kingchenc
b003321562
The fuzz suite previously covered only `Rsi(14)` and `Ema(20)` — 2 of 71 indicators, no OHLCV coverage at all. Audit finding R9 asked for ATR/ADX/Stochastic/PSAR as a minimum; this commit goes further and brings every indicator under fuzz. - `indicator_update` (rewritten): drives every scalar-input indicator through one streaming pass + one batch call per iteration. Covers 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, BollingerBands. A `drive` helper marked `#[inline(never)]` keeps each indicator on its own panic backtrace frame. - `indicator_update_candle` (new): chunks the fuzz `f64` stream into `[open, high, low, close, volume]` tuples, builds candles via `Candle::new` (skipping ones that fail OHLCV validation — that path is fuzz-tested separately), then drives every candle-input indicator through streaming + batch. Covers 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. - `fuzz/Cargo.toml` registers the new target; `fuzz/README.md` describes both expanded targets. - A `fuzz-smoke` CI job runs each of the five targets for 30 s on every push and pull-request — enough to catch a regression in the harness without slowing CI to a crawl. Long fuzz campaigns belong on dedicated infrastructure with persistent corpora.
91 lines
3.9 KiB
Rust
91 lines
3.9 KiB
Rust
#![no_main]
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//! Fuzz scalar-input indicator updates with arbitrary `f64` sequences.
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//!
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//! Every scalar indicator must tolerate any finite-or-not input stream — NaN,
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//! ±inf, subnormals, abrupt jumps — without panicking. Each fuzz iteration
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//! runs the **same** input sequence through every scalar indicator twice:
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//! once as a streaming `update` loop and once as a full `batch` call. Neither
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//! path may panic; `batch` is also expected to agree with the streaming path
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//! (the `BatchExt` blanket implementation replays `update` internally, so the
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//! agreement is structural — but exercising both paths surfaces any
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//! state-mutation bugs in `update` that would only manifest mid-batch).
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//!
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//! Audit finding R9: the previous version covered only `Rsi(14)` and
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//! `Ema(20)`. This target now covers every scalar indicator in the catalogue.
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use libfuzzer_sys::fuzz_target;
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use wickra_core::{
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BatchExt, BollingerBands, Cmo, Coppock, Dema, Dpo, Ema, HistoricalVolatility, Hma, Indicator,
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Kama, LinRegAngle, LinRegSlope, LinearRegression, MacdIndicator, Mom, Pmo, Ppo, Roc, Rsi, Sma,
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Smma, StdDev, StochRsi, T3, Tema, Trima, Trix, Tsi, UlcerIndex, VerticalHorizontalFilter, Wma,
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ZScore, Zlema,
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};
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/// Drive a single streaming + batch run through one scalar indicator. Marked
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/// `#[inline(never)]` so a panic backtrace pin-points the specific indicator.
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#[inline(never)]
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fn drive<I>(make: impl Fn() -> I, data: &[f64])
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where
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I: Indicator<Input = f64, Output = f64> + BatchExt,
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{
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let mut streaming = make();
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for &x in data {
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let _ = streaming.update(x);
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}
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let _ = make().batch(data);
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}
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fuzz_target!(|data: Vec<f64>| {
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// Bounded periods keep each iteration cheap and bias the fuzzer toward
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// adversarial input patterns rather than enormous windows. The constants
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// mirror the README's "common defaults" so we cover the parameterisations
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// most users actually instantiate.
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drive(|| Sma::new(14).unwrap(), &data);
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drive(|| Ema::new(20).unwrap(), &data);
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drive(|| Wma::new(14).unwrap(), &data);
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drive(|| Rsi::new(14).unwrap(), &data);
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drive(|| Dema::new(14).unwrap(), &data);
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drive(|| Tema::new(14).unwrap(), &data);
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drive(|| Hma::new(14).unwrap(), &data);
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drive(|| Roc::new(14).unwrap(), &data);
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drive(|| Trix::new(14).unwrap(), &data);
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drive(|| Smma::new(14).unwrap(), &data);
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drive(|| Trima::new(14).unwrap(), &data);
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drive(|| Zlema::new(14).unwrap(), &data);
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drive(|| Kama::new(10, 2, 30).unwrap(), &data);
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drive(|| T3::new(14, 0.7).unwrap(), &data);
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drive(|| Mom::new(14).unwrap(), &data);
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drive(|| Cmo::new(14).unwrap(), &data);
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drive(|| Tsi::new(25, 13).unwrap(), &data);
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drive(|| Pmo::new(35, 20).unwrap(), &data);
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drive(|| StochRsi::new(14, 14).unwrap(), &data);
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drive(|| Dpo::new(14).unwrap(), &data);
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drive(|| Ppo::new(12, 26).unwrap(), &data);
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drive(|| Coppock::new(14, 11, 10).unwrap(), &data);
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drive(|| StdDev::new(14).unwrap(), &data);
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drive(|| UlcerIndex::new(14).unwrap(), &data);
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drive(|| HistoricalVolatility::new(14, 252).unwrap(), &data);
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drive(|| LinearRegression::new(14).unwrap(), &data);
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drive(|| LinRegSlope::new(14).unwrap(), &data);
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drive(|| LinRegAngle::new(14).unwrap(), &data);
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drive(|| VerticalHorizontalFilter::new(14).unwrap(), &data);
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drive(|| ZScore::new(14).unwrap(), &data);
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// MACD and Bollinger Bands have non-`f64` outputs, so they cannot use the
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// generic `drive` helper above. Streaming + batch are still both exercised.
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{
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let mut macd = MacdIndicator::new(12, 26, 9).unwrap();
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for &x in &data {
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let _ = macd.update(x);
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}
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let _ = MacdIndicator::new(12, 26, 9).unwrap().batch(&data);
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}
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{
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let mut bb = BollingerBands::new(20, 2.0).unwrap();
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for &x in &data {
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let _ = bb.update(x);
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}
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let _ = BollingerBands::new(20, 2.0).unwrap().batch(&data);
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}
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});
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