kingchenc
05fcdd9a5e
* feat(family-12): add 13 Statistik/Regression indicators Brings the Price Statistics family to 20 indicators (7 → 20) and the total catalogue to 84 (71 → 84). Every indicator ships in the Rust core plus Python, Node, and WASM bindings with full streaming ↔ batch parity, fuzz coverage, and benches. Scalar (f64 → f64): - Variance, CoefficientOfVariation: rolling population variance and its dimensionless ratio with the mean. O(1) updates. - Skewness, Kurtosis: rolling Pearson skewness and excess kurtosis, derived from running sums of x, x², x³, x⁴ via the binomial identities — also O(1) per bar. - StandardError, DetrendedStdDev: standard error of estimate (n − 2) and population StdDev (n) of OLS residuals, sharing the LinReg O(1) sliding sums. - RSquared: coefficient of determination of the rolling OLS fit; the trend-quality filter, clamped to [0, 1]. - MedianAbsoluteDeviation: robust dispersion estimator; O(period log period) per emission via two in-place sorts of a reusable scratch buffer. - Autocorrelation(period, lag): rolling lag-k Pearson autocorrelation. - HurstExponent(period, chunks): R/S-analysis trend-persistence estimator clamped to [0, 1]. Pair indicators (Input = (f64, f64)): - PearsonCorrelation: rolling cross-series Pearson, O(1). - Beta: rolling OLS slope of asset vs. benchmark (CAPM). - SpearmanCorrelation: rolling rank correlation with mid-rank tie handling; O(period log period). Touchpoints: - crates/wickra-core: 13 new indicator modules + mod.rs / lib.rs re-exports. - bindings/python: pyclasses + add_class registration + __init__.py import & __all__ updates. The pair indicators expose update(x, y) and batch(x, y) over two equally-sized numpy arrays. - bindings/node: scalar indicators via node_scalar_indicator! macro; pair indicators via new node_pair_indicator! macro; explicit structs for Autocorrelation and HurstExponent (two-arg ctors). index.js extended with the new exports. - bindings/wasm: scalar wrappers via wasm_scalar_indicator!; pair wrappers via new wasm_pair_indicator! macro. - fuzz: every scalar drove through the generic helper; pair indicators stress-tested by pairing adjacent samples of the fuzz input. - Python tests (test_new_indicators.py): added to SCALAR parametrisation, plus algebraic reference values (variance of [2,4,6] = 8/3, MAD ignoring outlier = 0, monotone non-linear Spearman = 1, two-to-one Beta = 2, etc.) and a streaming-vs-batch test for the pair indicators. - Node tests (indicators.test.js): extended the scalar factories map and added a pair-indicator section with the same algebraic reference values. - crates/wickra/benches: bench_scalar entries for all 10 single- input new indicators. - README: counter 71 → 84; Price Statistics family-table row expanded with the 13 new indicators. - CHANGELOG: Unreleased section documents the family addition. Wiki drafts (ghost-ignored, manual sync to wickra.wiki at release time): indicator-ideas/families/wiki/family-12-statistik-regression/ contains 13 deep-dive pages plus _Sidebar / Indicators-Overview / Warmup-Periods / Home fragments for the curator merge. cargo check --workspace --all-features: clean. * fix(family-12): remove unreachable defensive guards in hurst_exponent The three guards (m < 2 continue, end > buf.len() break, denom == 0.0 return) are by-construction unreachable given the constructor invariant period >= 2 * chunks: m = period / k for k in 1..=chunks always satisfies m >= 2 and end = (c+1) * m <= k * m <= period = buf.len(), and m_1 = period and m_2 = period / 2 are always distinct so the slope denominator is strictly positive. Removing them brings codecov/patch back to 100%.
Fuzzing Wickra
cargo-fuzz harnesses for
the parsing and stateful entry points of Wickra. Fuzzing requires a nightly
Rust toolchain.
Setup
cargo install cargo-fuzz
rustup toolchain install nightly
Targets
| Target | What it exercises |
|---|---|
csv_reader |
CandleReader over arbitrary bytes — headers, cells, BOM, binary noise. |
binance_envelope |
RawWsEnvelope deserialization from arbitrary strings. |
indicator_update |
Every scalar-input indicator (SMA / EMA / WMA / RSI / DEMA / TEMA / HMA / ROC / TRIX / SMMA / TRIMA / ZLEMA / KAMA / T3 / MOM / CMO / TSI / PMO / StochRSI / DPO / PPO / Coppock / StdDev / UlcerIndex / HistoricalVolatility / LinearRegression / LinRegSlope / LinRegAngle / VHF / ZScore / MACD / Bollinger) streamed + batched over arbitrary f64 sequences (NaN, ±inf, jumps). |
indicator_update_candle |
Every candle-input indicator (ATR, NATR, TrueRange, ChaikinVolatility, Keltner, Donchian, PSAR, SuperTrend, ChandelierExit, ChandeKrollStop, ATRTrailingStop, ADX, Aroon, AroonOscillator, Vortex, MassIndex, ChoppinessIndex, CCI, WilliamsR, AwesomeOscillator, AcceleratorOscillator, UltimateOscillator, BalanceOfPower, OBV, MFI, VWAP, RollingVWAP, VWMA, ADL, VPT, CMF, ChaikinOscillator, ForceIndex, EaseOfMovement, TypicalPrice, MedianPrice, WeightedClose, Stochastic) streamed + batched over fuzz-derived OHLCV candles. |
tick_aggregator |
TickAggregator over arbitrary (price, volume, timestamp) triples. |
Run
# From the repository root:
cargo +nightly fuzz run csv_reader
cargo +nightly fuzz run binance_envelope
cargo +nightly fuzz run indicator_update
cargo +nightly fuzz run indicator_update_candle
cargo +nightly fuzz run tick_aggregator
Each run continues until a crash is found or it is interrupted. A short time-boxed smoke run is useful in CI:
cargo +nightly fuzz run csv_reader -- -max_total_time=60
The expectation for every target is that it never panics: malformed or
adversarial input must surface as an Err, never a crash.