* 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 fromec269d8The 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 from733afd9* 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.
143 lines
6.0 KiB
Rust
143 lines
6.0 KiB
Rust
#![no_main]
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//! Fuzz OHLCV-input indicator updates with arbitrary candle sequences.
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//!
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//! Every candle-input indicator must tolerate any sequence of validated OHLCV
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//! candles — extreme magnitudes, micro-spreads, zero-volume bars, abrupt
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//! reversals — without panicking. The fuzzer chunks the raw `f64` stream into
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//! `[open, high, low, close, volume]` tuples and constructs each candle via
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//! `Candle::new`; entries that fail OHLCV-invariant validation are skipped so
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//! the indicator only ever sees structurally-valid candles. Each iteration
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//! then drives that candle stream through every candle-input indicator twice
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//! (streaming `update` + batch).
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//!
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//! Audit finding R9: the previous fuzz suite had no candle-input coverage at
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//! all. This target now covers every candle-input indicator including the
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//! ones the audit named explicitly (ATR, ADX, Stochastic, PSAR) plus the
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//! complete catalogue: Keltner, Donchian, SuperTrend, Chandelier Exit, ATR
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//! Trailing Stop, Aroon, AwesomeOscillator, CCI, WilliamsR, MFI, OBV, VWAP,
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//! RollingVWAP, ADL, VPT, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex,
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//! EaseOfMovement, NATR, AroonOscillator, ChandeKrollStop, Vortex, MassIndex,
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//! ChoppinessIndex, TrueRange, ChaikinVolatility, AcceleratorOscillator,
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//! BalanceOfPower, UltimateOscillator, VWMA, TypicalPrice, MedianPrice,
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//! WeightedClose.
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use libfuzzer_sys::fuzz_target;
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use wickra_core::{
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AcceleratorOscillator, Adl, Adx, Alligator, Aroon, AroonOscillator, Atr, AtrTrailingStop,
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AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower, BatchExt, Candle, Cci,
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ChaikinMoneyFlow, ChaikinOscillator,
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ChaikinVolatility, ChandeKrollStop, ChandelierExit, ChoppinessIndex, Donchian, EaseOfMovement,
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Evwma, ForceIndex, Indicator, Inertia, Keltner, MassIndex, MedianPrice, Mfi, Natr, Obv, Pgo,
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Psar, RollingVwap, Rvi, Smi,
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Stochastic, SuperTrend, TrueRange, TypicalPrice, UltimateOscillator, VolumePriceTrend, Vortex,
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Vwap, Vwma, WeightedClose, WilliamsR,
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};
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/// Convert a flat `f64` stream into a `Vec<Candle>` by chunking it into
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/// `[open, high, low, close, volume]` groups. Tuples that fail OHLCV
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/// validation are dropped so the indicator under test only ever sees a
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/// structurally-valid candle stream (the *parser* is fuzz-tested elsewhere;
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/// this target focuses on indicator robustness).
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fn candles_from(data: &[f64]) -> Vec<Candle> {
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data.chunks_exact(5)
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.enumerate()
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.filter_map(|(i, ch)| {
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// A monotonic timestamp avoids surprising any indicator that might
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// care about ordering. The fuzz input drives OHLCV; time is just a
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// tie-breaker.
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Candle::new(ch[0], ch[1], ch[2], ch[3], ch[4], i as i64).ok()
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})
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.collect()
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}
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/// Streaming + batch sweep through one candle-input indicator. `#[inline(never)]`
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/// keeps each indicator on its own frame in any panic backtrace.
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#[inline(never)]
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fn drive<I, O>(make: impl Fn() -> I, candles: &[Candle])
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where
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I: Indicator<Input = Candle, Output = O> + BatchExt,
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{
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let mut streaming = make();
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for c in candles {
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let _ = streaming.update(*c);
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}
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let _ = make().batch(candles);
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}
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fuzz_target!(|data: Vec<f64>| {
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let candles = candles_from(&data);
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if candles.is_empty() {
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return;
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}
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// --- Volatility & ATR family ---
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drive(|| Atr::new(14).unwrap(), &candles);
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drive(|| Natr::new(14).unwrap(), &candles);
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drive(TrueRange::new, &candles);
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drive(|| ChaikinVolatility::new(10, 10).unwrap(), &candles);
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// --- Bands & Channels ---
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drive(|| Keltner::new(20, 10, 2.0).unwrap(), &candles);
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drive(|| Donchian::new(20).unwrap(), &candles);
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// --- Trailing Stops ---
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drive(|| Psar::new(0.02, 0.02, 0.20).unwrap(), &candles);
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drive(|| SuperTrend::new(14, 3.0).unwrap(), &candles);
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drive(|| ChandelierExit::new(22, 3.0).unwrap(), &candles);
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drive(|| ChandeKrollStop::new(10, 1.0, 9).unwrap(), &candles);
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drive(|| AtrTrailingStop::new(14, 3.0).unwrap(), &candles);
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// --- Trend & Directional ---
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drive(|| Adx::new(14).unwrap(), &candles);
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drive(|| Aroon::new(14).unwrap(), &candles);
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drive(|| Alligator::new(13, 8, 5).unwrap(), &candles);
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drive(|| AroonOscillator::new(14).unwrap(), &candles);
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drive(|| Vortex::new(14).unwrap(), &candles);
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drive(|| MassIndex::new(9, 25).unwrap(), &candles);
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drive(|| ChoppinessIndex::new(14).unwrap(), &candles);
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// --- Momentum & Oscillators ---
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drive(|| Cci::new(20).unwrap(), &candles);
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drive(|| Rvi::new(10).unwrap(), &candles);
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drive(|| Inertia::new(14, 20).unwrap(), &candles);
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drive(|| Pgo::new(14).unwrap(), &candles);
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drive(|| Smi::classic(), &candles);
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drive(|| WilliamsR::new(14).unwrap(), &candles);
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drive(|| AwesomeOscillator::new(5, 34).unwrap(), &candles);
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drive(
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|| AwesomeOscillatorHistogram::new(5, 34, 5).unwrap(),
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&candles,
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);
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drive(|| AcceleratorOscillator::new(5, 34, 5).unwrap(), &candles);
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drive(|| UltimateOscillator::new(7, 14, 28).unwrap(), &candles);
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drive(BalanceOfPower::new, &candles);
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// --- Volume ---
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drive(Obv::new, &candles);
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drive(|| Mfi::new(14).unwrap(), &candles);
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drive(Vwap::new, &candles);
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drive(|| RollingVwap::new(20).unwrap(), &candles);
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drive(|| Vwma::new(20).unwrap(), &candles);
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drive(|| Evwma::new(20).unwrap(), &candles);
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drive(Adl::new, &candles);
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drive(VolumePriceTrend::new, &candles);
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drive(|| ChaikinMoneyFlow::new(20).unwrap(), &candles);
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drive(|| ChaikinOscillator::new(3, 10).unwrap(), &candles);
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drive(|| ForceIndex::new(13).unwrap(), &candles);
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drive(|| EaseOfMovement::with_divisor(14, 1e8).unwrap(), &candles);
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// --- Price transformations ---
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drive(TypicalPrice::new, &candles);
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drive(MedianPrice::new, &candles);
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drive(WeightedClose::new, &candles);
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// --- Stochastic (multi-output) ---
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{
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let mut s = Stochastic::new(14, 3).unwrap();
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for c in &candles {
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let _ = s.update(*c);
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}
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let _ = Stochastic::new(14, 3).unwrap().batch(&candles);
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}
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});
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