feat: order-book microstructure indicators (part 1 of 4) (#112)
* feat(core): add microstructure input types (OrderBook, Trade, TradeQuote) New non-OHLCV value types for the order-book / trade-flow indicator family: Level, OrderBook (sorted, uncrossed depth snapshot), Side, Trade (with aggressor side), and TradeQuote (trade paired with prevailing mid). Each has a validating constructor plus a new_unchecked hot-path constructor, with full unit coverage. Adds InvalidOrderBook / InvalidTrade error variants. * feat(core): add 5 order-book microstructure indicators OrderBookImbalanceTop1/TopN/Full (signed depth imbalance), Microprice (size-weighted fair value), and QuotedSpread (top-of-book spread in bps). All consume the OrderBook snapshot type, emit f64, are stateless and ready after the first snapshot, with full unit coverage. Registers a new Microstructure family in the taxonomy. * feat(bindings): expose order-book microstructure indicators Python, Node, and WASM bindings for OrderBookImbalanceTop1/TopN/Full, Microprice and QuotedSpread. Each takes a depth snapshot via four equal-length (bid_px, bid_sz, ask_px, ask_sz) arrays. Python and Node expose a batch over a list of snapshots; WASM exposes per-snapshot update (the streaming model that fits a browser book feed). Regenerates node index.d.ts/.js and registers the new InvalidOrderBook/InvalidTrade arms in the Python error mapping. * test(bindings,fuzz): cover order-book microstructure indicators Python: smoke, reference values, streaming-vs-batch, lifecycle/repr and input validation (mismatched lengths, crossed book, misordered levels, zero levels) for all five order-book indicators. Node: reference values, streaming-vs-batch, and rejection cases. Adds an indicator_update_orderbook fuzz target driving every order-book indicator over arbitrary (incl. degenerate) snapshots. * bench(microstructure): synthetic order-book benchmarks Add a bench_orderbook_input harness and synthesise a five-level book around each candle close (no order-book dataset ships with the repo). Benches the cheapest (top-of-book imbalance) and most-expensive (full-depth imbalance) plus microprice, matching the curated cheapest/expensive-per-family approach. * docs: add Microstructure family + bump indicator counter to 224 README gains the Microstructure family row (order-book imbalance, microprice, quoted spread) and the indicator counter goes 219 -> 224 across seventeen families; CHANGELOG records the new order-book indicators and value types.
This commit is contained in:
kingchenc
@@ -912,3 +912,42 @@ test('Doji signed mode encodes dragonfly/gravestone/neutral direction', () => {
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assert.equal(d.update(10, 12, 8, 10), 0); // long-legged -> neutral 0
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assert.equal(d.update(10, 12, 10, 12), 0); // not a doji -> 0
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});
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test('order-book indicators reference values', () => {
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// Top-1: (3 - 1) / (3 + 1) = 0.5.
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assert.equal(new wickra.OrderBookImbalanceTop1().update([100], [3], [101], [1]), 0.5);
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// Top-2: bidDepth 3, askDepth 2 -> (3 - 2) / 5 = 0.2.
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assert.ok(
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Math.abs(new wickra.OrderBookImbalanceTopN(2).update([100, 99], [2, 1], [101, 102], [1, 1]) - 0.2) < 1e-12,
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);
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// Full: bidDepth 1, askDepth 3 -> -0.5.
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assert.equal(new wickra.OrderBookImbalanceFull().update([100], [1], [101, 102], [2, 1]), -0.5);
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// Microprice: (100*3 + 101*1) / 4 = 100.25.
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assert.equal(new wickra.Microprice().update([100], [1], [101], [3]), 100.25);
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// Quoted spread: 1 / 100.5 * 10000 ≈ 99.5025 bps.
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assert.ok(Math.abs(new wickra.QuotedSpread().update([100], [1], [101], [1]) - 99.50248756) < 1e-6);
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});
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test('order-book streaming update matches batch', () => {
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const snaps = Array.from({ length: 30 }, (_, i) => ({
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bidPx: [100, 99],
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bidSz: [1 + (i % 5), 1],
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askPx: [101, 102],
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askSz: [1 + ((i + 1) % 3), 1],
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}));
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const batch = new wickra.Microprice().batch(snaps);
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const streamer = new wickra.Microprice();
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assert.equal(batch.length, snaps.length);
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for (let i = 0; i < snaps.length; i++) {
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const s = streamer.update(snaps[i].bidPx, snaps[i].bidSz, snaps[i].askPx, snaps[i].askSz);
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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('order-book TopN rejects zero levels', () => {
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assert.throws(() => new wickra.OrderBookImbalanceTopN(0));
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});
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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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Vendored
+52
@@ -279,6 +279,13 @@ export interface OpeningRangeValue {
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low: number
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breakoutDistance: number
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}
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/** One order-book depth snapshot for batch evaluation. */
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export interface ObSnapshot {
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bidPx: Array<number>
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bidSz: Array<number>
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askPx: Array<number>
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askSz: Array<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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@@ -2189,6 +2196,51 @@ export declare class ThreeOutside {
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isReady(): boolean
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warmupPeriod(): number
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}
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export type OrderBookImbalanceTop1Node = OrderBookImbalanceTop1
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export declare class OrderBookImbalanceTop1 {
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constructor()
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update(bidPx: Array<number>, bidSz: Array<number>, askPx: Array<number>, askSz: Array<number>): number | null
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batch(snapshots: Array<ObSnapshot>): 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 OrderBookImbalanceFullNode = OrderBookImbalanceFull
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export declare class OrderBookImbalanceFull {
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constructor()
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update(bidPx: Array<number>, bidSz: Array<number>, askPx: Array<number>, askSz: Array<number>): number | null
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batch(snapshots: Array<ObSnapshot>): 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 MicropriceNode = Microprice
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export declare class Microprice {
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constructor()
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update(bidPx: Array<number>, bidSz: Array<number>, askPx: Array<number>, askSz: Array<number>): number | null
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batch(snapshots: Array<ObSnapshot>): 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 QuotedSpreadNode = QuotedSpread
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export declare class QuotedSpread {
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constructor()
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update(bidPx: Array<number>, bidSz: Array<number>, askPx: Array<number>, askSz: Array<number>): number | null
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batch(snapshots: Array<ObSnapshot>): 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 OrderBookImbalanceTopNNode = OrderBookImbalanceTopN
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export declare class OrderBookImbalanceTopN {
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constructor(levels: number)
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update(bidPx: Array<number>, bidSz: Array<number>, askPx: Array<number>, askSz: Array<number>): number | null
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batch(snapshots: Array<ObSnapshot>): 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, 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, 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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@@ -515,6 +515,11 @@ module.exports.Tweezer = Tweezer
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module.exports.SpinningTop = SpinningTop
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module.exports.ThreeInside = ThreeInside
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module.exports.ThreeOutside = ThreeOutside
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module.exports.OrderBookImbalanceTop1 = OrderBookImbalanceTop1
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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.SharpeRatio = SharpeRatio
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module.exports.SortinoRatio = SortinoRatio
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module.exports.CalmarRatio = CalmarRatio
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@@ -8754,6 +8754,166 @@ node_candle_pattern!(SpinningTopNode, wc::SpinningTop, "SpinningTop");
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node_candle_pattern!(ThreeInsideNode, wc::ThreeInside, "ThreeInside");
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node_candle_pattern!(ThreeOutsideNode, wc::ThreeOutside, "ThreeOutside");
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// ============================== Microstructure: Order Book ==============================
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//
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// Order-book indicators consume a depth snapshot rather than OHLCV. Streaming
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// `update(bidPx, bidSz, askPx, askSz)` takes four equal-length arrays for one
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// snapshot (bids best-first = descending price, asks best-first = ascending
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// price); `batch` takes an array of `{ bidPx, bidSz, askPx, askSz }` snapshots
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// and returns one value per snapshot.
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/// One order-book depth snapshot for batch evaluation.
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#[napi(object)]
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pub struct ObSnapshot {
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pub bid_px: Vec<f64>,
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pub bid_sz: Vec<f64>,
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pub ask_px: Vec<f64>,
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pub ask_sz: Vec<f64>,
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}
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fn build_order_book(
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bid_px: &[f64],
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bid_sz: &[f64],
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ask_px: &[f64],
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ask_sz: &[f64],
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) -> napi::Result<wc::OrderBook> {
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if bid_px.len() != bid_sz.len() || ask_px.len() != ask_sz.len() {
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return Err(NapiError::from_reason(
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"bid/ask price and size arrays must be equal length".to_string(),
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));
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}
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let bids = bid_px
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.iter()
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.zip(bid_sz)
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.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
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.collect();
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let asks = ask_px
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.iter()
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.zip(ask_sz)
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.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
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.collect();
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wc::OrderBook::new(bids, asks).map_err(map_err)
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}
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macro_rules! node_ob_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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bid_px: Vec<f64>,
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bid_sz: Vec<f64>,
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ask_px: Vec<f64>,
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ask_sz: Vec<f64>,
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) -> napi::Result<Option<f64>> {
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let book = build_order_book(&bid_px, &bid_sz, &ask_px, &ask_sz)?;
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Ok(self.inner.update(book))
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}
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#[napi]
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pub fn batch(&mut self, snapshots: Vec<ObSnapshot>) -> napi::Result<Vec<f64>> {
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let mut out = Vec::with_capacity(snapshots.len());
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for snap in &snapshots {
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let book =
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build_order_book(&snap.bid_px, &snap.bid_sz, &snap.ask_px, &snap.ask_sz)?;
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out.push(self.inner.update(book).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_ob_indicator!(
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OrderBookImbalanceTop1Node,
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wc::OrderBookImbalanceTop1,
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"OrderBookImbalanceTop1"
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);
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node_ob_indicator!(
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OrderBookImbalanceFullNode,
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wc::OrderBookImbalanceFull,
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"OrderBookImbalanceFull"
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);
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node_ob_indicator!(MicropriceNode, wc::Microprice, "Microprice");
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node_ob_indicator!(QuotedSpreadNode, wc::QuotedSpread, "QuotedSpread");
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// Top-N imbalance carries a `levels` parameter, so it is hand-written.
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#[napi(js_name = "OrderBookImbalanceTopN")]
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pub struct OrderBookImbalanceTopNNode {
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inner: wc::OrderBookImbalanceTopN,
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}
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#[napi]
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impl OrderBookImbalanceTopNNode {
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#[napi(constructor)]
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pub fn new(levels: u32) -> napi::Result<Self> {
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Ok(Self {
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inner: wc::OrderBookImbalanceTopN::new(levels 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(
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&mut self,
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bid_px: Vec<f64>,
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bid_sz: Vec<f64>,
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ask_px: Vec<f64>,
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ask_sz: Vec<f64>,
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) -> napi::Result<Option<f64>> {
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let book = build_order_book(&bid_px, &bid_sz, &ask_px, &ask_sz)?;
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Ok(self.inner.update(book))
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}
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#[napi]
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||||
pub fn batch(&mut self, snapshots: Vec<ObSnapshot>) -> napi::Result<Vec<f64>> {
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let mut out = Vec::with_capacity(snapshots.len());
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for snap in &snapshots {
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let book = build_order_book(&snap.bid_px, &snap.bid_sz, &snap.ask_px, &snap.ask_sz)?;
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out.push(self.inner.update(book).unwrap_or(f64::NAN));
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}
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Ok(out)
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}
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#[napi]
|
||||
pub fn reset(&mut self) {
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self.inner.reset();
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||||
}
|
||||
#[napi(js_name = "isReady")]
|
||||
pub fn is_ready(&self) -> bool {
|
||||
self.inner.is_ready()
|
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}
|
||||
#[napi(js_name = "warmupPeriod")]
|
||||
pub fn warmup_period(&self) -> u32 {
|
||||
self.inner.warmup_period() as u32
|
||||
}
|
||||
}
|
||||
|
||||
// ============================== Family 15: Risk / Performance ==============================
|
||||
|
||||
// Risk metrics with fallible `new` (most need `period >= 2`), so each wrapper
|
||||
|
||||
@@ -240,6 +240,12 @@ from ._wickra import (
|
||||
SpinningTop,
|
||||
ThreeInside,
|
||||
ThreeOutside,
|
||||
# Microstructure: order book
|
||||
OrderBookImbalanceTop1,
|
||||
OrderBookImbalanceTopN,
|
||||
OrderBookImbalanceFull,
|
||||
Microprice,
|
||||
QuotedSpread,
|
||||
# Risk / Performance
|
||||
SharpeRatio,
|
||||
SortinoRatio,
|
||||
@@ -477,6 +483,12 @@ __all__ = [
|
||||
"SpinningTop",
|
||||
"ThreeInside",
|
||||
"ThreeOutside",
|
||||
# Microstructure: order book
|
||||
"OrderBookImbalanceTop1",
|
||||
"OrderBookImbalanceTopN",
|
||||
"OrderBookImbalanceFull",
|
||||
"Microprice",
|
||||
"QuotedSpread",
|
||||
# Risk / Performance
|
||||
"SharpeRatio",
|
||||
"SortinoRatio",
|
||||
|
||||
+165
-1
@@ -26,7 +26,9 @@ fn map_err(e: wc::Error) -> PyErr {
|
||||
| wc::Error::NonPositiveMultiplier
|
||||
| wc::Error::NonFiniteInput
|
||||
| wc::Error::InvalidCandle { .. }
|
||||
| wc::Error::InvalidTick { .. } => PyValueError::new_err(e.to_string()),
|
||||
| wc::Error::InvalidTick { .. }
|
||||
| wc::Error::InvalidOrderBook { .. }
|
||||
| wc::Error::InvalidTrade { .. } => PyValueError::new_err(e.to_string()),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -11613,6 +11615,162 @@ candle_pattern_no_param!(PySpinningTop, wc::SpinningTop, "SpinningTop");
|
||||
candle_pattern_no_param!(PyThreeInside, wc::ThreeInside, "ThreeInside");
|
||||
candle_pattern_no_param!(PyThreeOutside, wc::ThreeOutside, "ThreeOutside");
|
||||
|
||||
// ============================== Microstructure: Order Book ==============================
|
||||
//
|
||||
// Order-book indicators consume a depth snapshot rather than OHLCV. Streaming
|
||||
// `update(bid_px, bid_sz, ask_px, ask_sz)` takes four equal-length sequences
|
||||
// describing one snapshot (bids best-first = descending price, asks best-first
|
||||
// = ascending price); `batch` takes a list of such `(bid_px, bid_sz, ask_px,
|
||||
// ask_sz)` tuples and returns one value per snapshot.
|
||||
|
||||
fn build_order_book(
|
||||
bid_px: &[f64],
|
||||
bid_sz: &[f64],
|
||||
ask_px: &[f64],
|
||||
ask_sz: &[f64],
|
||||
) -> PyResult<wc::OrderBook> {
|
||||
if bid_px.len() != bid_sz.len() || ask_px.len() != ask_sz.len() {
|
||||
return Err(PyValueError::new_err(
|
||||
"bid/ask price and size arrays must be equal length",
|
||||
));
|
||||
}
|
||||
let bids = bid_px
|
||||
.iter()
|
||||
.zip(bid_sz)
|
||||
.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
|
||||
.collect();
|
||||
let asks = ask_px
|
||||
.iter()
|
||||
.zip(ask_sz)
|
||||
.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
|
||||
.collect();
|
||||
wc::OrderBook::new(bids, asks).map_err(map_err)
|
||||
}
|
||||
|
||||
macro_rules! py_ob_indicator {
|
||||
($name:ident, $inner:ty, $repr:expr) => {
|
||||
#[pyclass(name = $repr, module = "wickra._wickra", skip_from_py_object)]
|
||||
#[derive(Clone)]
|
||||
struct $name {
|
||||
inner: $inner,
|
||||
}
|
||||
|
||||
#[pymethods]
|
||||
impl $name {
|
||||
#[new]
|
||||
fn new() -> Self {
|
||||
Self {
|
||||
inner: <$inner>::new(),
|
||||
}
|
||||
}
|
||||
fn update(
|
||||
&mut self,
|
||||
bid_px: Vec<f64>,
|
||||
bid_sz: Vec<f64>,
|
||||
ask_px: Vec<f64>,
|
||||
ask_sz: Vec<f64>,
|
||||
) -> PyResult<Option<f64>> {
|
||||
let book = build_order_book(&bid_px, &bid_sz, &ask_px, &ask_sz)?;
|
||||
Ok(self.inner.update(book))
|
||||
}
|
||||
#[allow(clippy::type_complexity)]
|
||||
fn batch<'py>(
|
||||
&mut self,
|
||||
py: Python<'py>,
|
||||
snapshots: Vec<(Vec<f64>, Vec<f64>, Vec<f64>, Vec<f64>)>,
|
||||
) -> PyResult<Bound<'py, PyArray1<f64>>> {
|
||||
let mut out = Vec::with_capacity(snapshots.len());
|
||||
for (bid_px, bid_sz, ask_px, ask_sz) in &snapshots {
|
||||
let book = build_order_book(bid_px, bid_sz, ask_px, ask_sz)?;
|
||||
out.push(self.inner.update(book).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!("{}()", $repr)
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
py_ob_indicator!(
|
||||
PyOrderBookImbalanceTop1,
|
||||
wc::OrderBookImbalanceTop1,
|
||||
"OrderBookImbalanceTop1"
|
||||
);
|
||||
py_ob_indicator!(
|
||||
PyOrderBookImbalanceFull,
|
||||
wc::OrderBookImbalanceFull,
|
||||
"OrderBookImbalanceFull"
|
||||
);
|
||||
py_ob_indicator!(PyMicroprice, wc::Microprice, "Microprice");
|
||||
py_ob_indicator!(PyQuotedSpread, wc::QuotedSpread, "QuotedSpread");
|
||||
|
||||
// Top-N imbalance carries a `levels` parameter, so it is hand-written.
|
||||
#[pyclass(
|
||||
name = "OrderBookImbalanceTopN",
|
||||
module = "wickra._wickra",
|
||||
skip_from_py_object
|
||||
)]
|
||||
#[derive(Clone)]
|
||||
struct PyOrderBookImbalanceTopN {
|
||||
inner: wc::OrderBookImbalanceTopN,
|
||||
}
|
||||
|
||||
#[pymethods]
|
||||
impl PyOrderBookImbalanceTopN {
|
||||
#[new]
|
||||
fn new(levels: usize) -> PyResult<Self> {
|
||||
Ok(Self {
|
||||
inner: wc::OrderBookImbalanceTopN::new(levels).map_err(map_err)?,
|
||||
})
|
||||
}
|
||||
fn update(
|
||||
&mut self,
|
||||
bid_px: Vec<f64>,
|
||||
bid_sz: Vec<f64>,
|
||||
ask_px: Vec<f64>,
|
||||
ask_sz: Vec<f64>,
|
||||
) -> PyResult<Option<f64>> {
|
||||
let book = build_order_book(&bid_px, &bid_sz, &ask_px, &ask_sz)?;
|
||||
Ok(self.inner.update(book))
|
||||
}
|
||||
#[allow(clippy::type_complexity)]
|
||||
fn batch<'py>(
|
||||
&mut self,
|
||||
py: Python<'py>,
|
||||
snapshots: Vec<(Vec<f64>, Vec<f64>, Vec<f64>, Vec<f64>)>,
|
||||
) -> PyResult<Bound<'py, PyArray1<f64>>> {
|
||||
let mut out = Vec::with_capacity(snapshots.len());
|
||||
for (bid_px, bid_sz, ask_px, ask_sz) in &snapshots {
|
||||
let book = build_order_book(bid_px, bid_sz, ask_px, ask_sz)?;
|
||||
out.push(self.inner.update(book).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!("OrderBookImbalanceTopN(levels={})", self.inner.levels())
|
||||
}
|
||||
}
|
||||
|
||||
// ============================== Family 15: Risk / Performance ==============================
|
||||
|
||||
#[pyclass(name = "SharpeRatio", module = "wickra._wickra", skip_from_py_object)]
|
||||
@@ -12718,6 +12876,12 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
|
||||
m.add_class::<PySpinningTop>()?;
|
||||
m.add_class::<PyThreeInside>()?;
|
||||
m.add_class::<PyThreeOutside>()?;
|
||||
// Microstructure: order book.
|
||||
m.add_class::<PyOrderBookImbalanceTop1>()?;
|
||||
m.add_class::<PyOrderBookImbalanceTopN>()?;
|
||||
m.add_class::<PyOrderBookImbalanceFull>()?;
|
||||
m.add_class::<PyMicroprice>()?;
|
||||
m.add_class::<PyQuotedSpread>()?;
|
||||
// Family 15: Risk / Performance metrics.
|
||||
m.add_class::<PySharpeRatio>()?;
|
||||
m.add_class::<PySortinoRatio>()?;
|
||||
|
||||
@@ -166,3 +166,28 @@ def test_family_10_ehlers_rejects_invalid_parameters():
|
||||
ta.MAMA(0.05, 0.5)
|
||||
with pytest.raises(ValueError):
|
||||
ta.EmpiricalModeDecomposition(20, 0.0)
|
||||
|
||||
|
||||
def test_orderbook_topn_zero_levels_raises():
|
||||
with pytest.raises(ValueError):
|
||||
ta.OrderBookImbalanceTopN(0)
|
||||
|
||||
|
||||
def test_orderbook_unequal_price_size_lengths_raise():
|
||||
# bid_px has 2 entries but bid_sz has 1 -> mismatched -> ValueError.
|
||||
with pytest.raises(ValueError):
|
||||
ta.OrderBookImbalanceTop1().update([100.0, 99.0], [1.0], [101.0], [1.0])
|
||||
with pytest.raises(ValueError):
|
||||
ta.Microprice().update([100.0], [1.0], [101.0, 102.0], [1.0])
|
||||
|
||||
|
||||
def test_orderbook_crossed_book_raises():
|
||||
# best_bid (102) >= best_ask (101) is a crossed book -> rejected.
|
||||
with pytest.raises(ValueError):
|
||||
ta.QuotedSpread().update([102.0], [1.0], [101.0], [1.0])
|
||||
|
||||
|
||||
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])
|
||||
|
||||
@@ -847,3 +847,26 @@ def test_doji_signed_dragonfly_gravestone_neutral():
|
||||
assert d.update((10.0, 12.0, 8.0, 10.0, 1.0, 2)) == pytest.approx(0.0)
|
||||
# A large body is not a doji at all -> 0 regardless of position.
|
||||
assert d.update((10.0, 12.0, 10.0, 12.0, 1.0, 3)) == pytest.approx(0.0)
|
||||
|
||||
|
||||
def test_orderbook_imbalance_reference_values():
|
||||
# Top-1: (3 - 1) / (3 + 1) = 0.5.
|
||||
assert ta.OrderBookImbalanceTop1().update([100.0], [3.0], [101.0], [1.0]) == pytest.approx(0.5)
|
||||
# Top-2: bidDepth 3, askDepth 2 -> (3 - 2) / 5 = 0.2.
|
||||
topn = ta.OrderBookImbalanceTopN(2)
|
||||
assert topn.update([100.0, 99.0], [2.0, 1.0], [101.0, 102.0], [1.0, 1.0]) == pytest.approx(0.2)
|
||||
# Full: bidDepth 1, askDepth 3 -> (1 - 3) / 4 = -0.5.
|
||||
full = ta.OrderBookImbalanceFull()
|
||||
assert full.update([100.0], [1.0], [101.0, 102.0], [2.0, 1.0]) == pytest.approx(-0.5)
|
||||
|
||||
|
||||
def test_microprice_reference_value():
|
||||
# (100*3 + 101*1) / (1 + 3) = 401 / 4 = 100.25 — heavy ask pulls toward bid.
|
||||
mp = ta.Microprice()
|
||||
assert mp.update([100.0], [1.0], [101.0], [3.0]) == pytest.approx(100.25)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
@@ -129,3 +129,24 @@ def test_ehlers_indicators_lifecycle():
|
||||
assert ind.is_ready()
|
||||
ind.reset()
|
||||
assert not ind.is_ready()
|
||||
|
||||
|
||||
def test_orderbook_lifecycle():
|
||||
snapshot = ([100.0], [1.0], [101.0], [1.0])
|
||||
for ind in [
|
||||
ta.OrderBookImbalanceTop1(),
|
||||
ta.OrderBookImbalanceTopN(3),
|
||||
ta.OrderBookImbalanceFull(),
|
||||
ta.Microprice(),
|
||||
ta.QuotedSpread(),
|
||||
]:
|
||||
assert ind.warmup_period() == 1
|
||||
assert not ind.is_ready()
|
||||
ind.update(*snapshot)
|
||||
assert ind.is_ready()
|
||||
ind.reset()
|
||||
assert not ind.is_ready()
|
||||
|
||||
|
||||
def test_orderbook_topn_repr():
|
||||
assert repr(ta.OrderBookImbalanceTopN(5)) == "OrderBookImbalanceTopN(levels=5)"
|
||||
|
||||
@@ -1863,3 +1863,38 @@ def test_new_indicators_expose_lifecycle():
|
||||
assert ind.warmup_period() >= 1
|
||||
ind.reset()
|
||||
assert ind.is_ready() is False
|
||||
|
||||
|
||||
def _orderbook_snapshots(n: int) -> list:
|
||||
"""A deterministic varying sequence of order-book snapshots."""
|
||||
snaps = []
|
||||
for i in range(n):
|
||||
bid_sz = 1.0 + (i % 5)
|
||||
ask_sz = 1.0 + ((i + 2) % 4)
|
||||
snaps.append(
|
||||
(
|
||||
[100.0, 99.0],
|
||||
[bid_sz, 1.0],
|
||||
[101.0, 102.0],
|
||||
[ask_sz, 1.0],
|
||||
)
|
||||
)
|
||||
return snaps
|
||||
|
||||
|
||||
def test_orderbook_indicators_streaming_equals_batch():
|
||||
snaps = _orderbook_snapshots(40)
|
||||
for make in (
|
||||
ta.OrderBookImbalanceTop1,
|
||||
lambda: ta.OrderBookImbalanceTopN(2),
|
||||
ta.OrderBookImbalanceFull,
|
||||
ta.Microprice,
|
||||
ta.QuotedSpread,
|
||||
):
|
||||
batch = make().batch(snaps)
|
||||
streamer = make()
|
||||
streamed = np.array(
|
||||
[streamer.update(*snap) for snap in snaps], dtype=np.float64
|
||||
)
|
||||
assert batch.shape == (len(snaps),)
|
||||
assert _eq_nan(batch, streamed)
|
||||
|
||||
@@ -97,3 +97,25 @@ def test_ehlers_super_smoother_batch_shape(sine_prices):
|
||||
def test_mama_batch_shape(sine_prices):
|
||||
out = ta.MAMA().batch(sine_prices)
|
||||
assert out.shape == (sine_prices.size, 2)
|
||||
|
||||
|
||||
def test_orderbook_indicators_construct_and_emit():
|
||||
# All five order-book indicators accept a four-array snapshot and emit a float.
|
||||
snapshot = ([100.0, 99.0], [2.0, 1.0], [101.0, 102.0], [1.0, 1.0])
|
||||
indicators = [
|
||||
ta.OrderBookImbalanceTop1(),
|
||||
ta.OrderBookImbalanceTopN(2),
|
||||
ta.OrderBookImbalanceFull(),
|
||||
ta.Microprice(),
|
||||
ta.QuotedSpread(),
|
||||
]
|
||||
for ind in indicators:
|
||||
out = ind.update(*snapshot)
|
||||
assert isinstance(out, float)
|
||||
|
||||
|
||||
def test_orderbook_batch_returns_one_value_per_snapshot():
|
||||
snapshots = [([100.0], [3.0], [101.0], [1.0])] * 5
|
||||
out = ta.OrderBookImbalanceTop1().batch(snapshots)
|
||||
assert out.shape == (5,)
|
||||
assert out.dtype == np.float64
|
||||
|
||||
@@ -201,3 +201,19 @@ def test_opening_range_streaming_matches_batch(ohlc_series):
|
||||
rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
|
||||
streamed = np.array(rows, dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
|
||||
def test_orderbook_streaming_matches_batch():
|
||||
snaps = [
|
||||
(
|
||||
[100.0, 99.0],
|
||||
[1.0 + (i % 5), 1.0],
|
||||
[101.0, 102.0],
|
||||
[1.0 + ((i + 1) % 3), 1.0],
|
||||
)
|
||||
for i in range(30)
|
||||
]
|
||||
batch = ta.Microprice().batch(snaps)
|
||||
streamer = ta.Microprice()
|
||||
streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
|
||||
assert _equal_with_nan(batch, streamed)
|
||||
|
||||
@@ -6331,6 +6331,134 @@ wasm_candle_pattern!(WasmSpinningTop, wc::SpinningTop, SpinningTop);
|
||||
wasm_candle_pattern!(WasmThreeInside, wc::ThreeInside, ThreeInside);
|
||||
wasm_candle_pattern!(WasmThreeOutside, wc::ThreeOutside, ThreeOutside);
|
||||
|
||||
// ============================== Microstructure: Order Book ==============================
|
||||
//
|
||||
// Order-book indicators consume a depth snapshot rather than OHLCV. Each
|
||||
// `update(bidPx, bidSz, askPx, askSz)` takes four equal-length typed arrays for
|
||||
// one snapshot (bids best-first = descending price, asks best-first = ascending
|
||||
// price) — the streaming model that fits a live browser book feed. Batch over a
|
||||
// ragged depth history is provided by the Python and Node bindings.
|
||||
|
||||
fn build_order_book(
|
||||
bid_px: &[f64],
|
||||
bid_sz: &[f64],
|
||||
ask_px: &[f64],
|
||||
ask_sz: &[f64],
|
||||
) -> Result<wc::OrderBook, JsError> {
|
||||
if bid_px.len() != bid_sz.len() || ask_px.len() != ask_sz.len() {
|
||||
return Err(JsError::new(
|
||||
"bid/ask price and size arrays must be equal length",
|
||||
));
|
||||
}
|
||||
let bids = bid_px
|
||||
.iter()
|
||||
.zip(bid_sz)
|
||||
.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
|
||||
.collect();
|
||||
let asks = ask_px
|
||||
.iter()
|
||||
.zip(ask_sz)
|
||||
.map(|(&p, &s)| wc::Level::new_unchecked(p, s))
|
||||
.collect();
|
||||
wc::OrderBook::new(bids, asks).map_err(map_err)
|
||||
}
|
||||
|
||||
macro_rules! wasm_ob_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,
|
||||
bid_px: &[f64],
|
||||
bid_sz: &[f64],
|
||||
ask_px: &[f64],
|
||||
ask_sz: &[f64],
|
||||
) -> Result<Option<f64>, JsError> {
|
||||
let book = build_order_book(bid_px, bid_sz, ask_px, ask_sz)?;
|
||||
Ok(self.inner.update(book))
|
||||
}
|
||||
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_ob_indicator!(
|
||||
WasmOrderBookImbalanceTop1,
|
||||
wc::OrderBookImbalanceTop1,
|
||||
OrderBookImbalanceTop1
|
||||
);
|
||||
wasm_ob_indicator!(
|
||||
WasmOrderBookImbalanceFull,
|
||||
wc::OrderBookImbalanceFull,
|
||||
OrderBookImbalanceFull
|
||||
);
|
||||
wasm_ob_indicator!(WasmMicroprice, wc::Microprice, Microprice);
|
||||
wasm_ob_indicator!(WasmQuotedSpread, wc::QuotedSpread, QuotedSpread);
|
||||
|
||||
// Top-N imbalance carries a `levels` parameter, so it is hand-written.
|
||||
#[wasm_bindgen(js_name = OrderBookImbalanceTopN)]
|
||||
pub struct WasmOrderBookImbalanceTopN {
|
||||
inner: wc::OrderBookImbalanceTopN,
|
||||
}
|
||||
|
||||
#[wasm_bindgen(js_class = OrderBookImbalanceTopN)]
|
||||
impl WasmOrderBookImbalanceTopN {
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(levels: usize) -> Result<WasmOrderBookImbalanceTopN, JsError> {
|
||||
Ok(Self {
|
||||
inner: wc::OrderBookImbalanceTopN::new(levels).map_err(map_err)?,
|
||||
})
|
||||
}
|
||||
pub fn update(
|
||||
&mut self,
|
||||
bid_px: &[f64],
|
||||
bid_sz: &[f64],
|
||||
ask_px: &[f64],
|
||||
ask_sz: &[f64],
|
||||
) -> Result<Option<f64>, JsError> {
|
||||
let book = build_order_book(bid_px, bid_sz, ask_px, ask_sz)?;
|
||||
Ok(self.inner.update(book))
|
||||
}
|
||||
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