Files
wickra/fuzz/fuzz_targets/indicator_update_candle.rs
T
kingchencandGitHub d9d3ad18aa feat: Family 03 MACD & Price Oscillators — APO / AO-Hist / CFO / Zero-Lag MACD / Elder Impulse / STC (#41)
* feat(apo): add Absolute Price Oscillator

EMA(close, fast) - EMA(close, slow). Like MACD without the signal EMA.
Defaults to (fast = 12, slow = 26); fast must be strictly less than
slow.

Touchpoints: apo.rs + mod.rs + lib.rs re-export, PyApo + __init__.py
+ test_new_indicators SCALAR + test_known_values flat reference,
ApoNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmApo via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(apo): add PyApo + ApoNode + WasmApo bindings missed from ec269d8

The previous APO commit (ec269d8) only registered APO in the Python
__init__.py / Node index.js / Node index.d.ts / fuzz / tests / docs.
The actual PyApo pyclass, ApoNode napi class, and WasmApo wasm class
edits silently no-op'd because the underlying lib.rs files had been
touched by a branch switch between Read and Edit. The bindings were
therefore advertising APO from the Python module / Node package /
WASM module but not actually exposing it.

Fix: insert PyApo block + add_class call in bindings/python/src/lib.rs,
ApoNode block in bindings/node/src/lib.rs, WasmApo macro line in
bindings/wasm/src/lib.rs. cargo test workspace stays at 615 (no new
tests added; the existing test_known_values + indicators.test.js
references would have failed at import once the bindings rebuilt
without these classes).

* feat(ao-histogram): add Awesome Oscillator Histogram

AO - SMA(AO, sma_period). A configurable variant of the existing
AcceleratorOscillator (which fixes fast=5, slow=34, sma=5).
Three parameters; defaults match Bill Williams' Accelerator.

Touchpoints: awesome_oscillator_histogram.rs + mod.rs + lib.rs
re-export, PyAoHist + __init__.py + test_new_indicators CANDLE_SCALAR
+ test_known_values flat reference, AwesomeOscillatorHistogramNode +
index.d.ts/index.js + indicators.test.js factory + reference,
WasmAoHist, candle-fuzz target, README + CHANGELOG.

* feat(cfo): add Chande Forecast Oscillator

100 * (close - LinReg(close, period)) / close. Positive when close
overshoots the linear forecast, negative when it undershoots. Holds
the previous value if the close is zero (percentage form undefined).
Single param period (default 14).

Touchpoints: cfo.rs + mod.rs + lib.rs re-export, PyCfo + __init__.py
+ test_new_indicators SCALAR + test_known_values linear reference,
CfoNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmCfo via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(cfo): add WasmCfo binding missed from 733afd9

* feat(zero-lag-macd): add Zero-Lag MACD

Classic MACD topology with ZLEMA substituted for EMA everywhere:
faster reaction to trend changes at the cost of slightly noisier
readings. Multi-output ZeroLagMacdOutput { macd, signal, histogram }.
Three parameters (fast = 12, slow = 26, signal = 9); fast must be
strictly less than slow.

Touchpoints: zero_lag_macd.rs + mod.rs + lib.rs re-export, PyZeroLagMacd
+ __init__.py + test_new_indicators MULTI + test_known_values flat
reference, ZeroLagMacdNode + ZeroLagMacdValue + index.d.ts/index.js +
indicators.test.js multi factory + reference, WasmZeroLagMacd, scalar
fuzz with hand-rolled drive (multi-output bypasses the f64-only
helper), README + CHANGELOG.

* feat(elder-impulse): add Alexander Elder Impulse System

Tri-state momentum gauge: +1 (green/buy) when EMA trend and MACD
histogram both rise, -1 (red/sell) when both fall, 0 (blue/neutral)
on disagreement. Four parameters (ema_period, macd_fast, macd_slow,
macd_signal); defaults (13, 12, 26, 9) match Elder.

Internally feeds both branches on every input so they warm in parallel;
needs one bar past the slowest branch to seed direction state.

Touchpoints: elder_impulse.rs + mod.rs + lib.rs re-export, PyElderImpulse
+ __init__.py + test_new_indicators SCALAR + test_known_values neutral
reference, ElderImpulseNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmElderImpulse via scalar macro, scalar-fuzz
target, README + CHANGELOG.

* feat(stc): add Schaff Trend Cycle

Doug Schaff's doubly-Stochastic-smoothed MACD. Bounded [0, 100]
reading that reacts faster than MACD by extracting the percentile of
MACD within a recent window, half-EMA-smoothing it, and re-stochasing
the smoothed series. Four parameters (fast = 23, slow = 50,
schaff_period = 10, factor = 0.5); fast must be strictly less than
slow and factor must lie in (0, 1].

Output clamped to [0, 100] to absorb floating-point rounding. The
stochastic stages clamp to 0 when their rolling range collapses (flat
input or perfectly monotone trend), so a flat series settles
deterministically at 0 after warmup.

Touchpoints: stc.rs + mod.rs + lib.rs re-export, PyStc + __init__.py
+ test_new_indicators SCALAR + test_known_values flat reference,
StcNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmStc via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(stc): rename last_stc -> last_value to satisfy clippy

* ci: Retry setup-node and setup-python on CDN flakes

Setup-node on Windows runners and setup-python across all OSes
occasionally fail with a silent hang or 5xx mid-download ("Attempting
to download 18..." → fail in <1s) — pure upstream CDN flake. The fix
ran on this branch's previous merge commit (24e723f) had to be
re-triggered manually via `gh run rerun --failed`.

Wrap both setup actions with continue-on-error and a follow-up retry
step that waits 30s and re-runs the same setup. The retry only fires
when the first attempt failed (steps.<id>.outcome == 'failure'), so a
green setup costs nothing extra. The retry uses the identical pinned
SHA so we still get supply-chain verification on both attempts.

Applied to ci.yml (Python matrix and Node matrix). release.yml has
the same setup-node / setup-python steps but is rarely re-run, so
the existing manual rerun pattern stays sufficient for now.

* test(zero-lag-macd): Fix MULTI dict shape mismatch + cover warmup_period

ZeroLagMACD was registered in the Python MULTI dict (which asserts a
(n, 2) batch shape) but actually emits (n, 3) — macd, signal,
histogram — like MACD. Moved out into its own standalone test
test_zero_lag_macd_streaming_matches_batch (3-tuple shape), and
included in the lifecycle sweep. Mirrors the existing Alligator
pattern for 3-output candle indicators.

Also adds a unit test for ZeroLagMacd::warmup_period that pins both
the (12, 26, 9) classic case and a small-period config — these four
lines were the codecov/patch miss on PR 41.
2026-05-25 17:26:46 +02:00

143 lines
6.0 KiB
Rust

#![no_main]
//! Fuzz OHLCV-input indicator updates with arbitrary candle sequences.
//!
//! Every candle-input indicator must tolerate any sequence of validated OHLCV
//! candles — extreme magnitudes, micro-spreads, zero-volume bars, abrupt
//! reversals — without panicking. The fuzzer chunks the raw `f64` stream into
//! `[open, high, low, close, volume]` tuples and constructs each candle via
//! `Candle::new`; entries that fail OHLCV-invariant validation are skipped so
//! the indicator only ever sees structurally-valid candles. Each iteration
//! then drives that candle stream through every candle-input indicator twice
//! (streaming `update` + batch).
//!
//! Audit finding R9: the previous fuzz suite had no candle-input coverage at
//! all. This target now covers every candle-input indicator including the
//! ones the audit named explicitly (ATR, ADX, Stochastic, PSAR) plus the
//! complete catalogue: Keltner, Donchian, SuperTrend, Chandelier Exit, ATR
//! Trailing Stop, Aroon, AwesomeOscillator, CCI, WilliamsR, MFI, OBV, VWAP,
//! RollingVWAP, ADL, VPT, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex,
//! EaseOfMovement, NATR, AroonOscillator, ChandeKrollStop, Vortex, MassIndex,
//! ChoppinessIndex, TrueRange, ChaikinVolatility, AcceleratorOscillator,
//! BalanceOfPower, UltimateOscillator, VWMA, TypicalPrice, MedianPrice,
//! WeightedClose.
use libfuzzer_sys::fuzz_target;
use wickra_core::{
AcceleratorOscillator, Adl, Adx, Alligator, Aroon, AroonOscillator, Atr, AtrTrailingStop,
AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower, BatchExt, Candle, Cci,
ChaikinMoneyFlow, ChaikinOscillator,
ChaikinVolatility, ChandeKrollStop, ChandelierExit, ChoppinessIndex, Donchian, EaseOfMovement,
Evwma, ForceIndex, Indicator, Inertia, Keltner, MassIndex, MedianPrice, Mfi, Natr, Obv, Pgo,
Psar, RollingVwap, Rvi, Smi,
Stochastic, SuperTrend, TrueRange, TypicalPrice, UltimateOscillator, VolumePriceTrend, Vortex,
Vwap, Vwma, WeightedClose, WilliamsR,
};
/// Convert a flat `f64` stream into a `Vec<Candle>` by chunking it into
/// `[open, high, low, close, volume]` groups. Tuples that fail OHLCV
/// validation are dropped so the indicator under test only ever sees a
/// structurally-valid candle stream (the *parser* is fuzz-tested elsewhere;
/// this target focuses on indicator robustness).
fn candles_from(data: &[f64]) -> Vec<Candle> {
data.chunks_exact(5)
.enumerate()
.filter_map(|(i, ch)| {
// A monotonic timestamp avoids surprising any indicator that might
// care about ordering. The fuzz input drives OHLCV; time is just a
// tie-breaker.
Candle::new(ch[0], ch[1], ch[2], ch[3], ch[4], i as i64).ok()
})
.collect()
}
/// Streaming + batch sweep through one candle-input indicator. `#[inline(never)]`
/// keeps each indicator on its own frame in any panic backtrace.
#[inline(never)]
fn drive<I, O>(make: impl Fn() -> I, candles: &[Candle])
where
I: Indicator<Input = Candle, Output = O> + BatchExt,
{
let mut streaming = make();
for c in candles {
let _ = streaming.update(*c);
}
let _ = make().batch(candles);
}
fuzz_target!(|data: Vec<f64>| {
let candles = candles_from(&data);
if candles.is_empty() {
return;
}
// --- Volatility & ATR family ---
drive(|| Atr::new(14).unwrap(), &candles);
drive(|| Natr::new(14).unwrap(), &candles);
drive(TrueRange::new, &candles);
drive(|| ChaikinVolatility::new(10, 10).unwrap(), &candles);
// --- Bands & Channels ---
drive(|| Keltner::new(20, 10, 2.0).unwrap(), &candles);
drive(|| Donchian::new(20).unwrap(), &candles);
// --- Trailing Stops ---
drive(|| Psar::new(0.02, 0.02, 0.20).unwrap(), &candles);
drive(|| SuperTrend::new(14, 3.0).unwrap(), &candles);
drive(|| ChandelierExit::new(22, 3.0).unwrap(), &candles);
drive(|| ChandeKrollStop::new(10, 1.0, 9).unwrap(), &candles);
drive(|| AtrTrailingStop::new(14, 3.0).unwrap(), &candles);
// --- Trend & Directional ---
drive(|| Adx::new(14).unwrap(), &candles);
drive(|| Aroon::new(14).unwrap(), &candles);
drive(|| Alligator::new(13, 8, 5).unwrap(), &candles);
drive(|| AroonOscillator::new(14).unwrap(), &candles);
drive(|| Vortex::new(14).unwrap(), &candles);
drive(|| MassIndex::new(9, 25).unwrap(), &candles);
drive(|| ChoppinessIndex::new(14).unwrap(), &candles);
// --- Momentum & Oscillators ---
drive(|| Cci::new(20).unwrap(), &candles);
drive(|| Rvi::new(10).unwrap(), &candles);
drive(|| Inertia::new(14, 20).unwrap(), &candles);
drive(|| Pgo::new(14).unwrap(), &candles);
drive(|| Smi::classic(), &candles);
drive(|| WilliamsR::new(14).unwrap(), &candles);
drive(|| AwesomeOscillator::new(5, 34).unwrap(), &candles);
drive(
|| AwesomeOscillatorHistogram::new(5, 34, 5).unwrap(),
&candles,
);
drive(|| AcceleratorOscillator::new(5, 34, 5).unwrap(), &candles);
drive(|| UltimateOscillator::new(7, 14, 28).unwrap(), &candles);
drive(BalanceOfPower::new, &candles);
// --- Volume ---
drive(Obv::new, &candles);
drive(|| Mfi::new(14).unwrap(), &candles);
drive(Vwap::new, &candles);
drive(|| RollingVwap::new(20).unwrap(), &candles);
drive(|| Vwma::new(20).unwrap(), &candles);
drive(|| Evwma::new(20).unwrap(), &candles);
drive(Adl::new, &candles);
drive(VolumePriceTrend::new, &candles);
drive(|| ChaikinMoneyFlow::new(20).unwrap(), &candles);
drive(|| ChaikinOscillator::new(3, 10).unwrap(), &candles);
drive(|| ForceIndex::new(13).unwrap(), &candles);
drive(|| EaseOfMovement::with_divisor(14, 1e8).unwrap(), &candles);
// --- Price transformations ---
drive(TypicalPrice::new, &candles);
drive(MedianPrice::new, &candles);
drive(WeightedClose::new, &candles);
// --- Stochastic (multi-output) ---
{
let mut s = Stochastic::new(14, 3).unwrap();
for c in &candles {
let _ = s.update(*c);
}
let _ = Stochastic::new(14, 3).unwrap().batch(&candles);
}
});