kingchenc
3287146f44
John McGinley's self-adjusting moving average with the recurrence MD + (price - MD) / (0.6 * period * (price / MD)^4). Speeds up when price falls below the indicator and damps when price runs above the indicator. Seeded with the simple average of the first period inputs. Reference: McGinley, Technical Analysis of Stocks & Commodities, 1990. Touchpoints: - crates/wickra-core: mcginley_dynamic.rs + mod.rs + lib.rs re-export - bindings/python: PyMcGinleyDynamic + __init__.py + test_new_indicators + test_known_values reference - bindings/node: McGinleyDynamicNode (scalar macro) + index.d.ts/index.js + indicators.test.js factory + reference value - bindings/wasm: wasm_scalar_indicator! macro - fuzz: indicator_update target covers McGinleyDynamic(10) - crates/wickra/benches: bench_scalar entry - README + CHANGELOG: Moving Averages row + Unreleased entry
93 lines
4.0 KiB
Rust
93 lines
4.0 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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Alma, BatchExt, BollingerBands, Cmo, Coppock, Dema, Dpo, Ema, HistoricalVolatility, Hma,
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Indicator, Kama, LinRegAngle, LinRegSlope, LinearRegression, MacdIndicator, McGinleyDynamic,
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Mom, Pmo, Ppo, Roc, Rsi, Sma, Smma, StdDev, StochRsi, T3, Tema, Trima, Trix, Tsi, UlcerIndex,
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VerticalHorizontalFilter, Wma, 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(|| Alma::new(9, 0.85, 6.0).unwrap(), &data);
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drive(|| McGinleyDynamic::new(10).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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