1 Commits

Include renames

Author	SHA1	Message	Date
kingchencandGitHub	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