(tUp, vUp), new TSeries(tDown, vDown)); + } + + public static (TSeries Results, Swings Indicator) Calculate(TBarSeries source, int lookback = DefaultLookback) + { + var indicator = new Swings(lookback); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/swings/Swings.md b/lib/reversals/swings/Swings.md new file mode 100644 index 00000000..12f39472 --- /dev/null +++ b/lib/reversals/swings/Swings.md @@ -0,0 +1,160 @@ +# SWINGS: Swing High/Low Detection + +> "The market tells you where it turned. You just have to listen long enough to be sure it actually meant it." + +Swing High/Low detection identifies local price extremes using a configurable lookback window. A Swing High marks a bar whose high strictly exceeds the highs of all bars within the lookback window on each side. A Swing Low marks a bar whose low is strictly less than all corresponding lows. The lookback parameter controls sensitivity: larger lookback windows require more confirmation and produce fewer, more significant signals. This generalizes Williams' fixed five-bar Fractals into a flexible structural analysis tool. + +## Historical Context + +Swing point detection predates formal technical analysis. Floor traders in the 1920s marked "pivot highs" and "pivot lows" on hand-drawn charts to identify support and resistance. W.D. Gann formalized the concept in the 1930s, using swing charts to filter noise and identify trend structure. The basic idea: a local maximum confirmed by subsequent lower prices marks resistance; a local minimum confirmed by subsequent higher prices marks support. + +Bill Williams codified a specific instance of this pattern as "Fractals" in *Trading Chaos* (1995), fixing the lookback to 2 bars (a five-bar window). TradingView's PineScript generalized this with `ta.pivothigh(source, leftbars, rightbars)` and `ta.pivotlow(source, leftbars, rightbars)`, allowing asymmetric lookback windows. This QuanTAlib implementation uses symmetric lookback (equal bars on both sides), matching the most common usage pattern. + +The choice of lookback period is a sensitivity-significance tradeoff. Lookback=2 (Williams Fractals) fires frequently but catches minor wiggles. Lookback=5 (the default here) requires substantial confirmation, producing signals that correspond to genuine structural turning points rather than intrabar noise. Lookback=10 or higher identifies swing points visible on lower timeframes, effectively performing multi-timeframe analysis within a single timeframe. + +The relationship between Swings and Fractals is straightforward: `Fractals()` is equivalent to `Swings(lookback: 2)`. Both use strict inequality (center must strictly exceed all neighbors, not merely equal them). This implementation follows PineScript convention: the swing point is reported on the confirming bar (when the full window is available), not retroactively placed on the center bar. + +## Architecture and Physics + +### 1. Configurable Window + +The indicator maintains two circular buffers of size $2 \times \text{lookback} + 1$: one for highs, one for lows. Each new bar shifts the window forward by one position using modular index arithmetic. + +### 2. Swing High Detection + +A Swing High is detected when the center bar's high strictly exceeds all neighbors in the window: + +$$ \text{SwingHigh}_t = \begin{cases} H_{t-L} & \text{if } H_{t-L} > H_j \text{ for all } j \in [t-2L, t] \text{ where } j \neq t-L \\ \text{NaN} & \text{otherwise} \end{cases} $$ + +Where $L$ is the lookback period and $t$ is the current bar index. + +### 3. Swing Low Detection + +A Swing Low is detected when the center bar's low is strictly less than all neighbors: + +$$ \text{SwingLow}_t = \begin{cases} L_{t-L} & \text{if } L_{t-L} < L_j \text{ for all } j \in [t-2L, t] \text{ where } j \neq t-L \\ \text{NaN} & \text{otherwise} \end{cases} $$ + +### 4. Persistent Last-Swing Levels + +Unlike per-bar SwingHigh/SwingLow (which are NaN when no pattern is present), `LastSwingHigh` and `LastSwingLow` persist the most recently confirmed swing level until superseded. These provide continuous support/resistance references. + +### 5. Dual Output + +Both swing values are available simultaneously. At any given bar, either, both, or neither swing may be present. The primary output (`Last.Val`) defaults to `SwingHigh` for overlay plotting. + +### Signal Interpretation + +| Condition | Interpretation | +| :--- | :--- | +| SwingHigh is not NaN | Local high identified $L$ bars ago; potential resistance level | +| SwingLow is not NaN | Local low identified $L$ bars ago; potential support level | +| Both present | Simultaneous peak and trough (rare; indicates extreme volatility) | +| Neither present | No pattern formed; trend continuation or consolidation | +| LastSwingHigh rising | Higher highs in structural terms; bullish tendency | +| LastSwingLow rising | Higher lows in structural terms; bullish tendency | + +## Mathematical Foundation + +### Parameters + +| Parameter | Default | Range | Notes | +| :--- | :---: | :---: | :--- | +| Lookback | 5 | 1-100 | Bars on each side of center for confirmation | + +### Derived Constants + +| Constant | Formula | Default Value | +| :--- | :--- | :--- | +| Window Size | $2L + 1$ | 11 | +| Warmup Period | $2L + 1$ | 11 | +| Reporting Delay | $L$ bars | 5 bars | + +### Warmup Period + +$$ W = 2L + 1 $$ + +The indicator requires $W$ bars before producing valid output. Prior to warmup completion, both SwingHigh and SwingLow output NaN. + +### Relationship to Williams Fractals + +$$ \text{Fractals}() \equiv \text{Swings}(\text{lookback} = 2) $$ + +Both use strict inequality. The five-bar pattern ($2 \times 2 + 1 = 5$) is the simplest non-trivial swing detection window. Increasing lookback trades detection frequency for signal significance. + +### Expected Detection Frequency + +In random walk data with GBM dynamics ($\mu = 0.05$, $\sigma = 0.20$), empirical swing high frequency is approximately: + +| Lookback | Window | Approx. Swing High Frequency | +| :--- | :--- | :--- | +| 2 | 5 bars | ~15-25% of bars | +| 3 | 7 bars | ~10-18% of bars | +| 5 | 11 bars | ~5-12% of bars | +| 10 | 21 bars | ~2-6% of bars | + +## Performance Profile + +### Implementation Design + +The implementation uses two circular buffers with modular index arithmetic. Pattern evaluation checks $2L$ comparisons per direction (all neighbors against center), with early termination when both swing high and swing low are ruled out. + +| Metric | Score | Notes | +| :--- | :--- | :--- | +| **Complexity** | O(L) per update | Linear in lookback; comparisons against all neighbors | +| **Allocations** | 0 | Hot path is allocation-free; fixed-size buffers | +| **Warmup** | $2L+1$ bars | Minimum viable for the pattern | +| **Accuracy** | 10/10 | Exact computation; no approximation or floating-point accumulation | +| **Timeliness** | Variable | Inherent $L$-bar reporting delay | +| **Smoothness** | N/A | Binary signal; smooth/noisy not applicable | + +### State Management + +Internal state uses a `record struct` with local copy pattern for JIT struct promotion. The state tracks last-valid values for high, low, and close (NaN/Infinity substitution) plus persistent LastSwingHigh/LastSwingLow levels. Bar correction via `isNew` flag enables same-timestamp rewrites. + +### SIMD Applicability + +Not applicable. The window-based comparison is inherently sequential due to the circular buffer state. For the span-based `Batch` API, each window evaluation is independent and could theoretically be parallelized, but the comparison count per window ($2L$) is small enough that SIMD overhead exceeds the benefit. + +## Validation + +Self-consistency validation confirms all API modes produce identical results: + +| Mode | Status | Notes | +| :--- | :--- | :--- | +| **Streaming** (`Update`) | Passed | Bar-by-bar with `isNew` support | +| **Batch** (`Batch(TBarSeries)`) | Passed | Matches streaming output | +| **Span** (`Batch(Span)`) | Passed | Matches streaming output | +| **BatchDual** | Passed | Both SwingHighs and SwingLows match span output | +| **Event** (`Pub` subscription) | Passed | Matches streaming output | + +| Library | Status | Notes | +| :--- | :--- | :--- | +| **QuanTAlib** | Passed | All modes self-consistent; mathematical correctness verified | +| **Skender** | N/A | No configurable swings API | +| **TA-Lib** | N/A | Not implemented | +| **Tulip** | N/A | Not implemented | +| **Ooples** | N/A | Not validated | + +Mathematical correctness is verified by confirming that every reported SwingHigh is a genuine local maximum (strictly greater than all neighbors) and every reported SwingLow is a genuine local minimum (strictly less than all neighbors) across GBM-generated test data. + +## Common Pitfalls + +1. **Lookback vs. window size confusion.** Lookback is the number of bars on each side, not the total window. `Swings(lookback: 5)` evaluates an 11-bar window ($2 \times 5 + 1$), not a 5-bar window. If you want Williams Fractals behavior (5-bar window), use `lookback: 2`. + +2. **Reporting delay scales with lookback.** A lookback of 5 means the swing point occurred 5 bars ago. In a fast-moving market, the price may have traveled significantly from the swing level by the time it is confirmed. This is inherent to the detection method, not a bug. + +3. **Strict inequality excludes equal highs/lows.** If the center bar's high equals any neighbor's high, no swing high is detected. In flat or low-volatility markets, this produces sparse signals. Use a smaller lookback for tighter detection in low-volatility regimes. + +4. **NaN output is the normal case.** Most bars do not form swing points. At lookback=5, roughly 90-95% of bars return NaN for both outputs. Design strategies accordingly; swing detection is an event, not a continuous signal. + +5. **LastSwingHigh/LastSwingLow may be stale.** These persistent levels hold indefinitely until the next swing is confirmed. In trending markets, LastSwingLow (in an uptrend) may lag far behind current price. Use `IsHot` and recency checks if staleness matters. + +6. **Asymmetric lookback not supported.** PineScript's `ta.pivothigh(src, leftbars, rightbars)` allows different left and right lookback values. This implementation uses symmetric lookback only. For asymmetric detection, chain two separate instances or modify the source. + +7. **Different lookback periods detect different market structure.** A lookback of 2 catches minor intraday reversals. A lookback of 10 catches significant multi-day swing points. There is no universally correct value; the choice depends on the analysis timeframe and trading horizon. + +## References + +- Williams, B. M. (1995). *Trading Chaos: Applying Expert Techniques to Maximize Your Profits*. John Wiley and Sons. +- Gann, W. D. (1935). *New Stock Trend Detector*. Financial Guardian Publishing. +- TradingView PineScript Reference: [`ta.pivothigh()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivothigh), [`ta.pivotlow()`](https://www.tradingview.com/pine-script-reference/v5/#fun_ta.pivotlow) diff --git a/lib/reversals/ttm_scalper/TtmScalper.Quantower.Tests.cs b/lib/reversals/ttm_scalper/TtmScalper.Quantower.Tests.cs new file mode 100644 index 00000000..12258048 --- /dev/null +++ b/lib/reversals/ttm_scalper/TtmScalper.Quantower.Tests.cs @@ -0,0 +1,128 @@ +using TradingPlatform.BusinessLayer; +using QuanTAlib; + +namespace QuanTAlib.Tests; + +public sealed class TtmScalperIndicatorTests +{ + [Fact] + public void TtmScalperIndicator_Constructor_SetsDefaults() + { + var indicator = new TtmScalperIndicator(); + + Assert.True(indicator.ShowColdValues); + Assert.False(indicator.UseCloses); + Assert.Contains("TTM_SCALPER", indicator.Name, StringComparison.Ordinal); + Assert.False(indicator.SeparateWindow); + Assert.True(indicator.OnBackGround); + } + + [Fact] + public void TtmScalperIndicator_MinHistoryDepths_EqualsZero() + { + var indicator = new TtmScalperIndicator(); + + Assert.Equal(0, TtmScalperIndicator.MinHistoryDepths); + IWatchlistIndicator watchlistIndicator = indicator; + Assert.Equal(0, watchlistIndicator.MinHistoryDepths); + } + + [Fact] + public void TtmScalperIndicator_ShortName_IsTtmScalper() + { + var indicator = new TtmScalperIndicator(); + indicator.Initialize(); + + Assert.Contains("TTM_SCALPER", indicator.ShortName, StringComparison.Ordinal); + } + + [Fact] + public void TtmScalperIndicator_SourceCodeLink_IsValid() + { + var indicator = new TtmScalperIndicator(); + + Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal); + Assert.Contains("TtmScalper", indicator.SourceCodeLink, StringComparison.Ordinal); + } + + [Fact] + public void TtmScalperIndicator_Initialize_CreatesInternalIndicator() + { + var indicator = new TtmScalperIndicator(); + + indicator.Initialize(); + + // After init, line series should exist (PivotHigh + PivotLow) + Assert.Equal(2, indicator.LinesSeries.Count); + } + + [Fact] + public void TtmScalperIndicator_ProcessUpdate_HistoricalBar_ComputesValue() + { + var indicator = new TtmScalperIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 20; i++) + { + // Create a pattern with varying highs/lows to generate pivots + double basePrice = 100 + (i % 3 == 1 ? 10 : 0); // spike every 3rd bar at position 1 + indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2); + + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + double pivotHigh = indicator.LinesSeries[0].GetValue(0); + double pivotLow = indicator.LinesSeries[1].GetValue(0); + + // Values should be set (either finite pivot or NaN=no pivot) + Assert.True(double.IsFinite(pivotHigh) || double.IsNaN(pivotHigh)); + Assert.True(double.IsFinite(pivotLow) || double.IsNaN(pivotLow)); + } + + [Fact] + public void TtmScalperIndicator_ProcessUpdate_NewBar_ComputesValue() + { + var indicator = new TtmScalperIndicator(); + indicator.Initialize(); + + var now = DateTime.UtcNow; + for (int i = 0; i < 10; i++) + { + indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i); + var args = new UpdateArgs(UpdateReason.HistoricalBar); + indicator.ProcessUpdate(args); + } + + // Simulate a new bar + indicator.HistoricalData.AddBar(now.AddMinutes(10), 110, 120, 100, 115); + var newArgs = new UpdateArgs(UpdateReason.NewBar); + indicator.ProcessUpdate(newArgs); + + double pivotHigh = indicator.LinesSeries[0].GetValue(0); + Assert.True(double.IsFinite(pivotHigh) || double.IsNaN(pivotHigh)); + } + + [Fact] + public void TtmScalperIndicator_TwoLineSeries_ArePresent() + { + var indicator = new TtmScalperIndicator(); + indicator.Initialize(); + + // PivotHigh is index 0 (red), PivotLow is index 1 (green) + Assert.Equal(2, indicator.LinesSeries.Count); + Assert.Contains("High", indicator.LinesSeries[0].Name, StringComparison.OrdinalIgnoreCase); + Assert.Contains("Low", indicator.LinesSeries[1].Name, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void TtmScalperIndicator_Description_IsSet() + { + var indicator = new TtmScalperIndicator(); + + Assert.NotNull(indicator.Description); + Assert.NotEmpty(indicator.Description); + Assert.Contains("pivot", indicator.Description, StringComparison.OrdinalIgnoreCase); + } +} diff --git a/lib/reversals/ttm_scalper/TtmScalper.Quantower.cs b/lib/reversals/ttm_scalper/TtmScalper.Quantower.cs new file mode 100644 index 00000000..60a87709 --- /dev/null +++ b/lib/reversals/ttm_scalper/TtmScalper.Quantower.cs @@ -0,0 +1,55 @@ +using System.Drawing; +using System.Runtime.CompilerServices; +using TradingPlatform.BusinessLayer; + +namespace QuanTAlib; + +[SkipLocalsInit] +public sealed class TtmScalperIndicator : Indicator, IWatchlistIndicator +{ + [InputParameter("Use closes", sortIndex: 10)] + public bool UseCloses { get; set; } + + [InputParameter("Show cold values", sortIndex: 21)] + public bool ShowColdValues { get; set; } = true; + + private TtmScalper _indicator = null!; + private readonly LineSeries _pivotHighSeries; + private readonly LineSeries _pivotLowSeries; + + public static int MinHistoryDepths => 0; + int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths; + + public override string ShortName => "TTM_SCALPER"; + public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/reversals/ttm_scalper/TtmScalper.cs"; + + public TtmScalperIndicator() + { + OnBackGround = true; + SeparateWindow = false; + Name = "TTM_SCALPER - TTM Scalper Alert"; + Description = "Three-bar pivot pattern detecting potential reversal points for scalping entries."; + + _pivotHighSeries = new LineSeries(name: "Pivot High", color: Color.Red, width: 2, style: LineStyle.Dot); + _pivotLowSeries = new LineSeries(name: "Pivot Low", color: Color.Green, width: 2, style: LineStyle.Dot); + + AddLineSeries(_pivotHighSeries); + AddLineSeries(_pivotLowSeries); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnInit() + { + _indicator = new TtmScalper(UseCloses); + base.OnInit(); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + protected override void OnUpdate(UpdateArgs args) + { + _ = _indicator.Update(this.GetInputBar(args), args.IsNewBar()); + + _pivotHighSeries.SetValue(_indicator.PivotHigh, _indicator.IsHot, ShowColdValues); + _pivotLowSeries.SetValue(_indicator.PivotLow, _indicator.IsHot, ShowColdValues); + } +} diff --git a/lib/reversals/ttm_scalper/TtmScalper.Tests.cs b/lib/reversals/ttm_scalper/TtmScalper.Tests.cs new file mode 100644 index 00000000..cdd38564 --- /dev/null +++ b/lib/reversals/ttm_scalper/TtmScalper.Tests.cs @@ -0,0 +1,572 @@ +// TTM_SCALPER Tests - TTM Scalper Alert + +namespace QuanTAlib.Tests; + +// -- A) Constructor Validation ------------------------------------------------ +public sealed class TtmScalperConstructorTests +{ + [Fact] + public void Constructor_Default_SetsProperties() + { + var ts = new TtmScalper(); + + Assert.Equal(3, ts.WarmupPeriod); + Assert.Contains("TtmScalper", ts.Name, StringComparison.Ordinal); + Assert.False(ts.IsHot); + Assert.False(ts.UseCloses); + } + + [Fact] + public void Constructor_UseCloses_SetsFlag() + { + var ts = new TtmScalper(useCloses: true); + + Assert.True(ts.UseCloses); + Assert.Contains("True", ts.Name, StringComparison.Ordinal); + } + + [Fact] + public void Constructor_InitialState_NaN() + { + var ts = new TtmScalper(); + + Assert.True(double.IsNaN(ts.PivotHigh)); + Assert.True(double.IsNaN(ts.PivotLow)); + } +} + +// -- B) Basic Calculation ----------------------------------------------------- +public sealed class TtmScalperBasicTests +{ + [Fact] + public void Update_ReturnsTValue() + { + var ts = new TtmScalper(); + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + TValue result = ts.Update(bar); + + Assert.IsType(result); + } + + [Fact] + public void Update_Last_IsAccessible() + { + var ts = new TtmScalper(); + var bar = new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000); + + _ = ts.Update(bar); + + Assert.True(double.IsFinite(ts.Last.Value) || double.IsNaN(ts.Last.Value)); + } + + [Fact] + public void Update_KnownPivotHigh_Detected() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + // TBar(DateTime, open, high, low, close, volume) + // Pattern: bar[2] low high, bar[1] HIGH peak, bar[0] low high + _ = ts.Update(new TBar(dt.AddMinutes(0), 97, 100, 95, 98, 1000), isNew: true); // bar[2]: high=100 + _ = ts.Update(new TBar(dt.AddMinutes(1), 104, 110, 92, 105, 1000), isNew: true); // bar[1]: high=110 (peak) + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 101, 96, 99, 1000), isNew: true); // bar[0]: high=101 + + // bar[1].High=110 > bar[2].High=100 AND bar[1].High=110 > bar[0].High=101 + Assert.Equal(110.0, ts.PivotHigh); + } + + [Fact] + public void Update_KnownPivotLow_Detected() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + // TBar(DateTime, open, high, low, close, volume) + // Pattern: bar[2] high low, bar[1] LOW trough, bar[0] high low + _ = ts.Update(new TBar(dt.AddMinutes(0), 102, 105, 100, 103, 1000), isNew: true); // bar[2]: low=100 + _ = ts.Update(new TBar(dt.AddMinutes(1), 94, 102, 88, 95, 1000), isNew: true); // bar[1]: low=88 (trough) + _ = ts.Update(new TBar(dt.AddMinutes(2), 102, 106, 99, 103, 1000), isNew: true); // bar[0]: low=99 + + // bar[1].Low=88 < bar[2].Low=100 AND bar[1].Low=88 < bar[0].Low=99 + Assert.Equal(88.0, ts.PivotLow); + } + + [Fact] + public void Update_NoPivot_ReturnsNaN() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + // Monotone ascending — no pivot + for (int i = 0; i < 3; i++) + { + double price = 100.0 + i * 5; + _ = ts.Update(new TBar(dt.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000), isNew: true); + } + + Assert.True(double.IsNaN(ts.PivotHigh)); + } + + [Fact] + public void Update_UseCloses_PivotHigh_Detected() + { + var ts = new TtmScalper(useCloses: true); + var dt = DateTime.UtcNow; + + // Close-based: close[1] > close[2] AND close[1] > close[0] + _ = ts.Update(new TBar(dt.AddMinutes(0), 97, 100, 95, 98, 1000), isNew: true); // bar[2]: close=98 + _ = ts.Update(new TBar(dt.AddMinutes(1), 104, 110, 92, 108, 1000), isNew: true); // bar[1]: close=108 (peak) + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 101, 96, 99, 1000), isNew: true); // bar[0]: close=99 + + Assert.Equal(108.0, ts.PivotHigh); + } + + [Fact] + public void Update_UseCloses_PivotLow_Detected() + { + var ts = new TtmScalper(useCloses: true); + var dt = DateTime.UtcNow; + + // Close-based: close[1] < close[2] AND close[1] < close[0] + _ = ts.Update(new TBar(dt.AddMinutes(0), 102, 105, 100, 103, 1000), isNew: true); // bar[2]: close=103 + _ = ts.Update(new TBar(dt.AddMinutes(1), 94, 102, 88, 90, 1000), isNew: true); // bar[1]: close=90 (trough) + _ = ts.Update(new TBar(dt.AddMinutes(2), 102, 106, 99, 103, 1000), isNew: true); // bar[0]: close=103 + + Assert.Equal(90.0, ts.PivotLow); + } + + [Fact] + public void Name_ContainsTtmScalper() + { + var ts = new TtmScalper(); + Assert.Contains("TtmScalper", ts.Name, StringComparison.Ordinal); + } +} + +// -- C) State + Bar Correction ------------------------------------------------ +public sealed class TtmScalperStateCorrectionTests +{ + [Fact] + public void IsNew_True_AdvancesState() + { + var ts = new TtmScalper(); + + _ = ts.Update(new TBar(DateTime.UtcNow, 100, 105, 95, 100, 1000), isNew: true); + var first = ts.Last; + + _ = ts.Update(new TBar(DateTime.UtcNow.AddMinutes(1), 105, 110, 100, 105, 1000), isNew: true); + var second = ts.Last; + + Assert.NotEqual(first.Time, second.Time); + } + + [Fact] + public void IsNew_False_CorrectionRestoresState() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + // Feed 2 bars + for (int i = 0; i < 2; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + // New bar + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 110, 85, 100, 1000), isNew: true); + + // Correct the bar (isNew=false with different values) + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 111, 84, 100, 1000), isNew: false); + + // Another correction with same values should produce same result + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 111, 84, 100, 1000), isNew: false); + var corrected1High = ts.PivotHigh; + var corrected1Low = ts.PivotLow; + + _ = ts.Update(new TBar(dt.AddMinutes(2), 98, 111, 84, 100, 1000), isNew: false); + var corrected2High = ts.PivotHigh; + var corrected2Low = ts.PivotLow; + + Assert.Equal(corrected1High, corrected2High); + Assert.Equal(corrected1Low, corrected2Low); + } + + [Fact] + public void IterativeCorrections_ProduceSameResult() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 2; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000), isNew: true); + } + + _ = ts.Update(new TBar(dt.AddMinutes(2), 105, 110, 90, 100, 1000), isNew: true); + + double[] highResults = new double[3]; + double[] lowResults = new double[3]; + for (int i = 0; i < 3; i++) + { + _ = ts.Update(new TBar(dt.AddMinutes(2), 106, 112, 88, 102, 1000), isNew: false); + highResults[i] = ts.PivotHigh; + lowResults[i] = ts.PivotLow; + } + + Assert.Equal(highResults[0], highResults[1]); + Assert.Equal(highResults[1], highResults[2]); + Assert.Equal(lowResults[0], lowResults[1]); + Assert.Equal(lowResults[1], lowResults[2]); + } + + [Fact] + public void Reset_ClearsAllState() + { + var ts = new TtmScalper(); + + for (int i = 0; i < 10; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(ts.IsHot); + + ts.Reset(); + + Assert.False(ts.IsHot); + Assert.True(double.IsNaN(ts.PivotHigh)); + Assert.True(double.IsNaN(ts.PivotLow)); + } +} + +// -- D) Warmup / Convergence -------------------------------------------------- +public sealed class TtmScalperWarmupTests +{ + [Fact] + public void IsHot_FlipsAfterWarmup() + { + var ts = new TtmScalper(); + + // Feed 2 bars — should NOT be hot + for (int i = 0; i < 2; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + Assert.False(ts.IsHot, $"Should not be hot at bar {i}"); + } + + // Feed 3rd bar — should be hot + double p = 100.0 + 2; + _ = ts.Update(new TBar(DateTime.UtcNow.AddMinutes(2), p, p + 2, p - 2, p + 1, 1000)); + Assert.True(ts.IsHot, "Should be hot after 3 bars"); + } + + [Fact] + public void WarmupPeriod_Equals3() + { + var ts = new TtmScalper(); + Assert.Equal(3, ts.WarmupPeriod); + } +} + +// -- E) Robustness ------------------------------------------------------------ +public sealed class TtmScalperRobustnessTests +{ + [Fact] + public void NaN_Input_UsesLastValidValue() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + // Feed valid bars + for (int i = 0; i < 3; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + Assert.True(ts.IsHot); + + // Feed NaN bar + _ = ts.Update(new TBar(dt.AddMinutes(3), double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + // Should still be hot with valid outputs + Assert.True(ts.IsHot); + } + + [Fact] + public void Infinity_Input_UsesLastValidValue() + { + var ts = new TtmScalper(); + var dt = DateTime.UtcNow; + + for (int i = 0; i < 3; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(dt.AddMinutes(i), price, price + 5, price - 5, price + 1, 1000)); + } + + _ = ts.Update(new TBar(dt.AddMinutes(3), + double.PositiveInfinity, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 0)); + + Assert.True(ts.IsHot); + } + + [Fact] + public void FirstBar_NaN_ReturnsNaN() + { + var ts = new TtmScalper(); + + _ = ts.Update(new TBar(DateTime.UtcNow, double.NaN, double.NaN, double.NaN, double.NaN, 0)); + + Assert.True(double.IsNaN(ts.Last.Value)); + } +} + +// -- F) Consistency ----------------------------------------------------------- +public sealed class TtmScalperConsistencyTests +{ + private static TBarSeries CreateGbmBars(int count = 500) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + [Fact] + public void Streaming_MatchesBatch() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new TtmScalper(); + var streamHighResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHighResults[i] = streaming.PivotHigh; + } + + // Batch + var batchResults = TtmScalper.Batch(bars); + + int warmup = 2; // first 2 bars are NaN + for (int i = warmup; i < bars.Count; i++) + { + if (double.IsNaN(streamHighResults[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value)); + } + else + { + Assert.Equal(streamHighResults[i], batchResults[i].Value, precision: 10); + } + } + } + + [Fact] + public void Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new TtmScalper(); + var streamHighResults = new double[bars.Count]; + var streamLowResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHighResults[i] = streaming.PivotHigh; + streamLowResults[i] = streaming.PivotLow; + } + + // Span + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + TtmScalper.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanHigh, spanLow); + + for (int i = 2; i < bars.Count; i++) + { + if (double.IsNaN(streamHighResults[i])) + { + Assert.True(double.IsNaN(spanHigh[i]), $"PivotHigh mismatch at {i}"); + } + else + { + Assert.Equal(streamHighResults[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLowResults[i])) + { + Assert.True(double.IsNaN(spanLow[i]), $"PivotLow mismatch at {i}"); + } + else + { + Assert.Equal(streamLowResults[i], spanLow[i], precision: 10); + } + } + } + + [Fact] + public void TValue_Update_MatchesTBar_Update() + { + var f1 = new TtmScalper(); + var f2 = new TtmScalper(); + + double[] prices = [100, 102, 98, 105, 99, 103, 107, 95, 110, 108]; + + for (int i = 0; i < prices.Length; i++) + { + double p = prices[i]; + _ = f1.Update(new TBar(DateTime.UtcNow.AddMinutes(i), p, p, p, p, 0), isNew: true); + _ = f2.Update(new TValue(DateTime.UtcNow.AddMinutes(i), p), isNew: true); + } + + Assert.Equal(f1.PivotHigh, f2.PivotHigh); + Assert.Equal(f1.PivotLow, f2.PivotLow); + } + + [Fact] + public void UseCloses_Streaming_MatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming with useCloses=true + var streaming = new TtmScalper(useCloses: true); + var streamHighResults = new double[bars.Count]; + var streamLowResults = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHighResults[i] = streaming.PivotHigh; + streamLowResults[i] = streaming.PivotLow; + } + + // Span with useCloses=true + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + TtmScalper.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanHigh, spanLow, useCloses: true); + + for (int i = 2; i < bars.Count; i++) + { + if (double.IsNaN(streamHighResults[i])) + { + Assert.True(double.IsNaN(spanHigh[i]), $"UseCloses PivotHigh mismatch at {i}"); + } + else + { + Assert.Equal(streamHighResults[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLowResults[i])) + { + Assert.True(double.IsNaN(spanLow[i]), $"UseCloses PivotLow mismatch at {i}"); + } + else + { + Assert.Equal(streamLowResults[i], spanLow[i], precision: 10); + } + } + } +} + +// -- G) Span API Tests -------------------------------------------------------- +public sealed class TtmScalperSpanTests +{ + [Fact] + public void Batch_Span_MismatchedLengths_Throws() + { + var ex = Assert.Throws(() => + TtmScalper.Batch(new double[10], new double[5], new double[10], new double[10], new double[10])); + Assert.Equal("high", ex.ParamName); + } + + [Fact] + public void Batch_Span_HighOutputTooShort_Throws() + { + var ex = Assert.Throws(() => + TtmScalper.Batch(new double[10], new double[10], new double[10], new double[5], new double[10])); + Assert.Equal("highOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_LowOutputTooShort_Throws() + { + var ex = Assert.Throws(() => + TtmScalper.Batch(new double[10], new double[10], new double[10], new double[10], new double[5])); + Assert.Equal("lowOutput", ex.ParamName); + } + + [Fact] + public void Batch_Span_Empty_NoException() + { + var ex = Record.Exception(() => + TtmScalper.Batch(ReadOnlySpan.Empty, ReadOnlySpan.Empty, + ReadOnlySpan.Empty, Span.Empty, Span.Empty)); + Assert.Null(ex); + } +} + +// -- H) Event / Chainability ------------------------------------------------- +public sealed class TtmScalperEventTests +{ + [Fact] + public void Pub_FiresOnUpdate() + { + var ts = new TtmScalper(); + int fireCount = 0; + + ts.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + _ = ts.Update(new TBar(DateTime.UtcNow, 97, 100, 95, 98, 1000)); + + Assert.Equal(1, fireCount); + } + + [Fact] + public void Pub_FiresOnEachUpdate() + { + var ts = new TtmScalper(); + int fireCount = 0; + + ts.Pub += (object? _, in TValueEventArgs _e) => { fireCount++; }; + + for (int i = 0; i < 5; i++) + { + double price = 100.0 + i; + _ = ts.Update(new TBar(DateTime.UtcNow.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000)); + } + + Assert.Equal(5, fireCount); + } +} + +// -- I) Prime Tests ----------------------------------------------------------- +public sealed class TtmScalperPrimeTests +{ + [Fact] + public void Prime_TBarSeries_SetsState() + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: 42); + var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + + var ts = new TtmScalper(); + ts.Prime(bars); + + Assert.True(ts.IsHot); + } + + [Fact] + public void Prime_EmptySource_NoException() + { + var ts = new TtmScalper(); + var bars = new TBarSeries(); + + var ex = Record.Exception(() => ts.Prime(bars)); + Assert.Null(ex); + Assert.False(ts.IsHot); + } +} diff --git a/lib/reversals/ttm_scalper/TtmScalper.Validation.Tests.cs b/lib/reversals/ttm_scalper/TtmScalper.Validation.Tests.cs new file mode 100644 index 00000000..a2e2dfad --- /dev/null +++ b/lib/reversals/ttm_scalper/TtmScalper.Validation.Tests.cs @@ -0,0 +1,231 @@ +// TTM_SCALPER Validation Tests - TTM Scalper Alert +// Self-consistency validation (no external library has TTM Scalper implementation) +// Validates: streaming == batch == span, determinism, dual output, useCloses mode + +namespace QuanTAlib.Tests; + +public sealed class TtmScalperValidationTests +{ + private static TBarSeries CreateGbmBars(int count = 500, int seed = 42) + { + var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.20, seed: seed); + return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)); + } + + // -- Self-Consistency: Streaming == Batch (PivotHigh) ------------------------- + + [Fact] + public void StreamingMatchesBatch_PivotHigh() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new TtmScalper(); + var streamHigh = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHigh[i] = streaming.PivotHigh; + } + + // Batch + var batchResults = TtmScalper.Batch(bars); + + for (int i = 2; i < bars.Count; i++) + { + if (double.IsNaN(streamHigh[i])) + { + Assert.True(double.IsNaN(batchResults[i].Value), + $"Mismatch at {i}: streaming=NaN, batch={batchResults[i].Value}"); + } + else + { + Assert.Equal(streamHigh[i], batchResults[i].Value, precision: 10); + } + } + } + + // -- Self-Consistency: Streaming == Span (Both Pivots) ------------------------ + + [Fact] + public void StreamingMatchesSpan_BothPivots() + { + var bars = CreateGbmBars(); + + // Streaming + var streaming = new TtmScalper(); + var streamHigh = new double[bars.Count]; + var streamLow = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHigh[i] = streaming.PivotHigh; + streamLow[i] = streaming.PivotLow; + } + + // Span + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + TtmScalper.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, spanHigh, spanLow); + + for (int i = 2; i < bars.Count; i++) + { + if (double.IsNaN(streamHigh[i])) + { + Assert.True(double.IsNaN(spanHigh[i])); + } + else + { + Assert.Equal(streamHigh[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLow[i])) + { + Assert.True(double.IsNaN(spanLow[i])); + } + else + { + Assert.Equal(streamLow[i], spanLow[i], precision: 10); + } + } + } + + // -- UseCloses: Streaming == Span --------------------------------------------- + + [Fact] + public void UseCloses_StreamingMatchesSpan() + { + var bars = CreateGbmBars(); + + // Streaming with useCloses=true + var streaming = new TtmScalper(useCloses: true); + var streamHigh = new double[bars.Count]; + var streamLow = new double[bars.Count]; + for (int i = 0; i < bars.Count; i++) + { + _ = streaming.Update(bars[i], isNew: true); + streamHigh[i] = streaming.PivotHigh; + streamLow[i] = streaming.PivotLow; + } + + // Span with useCloses=true + var spanHigh = new double[bars.Count]; + var spanLow = new double[bars.Count]; + TtmScalper.Batch(bars.HighValues, bars.LowValues, bars.CloseValues, + spanHigh, spanLow, useCloses: true); + + for (int i = 2; i < bars.Count; i++) + { + if (double.IsNaN(streamHigh[i])) + { + Assert.True(double.IsNaN(spanHigh[i])); + } + else + { + Assert.Equal(streamHigh[i], spanHigh[i], precision: 10); + } + + if (double.IsNaN(streamLow[i])) + { + Assert.True(double.IsNaN(spanLow[i])); + } + else + { + Assert.Equal(streamLow[i], spanLow[i], precision: 10); + } + } + } + + // -- Determinism --------------------------------------------------------------- + + [Fact] + public void SameInput_ProducesSameOutput() + { + var bars = CreateGbmBars(count: 200, seed: 123); + + var ts1 = new TtmScalper(); + var ts2 = new TtmScalper(); + + for (int i = 0; i < bars.Count; i++) + { + _ = ts1.Update(bars[i], isNew: true); + _ = ts2.Update(bars[i], isNew: true); + } + + Assert.Equal(ts1.PivotHigh, ts2.PivotHigh); + Assert.Equal(ts1.PivotLow, ts2.PivotLow); + } + + // -- Calculate Returns Valid Indicator ----------------------------------------- + + [Fact] + public void Calculate_ReturnsValidIndicatorAndResults() + { + var bars = CreateGbmBars(count: 100); + + var (results, indicator) = TtmScalper.Calculate(bars); + + Assert.NotNull(results); + Assert.Equal(bars.Count, results.Count); + Assert.True(indicator.IsHot); + } + + // -- BatchDual Returns Both Pivots ------------------------------------------- + + [Fact] + public void BatchDual_ReturnsBothSeries() + { + var bars = CreateGbmBars(count: 100); + + var (highSeries, lowSeries) = TtmScalper.BatchDual(bars); + + Assert.Equal(bars.Count, highSeries.Count); + Assert.Equal(bars.Count, lowSeries.Count); + + // With 100 bars of GBM data, should detect both pivot types + bool hasHigh = false; + bool hasLow = false; + for (int i = 0; i < highSeries.Count; i++) + { + if (double.IsFinite(highSeries[i].Value)) + { + hasHigh = true; + } + if (double.IsFinite(lowSeries[i].Value)) + { + hasLow = true; + } + } + + Assert.True(hasHigh, "Should detect at least one pivot high in 100 bars"); + Assert.True(hasLow, "Should detect at least one pivot low in 100 bars"); + } + + // -- UseCloses produces different results than default ------------------------- + + [Fact] + public void UseCloses_ProducesDifferentResults() + { + var bars = CreateGbmBars(count: 200); + + var defaultMode = new TtmScalper(useCloses: false); + var closesMode = new TtmScalper(useCloses: true); + + int differenceCount = 0; + for (int i = 0; i < bars.Count; i++) + { + _ = defaultMode.Update(bars[i], isNew: true); + _ = closesMode.Update(bars[i], isNew: true); + + bool defaultHasHigh = double.IsFinite(defaultMode.PivotHigh); + bool closesHasHigh = double.IsFinite(closesMode.PivotHigh); + if (defaultHasHigh != closesHasHigh) + { + differenceCount++; + } + } + + // With GBM data, high/low and close patterns should diverge at least once + Assert.True(differenceCount > 0, "UseCloses should produce different pivot detection than default mode"); + } +} diff --git a/lib/reversals/ttm_scalper/TtmScalper.cs b/lib/reversals/ttm_scalper/TtmScalper.cs new file mode 100644 index 00000000..cc3204bb --- /dev/null +++ b/lib/reversals/ttm_scalper/TtmScalper.cs @@ -0,0 +1,447 @@ +// TTM_SCALPER: TTM Scalper Alert (John Carter) +// Three-bar pivot pattern detecting potential reversal points for scalping entries. +// Simpler cousin of Williams Fractals — uses 3-bar window instead of 5. + +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +namespace QuanTAlib; + +/// +/// TTM_SCALPER: TTM Scalper Alert +/// +/// +/// A retrospective 3-bar pattern detector. A pivot high occurs when bar[1].High +/// is strictly greater than both neighbors' highs. A pivot low occurs when +/// bar[1].Low is strictly less than both neighbors' lows. Optional close-based mode +/// uses close prices instead of high/low. +/// +/// Calculation (default high/low mode): +/// +/// PivotHigh = high[1] > high[2] AND high[1] > high[0] ? high[1] : NaN +/// PivotLow = low[1] < low[2] AND low[1] < low[0] ? low[1] : NaN +/// +/// +/// Calculation (close-based mode): +/// +/// PivotHigh = close[1] > close[2] AND close[1] > close[0] ? close[1] : NaN +/// PivotLow = close[1] < close[2] AND close[1] < close[0] ? close[1] : NaN +/// +/// +/// Key characteristics: +/// - O(1) update via 3-element circular buffer +/// - Outputs are naturally delayed by 1 bar (the pivot is at bar[1]) +/// - Dual output: PivotHigh (bearish reversal) and PivotLow (bullish reversal) +/// - Optional UseCloses parameter for close-based detection +/// - WarmupPeriod = 3 (need exactly 3 bars to detect the first pivot) +/// +/// Detailed documentation +[SkipLocalsInit] +public sealed class TtmScalper : ITValuePublisher +{ + private const int WindowSize = 3; + + // Circular buffers for highs, lows, and closes — fixed 3 elements + private readonly double[] _hBuf; + private readonly double[] _lBuf; + private readonly double[] _cBuf; + + private int _count; + private long _index; + + [StructLayout(LayoutKind.Auto)] + private record struct State( + double LastValidHigh, + double LastValidLow, + double LastValidClose); + + private State _s; + private State _ps; + + private readonly bool _useCloses; + private readonly TBarPublishedHandler _barHandler; + + /// Display name for the indicator. + public string Name { get; } + + /// Bars required for the indicator to warm up. + public int WarmupPeriod { get; } + + /// Whether to use close prices instead of high/low for detection. + public bool UseCloses => _useCloses; + + /// Current pivot high price (NaN if no pivot high at current position). + public double PivotHigh { get; private set; } + + /// Current pivot low price (NaN if no pivot low at current position). + public double PivotLow { get; private set; } + + /// Primary output value (PivotHigh as TValue for overlay plotting). + public TValue Last { get; private set; } + + /// True when enough bars have been processed for valid output. + public bool IsHot => _count >= WindowSize; + + public event TValuePublishedHandler? Pub; + + /// + /// Creates a TTM Scalper Alert indicator. + /// + /// Use close prices instead of high/low for pivot detection. + public TtmScalper(bool useCloses = false) + { + _useCloses = useCloses; + _hBuf = new double[WindowSize]; + _lBuf = new double[WindowSize]; + _cBuf = new double[WindowSize]; + + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN); + _ps = _s; + PivotHigh = double.NaN; + PivotLow = double.NaN; + + Name = $"TtmScalper({useCloses})"; + WarmupPeriod = WindowSize; + _barHandler = HandleBar; + } + + /// + /// Creates a TTM Scalper Alert indicator chained to a TBarSeries source. + /// + public TtmScalper(TBarSeries source, bool useCloses = false) + : this(useCloses) + { + Prime(source); + source.Pub += _barHandler; + } + + private void HandleBar(object? sender, in TBarEventArgs e) => Update(e.Value, e.IsNew); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + private void PubEvent(TValue value, bool isNew = true) => + Pub?.Invoke(this, new TValueEventArgs { Value = value, IsNew = isNew }); + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TBar input, bool isNew = true) + { + if (isNew) + { + _ps = _s; + _index++; + _count++; + } + else + { + _s = _ps; + } + + var s = _s; + + // Validate inputs — substitute last-valid on NaN/Infinity + double high = input.High; + double low = input.Low; + double close = input.Close; + + if (double.IsFinite(high)) { s.LastValidHigh = high; } + else { high = s.LastValidHigh; } + + if (double.IsFinite(low)) { s.LastValidLow = low; } + else { low = s.LastValidLow; } + + if (double.IsFinite(close)) { s.LastValidClose = close; } + else { close = s.LastValidClose; } + + // If still no valid data, return NaN + if (double.IsNaN(high) || double.IsNaN(low) || double.IsNaN(close)) + { + _s = s; + PivotHigh = double.NaN; + PivotLow = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // Store in circular buffer + int bufIdx = (int)(_index % WindowSize); + _hBuf[bufIdx] = high; + _lBuf[bufIdx] = low; + _cBuf[bufIdx] = close; + + // Need at least 3 bars to evaluate a pivot + if (_count < WindowSize) + { + _s = s; + PivotHigh = double.NaN; + PivotLow = double.NaN; + Last = new TValue(input.Time, double.NaN); + PubEvent(Last, isNew); + return Last; + } + + // The pivot candidate is at position [1] relative to current: + // Current bar = index 0 (newest), we look at bar[1] = 1 bar ago + // In circular buffer terms: + // bar[0] = bufIdx + // bar[1] = (bufIdx - 1 + 3) % 3 <- the candidate + // bar[2] = (bufIdx - 2 + 3) % 3 + + int i0 = bufIdx; + int i1 = (bufIdx + WindowSize - 1) % WindowSize; // candidate + int i2 = (bufIdx + WindowSize - 2) % WindowSize; + + if (_useCloses) + { + double c1 = _cBuf[i1]; + + // Pivot high: close[1] > close[2] AND close[1] > close[0] + PivotHigh = (c1 > _cBuf[i2] && c1 > _cBuf[i0]) + ? c1 + : double.NaN; + + // Pivot low: close[1] < close[2] AND close[1] < close[0] + PivotLow = (c1 < _cBuf[i2] && c1 < _cBuf[i0]) + ? c1 + : double.NaN; + } + else + { + double h1 = _hBuf[i1]; + double l1 = _lBuf[i1]; + + // Pivot high: high[1] > high[2] AND high[1] > high[0] + PivotHigh = (h1 > _hBuf[i2] && h1 > _hBuf[i0]) + ? h1 + : double.NaN; + + // Pivot low: low[1] < low[2] AND low[1] < low[0] + PivotLow = (l1 < _lBuf[i2] && l1 < _lBuf[i0]) + ? l1 + : double.NaN; + } + + _s = s; + + Last = new TValue(input.Time, PivotHigh); + PubEvent(Last, isNew); + return Last; + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public TValue Update(TValue input, bool isNew = true) => + Update(new TBar(input.Time, input.Value, input.Value, input.Value, input.Value, 0), isNew); + + public TSeries Update(TBarSeries source) + { + if (source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v), downBuf, _useCloses); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + // Prime internal state for continued streaming + Prime(source); + + var lastTime = new DateTime(source.Times[^1], DateTimeKind.Utc); + Last = new TValue(lastTime, CollectionsMarshal.AsSpan(v)[^1]); + + return new TSeries(t, v); + } + + public void Prime(TBarSeries source) + { + Reset(); + + if (source.Count == 0) + { + return; + } + + for (int i = 0; i < source.Count; i++) + { + Update(source[i], isNew: true); + } + } + + public void Prime(ReadOnlySpan source, TimeSpan? step = null) + { + Reset(); + + if (source.Length == 0) + { + return; + } + + long t = DateTime.UtcNow.Ticks; + long stepTicks = (step ?? TimeSpan.FromMinutes(1)).Ticks; + + for (int i = 0; i < source.Length; i++) + { + double val = source[i]; + Update(new TBar(t, val, val, val, val, 0), isNew: true); + t += stepTicks; + } + } + + public void Reset() + { + Array.Clear(_hBuf); + Array.Clear(_lBuf); + Array.Clear(_cBuf); + _count = 0; + _index = -1; + _s = new State(double.NaN, double.NaN, double.NaN); + _ps = _s; + PivotHigh = double.NaN; + PivotLow = double.NaN; + Last = default; + } + + /// + /// Batch computation of TTM Scalper pivots over span data. + /// Writes PivotHigh values to and PivotLow values to . + /// + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static void Batch( + ReadOnlySpan high, + ReadOnlySpan low, + ReadOnlySpan close, + Span highOutput, + Span lowOutput, + bool useCloses = false) + { + if (high.Length != low.Length || high.Length != close.Length) + { + throw new ArgumentException("Input spans must have the same length.", nameof(high)); + } + if (highOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(highOutput)); + } + if (lowOutput.Length < high.Length) + { + throw new ArgumentException("Output span must be at least as long as input.", nameof(lowOutput)); + } + + int len = high.Length; + if (len == 0) + { + return; + } + + // Fill first 2 bars with NaN (need 3 bars for first pivot) + int warmup = Math.Min(WindowSize - 1, len); + for (int i = 0; i < warmup; i++) + { + highOutput[i] = double.NaN; + lowOutput[i] = double.NaN; + } + + if (useCloses) + { + for (int i = WindowSize - 1; i < len; i++) + { + double c1 = close[i - 1]; + + highOutput[i] = (c1 > close[i - 2] && c1 > close[i]) + ? c1 + : double.NaN; + + lowOutput[i] = (c1 < close[i - 2] && c1 < close[i]) + ? c1 + : double.NaN; + } + } + else + { + for (int i = WindowSize - 1; i < len; i++) + { + double h1 = high[i - 1]; + double l1 = low[i - 1]; + + highOutput[i] = (h1 > high[i - 2] && h1 > high[i]) + ? h1 + : double.NaN; + + lowOutput[i] = (l1 < low[i - 2] && l1 < low[i]) + ? l1 + : double.NaN; + } + } + } + + public static TSeries Batch(TBarSeries source, bool useCloses = false) + { + if (source == null || source.Count == 0) + { + return new TSeries([], []); + } + + int len = source.Count; + var t = new List(len); + var v = new List(len); + + CollectionsMarshal.SetCount(t, len); + CollectionsMarshal.SetCount(v, len); + + var downBuf = new double[len]; + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(v), downBuf, useCloses); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(t)); + + return new TSeries(t, v); + } + + /// + /// Batch computation returning both PivotHigh and PivotLow TSeries. + /// + public static (TSeries PivotHighs, TSeries PivotLows) BatchDual(TBarSeries source, bool useCloses = false) + { + if (source == null || source.Count == 0) + { + return (new TSeries([], []), new TSeries([], [])); + } + + int len = source.Count; + var tHigh = new List(len); + var vHigh = new List(len); + var tLow = new List(len); + var vLow = new List(len); + + CollectionsMarshal.SetCount(tHigh, len); + CollectionsMarshal.SetCount(vHigh, len); + CollectionsMarshal.SetCount(tLow, len); + CollectionsMarshal.SetCount(vLow, len); + + Batch(source.HighValues, source.LowValues, source.CloseValues, + CollectionsMarshal.AsSpan(vHigh), CollectionsMarshal.AsSpan(vLow), useCloses); + + source.Times.CopyTo(CollectionsMarshal.AsSpan(tHigh)); + source.Times.CopyTo(CollectionsMarshal.AsSpan(tLow)); + + return (new TSeries(tHigh, vHigh), new TSeries(tLow, vLow)); + } + + [MethodImpl(MethodImplOptions.AggressiveInlining)] + public static (TSeries Results, TtmScalper Indicator) Calculate(TBarSeries source, bool useCloses = false) + { + var indicator = new TtmScalper(useCloses); + var results = indicator.Update(source); + return (results, indicator); + } +} diff --git a/lib/reversals/ttm_scalper/ttmscalper.pine b/lib/reversals/ttm_scalper/ttmscalper.pine new file mode 100644 index 00000000..4e612a44 --- /dev/null +++ b/lib/reversals/ttm_scalper/ttmscalper.pine @@ -0,0 +1,40 @@ +// The MIT License (MIT) +// © mihakralj +//@version=6 +indicator("TTM Scalper Alert", "TTM_SCALPER", overlay=true) + +//@function Detects 3-bar pivot patterns for scalping entry signals +//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/reversals/ttmscalper.md +//@returns Tuple [pivot_high_price, pivot_low_price] with pivot values (na if no pivot) +ttmscalper(bool use_closes = false) => + bool is_pivot_high = false + bool is_pivot_low = false + + if bar_index >= 2 + if use_closes + is_pivot_high := close[1] > close[2] and close[1] > close[0] + is_pivot_low := close[1] < close[2] and close[1] < close[0] + else + is_pivot_high := high[1] > high[2] and high[1] > high[0] + is_pivot_low := low[1] < low[2] and low[1] < low[0] + + float pivot_high_price = is_pivot_high ? (use_closes ? close[1] : high[1]) : na + float pivot_low_price = is_pivot_low ? (use_closes ? close[1] : low[1]) : na + + [pivot_high_price, pivot_low_price] + +// ---------- Main loop ---------- + +// Inputs +i_use_closes = input.bool(false, "Use Closes", tooltip="Use close prices instead of high/low for pivot detection") +i_show_high = input.bool(true, "Show Pivot Highs", tooltip="Display bearish pivot markers") +i_show_low = input.bool(true, "Show Pivot Lows", tooltip="Display bullish pivot markers") +i_color_high = input.color(color.red, "Pivot High Color") +i_color_low = input.color(color.green, "Pivot Low Color") + +// Calculation +[pivot_high, pivot_low] = ttmscalper(i_use_closes) + +// Plot pivot markers +plotshape(i_show_high and not na(pivot_high) ? pivot_high : na, "Pivot High", style=shape.triangledown, location=location.absolute, color=i_color_high, size=size.small, offset=-1) +plotshape(i_show_low and not na(pivot_low) ? pivot_low : na, "Pivot Low", style=shape.triangleup, location=location.absolute, color=i_color_low, size=size.small, offset=-1) diff --git a/lib/trends_FIR/blma/Blma.cs b/lib/trends_FIR/blma/Blma.cs index 180e643b..536bac4d 100644 --- a/lib/trends_FIR/blma/Blma.cs +++ b/lib/trends_FIR/blma/Blma.cs @@ -122,7 +122,7 @@ public sealed class Blma : AbstractBase result = ComputeWeightedAverage( currentWeightSum, CalculateWeightedSum(_buffer, currentWeights), - _buffer.Average()); + _buffer.Average); } } else @@ -131,7 +131,7 @@ public sealed class Blma : AbstractBase result = ComputeWeightedAverage( _weightSum, CalculateWeightedSum(_buffer, _weights), - _buffer.Average()); + _buffer.Average); } var tValue = new TValue(input.Time, result); diff --git a/plans/missing-indicators-report.md b/plans/missing-indicators-report.md deleted file mode 100644 index 53b0007d..00000000 --- a/plans/missing-indicators-report.md +++ /dev/null @@ -1,337 +0,0 @@ -# Missing Indicators Report - -> Generated: 2026-02-13 | Refreshed: 2026-02-17 | Source: Cross-reference of `_index.md` files vs actual filesystem + planned additions - -## Summary - -| Status | Count | Description | -|--------|------:|-------------| -| **Fully Implemented** | 276 | `.cs` + tests + docs | -| **Pine-Only** (has spec, no C#) | 9 | Directory exists with `.pine` file only | -| **No Directory** (listed in master index or planned, no files) | 98 | Planned but nothing on disk | -| **Doc-Only** | 1 | Only `.md` file exists | -| **Index Discrepancies** | 0 | All 7 mismatches in `lib/_index.md` fixed on 2026-02-16 | - ---- - -## Implemented Categories (fully in C#) - -| Category | Subdirs | Implemented | Notes | -|----------|--------:|------------:|-------| -| Trends (FIR) | 17 | **17** | All complete; 23 more planned | -| Trends (IIR) | 23 | **23** | All complete; 17 more planned | -| Trends (Adaptive) | — | **0** | New category; 5 planned | -| Filters | 18 | **18** | All complete; 12 more planned | -| Oscillators | 19 | **19** | All complete; 20 more planned | -| Dynamics | 18 | **18** | All complete | -| Momentum | 16 | **16** | All complete | -| Volatility | 26 | **26** | All complete | -| Volume | 26 | **26** | All complete; 1 more planned | -| Channels | 23 | **23** | All complete | -| Cycles | 14 | **14** | All complete; 2 more planned | -| Errors | 26 | **26** | All complete (no Quantower wrappers) | -| Numerics | 15 | **15** | All complete; 14 distributions planned | -| Forecasts | 1 | **1** | AFIRMA only (MLP planned) | -| Statistics | 30 | **30** | ✅ All complete (was 10 pine-only, implemented 2026-02-14 through 2026-02-17) | -| Reversals | 12 | **2** | 9 pine-only + 1 doc-only + 1 planned | -| Feeds | 3 | **3** | CSV, GBM, IFeed | - ---- - -## 1. PINE-ONLY Indicators (spec exists, no C# implementation) - -These have directories with `.pine` reference files but **zero `.cs` files**. They need full implementation. - -### Reversals (9 indicators — pine-only) - -| Indicator | Directory | -|-----------|-----------| -| FRACTALS | `lib/reversals/fractals/` | -| PIVOT | `lib/reversals/pivot/` | -| PIVOTCAM | `lib/reversals/pivotcam/` | -| PIVOTDEM | `lib/reversals/pivotdem/` | -| PIVOTEXT | `lib/reversals/pivotext/` | -| PIVOTFIB | `lib/reversals/pivotfib/` | -| PIVOTWOOD | `lib/reversals/pivotwood/` | -| PSAR | `lib/reversals/psar/` | -| SWINGS | `lib/reversals/swings/` | - ---- - -## 2. NO DIRECTORY at All (planned, no files on disk) - -These indicators are planned but have **no directory, no files**. Organized by category. - -### Filters (12 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| LAGUERRE | Laguerre Filter | Ehlers; 4-element IIR with damping factor | -| ALAGUERRE | Adaptive Laguerre Filter | Ehlers; variable-gamma Laguerre | -| ROOFING | Roofing Filter | Ehlers; HP + super smoother composite | -| VOSS | Voss Predictive Filter | Ehlers; predictive bandpass | -| AGC | Automatic Gain Control | Ehlers; amplitude normalization | -| SPBF | Super Passband Filter | Ehlers; wide-band bandpass | -| LMS | Least Mean Squares Adaptive Filter | Widrow-Hoff adaptive FIR | -| RLS | Recursive Least Squares Adaptive Filter | Faster convergence than LMS | -| WAVELET | Denoising Wavelet Filter | Wavelet-based noise removal (distinct from CWT/DWT transforms) | -| BAXTERKING | Baxter-King Filter | Symmetric band-pass for business cycle extraction | -| CHRISTIANOFITZGERALD | Christiano-Fitzgerald Filter | Asymmetric band-pass; handles endpoints | -| ONEEURO | One Euro Filter | Low-latency jitter removal; speed-adaptive | - -### Trends — IIR (17 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| DECYCLER | Decycler | Ehlers; HP subtracted from price | -| TRENDFLEX | Trendflex Indicator | Ehlers; trend-following zero-lag | -| PMA | Predictive Moving Average | Ehlers; 2×EMA − EMA(EMA) extrapolation | -| REVERSEEMA | Reverse Exponential Moving Average | Ehlers; backward-looking EMA reconstruction | -| EVWMA | Elastic Volume Weighted Moving Average | Volume-elastic EMA variant | -| MAVP | Moving Average Variable Period | TA-Lib; period varies per bar | -| EHMA | Exponential Hull Moving Average | Hull concept with EMA instead of WMA | -| HOLT | Holt Exponential Smoothing | Double exponential smoothing (level + trend) | -| HWTS | Holt-Winters Triple Smoothing | Triple smoothing (level+trend+seasonality); ⚠️ check overlap with HWMA | -| CORAL | Coral Trend Filter | LazyBear; multi-pole IIR with color coding | -| GDEMA | Generalized Double EMA | Generalized DEMA with tunable volume factor | -| NLMA | Non-Lag Moving Average | Zero-lag via Kalman-like error correction | -| LEMA | Leader Exponential Moving Average | EMA + momentum lead term | -| MCNMA | McNicholl EMA | McNicholl's zero-lag EMA variant | -| NYQMA | Nyquist Moving Average | Nyquist-frequency-aware smoothing | -| AHRENS | Ahrens Moving Average | Richard Ahrens' recursive MA | -| RAINBOW | Rainbow Moving Average | Cascaded SMA stack (10 layers averaged) | - -### Trends — Adaptive (5 planned — new category) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| ADXVMA | ADX Variable Moving Average | ADX-scaled smoothing factor | -| TRAMA | Trend Regularity Adaptive MA | Adapts to trend regularity | -| NMA | Natural Moving Average | Natural cycle-adaptive MA | -| VMA | Variable Moving Average | Tushar Chande; volatility-adaptive | -| EDCF | Distance Coefficient Filter | Distance-based adaptive coefficient | - -### Trends — FIR (23 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| RWMA | Range Weighted Moving Average | Weights by bar range | -| MIDPOINT | Midpoint | (Highest + Lowest) / 2 over period; ⚠️ name collision with `numerics/midpoint` | -| MIDPRICE | Midprice | (Highest High + Lowest Low) / 2 over period | -| SWMA | Symmetric Weighted Moving Average | Symmetric triangular weights | -| KAISER | Kaiser Window Moving Average | Kaiser-Bessel window FIR | -| BLACKMANHARRIS | Blackman-Harris Window MA | 4-term Blackman-Harris window | -| LANCZOS | Lanczos Window Moving Average | Lanczos (sinc) window FIR | -| PARZEN | Parzen Window Moving Average | Parzen (de la Vallée-Poussin) window | -| NUTTALL | Nuttall Window Moving Average | 4-term Nuttall window; minimal sidelobe | -| BOHMAN | Bohman Window Moving Average | Bohman window FIR | -| DOLPH | Dolph-Chebyshev Window MA | Equiripple sidelobe window | -| TSF | Time Series Forecast | Linear regression extrapolation (moved from Statistics) | -| QRMA | Quadratic Regression MA | 2nd-order polynomial regression | -| CRMA | Cubic Regression MA | 3rd-order polynomial regression | -| NW | Nadaraya-Watson Kernel Regression | Gaussian kernel weighted regression | -| HENDERSON | Henderson Moving Average | Henderson symmetric filter (ABS standard) | -| SP15 | Spencer 15-Point Moving Average | Spencer's classic 15-weight filter | -| SP21 | Spencer 21-Point Moving Average | Spencer's extended 21-weight filter | -| TRIMMED | Trimmed Mean Moving Average | Outlier-trimmed arithmetic mean | -| WINSOR | Winsorized Mean Moving Average | Outlier-capped (Winsorized) mean | -| GEOMMA | Geometric Mean Moving Average | Geometric mean as FIR weight basis | -| HARMMA | Harmonic Mean Moving Average | Harmonic mean as FIR weight basis | - -> **Note:** HAMMING and BLACKMAN window MAs already exist as **HAMMA** and **BLMA** respectively. - -### Oscillators (20 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| BBI | Bulls Bears Index | — | -| BRAR | BRAR | — | -| COPPOCK | Coppock Curve | — | -| CRSI | Connors RSI | — | -| CTI | Correlation Trend Indicator | — | -| DECO | Decycler Oscillator | Ehlers; HP component of Decycler | -| DOSC | Derivative Oscillator | — | -| ER | Efficiency Ratio | — | -| ERI | Elder Ray Index | — | -| FOSC | Forecast Oscillator | — | -| KRI | Kairi Relative Index | — | -| KST | KST Oscillator | — | -| PSL | Psychological Line | — | -| QQE | Quantitative Qualitative Estimation | — | -| REFLEX | Reflex Indicator | Ehlers; momentum with cycle correction | -| RVGI | Relative Vigor Index | — | -| SQUEEZE | Squeeze | — | -| TD_SEQ | TD Sequential | — | -| CYBERCYCLE | Cyber Cycle | Ehlers; 2-pole IIR cycle isolator | -| MSTOCH | MESA Stochastic | Ehlers; Hilbert-based stochastic | - -### Cycles (2 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| CCOR | Correlation Cycle | Ehlers; cycle detection via autocorrelation | -| GOERTZEL | Goertzel Frequency Detector | Single-bin DFT for dominant cycle | - -### Volume (1 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| FVE | Finite Volume Elements | Markos Katsanos; volume-price flow | - -### Reversals (1 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| SAREXT | Parabolic SAR Extended | TA-Lib extended SAR with configurable acceleration | - -### Statistics (2 planned) - -| Indicator | Full Name | Notes | -|-----------|-----------|-------| -| POLYFIT | Polynomial Fitting | — | -| WAVG | Weighted Average | — | - -### Numerics (14 planned — distributions and transforms) - -| Indicator | Full Name | -|-----------|-----------| -| BETADIST | Beta Distribution | -| BINOMDIST | Binomial Distribution | -| CWT | Continuous Wavelet Transform | -| DWT | Discrete Wavelet Transform | -| EXPDIST | Exponential Distribution | -| FDIST | F-Distribution | -| FFT | Fast Fourier Transform | -| GAMMADIST | Gamma Distribution | -| IFFT | Inverse Fast Fourier Transform | -| LOGNORMDIST | Log-normal Distribution | -| NORMDIST | Normal Distribution | -| POISSONDIST | Poisson Distribution | -| TDIST | Student's t-Distribution | -| WEIBULLDIST | Weibull Distribution | - -### Forecasts (1 planned) - -| Indicator | Full Name | -|-----------|-----------| -| MLP | Multilayer Perceptron | - ---- - -## 3. DOC-ONLY (has `.md` but no `.cs` or `.pine`) - -| Indicator | Directory | Files Present | -|-----------|-----------|---------------| -| TTM_SCALPER | `lib/reversals/ttm_scalper/` | `TtmScalper.md` only | - ---- - -## 4. Index Discrepancies (FIXED 2026-02-16) - -All category mismatches and count errors in `lib/_index.md` have been corrected. - -### Fixes Applied - -| Issue | Fix | Status | -|-------|-----|--------| -| **APO** category wrong (Momentum → Oscillators) | Path changed to `oscillators/apo/Apo.md`, category to Oscillators | ✅ Fixed | -| **PHASOR** broken link (subdir doesn't exist) | Removed markdown link, kept as plain text planned entry | ✅ Fixed | -| **CHANDELIER** missing link (now implemented) | Added `[CHANDELIER](reversals/chandelier/Chandelier.md)` | ✅ Fixed | -| **CKSTOP** missing link (now implemented) | Added `[CKSTOP](reversals/ckstop/Ckstop.md)` | ✅ Fixed | -| **IMPULSE** missing link (now implemented) | Added `[IMPULSE](dynamics/impulse/Impulse.md)` | ✅ Fixed | -| **TSF** wrong category (Statistics → Trends FIR) | Category changed to `Trends (FIR)` | ✅ Fixed | -| **All category counts** | Updated to match actual subdirectory counts | ✅ Fixed | -| **Total** | Updated from 278 → **284** | ✅ Fixed | - -### Name Collisions - -| Name | Existing | New (Planned) | Resolution | -|------|----------|---------------|------------| -| MIDPOINT | `lib/numerics/midpoint/` (math function) | Trends (FIR) — (highest+lowest)/2 | Different indicators; use distinct directory paths | -| TUKEY | `lib/errors/tukey/` (Tukey fence metric) | Trends (FIR) — Tukey window MA | Different indicators; use distinct directory paths | -| HAMMING | — | Trends (FIR) — Hamming window MA | **Already exists as HAMMA** (`lib/trends_FIR/hamma/`). Do not add. | -| BLACKMAN | — | Trends (FIR) — Blackman window MA | **Already exists as BLMA** (`lib/trends_FIR/blma/`). Do not add. | -| HWTS | — | Trends (IIR) — Holt-Winters Triple | **May overlap with HWMA** (`lib/trends_FIR/hwma/`). Review before implementing. | -| WAVELET | CWT/DWT in Numerics (transforms) | Filters — Denoising wavelet filter | Different purpose (denoising vs transform). Both valid. | -| TSF | Was under Statistics planned | Now Trends (FIR) | Moved to correct category | - ---- - -## 5. Priority Implementation Order - -### Tier 1 — High Value (pine-only, well-specified) - -1. ~~**Statistics** (10 pine-only)~~ — ✅ **ALL COMPLETE** (implemented 2026-02-14 through 2026-02-17) - -2. **Reversals** (9 pine-only) — Large category gap - - PSAR, FRACTALS, PIVOT, PIVOTCAM, PIVOTDEM, PIVOTEXT, PIVOTFIB, PIVOTWOOD, SWINGS - -### Tier 2 — Ehlers DSP Suite (high-value, well-documented algorithms) - -1. **Filters** — LAGUERRE, ALAGUERRE, ROOFING, VOSS, AGC, SPBF (all Ehlers) -2. **Oscillators** — DECO, REFLEX, CYBERCYCLE, MSTOCH (all Ehlers) -3. **Cycles** — CCOR, GOERTZEL (Ehlers cycle detection) -4. **Trends (IIR)** — DECYCLER, TRENDFLEX, PMA, REVERSEEMA (Ehlers trend) - -### Tier 3 — Window Functions & Regression MAs - -1. **Trends (FIR)** — Window functions: KAISER, BLACKMANHARRIS, LANCZOS, PARZEN, NUTTALL, BOHMAN, DOLPH -2. **Trends (FIR)** — Regression: TSF, QRMA, CRMA, NW, HENDERSON, SP15, SP21 -3. **Trends (FIR)** — Robust means: TRIMMED, WINSOR, GEOMMA, HARMMA - -### Tier 4 — IIR & Adaptive MAs - -1. **Trends (IIR)** — EHMA, HOLT, CORAL, GDEMA, NLMA, LEMA, MCNMA, NYQMA, AHRENS, RAINBOW, EVWMA, MAVP, HWTS -2. **Trends (Adaptive)** — ADXVMA, TRAMA, NMA, VMA, EDCF (new category) - -### Tier 5 — Adaptive Filters & Miscellaneous - -1. **Filters** — LMS, RLS, BAXTERKING, CHRISTIANOFITZGERALD, ONEEURO, WAVELET -2. **Oscillators** — Legacy: QQE, RVGI, COPPOCK, KST, CRSI, TD_SEQ, ERI, SQUEEZE, BBI, BRAR, CTI, DOSC, ER, FOSC, KRI, PSL -3. **Volume** — FVE -4. **Reversals** — SAREXT - -### Tier 6 — Lower Priority - -1. **Statistics** — POLYFIT, WAVG -2. **Numerics** — 14 distributions + transforms -3. **Trends (IIR)** — LTMA -4. **Forecasts** — MLP -5. **Cycles** — PHASOR -6. **Reversals** — TTM_SCALPER (doc exists, needs C#) - ---- - -## 6. Planned Count by Category - -| Category | Existing | Pine-Only | Planned (no dir) | Total When Done | -|----------|--------:|----------:|-----------------:|----------------:| -| Trends (FIR) | 17 | 0 | 23 | 40 | -| Trends (IIR) | 23 | 0 | 17 | 40 | -| Trends (Adaptive) | 0 | 0 | 5 | 5 | -| Filters | 18 | 0 | 12 | 30 | -| Oscillators | 19 | 0 | 20 | 39 | -| Dynamics | 18 | 0 | 0 | 18 | -| Momentum | 16 | 0 | 0 | 16 | -| Volatility | 26 | 0 | 0 | 26 | -| Volume | 26 | 0 | 1 | 27 | -| Channels | 23 | 0 | 0 | 23 | -| Cycles | 14 | 0 | 2 | 16 | -| Errors | 26 | 0 | 0 | 26 | -| Numerics | 15 | 0 | 14 | 29 | -| Forecasts | 1 | 0 | 1 | 2 | -| Statistics | 30 | 0 | 2 | 32 | -| Reversals | 2 | 9 | 1 | 12 | -| Feeds | 3 | 0 | 0 | 3 | -| **Total** | **276** | **9** | **98** | **383** | - -## 7. Grand Totals - -| Type | Count | -|------|------:| -| Pine-only (spec ready, no C#) | **9** | -| No directory (planned only) | **98** | -| Doc-only | **1** | -| **Total missing indicators** | **108** | -| **Total implemented (C#)** | **276** | -| **Grand total (implemented + missing)** | **384** | diff --git a/plans/oscillator-docs-rewrite.md b/plans/oscillator-docs-rewrite.md deleted file mode 100644 index cf4cf57a..00000000 --- a/plans/oscillator-docs-rewrite.md +++ /dev/null @@ -1,106 +0,0 @@ -# Plan: Rewrite All Oscillator .md Files to Gold Standard Template - -## Context - -The gold standard documentation template lives at `temp/doc_template.md`. It was derived from comparative analysis of 8 existing indicator docs across tiers (Jma.md, Rsi.md, Willr.md, Obv.md, Ssf.md, Adx.md, Imi.md, Mom.md) and is stored in qdrant. - -The first application was `lib/oscillators/trix/Trix.md` (completed, verified against source with 22 verification points). This serves as the reference exemplar for oscillator-category docs. - -## Inventory (19 indicators, 1 done) - -| # | Indicator | Current Lines | File | Status | -|---|-----------|:------------:|------|--------| -| 1 | TRIX | 157 | `lib/oscillators/trix/Trix.md` | **Done** | -| 2 | WillR | 110 | `lib/oscillators/willr/Willr.md` | Pending | -| 3 | Stoch | 91 | `lib/oscillators/stoch/Stoch.md` | Pending | -| 4 | Stochf | 94 | `lib/oscillators/stochf/Stochf.md` | Pending | -| 5 | StochRSI | 157 | `lib/oscillators/stochrsi/Stochrsi.md` | Pending | -| 6 | SMI | 106 | `lib/oscillators/smi/Smi.md` | Pending | -| 7 | KDJ | 81 | `lib/oscillators/kdj/Kdj.md` | Pending | -| 8 | Fisher | 43 | `lib/oscillators/fisher/Fisher.md` | Pending | -| 9 | AC | 84 | `lib/oscillators/ac/Ac.md` | Pending | -| 10 | AO | 45 | `lib/oscillators/ao/Ao.md` | Pending | -| 11 | APO | 47 | `lib/oscillators/apo/Apo.md` | Pending | -| 12 | BBB | 71 | `lib/oscillators/bbb/Bbb.md` | Pending | -| 13 | BBS | 89 | `lib/oscillators/bbs/Bbs.md` | Pending | -| 14 | CFO | 102 | `lib/oscillators/cfo/Cfo.md` | Pending | -| 15 | DPO | 73 | `lib/oscillators/dpo/Dpo.md` | Pending | -| 16 | Inertia | 52 | `lib/oscillators/inertia/Inertia.md` | Pending | -| 17 | PGO | 82 | `lib/oscillators/pgo/Pgo.md` | Pending | -| 18 | TTM Wave | 106 | `lib/oscillators/ttm_wave/TtmWave.md` | Pending | -| 19 | Ultosc | 88 | `lib/oscillators/ultosc/Ultosc.md` | Pending | - -## Execution Workflow (per indicator) - -Each rewrite follows the same proven workflow used for TRIX: - -### Step 1: Gather Implementation Details -- Use `understand` (dotnet-semantic-mcp) scoped to the indicator class to get full source, hierarchy, references -- Read the `.Validation.Tests.cs` file to extract validation library coverage and tolerances -- Check for `.pine` file in the indicator directory (source material reference) - -### Step 2: Write the Document -Follow the gold standard template sections in order: -1. **Title + Quote** - `# ABBREV: Full Name` + witty one-liner -2. **Quick-ref card** - Category, Inputs, Parameters, Outputs, Output range, Warmup -3. **Key takeaways** - 5 bullets -4. **Historical Context** - 2-3 paragraphs -5. **What It Measures and Why It Matters** - 2-3 opinionated paragraphs -6. **Mathematical Foundation** - LaTeX formulas, parameter mapping, warmup period -7. **Architecture & Physics** - Pipeline description, state management, FMA usage, edge cases -8. **Interpretation and Signals** - Signal zones table, patterns, practical notes -9. **Related Indicators** - 2-4 with relative links -10. **Validation** - Simplified table (Status/Notes columns only) -11. **Performance Profile** - Key optimizations, operation count, SIMD analysis -12. **Common Pitfalls** - 5-7 numbered items -13. **FAQ** - Optional, for complex indicators -14. **References** - Academic and web sources - -### Step 3: Verify Against Source -Cross-check all technical claims against the `.cs` source: -- Default period, parameter constraints -- State struct fields and initialization -- Alpha/decay formulas -- Warmup period formula and IsHot condition -- FMA patterns used -- NaN/Infinity handling -- Bar correction logic -- Validation library coverage matches test file - -### Step 4: Store in qdrant -Record completion with tags for future reference. - -## Batching Strategy - -Process in groups of related indicators for cross-referencing efficiency: - -### Batch 1: Stochastic Family (share architecture patterns) -- Stoch, Stochf, StochRSI, KDJ, SMI - -### Batch 2: Bill Williams / Momentum Oscillators -- AC, AO, APO, CFO, DPO - -### Batch 3: Bollinger/Statistical Oscillators -- BBB, BBS, PGO, Inertia - -### Batch 4: Remaining -- Fisher, WillR, TtmWave, Ultosc - -## Template Rules Reminder - -- **Voice**: GRINGE blend. Skeptical-architect tone. -- **Anti-slop forbidden words**: delve, leverage, pivotal, tapestry, landscape, furthermore, seamless, ecosystem, transformative, foster -- **No em-dashes** -- **Quality Metrics section**: OMIT (oscillators are not trends/filters) -- **Validation table**: Simplified 2-column format (Status, Notes) -- **LaTeX**: Only in Mathematical Foundation section -- **Code blocks**: Only in Architecture & Physics section for pipeline diagrams -- **Exemplar**: `lib/oscillators/trix/Trix.md` (completed reference) - -## Dependencies - -- Gold standard template: `temp/doc_template.md` -- Reference exemplar: `lib/oscillators/trix/Trix.md` -- C# analysis: requires `__unlock_csharp_analysis__` per session -- All 19 indicators have validation tests (confirmed) -- All 19 indicators have existing .md files to be replaced