feat: footprint microstructure indicator (part 4 of 4) (#123)
This commit is contained in:
kingchenc
@@ -1063,3 +1063,34 @@ test('price-impact rejects bad input', () => {
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assert.throws(() => new wickra.RealizedSpread(0));
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assert.throws(() => new wickra.KylesLambda(1));
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});
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test('footprint buckets buy and sell volume per price level', () => {
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const fp = new wickra.Footprint(1.0);
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fp.update(100.2, 2, true); // bucket 100 -> ask 2
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fp.update(100.7, 3, false); // bucket 101 -> bid 3
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const out = fp.update(100.1, 1, true); // bucket 100 -> ask 3
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assert.equal(out.length, 2);
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assert.deepEqual(
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{ price: out[0].price, bidVol: out[0].bidVol, askVol: out[0].askVol },
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{ price: 100.0, bidVol: 0.0, askVol: 3.0 },
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);
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assert.deepEqual(
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{ price: out[1].price, bidVol: out[1].bidVol, askVol: out[1].askVol },
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{ price: 101.0, bidVol: 3.0, askVol: 0.0 },
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);
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});
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test('footprint streaming update matches batch and rejects bad tick', () => {
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const n = 12;
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const price = Array.from({ length: n }, (_, i) => 100 + (i % 5) * 0.3);
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const size = Array.from({ length: n }, (_, i) => 1 + (i % 3));
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const isBuy = Array.from({ length: n }, (_, i) => i % 2 === 0);
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const batch = new wickra.Footprint(1.0).batch(price, size, isBuy);
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const streamer = new wickra.Footprint(1.0);
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assert.equal(batch.length, n);
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for (let i = 0; i < n; i++) {
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const s = streamer.update(price[i], size[i], isBuy[i]);
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assert.deepEqual(s, batch[i], `mismatch at ${i}`);
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}
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assert.throws(() => new wickra.Footprint(0));
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});
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Vendored
+15
@@ -286,6 +286,12 @@ export interface ObSnapshot {
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askPx: Array<number>
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askSz: Array<number>
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}
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/** One price bucket of a footprint. */
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export interface FootprintLevelValue {
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price: number
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bidVol: number
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askVol: number
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}
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export type SmaNode = SMA
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export declare class SMA {
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constructor(period: number)
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@@ -2304,6 +2310,15 @@ export declare class KylesLambda {
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isReady(): boolean
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warmupPeriod(): number
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}
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export type FootprintNode = Footprint
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export declare class Footprint {
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constructor(tickSize: number)
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update(price: number, size: number, isBuy: boolean): Array<FootprintLevelValue>
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batch(price: Array<number>, size: Array<number>, isBuy: Array<boolean>): Array<Array<FootprintLevelValue>>
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reset(): void
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isReady(): boolean
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warmupPeriod(): number
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}
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export type SharpeRatioNode = SharpeRatio
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export declare class SharpeRatio {
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constructor(period: number, riskFree: number)
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@@ -310,7 +310,7 @@ if (!nativeBinding) {
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throw new Error(`Failed to load native binding`)
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}
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const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, MOM, CMO, DPO, StdDev, UlcerIndex, VerticalHorizontalFilter, ZScore, McGinleyDynamic, FRAMA, SuperSmoother, FisherTransform, Decycler, CenterOfGravity, CyberneticCycle, InstantaneousTrendline, EhlersStochastic, RVIVolatility, Variance, CoefficientOfVariation, Skewness, Kurtosis, StandardError, DetrendedStdDev, RSquared, MedianAbsoluteDeviation, Autocorrelation, HurstExponent, PearsonCorrelation, Beta, PairwiseBeta, SpearmanCorrelation, PairSpreadZScore, LeadLagCrossCorrelation, Cointegration, RelativeStrengthAB, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, ADXR, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, RollingVWAP, AwesomeOscillator, Aroon, Inertia, ConnorsRSI, LaguerreRSI, SMI, KST, PGO, RVI, AwesomeOscillatorHistogram, STC, ElderImpulse, ZeroLagMACD, CFO, APO, KAMA, EVWMA, Alligator, JMA, VIDYA, ALMA, T3, TSI, PMO, TII, ADL, VolumePriceTrend, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex, NVI, PVI, VolumeOscillator, KVO, WilliamsAD, AnchoredVWAP, DemandIndex, TSV, VZO, MarketFacilitationIndex, EaseOfMovement, SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop, HiLoActivator, VoltyStop, YoyoExit, DonchianStop, PercentageTrailingStop, StepTrailingStop, RenkoTrailingStop, TypicalPrice, MedianPrice, WeightedClose, LinearRegression, LinRegSlope, AcceleratorOscillator, BalanceOfPower, ChoppinessIndex, TrueRange, ChaikinVolatility, YangZhangVolatility, RogersSatchellVolatility, GarmanKlassVolatility, ParkinsonVolatility, LinRegAngle, BollingerBandwidth, PercentB, NATR, HistoricalVolatility, AroonOscillator, WaveTrend, RWI, Vortex, MassIndex, StochRSI, UltimateOscillator, PPO, Coppock, VWMA, MaEnvelope, AccelerationBands, StarcBands, AtrBands, HurstChannel, LinRegChannel, StandardErrorBands, DoubleBollinger, TtmSqueeze, FractalChaosBands, VwapStdDevBands, ClassicPivots, FibonacciPivots, Camarilla, WoodiePivots, DemarkPivots, WilliamsFractals, ZigZag, TDSetup, TDSequential, TDDeMarker, TDREI, TDPressure, TDCombo, TDCountdown, TDLines, TDRangeProjection, TDDifferential, TDOpen, TDRiskLevel, InverseFisherTransform, DecyclerOscillator, RoofingFilter, EmpiricalModeDecomposition, HilbertDominantCycle, AdaptiveCycle, SineWave, MAMA, FAMA, Ichimoku, HeikinAshi, ValueArea, InitialBalance, OpeningRange, Doji, Hammer, InvertedHammer, HangingMan, ShootingStar, Engulfing, Harami, MorningEveningStar, ThreeSoldiersOrCrows, PiercingDarkCloud, Marubozu, Tweezer, SpinningTop, ThreeInside, ThreeOutside, OrderBookImbalanceTop1, OrderBookImbalanceFull, Microprice, QuotedSpread, DepthSlope, OrderBookImbalanceTopN, SignedVolume, CumulativeVolumeDelta, TradeImbalance, EffectiveSpread, RealizedSpread, KylesLambda, SharpeRatio, SortinoRatio, CalmarRatio, OmegaRatio, MaxDrawdown, AverageDrawdown, DrawdownDuration, PainIndex, ValueAtRisk, ConditionalValueAtRisk, ProfitFactor, GainLossRatio, RecoveryFactor, KellyCriterion, TreynorRatio, InformationRatio, Alpha } = nativeBinding
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const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, MOM, CMO, DPO, StdDev, UlcerIndex, VerticalHorizontalFilter, ZScore, McGinleyDynamic, FRAMA, SuperSmoother, FisherTransform, Decycler, CenterOfGravity, CyberneticCycle, InstantaneousTrendline, EhlersStochastic, RVIVolatility, Variance, CoefficientOfVariation, Skewness, Kurtosis, StandardError, DetrendedStdDev, RSquared, MedianAbsoluteDeviation, Autocorrelation, HurstExponent, PearsonCorrelation, Beta, PairwiseBeta, SpearmanCorrelation, PairSpreadZScore, LeadLagCrossCorrelation, Cointegration, RelativeStrengthAB, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, ADXR, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, RollingVWAP, AwesomeOscillator, Aroon, Inertia, ConnorsRSI, LaguerreRSI, SMI, KST, PGO, RVI, AwesomeOscillatorHistogram, STC, ElderImpulse, ZeroLagMACD, CFO, APO, KAMA, EVWMA, Alligator, JMA, VIDYA, ALMA, T3, TSI, PMO, TII, ADL, VolumePriceTrend, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex, NVI, PVI, VolumeOscillator, KVO, WilliamsAD, AnchoredVWAP, DemandIndex, TSV, VZO, MarketFacilitationIndex, EaseOfMovement, SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop, HiLoActivator, VoltyStop, YoyoExit, DonchianStop, PercentageTrailingStop, StepTrailingStop, RenkoTrailingStop, TypicalPrice, MedianPrice, WeightedClose, LinearRegression, LinRegSlope, AcceleratorOscillator, BalanceOfPower, ChoppinessIndex, TrueRange, ChaikinVolatility, YangZhangVolatility, RogersSatchellVolatility, GarmanKlassVolatility, ParkinsonVolatility, LinRegAngle, BollingerBandwidth, PercentB, NATR, HistoricalVolatility, AroonOscillator, WaveTrend, RWI, Vortex, MassIndex, StochRSI, UltimateOscillator, PPO, Coppock, VWMA, MaEnvelope, AccelerationBands, StarcBands, AtrBands, HurstChannel, LinRegChannel, StandardErrorBands, DoubleBollinger, TtmSqueeze, FractalChaosBands, VwapStdDevBands, ClassicPivots, FibonacciPivots, Camarilla, WoodiePivots, DemarkPivots, WilliamsFractals, ZigZag, TDSetup, TDSequential, TDDeMarker, TDREI, TDPressure, TDCombo, TDCountdown, TDLines, TDRangeProjection, TDDifferential, TDOpen, TDRiskLevel, InverseFisherTransform, DecyclerOscillator, RoofingFilter, EmpiricalModeDecomposition, HilbertDominantCycle, AdaptiveCycle, SineWave, MAMA, FAMA, Ichimoku, HeikinAshi, ValueArea, InitialBalance, OpeningRange, Doji, Hammer, InvertedHammer, HangingMan, ShootingStar, Engulfing, Harami, MorningEveningStar, ThreeSoldiersOrCrows, PiercingDarkCloud, Marubozu, Tweezer, SpinningTop, ThreeInside, ThreeOutside, OrderBookImbalanceTop1, OrderBookImbalanceFull, Microprice, QuotedSpread, DepthSlope, OrderBookImbalanceTopN, SignedVolume, CumulativeVolumeDelta, TradeImbalance, EffectiveSpread, RealizedSpread, KylesLambda, Footprint, SharpeRatio, SortinoRatio, CalmarRatio, OmegaRatio, MaxDrawdown, AverageDrawdown, DrawdownDuration, PainIndex, ValueAtRisk, ConditionalValueAtRisk, ProfitFactor, GainLossRatio, RecoveryFactor, KellyCriterion, TreynorRatio, InformationRatio, Alpha } = nativeBinding
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module.exports.version = version
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module.exports.SMA = SMA
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@@ -527,6 +527,7 @@ module.exports.TradeImbalance = TradeImbalance
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module.exports.EffectiveSpread = EffectiveSpread
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module.exports.RealizedSpread = RealizedSpread
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module.exports.KylesLambda = KylesLambda
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module.exports.Footprint = Footprint
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module.exports.SharpeRatio = SharpeRatio
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module.exports.SortinoRatio = SortinoRatio
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module.exports.CalmarRatio = CalmarRatio
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@@ -9264,6 +9264,94 @@ impl KylesLambdaNode {
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}
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}
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// ============================== Microstructure: Footprint ==============================
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//
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// Footprint is a multi-output, variable-length indicator. Each `update(price,
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// size, isBuy)` returns the full bar footprint accumulated since the last
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// `reset()` as an array of `{ price, bidVol, askVol }` rows (sorted ascending
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// by price); `batch` returns an array of such arrays, one per trade.
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/// One price bucket of a footprint.
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#[napi(object)]
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pub struct FootprintLevelValue {
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pub price: f64,
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pub bid_vol: f64,
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pub ask_vol: f64,
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}
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fn footprint_levels(out: &wc::FootprintOutput) -> Vec<FootprintLevelValue> {
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out.levels
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.iter()
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.map(|level| FootprintLevelValue {
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price: level.price,
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bid_vol: level.bid_vol,
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ask_vol: level.ask_vol,
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})
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.collect()
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}
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#[napi(js_name = "Footprint")]
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pub struct FootprintNode {
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inner: wc::Footprint,
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}
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#[napi]
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impl FootprintNode {
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#[napi(constructor)]
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pub fn new(tick_size: f64) -> napi::Result<Self> {
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Ok(Self {
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inner: wc::Footprint::new(tick_size).map_err(map_err)?,
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})
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}
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#[napi]
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pub fn update(
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&mut self,
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price: f64,
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size: f64,
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is_buy: bool,
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) -> napi::Result<Vec<FootprintLevelValue>> {
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let out = self
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.inner
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.update(build_trade(price, size, is_buy)?)
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.expect("footprint emits on every trade");
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Ok(footprint_levels(&out))
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}
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#[napi]
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pub fn batch(
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&mut self,
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price: Vec<f64>,
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size: Vec<f64>,
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is_buy: Vec<bool>,
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) -> napi::Result<Vec<Vec<FootprintLevelValue>>> {
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if price.len() != size.len() || size.len() != is_buy.len() {
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return Err(NapiError::from_reason(
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"price, size, is_buy must be equal length".to_string(),
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));
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}
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let mut out = Vec::with_capacity(price.len());
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for i in 0..price.len() {
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let snapshot = self
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.inner
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.update(build_trade(price[i], size[i], is_buy[i])?)
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.expect("footprint emits on every trade");
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out.push(footprint_levels(&snapshot));
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}
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Ok(out)
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}
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#[napi]
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pub fn reset(&mut self) {
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self.inner.reset();
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}
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#[napi(js_name = "isReady")]
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pub fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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#[napi(js_name = "warmupPeriod")]
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pub fn warmup_period(&self) -> u32 {
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self.inner.warmup_period() as u32
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}
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}
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// ============================== Family 15: Risk / Performance ==============================
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// Risk metrics with fallible `new` (most need `period >= 2`), so each wrapper
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@@ -255,6 +255,8 @@ from ._wickra import (
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EffectiveSpread,
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RealizedSpread,
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KylesLambda,
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# Microstructure: footprint
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Footprint,
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# Risk / Performance
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SharpeRatio,
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SortinoRatio,
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@@ -507,6 +509,8 @@ __all__ = [
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"EffectiveSpread",
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"RealizedSpread",
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"KylesLambda",
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# Microstructure: footprint
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"Footprint",
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# Risk / Performance
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"SharpeRatio",
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"SortinoRatio",
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@@ -12095,6 +12095,93 @@ impl PyKylesLambda {
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}
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}
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// ============================== Microstructure: Footprint ==============================
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//
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// Footprint is a multi-output, variable-length indicator: each `update(price,
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// size, is_buy)` returns the full bar footprint accumulated since the last
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// `reset()` as a `(k, 3)` array with columns `[price, bid_vol, ask_vol]`, one
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// row per touched price bucket (sorted ascending by price). `batch` returns a
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// list of such arrays, one per trade.
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fn footprint_to_array<'py>(
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py: Python<'py>,
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out: &wc::FootprintOutput,
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) -> Bound<'py, PyArray2<f64>> {
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let rows = out.levels.len();
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let mut data = Vec::with_capacity(rows * 3);
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for level in &out.levels {
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data.push(level.price);
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data.push(level.bid_vol);
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data.push(level.ask_vol);
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}
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numpy::ndarray::Array2::from_shape_vec((rows, 3), data)
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.expect("shape consistent")
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.into_pyarray(py)
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}
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#[pyclass(name = "Footprint", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PyFootprint {
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inner: wc::Footprint,
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}
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#[pymethods]
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impl PyFootprint {
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#[new]
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fn new(tick_size: f64) -> PyResult<Self> {
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Ok(Self {
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inner: wc::Footprint::new(tick_size).map_err(map_err)?,
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})
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}
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fn update<'py>(
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&mut self,
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py: Python<'py>,
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price: f64,
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size: f64,
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is_buy: bool,
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) -> PyResult<Bound<'py, PyArray2<f64>>> {
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let out = self
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.inner
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.update(build_trade(price, size, is_buy)?)
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.expect("footprint emits on every trade");
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Ok(footprint_to_array(py, &out))
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}
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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price: Vec<f64>,
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size: Vec<f64>,
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is_buy: Vec<bool>,
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) -> PyResult<Vec<Bound<'py, PyArray2<f64>>>> {
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if price.len() != size.len() || size.len() != is_buy.len() {
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return Err(PyValueError::new_err(
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"price, size, is_buy must be equal length",
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));
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}
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let mut out = Vec::with_capacity(price.len());
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for i in 0..price.len() {
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let snapshot = self
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.inner
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.update(build_trade(price[i], size[i], is_buy[i])?)
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.expect("footprint emits on every trade");
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out.push(footprint_to_array(py, &snapshot));
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}
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Ok(out)
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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format!("Footprint(tick_size={})", self.inner.tick_size())
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}
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}
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// ============================== Family 15: Risk / Performance ==============================
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#[pyclass(name = "SharpeRatio", module = "wickra._wickra", skip_from_py_object)]
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@@ -13215,6 +13302,8 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
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m.add_class::<PyEffectiveSpread>()?;
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m.add_class::<PyRealizedSpread>()?;
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m.add_class::<PyKylesLambda>()?;
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// Microstructure: footprint.
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m.add_class::<PyFootprint>()?;
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// Family 15: Risk / Performance metrics.
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m.add_class::<PySharpeRatio>()?;
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m.add_class::<PySortinoRatio>()?;
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@@ -231,3 +231,10 @@ def test_realized_spread_zero_horizon_raises():
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def test_kyles_lambda_window_below_two_raises():
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with pytest.raises(ValueError):
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ta.KylesLambda(1)
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def test_footprint_non_positive_tick_raises():
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with pytest.raises(ValueError):
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ta.Footprint(0.0)
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with pytest.raises(ValueError):
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ta.Footprint(-1.0)
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@@ -882,6 +882,17 @@ def test_depth_slope_reference_value():
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assert ta.DepthSlope().update([100.0], [1.0], [101.0], [1.0]) == pytest.approx(0.0)
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def test_footprint_buckets_buy_and_sell_volume():
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fp = ta.Footprint(1.0)
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fp.update(100.2, 2.0, True) # bucket 100 -> ask 2
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fp.update(100.7, 3.0, False) # bucket 101 -> bid 3
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out = fp.update(100.1, 1.0, True) # bucket 100 -> ask 3
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# Columns are [price, bid_vol, ask_vol], rows sorted ascending by price.
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assert out.shape == (2, 3)
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assert list(out[0]) == [100.0, 0.0, 3.0]
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assert list(out[1]) == [101.0, 3.0, 0.0]
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def test_signed_volume_reference_values():
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assert ta.SignedVolume().update(100.0, 2.0, True) == pytest.approx(2.0)
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assert ta.SignedVolume().update(100.0, 3.0, False) == pytest.approx(-3.0)
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@@ -207,3 +207,14 @@ def test_kyles_lambda_lifecycle_and_repr():
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kl.reset()
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assert not kl.is_ready()
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assert repr(ta.KylesLambda(7)) == "KylesLambda(window=7)"
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def test_footprint_lifecycle_and_repr():
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fp = ta.Footprint(0.5)
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assert fp.warmup_period() == 1
|
||||
assert not fp.is_ready()
|
||||
fp.update(100.0, 1.0, True)
|
||||
assert fp.is_ready()
|
||||
fp.reset()
|
||||
assert not fp.is_ready()
|
||||
assert repr(ta.Footprint(0.25)) == "Footprint(tick_size=0.25)"
|
||||
|
||||
@@ -1939,3 +1939,16 @@ def test_price_impact_indicators_streaming_equals_batch():
|
||||
)
|
||||
assert batch.shape == (n,)
|
||||
assert _eq_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_footprint_streaming_equals_batch():
|
||||
n = 20
|
||||
price = [100.0 + (i % 5) * 0.3 for i in range(n)]
|
||||
size = [1.0 + (i % 3) for i in range(n)]
|
||||
is_buy = [i % 2 == 0 for i in range(n)]
|
||||
batch = ta.Footprint(1.0).batch(price, size, is_buy)
|
||||
streamer = ta.Footprint(1.0)
|
||||
assert len(batch) == n
|
||||
for i in range(n):
|
||||
streamed = streamer.update(price[i], size[i], is_buy[i])
|
||||
assert np.array_equal(streamed, batch[i])
|
||||
|
||||
@@ -154,3 +154,16 @@ def test_price_impact_batch_returns_one_value_per_trade():
|
||||
out = ind.batch(price, size, is_buy, mid)
|
||||
assert out.shape == (4,)
|
||||
assert out.dtype == np.float64
|
||||
|
||||
|
||||
def test_footprint_constructs_and_emits():
|
||||
out = ta.Footprint(1.0).update(100.2, 2.0, True)
|
||||
assert out.shape == (1, 3)
|
||||
assert out.dtype == np.float64
|
||||
|
||||
|
||||
def test_footprint_batch_returns_list_of_arrays():
|
||||
res = ta.Footprint(1.0).batch([100.2, 100.7], [2.0, 3.0], [True, False])
|
||||
assert isinstance(res, list)
|
||||
assert len(res) == 2
|
||||
assert res[-1].shape[1] == 3
|
||||
|
||||
@@ -9,7 +9,7 @@
|
||||
#![allow(clippy::needless_pass_by_value)]
|
||||
#![allow(missing_debug_implementations)] // wasm_bindgen wrappers expose JS objects, no need for Debug
|
||||
|
||||
use js_sys::{Float64Array, Object, Reflect};
|
||||
use js_sys::{Array, Float64Array, Object, Reflect};
|
||||
use wasm_bindgen::prelude::*;
|
||||
use wickra_core as wc;
|
||||
use wickra_core::{BatchExt, Indicator};
|
||||
@@ -6699,6 +6699,54 @@ impl WasmKylesLambda {
|
||||
}
|
||||
}
|
||||
|
||||
// ============================== Microstructure: Footprint ==============================
|
||||
//
|
||||
// Footprint is a multi-output, variable-length indicator. Each `update(price,
|
||||
// size, isBuy)` returns the full bar footprint accumulated since the last
|
||||
// `reset()` as an array of `{ price, bidVol, askVol }` objects (sorted ascending
|
||||
// by price) — the streaming model for a live browser trade feed.
|
||||
|
||||
#[wasm_bindgen(js_name = Footprint)]
|
||||
pub struct WasmFootprint {
|
||||
inner: wc::Footprint,
|
||||
}
|
||||
|
||||
#[wasm_bindgen(js_class = Footprint)]
|
||||
impl WasmFootprint {
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(tick_size: f64) -> Result<WasmFootprint, JsError> {
|
||||
Ok(Self {
|
||||
inner: wc::Footprint::new(tick_size).map_err(map_err)?,
|
||||
})
|
||||
}
|
||||
pub fn update(&mut self, price: f64, size: f64, is_buy: bool) -> Result<JsValue, JsError> {
|
||||
let out = self
|
||||
.inner
|
||||
.update(build_trade(price, size, is_buy)?)
|
||||
.expect("footprint emits on every trade");
|
||||
let levels = Array::new();
|
||||
for level in &out.levels {
|
||||
let obj = Object::new();
|
||||
Reflect::set(&obj, &"price".into(), &level.price.into()).ok();
|
||||
Reflect::set(&obj, &"bidVol".into(), &level.bid_vol.into()).ok();
|
||||
Reflect::set(&obj, &"askVol".into(), &level.ask_vol.into()).ok();
|
||||
levels.push(&obj);
|
||||
}
|
||||
Ok(levels.into())
|
||||
}
|
||||
pub fn reset(&mut self) {
|
||||
self.inner.reset();
|
||||
}
|
||||
#[wasm_bindgen(js_name = isReady)]
|
||||
pub fn is_ready(&self) -> bool {
|
||||
self.inner.is_ready()
|
||||
}
|
||||
#[wasm_bindgen(js_name = warmupPeriod)]
|
||||
pub fn warmup_period(&self) -> usize {
|
||||
self.inner.warmup_period()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
Reference in New Issue
Block a user