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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 d2f99efd78 F13c: restructure the indicator catalogue into eight families 
The original taxonomy was four classical families plus a statistics group,
with the F1-F12 expansion slotted in as sub-categories. This regroups the
whole 71-indicator catalogue into eight top-level families, each with at
least five members:

  Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9),
  Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5),
  Volume (9), Price Statistics (7).

- Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71
  indicator pages moved with `git mv`. Every internal cross-link is
  normalised to `../<family>/Indicator-X.md`, each page's `Family` field is
  set to its new family, and two pre-existing `../Indicator-Chaining.md`
  links (should have been `../../`) are corrected. A link check confirms
  every relative wiki link resolves.
- Indicators-Overview.md fully rewritten around the eight families;
  Home.md indicator reference and the README family table follow suit.
- Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the
  46-indicator expansion (25 -> 71) and the eight-family taxonomy.
- Tests: Node indicators.test.js and Python test_new_indicators.py cover
  all eight new indicators (Node 91/91, Python 117/117 green).

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests,
25 data tests and 74 doctests green.
 2026-05-22 21:21:56 +02:00
kingchenc 2f3b5cc3be F-Abschluss: wire the Python package, refresh docs and extend the test suites 
Finalises the F1-F12 indicator expansion (25 -> 63 indicators).

- Python `wickra/__init__.py`: import and re-export all 63 indicators,
  grouped by family, with a matching `__all__`. The package previously
  exposed only the original 25 even though the compiled module and the
  `.pyi` stubs already carried the rest.
- Docs: `Home.md` and `README.md` indicator counts and family tables
  updated to 63; `Indicators-Overview.md` already restructured per family
  in F10-F12; `Warmup-Periods.md` gains all 38 new indicators across the
  single- and multi-output tables (and the stale two-arg `Psar::new`
  example is corrected to three args); `CHANGELOG.md` `[Unreleased]` lists
  every new indicator by family.
- Tests: `bindings/node/__tests__/indicators.test.js` covers all 63
  indicators (streaming==batch plus four new reference-value checks),
  80/80 green; new `bindings/python/tests/test_new_indicators.py` covers
  the 38 additions (streaming==batch, shapes, reference values,
  lifecycle), Python suite 105/105 green.
- `bindings/node/index.js` regenerated by `napi build`.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests,
25 data tests, 66 doctests, 80 Node tests and 105 Python tests green;
`cargo check -p wickra-wasm --tests` green.
 2026-05-22 20:04:13 +02:00