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1cd5d1d8da551cb15925aa007e956281c0239cb8
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3 Commits

Author SHA1 Message Date
kingchenc 466faddd87 feat: Family 01 Moving Averages — ALMA / McGinley / FRAMA / VIDYA / JMA / Alligator / EVWMA (#39) 
* feat(alma): add Arnaud Legoux Moving Average

Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.

Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.

Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
  test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
  factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(mcginley): add McGinley Dynamic moving average

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

* feat(frama): add Fractal Adaptive Moving Average

Ehlers' FRAMA adapts its smoothing constant to the fractal dimension of
the recent window: tight tracking in trends, heavy smoothing in chop.
Uses the close-only variant where max/min over each window half drive
the dimension estimate. Period must be even (default 16).

Reference: Ehlers, Fractal Adaptive Moving Average, 2005.

Touchpoints:
- crates/wickra-core: frama.rs + mod.rs + lib.rs re-export
- bindings/python: PyFrama + __init__.py + test_new_indicators +
  test_known_values reference (constant series + uptrend tracking)
- bindings/node: FramaNode (scalar macro) + index.d.ts/index.js +
  indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers Frama(16)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(vidya): add Variable Index Dynamic Average

Chande's VIDYA — an EMA whose alpha scales with |CMO(cmo_period)| / 100.
Strong directional momentum lifts the smoothing constant toward the
EMA-of-period rate; flat or choppy windows shrink it toward zero so
VIDYA coasts on its previous value. Two parameters: period (14) and
cmo_period (9). Reuses the existing wickra-core Cmo internally.

Reference: Chande, Stocks & Commodities, 1992.

Also fixes a silent gap from d37fbd1 (feat(frama)): the PyFrama Python
class wrapper and its add_class registration were dropped because the
two edits hit "File has not been read yet" errors that scrolled past
in a batch. Adds them here alongside VIDYA's bindings.

Touchpoints (VIDYA): vidya.rs + mod.rs + lib.rs re-export, PyVidya +
__init__.py + test_new_indicators + test_known_values reference,
VidyaNode (manual two-param binding) + index.d.ts/index.js +
indicators.test.js factory + reference, wasm_scalar_indicator! macro,
fuzz target, bench, README + CHANGELOG.

* feat(jma): add Jurik Moving Average

Three-stage filter reconstruction of Mark Jurik's adaptive MA (the
algorithm is proprietary; this is the form used by most open-source
ports since the 1999 TASC article). Parameters: period (14), phase in
[-100, 100] (0), power in 1..=4 (2). State is seeded by setting
e0 = JMA = first input so a constant input stream is reproduced exactly.

Touchpoints: jma.rs + mod.rs + lib.rs re-export, PyJma + __init__.py +
test_new_indicators + test_known_values reference, JmaNode (manual
three-param binding) + index.d.ts/index.js + indicators.test.js factory
+ reference, wasm_scalar_indicator! macro, fuzz target, bench, README +
CHANGELOG.

* feat(alligator): add Bill Williams Alligator

Three SMMA lines (Jaw / Teeth / Lips) over the median price
(high + low) / 2 with default periods 13 / 8 / 5. Multi-output
indicator returning AlligatorOutput { jaw, teeth, lips }. The
original chart variant shifts each line forward for display; we
publish the unshifted SMMA values and leave the visual shift to
the consumer.

Reference: Bill Williams, Trading Chaos, 1995.

Touchpoints: alligator.rs + mod.rs + lib.rs re-export, PyAlligator
(Candle input, returns 3-tuple, ndarray (n, 3) batch) + __init__.py
+ test_new_indicators + test_known_values reference, AlligatorNode +
AlligatorValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmAlligator (manual JsValue object) +
candle-fuzz target + README + CHANGELOG.

* feat(evwma): add Elastic Volume-Weighted Moving Average

Christian P. Fries' elastic recurrence where the smoothing weight is the
bar's volume relative to the running window total:

  V_sum_t = sum of volumes over the last period candles
  EVWMA_t = ((V_sum_t - v_t) * EVWMA_{t-1} + v_t * close_t) / V_sum_t

A bar whose volume is small barely moves the average; a bar that
dominates the window pulls it strongly toward that bar's close. Seeded
with the close of the first full window; holds its previous value if
the entire window has zero volume.

Reference: Fries, Wilmott Magazine, 2001.

Touchpoints: evwma.rs + mod.rs + lib.rs re-export, PyEvwma (close +
volume batch) + __init__.py + test_new_indicators CANDLE_SCALAR +
test_known_values reference, EvwmaNode + index.d.ts/index.js +
indicators.test.js candleScalar factory + reference, WasmEvwma,
candle-fuzz target + README + CHANGELOG.

* ci: Force local wheel install in Python jobs

Use --no-index --no-deps so the Python matrix installs the freshly
built wheel from dist/ and never falls back to PyPI. Previously pip
sometimes picked the released 0.2.x wheel on macOS / Windows when its
platform tag was a wider match than the local build, which made the
job test the released package and miss any new symbols added in the
PR (e.g. AttributeError: module 'wickra' has no attribute 'ALMA').
numpy is already installed by the preceding pip step, so --no-deps
is safe.
 2026-05-25 15:01:14 +02:00
kingchenc b003321562 test(fuzz): cover every indicator, scalar and candle inputs (R9) 
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.
 2026-05-23 10:33:05 +02:00
kingchenc 78c31d1bed E16: add a cargo-fuzz harness 
The repository had no fuzzing setup despite several natural targets —
the CSV parser, the Binance envelope deserializer, and the stateful
indicator/aggregator update paths.

Add a fuzz/ cargo-fuzz crate (detached from the workspace via its own
[workspace] table and the parent's exclude) with four targets:

- csv_reader      — CandleReader over arbitrary bytes
- binance_envelope — RawWsEnvelope deserialization from arbitrary strings
- indicator_update — RSI/EMA streaming + batch over arbitrary f64 series
- tick_aggregator — TickAggregator over arbitrary tick triples

Each target asserts the no-panic contract: malformed input must surface
as an Err. fuzz/README.md documents running them (nightly + cargo-fuzz).
 2026-05-22 16:44:19 +02:00