feat: trade-flow microstructure indicators (part 2 of 4) (#113)
* feat(core): add 3 trade-flow microstructure indicators SignedVolume (per-trade size signed by aggressor), CumulativeVolumeDelta (running signed-volume total), and TradeImbalance (rolling buy/sell volume imbalance over a trade window). All consume the Trade type, with full unit coverage. Extends the Microstructure family. * feat(bindings): expose trade-flow microstructure indicators Python, Node and WASM bindings for SignedVolume, CumulativeVolumeDelta and TradeImbalance. Each takes a trade via update(price, size, is_buy); Python and Node expose a batch over three parallel arrays, WASM exposes per-trade update. Regenerates node index.d.ts/.js. * test(bindings,fuzz,bench): cover trade-flow microstructure indicators Python and Node: reference values, streaming-vs-batch, lifecycle/repr and input validation (zero window, negative size, non-positive price, mismatched batch lengths). New indicator_update_trade fuzz target. Synthetic trade-tape benches (signed_volume cheapest, trade_imbalance windowed/expensive). * docs: add trade-flow indicators + bump counter to 227 README Microstructure family row gains signed volume / CVD / trade imbalance and the counter goes 224 -> 227; CHANGELOG records the trade-flow indicators.
This commit is contained in:
kingchenc
@@ -951,3 +951,33 @@ test('order-book TopN rejects zero levels', () => {
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test('order-book update rejects a crossed book', () => {
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assert.throws(() => new wickra.QuotedSpread().update([102], [1], [101], [1]));
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});
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test('trade-flow indicators reference values', () => {
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assert.equal(new wickra.SignedVolume().update(100, 2, true), 2);
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assert.equal(new wickra.SignedVolume().update(100, 3, false), -3);
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const cvd = new wickra.CumulativeVolumeDelta();
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assert.equal(cvd.update(100, 5, true), 5);
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assert.equal(cvd.update(100, 2, false), 3);
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const ti = new wickra.TradeImbalance(2);
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assert.equal(ti.update(100, 3, true), null); // warming up
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assert.equal(ti.update(100, 1, false), 0.5); // (3 - 1) / 4
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});
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test('trade-flow streaming update matches batch', () => {
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const n = 30;
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const price = Array.from({ length: n }, () => 100);
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const size = Array.from({ length: n }, (_, i) => 1 + (i % 4));
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const isBuy = Array.from({ length: n }, (_, i) => i % 3 !== 0);
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const batch = new wickra.CumulativeVolumeDelta().batch(price, size, isBuy);
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const streamer = new wickra.CumulativeVolumeDelta();
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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.ok(Math.abs(s - batch[i]) < 1e-12, `mismatch at ${i}: ${s} vs ${batch[i]}`);
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}
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});
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test('trade-flow rejects bad input', () => {
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assert.throws(() => new wickra.TradeImbalance(0));
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assert.throws(() => new wickra.SignedVolume().update(100, -1, true));
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});
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Vendored
+27
@@ -2241,6 +2241,33 @@ export declare class OrderBookImbalanceTopN {
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isReady(): boolean
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warmupPeriod(): number
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}
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export type SignedVolumeNode = SignedVolume
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export declare class SignedVolume {
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constructor()
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update(price: number, size: number, isBuy: boolean): number | null
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batch(price: Array<number>, size: Array<number>, isBuy: Array<boolean>): Array<number>
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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 CumulativeVolumeDeltaNode = CumulativeVolumeDelta
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export declare class CumulativeVolumeDelta {
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constructor()
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update(price: number, size: number, isBuy: boolean): number | null
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batch(price: Array<number>, size: Array<number>, isBuy: Array<boolean>): Array<number>
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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 TradeImbalanceNode = TradeImbalance
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export declare class TradeImbalance {
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constructor(window: number)
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update(price: number, size: number, isBuy: boolean): number | null
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batch(price: Array<number>, size: Array<number>, isBuy: Array<boolean>): Array<number>
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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, OrderBookImbalanceTopN, 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, OrderBookImbalanceTopN, SignedVolume, CumulativeVolumeDelta, TradeImbalance, 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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@@ -520,6 +520,9 @@ module.exports.OrderBookImbalanceFull = OrderBookImbalanceFull
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module.exports.Microprice = Microprice
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module.exports.QuotedSpread = QuotedSpread
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module.exports.OrderBookImbalanceTopN = OrderBookImbalanceTopN
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module.exports.SignedVolume = SignedVolume
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module.exports.CumulativeVolumeDelta = CumulativeVolumeDelta
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module.exports.TradeImbalance = TradeImbalance
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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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@@ -8914,6 +8914,144 @@ impl OrderBookImbalanceTopNNode {
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}
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}
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// ============================== Microstructure: Trade Flow ==============================
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//
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// Trade-flow indicators consume a trade tape rather than OHLCV. Streaming
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// `update(price, size, isBuy)` takes one trade (`isBuy=true` for a
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// buyer-initiated trade); `batch` takes three equal-length arrays.
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fn build_trade(price: f64, size: f64, is_buy: bool) -> napi::Result<wc::Trade> {
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let side = if is_buy {
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wc::Side::Buy
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} else {
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wc::Side::Sell
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};
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wc::Trade::new(price, size, side, 0).map_err(map_err)
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}
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macro_rules! node_trade_indicator {
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($node:ident, $inner:ty, $js:literal) => {
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#[napi(js_name = $js)]
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pub struct $node {
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inner: $inner,
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}
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impl Default for $node {
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fn default() -> Self {
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Self::new()
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}
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}
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#[napi]
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impl $node {
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#[napi(constructor)]
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pub fn new() -> Self {
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Self {
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inner: <$inner>::new(),
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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<Option<f64>> {
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Ok(self.inner.update(build_trade(price, size, is_buy)?))
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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<f64>> {
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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 trade = build_trade(price[i], size[i], is_buy[i])?;
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out.push(self.inner.update(trade).unwrap_or(f64::NAN));
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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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};
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}
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node_trade_indicator!(SignedVolumeNode, wc::SignedVolume, "SignedVolume");
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node_trade_indicator!(
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CumulativeVolumeDeltaNode,
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wc::CumulativeVolumeDelta,
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"CumulativeVolumeDelta"
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);
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// Trade imbalance carries a `window` parameter, so it is hand-written.
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#[napi(js_name = "TradeImbalance")]
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pub struct TradeImbalanceNode {
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inner: wc::TradeImbalance,
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}
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#[napi]
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impl TradeImbalanceNode {
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#[napi(constructor)]
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pub fn new(window: u32) -> napi::Result<Self> {
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Ok(Self {
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inner: wc::TradeImbalance::new(window as usize).map_err(map_err)?,
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})
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}
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#[napi]
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pub fn update(&mut self, price: f64, size: f64, is_buy: bool) -> napi::Result<Option<f64>> {
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Ok(self.inner.update(build_trade(price, size, is_buy)?))
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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<f64>> {
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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 trade = build_trade(price[i], size[i], is_buy[i])?;
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out.push(self.inner.update(trade).unwrap_or(f64::NAN));
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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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@@ -246,6 +246,10 @@ from ._wickra import (
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OrderBookImbalanceFull,
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Microprice,
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QuotedSpread,
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# Microstructure: trade flow
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SignedVolume,
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CumulativeVolumeDelta,
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TradeImbalance,
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# Risk / Performance
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SharpeRatio,
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SortinoRatio,
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@@ -489,6 +493,10 @@ __all__ = [
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"OrderBookImbalanceFull",
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"Microprice",
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"QuotedSpread",
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# Microstructure: trade flow
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"SignedVolume",
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"CumulativeVolumeDelta",
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"TradeImbalance",
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# Risk / Performance
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"SharpeRatio",
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"SortinoRatio",
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@@ -11771,6 +11771,137 @@ impl PyOrderBookImbalanceTopN {
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}
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}
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// ============================== Microstructure: Trade Flow ==============================
|
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//
|
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// Trade-flow indicators consume a trade tape rather than OHLCV. Streaming
|
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// `update(price, size, is_buy)` takes one trade (`is_buy=True` for a
|
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// buyer-initiated trade); `batch` takes three equal-length arrays.
|
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|
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fn build_trade(price: f64, size: f64, is_buy: bool) -> PyResult<wc::Trade> {
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let side = if is_buy {
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wc::Side::Buy
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} else {
|
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wc::Side::Sell
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};
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wc::Trade::new(price, size, side, 0).map_err(map_err)
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}
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macro_rules! py_trade_indicator {
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($name:ident, $inner:ty, $repr:expr) => {
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#[pyclass(name = $repr, module = "wickra._wickra", skip_from_py_object)]
|
||||
#[derive(Clone)]
|
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struct $name {
|
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inner: $inner,
|
||||
}
|
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|
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#[pymethods]
|
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impl $name {
|
||||
#[new]
|
||||
fn new() -> Self {
|
||||
Self {
|
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inner: <$inner>::new(),
|
||||
}
|
||||
}
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fn update(&mut self, price: f64, size: f64, is_buy: bool) -> PyResult<Option<f64>> {
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Ok(self.inner.update(build_trade(price, size, is_buy)?))
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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<Bound<'py, PyArray1<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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let mut out = Vec::with_capacity(price.len());
|
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for i in 0..price.len() {
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let trade = build_trade(price[i], size[i], is_buy[i])?;
|
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out.push(self.inner.update(trade).unwrap_or(f64::NAN));
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}
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Ok(out.into_pyarray(py))
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}
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fn reset(&mut self) {
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self.inner.reset();
|
||||
}
|
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fn is_ready(&self) -> bool {
|
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self.inner.is_ready()
|
||||
}
|
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fn warmup_period(&self) -> usize {
|
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self.inner.warmup_period()
|
||||
}
|
||||
fn __repr__(&self) -> String {
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format!("{}()", $repr)
|
||||
}
|
||||
}
|
||||
};
|
||||
}
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py_trade_indicator!(PySignedVolume, wc::SignedVolume, "SignedVolume");
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py_trade_indicator!(
|
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PyCumulativeVolumeDelta,
|
||||
wc::CumulativeVolumeDelta,
|
||||
"CumulativeVolumeDelta"
|
||||
);
|
||||
|
||||
// Trade imbalance carries a `window` parameter, so it is hand-written.
|
||||
#[pyclass(
|
||||
name = "TradeImbalance",
|
||||
module = "wickra._wickra",
|
||||
skip_from_py_object
|
||||
)]
|
||||
#[derive(Clone)]
|
||||
struct PyTradeImbalance {
|
||||
inner: wc::TradeImbalance,
|
||||
}
|
||||
|
||||
#[pymethods]
|
||||
impl PyTradeImbalance {
|
||||
#[new]
|
||||
fn new(window: usize) -> PyResult<Self> {
|
||||
Ok(Self {
|
||||
inner: wc::TradeImbalance::new(window).map_err(map_err)?,
|
||||
})
|
||||
}
|
||||
fn update(&mut self, price: f64, size: f64, is_buy: bool) -> PyResult<Option<f64>> {
|
||||
Ok(self.inner.update(build_trade(price, size, is_buy)?))
|
||||
}
|
||||
fn batch<'py>(
|
||||
&mut self,
|
||||
py: Python<'py>,
|
||||
price: Vec<f64>,
|
||||
size: Vec<f64>,
|
||||
is_buy: Vec<bool>,
|
||||
) -> PyResult<Bound<'py, PyArray1<f64>>> {
|
||||
if price.len() != size.len() || size.len() != is_buy.len() {
|
||||
return Err(PyValueError::new_err(
|
||||
"price, size, is_buy must be equal length",
|
||||
));
|
||||
}
|
||||
let mut out = Vec::with_capacity(price.len());
|
||||
for i in 0..price.len() {
|
||||
let trade = build_trade(price[i], size[i], is_buy[i])?;
|
||||
out.push(self.inner.update(trade).unwrap_or(f64::NAN));
|
||||
}
|
||||
Ok(out.into_pyarray(py))
|
||||
}
|
||||
fn reset(&mut self) {
|
||||
self.inner.reset();
|
||||
}
|
||||
fn is_ready(&self) -> bool {
|
||||
self.inner.is_ready()
|
||||
}
|
||||
fn warmup_period(&self) -> usize {
|
||||
self.inner.warmup_period()
|
||||
}
|
||||
fn __repr__(&self) -> String {
|
||||
format!("TradeImbalance(window={})", self.inner.window())
|
||||
}
|
||||
}
|
||||
|
||||
// ============================== Family 15: Risk / Performance ==============================
|
||||
|
||||
#[pyclass(name = "SharpeRatio", module = "wickra._wickra", skip_from_py_object)]
|
||||
@@ -12882,6 +13013,10 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
|
||||
m.add_class::<PyOrderBookImbalanceFull>()?;
|
||||
m.add_class::<PyMicroprice>()?;
|
||||
m.add_class::<PyQuotedSpread>()?;
|
||||
// Microstructure: trade flow.
|
||||
m.add_class::<PySignedVolume>()?;
|
||||
m.add_class::<PyCumulativeVolumeDelta>()?;
|
||||
m.add_class::<PyTradeImbalance>()?;
|
||||
// Family 15: Risk / Performance metrics.
|
||||
m.add_class::<PySharpeRatio>()?;
|
||||
m.add_class::<PySortinoRatio>()?;
|
||||
|
||||
@@ -191,3 +191,23 @@ def test_orderbook_misordered_levels_raise():
|
||||
# Bids must be strictly descending in price.
|
||||
with pytest.raises(ValueError):
|
||||
ta.OrderBookImbalanceFull().update([99.0, 100.0], [1.0, 1.0], [101.0], [1.0])
|
||||
|
||||
|
||||
def test_trade_imbalance_zero_window_raises():
|
||||
with pytest.raises(ValueError):
|
||||
ta.TradeImbalance(0)
|
||||
|
||||
|
||||
def test_trade_negative_size_raises():
|
||||
with pytest.raises(ValueError):
|
||||
ta.SignedVolume().update(100.0, -1.0, True)
|
||||
|
||||
|
||||
def test_trade_non_positive_price_raises():
|
||||
with pytest.raises(ValueError):
|
||||
ta.CumulativeVolumeDelta().update(0.0, 1.0, True)
|
||||
|
||||
|
||||
def test_trade_batch_unequal_lengths_raise():
|
||||
with pytest.raises(ValueError):
|
||||
ta.SignedVolume().batch([100.0, 100.0], [1.0], [True, False])
|
||||
|
||||
@@ -870,3 +870,22 @@ def test_quoted_spread_reference_value():
|
||||
# spread 1.0, mid 100.5 -> 1 / 100.5 * 10_000 ≈ 99.5025 bps.
|
||||
qs = ta.QuotedSpread()
|
||||
assert qs.update([100.0], [1.0], [101.0], [1.0]) == pytest.approx(99.50248756, abs=1e-6)
|
||||
|
||||
|
||||
def test_signed_volume_reference_values():
|
||||
assert ta.SignedVolume().update(100.0, 2.0, True) == pytest.approx(2.0)
|
||||
assert ta.SignedVolume().update(100.0, 3.0, False) == pytest.approx(-3.0)
|
||||
|
||||
|
||||
def test_cumulative_volume_delta_reference_values():
|
||||
cvd = ta.CumulativeVolumeDelta()
|
||||
assert cvd.update(100.0, 5.0, True) == pytest.approx(5.0)
|
||||
assert cvd.update(100.0, 2.0, False) == pytest.approx(3.0)
|
||||
assert cvd.update(100.0, 4.0, False) == pytest.approx(-1.0)
|
||||
|
||||
|
||||
def test_trade_imbalance_reference_value():
|
||||
ti = ta.TradeImbalance(2)
|
||||
assert ti.update(100.0, 3.0, True) is None # warming up
|
||||
# Window full: buyVol 3, sellVol 1 -> (3 - 1) / 4 = 0.5.
|
||||
assert ti.update(100.0, 1.0, False) == pytest.approx(0.5)
|
||||
|
||||
@@ -150,3 +150,25 @@ def test_orderbook_lifecycle():
|
||||
|
||||
def test_orderbook_topn_repr():
|
||||
assert repr(ta.OrderBookImbalanceTopN(5)) == "OrderBookImbalanceTopN(levels=5)"
|
||||
|
||||
|
||||
def test_tradeflow_lifecycle():
|
||||
for ind in [ta.SignedVolume(), ta.CumulativeVolumeDelta()]:
|
||||
assert ind.warmup_period() == 1
|
||||
assert not ind.is_ready()
|
||||
ind.update(100.0, 1.0, True)
|
||||
assert ind.is_ready()
|
||||
ind.reset()
|
||||
assert not ind.is_ready()
|
||||
|
||||
|
||||
def test_trade_imbalance_lifecycle_and_repr():
|
||||
ti = ta.TradeImbalance(3)
|
||||
assert ti.warmup_period() == 3
|
||||
assert not ti.is_ready()
|
||||
for _ in range(3):
|
||||
ti.update(100.0, 1.0, True)
|
||||
assert ti.is_ready()
|
||||
ti.reset()
|
||||
assert not ti.is_ready()
|
||||
assert repr(ta.TradeImbalance(4)) == "TradeImbalance(window=4)"
|
||||
|
||||
@@ -1898,3 +1898,23 @@ def test_orderbook_indicators_streaming_equals_batch():
|
||||
)
|
||||
assert batch.shape == (len(snaps),)
|
||||
assert _eq_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_tradeflow_indicators_streaming_equals_batch():
|
||||
n = 40
|
||||
price = np.full(n, 100.0)
|
||||
size = np.array([1.0 + (i % 5) for i in range(n)], dtype=np.float64)
|
||||
is_buy = [i % 2 == 0 for i in range(n)]
|
||||
for make in (
|
||||
ta.SignedVolume,
|
||||
ta.CumulativeVolumeDelta,
|
||||
lambda: ta.TradeImbalance(5),
|
||||
):
|
||||
batch = make().batch(price, size, is_buy)
|
||||
streamer = make()
|
||||
streamed = np.array(
|
||||
[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert batch.shape == (n,)
|
||||
assert _eq_nan(batch, streamed)
|
||||
|
||||
@@ -119,3 +119,19 @@ def test_orderbook_batch_returns_one_value_per_snapshot():
|
||||
out = ta.OrderBookImbalanceTop1().batch(snapshots)
|
||||
assert out.shape == (5,)
|
||||
assert out.dtype == np.float64
|
||||
|
||||
|
||||
def test_tradeflow_indicators_construct_and_emit():
|
||||
# SignedVolume and CVD emit from the first trade; TradeImbalance(1) too.
|
||||
assert isinstance(ta.SignedVolume().update(100.0, 2.0, True), float)
|
||||
assert isinstance(ta.CumulativeVolumeDelta().update(100.0, 2.0, True), float)
|
||||
assert isinstance(ta.TradeImbalance(1).update(100.0, 2.0, True), float)
|
||||
|
||||
|
||||
def test_tradeflow_batch_returns_one_value_per_trade():
|
||||
price = np.full(6, 100.0)
|
||||
size = np.array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0])
|
||||
is_buy = [True, False, True, False, True, False]
|
||||
out = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
|
||||
assert out.shape == (6,)
|
||||
assert out.dtype == np.float64
|
||||
|
||||
@@ -217,3 +217,17 @@ def test_orderbook_streaming_matches_batch():
|
||||
streamer = ta.Microprice()
|
||||
streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_tradeflow_streaming_matches_batch():
|
||||
n = 30
|
||||
price = np.full(n, 100.0)
|
||||
size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
|
||||
is_buy = [i % 3 != 0 for i in range(n)]
|
||||
batch = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
|
||||
streamer = ta.CumulativeVolumeDelta()
|
||||
streamed = np.array(
|
||||
[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
@@ -6459,6 +6459,102 @@ impl WasmOrderBookImbalanceTopN {
|
||||
}
|
||||
}
|
||||
|
||||
// ============================== Microstructure: Trade Flow ==============================
|
||||
//
|
||||
// Trade-flow indicators consume a trade tape rather than OHLCV. Each
|
||||
// `update(price, size, isBuy)` takes one trade (`isBuy=true` for a
|
||||
// buyer-initiated trade) — the streaming model for a live browser trade feed.
|
||||
|
||||
fn build_trade(price: f64, size: f64, is_buy: bool) -> Result<wc::Trade, JsError> {
|
||||
let side = if is_buy {
|
||||
wc::Side::Buy
|
||||
} else {
|
||||
wc::Side::Sell
|
||||
};
|
||||
wc::Trade::new(price, size, side, 0).map_err(map_err)
|
||||
}
|
||||
|
||||
macro_rules! wasm_trade_indicator {
|
||||
($wasm:ident, $inner:ty, $js:ident) => {
|
||||
#[wasm_bindgen(js_name = $js)]
|
||||
pub struct $wasm {
|
||||
inner: $inner,
|
||||
}
|
||||
|
||||
impl Default for $wasm {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
#[wasm_bindgen(js_class = $js)]
|
||||
impl $wasm {
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new() -> $wasm {
|
||||
Self {
|
||||
inner: <$inner>::new(),
|
||||
}
|
||||
}
|
||||
pub fn update(
|
||||
&mut self,
|
||||
price: f64,
|
||||
size: f64,
|
||||
is_buy: bool,
|
||||
) -> Result<Option<f64>, JsError> {
|
||||
Ok(self.inner.update(build_trade(price, size, is_buy)?))
|
||||
}
|
||||
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()
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
wasm_trade_indicator!(WasmSignedVolume, wc::SignedVolume, SignedVolume);
|
||||
wasm_trade_indicator!(
|
||||
WasmCumulativeVolumeDelta,
|
||||
wc::CumulativeVolumeDelta,
|
||||
CumulativeVolumeDelta
|
||||
);
|
||||
|
||||
// Trade imbalance carries a `window` parameter, so it is hand-written.
|
||||
#[wasm_bindgen(js_name = TradeImbalance)]
|
||||
pub struct WasmTradeImbalance {
|
||||
inner: wc::TradeImbalance,
|
||||
}
|
||||
|
||||
#[wasm_bindgen(js_class = TradeImbalance)]
|
||||
impl WasmTradeImbalance {
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(window: usize) -> Result<WasmTradeImbalance, JsError> {
|
||||
Ok(Self {
|
||||
inner: wc::TradeImbalance::new(window).map_err(map_err)?,
|
||||
})
|
||||
}
|
||||
pub fn update(&mut self, price: f64, size: f64, is_buy: bool) -> Result<Option<f64>, JsError> {
|
||||
Ok(self.inner.update(build_trade(price, size, is_buy)?))
|
||||
}
|
||||
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