7f1a6df202ff6a7fc2cb4b2b27e8b83a7ea7657f
23 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 kingchenc
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24e723fa7d |
feat: Family 02 Momentum Oscillators — RVI / PGO / KST / SMI / Laguerre / Connors / Inertia (#40)
* feat(rvi): add Relative Vigor Index
Dorsey's RVI = SMA(close - open, period) / SMA(high - low, period) over
a rolling window of period candles. Candle input, single parameter
period (default 10). Positive on average-bullish windows, negative on
average-bearish. Holds the previous value if the entire window has
zero range (denominator undefined).
Reference: Donald Dorsey, also pandas-ta rvi.
Touchpoints: rvi.rs + mod.rs + lib.rs re-export, PyRvi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values reference,
RviNode (4-column OHLC batch) + index.d.ts/index.js + indicators.test
.js factory + reference, WasmRvi + make_candle_ohlc helper, candle-fuzz
target + criterion bench, README + CHANGELOG.
* feat(pgo): add Pretty Good Oscillator
Mark Johnson's PGO = (close - SMA(close, period)) / EMA(TR, period).
Counts roughly how many ATR-equivalents the close sits from its
period-bar mean. Candle input, single parameter period (default 14).
Johnson's heuristic uses +3/-3 crossings as entry signals.
Touchpoints: pgo.rs + mod.rs + lib.rs re-export, PyPgo + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-close
reference, PgoNode (h/l/c) + index.d.ts/index.js + indicators.test.js
factory + reference, WasmPgo, candle-fuzz target + bench, README +
CHANGELOG.
* feat(kst): add Know Sure Thing (Pring)
Pring's long-horizon momentum oscillator: weighted sum of four
SMA-smoothed ROC series with fixed weights 1, 2, 3, 4, plus an SMA
signal line. Nine parameters (four ROC periods, four SMA periods, one
signal period); classic() applies Pring's recommended defaults.
Multi-output indicator emitting KstOutput { kst, signal }.
Touchpoints: kst.rs + mod.rs + lib.rs re-export, PyKst + __init__.py
+ test_new_indicators MULTI + test_known_values flat-input reference,
KstNode + KstValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmKst (manual JsValue object), scalar-fuzz
target (handled outside the f64-output drive helper), README +
CHANGELOG.
* feat(smi): add Stochastic Momentum Index (Blau)
Blau's doubly-EMA-smoothed bounded oscillator: measures the close's
displacement from the centre of the recent high-low range, scaled by
the smoothed range. Candle input, three parameters (period, d_period,
d2_period) with defaults 5 / 3 / 3.
Internally feeds both the displacement-EMA stack and the range-EMA
stack on every candle so they warm up in parallel (gating either
behind the other starves the second by one input).
Touchpoints: smi.rs + mod.rs + lib.rs re-export, PySmi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-input
reference, SmiNode + index.d.ts/index.js + indicators.test.js factory
+ reference, WasmSmi, candle-fuzz target, README + CHANGELOG.
* feat(laguerre-rsi): add Ehlers Laguerre RSI
Four-stage Laguerre polynomial filter wrapped in an RSI-style up/down
accumulator. Single gamma in [0, 1] (default 0.5) trades lag for
smoothness. State is seeded by setting all four L_i to the first input
so a constant series stays at the neutral 50. Output clamped to
[0, 100] to absorb floating-point rounding.
Reference: Ehlers, Time Warp - Without Space Travel, 2002.
Touchpoints: laguerre_rsi.rs + mod.rs + lib.rs re-export, PyLaguerreRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values neutral
reference, LaguerreRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmLaguerreRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(connors-rsi): add Connors RSI (CRSI)
Larry Connors' 3-component aggregate: RSI(close), RSI(streak), and
PercentRank of the 1-period return over the last period_rank returns.
Each component is bounded in [0, 100] so the aggregate is too.
Three parameters (period_rsi, period_streak, period_rank) with
defaults 3 / 2 / 100. Streak tracks consecutive up/down runs (resets
to 0 on unchanged close).
Touchpoints: connors_rsi.rs + mod.rs + lib.rs re-export, PyConnorsRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values bounded
reference, ConnorsRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmConnorsRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(inertia): add Dorsey Inertia (RVI + LinReg)
Donald Dorsey's Inertia — a LinearRegression smoothing of the RVI
series. Endpoint of an n-bar least-squares fit of RVI is the indicator
reading. Preserves trend direction while damping the ratio. Candle
input, two parameters (rvi_period, linreg_period) with defaults 14 / 20.
Touchpoints: inertia.rs + mod.rs + lib.rs re-export, PyInertia +
__init__.py + test_new_indicators CANDLE_SCALAR + test_known_values
constant reference, InertiaNode (4-column OHLC batch) + index.d.ts /
index.js + indicators.test.js factory + reference, WasmInertia,
candle-fuzz target, README + CHANGELOG.
* test(kst): Move KST out of MULTI dict (it is scalar-input)
KST sits in the MULTI dict (candle-input, multi-output) but its
update() takes a single f64, not a candle tuple. The shared streaming
loop in test_multi_streaming_matches_batch fed the OHLCV tuple in,
which crashed with `TypeError: argument 'value': must be real number,
not tuple` on every Python matrix entry.
Split into a new MULTI_SCALAR_INPUT dict with its own test function
that feeds the close-price stream as floats. KST is currently the
only such indicator; structure is ready for future scalar-input
multi-output additions (e.g. some MACD-shaped indicators).
* test(coverage): Cover SMI zero-range and ConnorsRsi zero-prev cold paths
codecov/patch on PR 40 flagged two uncovered defensive branches:
- SMI returns self.current early when the smoothed range collapses to
zero (`r2 <= 0.0`) so the formula stays defined. Exercised by feeding
bars where high == low.
- ConnorsRsi skips the ROC ring-buffer update when the previous price
is exactly zero so the divide-by-zero in `(input - prev) / prev` is
impossible. Exercised by seeding the first bar at 0.0.
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kingchenc
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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
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kingchenc
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183ebec7ba |
fix(core): skip non-positive HV prices and add Error::InvalidTick (R13, R14)
R13 — `HistoricalVolatility::update` previously substituted `0.0` for
the log-return whenever `prev <= 0` or `input <= 0`. The log-return is
undefined there, and silently treating bad ticks as "no movement"
underreports realised volatility on broken data feeds. The fix skips
non-positive prices entirely: `self.last` is returned, state is left
untouched, and the next real tick re-anchors against the previous
*valid* `prev_price`. This matches how every other indicator handles
invalid inputs (SMA / EMA / ROC / Bollinger).
A new test `skips_non_positive_prices` proves the invariant: after a
warmed-up indicator, two consecutive bad ticks (`-5.0` and `0.0`) must
return the baseline value, and a subsequent real positive tick must
produce the same output as a control indicator that simply never saw
the bad ticks.
R14 — `Tick::new` previously returned `Error::InvalidCandle` for
negative volume. A tick is not a candle; downstream tick-stream
pipelines should be able to match on a semantically-correct error. A
new `Error::InvalidTick { message }` variant is added; the existing
test is updated to assert against it. Python's `map_err` is extended
to forward the new variant as `PyValueError`; the Node and WASM
bindings format via `Error::to_string()` and pick the new variant up
automatically without source changes.
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kingchenc
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efcd6216c1 |
feat(bindings): expose RollingVWAP in Python, Node and WASM (R4)
The rolling-window VWAP indicator (`wickra_core::RollingVwap`) was only available in the Rust crate, even though the README's Volume-family table already advertised "VWAP (cumulative + rolling)" as a cross- language feature. Users on Python, Node or in the browser had to fall back to the cumulative `VWAP` or re-implement the rolling variant themselves. This commit closes the gap end-to-end: - Python: `wickra.RollingVWAP(period)` — same constructor / `update` / `batch` / `reset` / `is_ready` / `warmup_period` surface as `VWAP`, plus a `period` property and a typed `__repr__`. The `__init__.py` re-exports it and `__all__` lists it; the `.pyi` stub matches. - Node: `RollingVWAP(period)` — napi class with the same lifecycle, exported from `index.js` and declared in `index.d.ts`. - WASM: `RollingVWAP(period)` — wasm-bindgen class with the same `Float64Array` I/O as `VWAP`. Tests added: - Python: `test_rolling_vwap_streaming_matches_batch` — exercises `update == batch` plus the full lifecycle on the shared OHLC fixture. - Node: `RollingVWAP` row in the `candleScalar` parity table — covered by the generic streaming-vs-batch + lifecycle harness. - WASM: dedicated `wasm-bindgen-test` mirrors the Python test. The wiki page `Indicator-Vwap.md` drops the "Rust-only" caveat and gains Python / Node / WASM examples. |
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kingchenc
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c99cf54a1f |
fix(security): upgrade pyo3 and numpy to 0.28, fix RUSTSEC-2025-0020
Bumps the Python binding from pyo3 0.22 / numpy 0.22 to 0.28 / 0.28, which resolves RUSTSEC-2025-0020 — a buffer overflow in `PyString::from_object` that affected every published Python wheel. Migration: - `into_pyarray_bound(py)` → `into_pyarray(py)` (numpy 0.23 dropped the `_bound` transitional suffix; the method now returns `Bound<'py, _>` directly). - `downcast::<PyDict>` → `cast::<PyDict>` (pyo3 renamed the method on `PyAnyMethods`). - Every `#[pyclass]` declares `skip_from_py_object` to opt out of the now-deprecated automatic `FromPyObject` derive for `Clone` types. Indicators are stateful — silently extracting them by value-clone is never the intended FFI semantics. - Workspace clippy gains `unused_self = "allow"` on the python crate only: Python's `__repr__` protocol forces `&self` even for parameter- less indicators where the body does not read state. - `map_err` arms collapsed into a single `PyValueError` arm (clippy::match_same_arms). `deny.toml` no longer suppresses RUSTSEC-2025-0020; `cargo deny check` is green on advisories, bans, licenses and sources without exceptions. |
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kingchenc
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6643f7a81d |
F13b: add True Range, Chaikin Volatility, Z-Score and Linear Regression Angle
Second half of the eight indicators that fill out the new family taxonomy. - Rust core: true_range.rs (TrueRange — the raw single-bar volatility ATR averages), chaikin_volatility.rs (ChaikinVolatility — rate of change of a smoothed high-low spread), z_score.rs (ZScore — price normalised against its rolling mean and standard deviation) and linreg_angle.rs (LinRegAngle — the rolling regression slope as a degree angle). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python / Node / WASM: classes wired through all three bindings (ZScore and LinRegAngle ride the scalar macros where possible) plus .pyi stubs and __init__.py / __all__ entries. - Wiki: four new Indicator-*.md pages. The eight-family taxonomy restructure (Overview / Home / README / folder layout) lands next in F13c. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests, 25 data tests and 74 doctests green. |
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kingchenc
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e452d35a27 |
F13a: add Accelerator Oscillator, Balance of Power, Choppiness Index and Vertical Horizontal Filter
First half of the eight indicators that fill out the new family taxonomy. - Rust core: accelerator_oscillator.rs (AcceleratorOscillator — AO minus a short SMA of itself), balance_of_power.rs (BalanceOfPower — per-bar (close-open)/(high-low)), choppiness_index.rs (ChoppinessIndex — summed true range over the high-low span, log-scaled) and vertical_horizontal_filter.rs (VerticalHorizontalFilter — net move over total move). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python / Node / WASM: classes wired through all three bindings (BalanceOfPower carries an explicit open column; VHF rides the scalar macros) plus .pyi stubs and __init__.py / __all__ entries. - Wiki: four new Indicator-*.md pages. The eight-family taxonomy restructure (Overview / Home / README / folder layout) lands in F13c once F13b's four indicators are in. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 481 core tests, 25 data tests and 70 doctests green. |
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kingchenc
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2d0ee926c5 |
F12: add price transforms and rolling linear regression
- Rust core: typical_price.rs ((H+L+C)/3), median_price.rs ((H+L)/2), weighted_close.rs ((H+L+2C)/4) — stateless per-bar OHLC transforms — and linreg.rs (LinearRegression — endpoint of a rolling ordinary-least-squares fit) and linreg_slope.rs (LinRegSlope — slope of that fit). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python: PyTypicalPrice / PyMedianPrice / PyWeightedClose / PyLinearRegression / PyLinRegSlope PyO3 classes + module registration + .pyi stubs. - Node: explicit TypicalPriceNode / MedianPriceNode / WeightedCloseNode / LinearRegressionNode / LinRegSlopeNode; index.d.ts and index.js updated. - WASM: explicit WasmTypicalPrice / WasmMedianPrice / WasmWeightedClose; WasmLinearRegression / WasmLinRegSlope via the scalar macro. - Wiki: a new indicators/statistics/ folder with five Indicator-*.md pages, a new "Statistics" family in Indicators-Overview.md and Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests, 25 data tests and 66 doctests green. |
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kingchenc
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21bbd521b3 |
F11: add SuperTrend, Chandelier Exit, Chande Kroll Stop and ATR Trailing Stop
- Rust core: super_trend.rs (SuperTrend — ATR-banded trailing stop with
flip logic; SuperTrendOutput { value, direction }), chandelier_exit.rs
(Chandelier Exit — ATR stop hung off the window's highest high / lowest
low; ChandelierExitOutput { long_stop, short_stop }),
chande_kroll_stop.rs (Chande Kroll Stop — a two-stage ATR stop;
ChandeKrollStopOutput { stop_long, stop_short }), atr_trailing_stop.rs
(ATR Trailing Stop — a single ratcheting close-based stop). Each with a
full Indicator impl, runnable doctest and reference / property / warmup
/ reset / batch==streaming tests.
- Python: PySuperTrend / PyChandelierExit / PyChandeKrollStop /
PyAtrTrailingStop PyO3 classes (struct outputs as tuples and (n, 2)
arrays) + module registration + .pyi stubs.
- Node: explicit SuperTrendNode / ChandelierExitNode / ChandeKrollStopNode
/ AtrTrailingStopNode with SuperTrendValue / ChandelierExitValue /
ChandeKrollStopValue objects; index.d.ts and index.js updated.
- WASM: WasmSuperTrend / WasmChandelierExit / WasmChandeKrollStop /
WasmAtrTrailingStop.
- Wiki: Indicator-SuperTrend/ChandelierExit/ChandeKrollStop/
AtrTrailingStop.md plus rows in the "Trailing stop" table of
Indicators-Overview.md and entries in Home.md.
- Add clippy.toml with doc-valid-idents for the proper noun "LeBeau".
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 427 core tests,
25 data tests and 61 doctests green.
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kingchenc
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0b11a523a0 |
F10: add Chaikin Money Flow, Chaikin Oscillator, Force Index and Ease of Movement
- Rust core: cmf.rs (Chaikin Money Flow — summed money-flow volume over summed volume, bounded to [-1, +1]), chaikin_oscillator.rs (Chaikin Oscillator — the MACD of the ADL, EMA(ADL, fast) - EMA(ADL, slow)), force_index.rs (Elder's Force Index — EMA of price change scaled by volume), ease_of_movement.rs (Arms' Ease of Movement — SMA of distance travelled per unit of volume). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python: PyChaikinMoneyFlow / PyChaikinOscillator / PyForceIndex / PyEaseOfMovement PyO3 classes + module registration + .pyi stubs. - Node: explicit ChaikinMoneyFlowNode / ChaikinOscillatorNode / ForceIndexNode / EaseOfMovementNode; index.d.ts and index.js updated. - WASM: WasmChaikinMoneyFlow / WasmChaikinOscillator / WasmForceIndex / WasmEaseOfMovement. - Wiki: Indicator-ChaikinMoneyFlow/ChaikinOscillator/ForceIndex/ EaseOfMovement.md plus a new "Oscillators" sub-table in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 402 core tests, 25 data tests and 57 doctests green. |
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kingchenc
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81962485af |
F9: add Accumulation/Distribution Line and Volume-Price Trend
Completes the F9 family (Cumulative volume) end to end: - Rust core: adl.rs (Accumulation/Distribution Line — cumulative range-weighted volume) and vpt.rs (Volume-Price Trend — cumulative volume scaled by percentage price change). Each with a full Indicator impl, runnable doctest and reference / cumulative-property / warmup / reset / batch==streaming tests. - Python: PyAdl / PyVolumePriceTrend PyO3 classes + module registration + .pyi stubs (no parameters, like OBV/VWAP). - Node: explicit AdlNode and VolumePriceTrendNode; index.d.ts and index.js updated. - WASM: WasmAdl and WasmVolumePriceTrend. - Wiki: Indicator-Adl.md and Indicator-VolumePriceTrend.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 373 core tests, 25 data tests and 53 doctests green. |
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kingchenc
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99dd144576 |
F8: add Bollinger Bandwidth and %b
Completes the F8 family (Bands & channels) end to end: - Rust core: bollinger_bandwidth.rs ((upper - lower) / middle — the squeeze gauge) and percent_b.rs ((price - lower) / (upper - lower) — price position within the bands, unclamped). Both wrap BollingerBands and carry a full Indicator impl, runnable doctest and reference / constant-series / definition-consistency / warmup / reset / batch==streaming tests. - Python: PyBollingerBandwidth / PyPercentB PyO3 classes + module registration + .pyi stubs (defaults (20, 2.0)). - Node: explicit BollingerBandwidthNode and PercentBNode; index.d.ts and index.js updated. - WASM: WasmBollingerBandwidth / WasmPercentB via the scalar macro. - Wiki: Indicator-BollingerBandwidth.md and Indicator-PercentB.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 362 core tests, 25 data tests and 51 doctests green. |
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kingchenc
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6c58d3827c |
F7: add NATR, StdDev, Ulcer Index and Historical Volatility
Completes the F7 family (Volatility) end to end: - Rust core: natr.rs (ATR as a percentage of close), std_dev.rs (rolling population standard deviation), ulcer_index.rs (RMS of trailing-high drawdowns — downside-only risk), historical_volatility.rs (annualised sample stddev of log returns). Each with a full Indicator impl, runnable doctest and reference / constant-series / warmup / reset / batch==streaming tests. - Python: PyNatr / PyStdDev / PyUlcerIndex / PyHistoricalVolatility PyO3 classes + module registration + .pyi stubs. - Node: StdDevNode / UlcerIndexNode via the scalar macro, explicit NatrNode and HistoricalVolatilityNode; index.d.ts and index.js updated. - WASM: WasmStdDev / WasmUlcerIndex / WasmHistoricalVolatility via the scalar macro, explicit WasmNatr. - Wiki: Indicator-Natr/StdDev/UlcerIndex/HistoricalVolatility.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 350 core tests, 25 data tests and 49 doctests green. |
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kingchenc
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16c0639f0c |
F6: add Aroon Oscillator, Vortex and Mass Index
Completes the F6 family (Trend strength) end to end: - Rust core: aroon_oscillator.rs (AroonUp - AroonDown, one-line trend gauge), vortex.rs (Vortex Indicator VI+/VI- with the VortexOutput struct), mass_index.rs (Dorsey's range-expansion sum of the EMA-of-range ratio). Each with a full Indicator impl, runnable doctest and reference / saturation / warmup / reset / batch==streaming tests. - Python: PyAroonOscillator / PyVortex / PyMassIndex PyO3 classes + module registration + .pyi stubs (defaults Aroon=14, Vortex=14, MassIndex=(9,25)). - Node: explicit AroonOscillatorNode, VortexNode (with VortexValue object) and MassIndexNode; index.d.ts and index.js updated. - WASM: WasmAroonOscillator, WasmVortex, WasmMassIndex. - Wiki: Indicator-AroonOscillator/Vortex/MassIndex.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 320 core tests, 25 data tests and 45 doctests green. |
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kingchenc
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54148cad5b |
F5: add PPO, DPO and Coppock Curve price oscillators
Completes the F5 family (Price oscillators) end to end: - Rust core: ppo.rs (Percentage Price Oscillator — MACD as a percentage of the slow EMA), dpo.rs (Detrended Price Oscillator — shifted price minus its SMA), coppock.rs (Coppock Curve — WMA of two summed ROCs). Each with a full Indicator impl, runnable doctest and reference / constant-series / warmup / reset / batch==streaming / non-finite tests. - Python: PyPpo / PyDpo / PyCoppock PyO3 classes + module registration + .pyi stubs (defaults PPO=(12,26), DPO=20, Coppock=(14,11,10)). - Node: DpoNode via the scalar macro, explicit PpoNode and CoppockNode; index.d.ts and index.js updated. - WASM: WasmDpo / WasmPpo / WasmCoppock via the scalar macro. - Wiki: Indicator-Ppo/Dpo/Coppock.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 300 core tests, 25 data tests and 42 doctests green. |
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kingchenc
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e24e7726ce |
F4: add StochRSI and Ultimate Oscillator
Completes the F4 family (Stochastic oscillators) end to end: - Rust core: stoch_rsi.rs (Stochastic Oscillator applied to the RSI series, bounded [0,100]) and ultimate_oscillator.rs (Larry Williams' weighted three-timeframe buying-pressure oscillator). Each with a full Indicator impl, runnable doctest and reference / saturation / bounds / warmup / reset / batch==streaming tests. - Python: PyStochRsi / PyUltimateOscillator PyO3 classes + module registration + .pyi stubs (defaults StochRSI=(14,14), UO=(7,14,28)). - Node: explicit StochRsiNode and UltimateOscillatorNode; index.d.ts and index.js updated. - WASM: WasmStochRsi via the scalar macro, explicit WasmUltimateOscillator. - Wiki: Indicator-StochRsi.md and Indicator-UltimateOscillator.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 278 core tests, 25 data tests and 39 doctests green. |
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kingchenc
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7728151c87 |
F3: add MOM, CMO, TSI and PMO momentum indicators
Completes the F3 family (Momentum) end to end: - Rust core: mom.rs (raw price-difference momentum), cmo.rs (Chande Momentum Oscillator — unsmoothed gain/loss sum, bounded [-100,100]), tsi.rs (True Strength Index — double-EMA-smoothed momentum ratio), pmo.rs (DecisionPoint Price Momentum Oscillator — doubly-smoothed ROC with the 2/period custom smoothing). Each with a full Indicator impl, runnable doctest and reference-value / saturation / warmup / reset / batch==streaming / non-finite tests. - Python: PyMom / PyCmo / PyTsi / PyPmo PyO3 classes + module registration + .pyi stubs (defaults MOM=10, CMO=14, TSI=(25,13), PMO=(35,20)). - Node: MomNode / CmoNode via the scalar macro, explicit TsiNode and PmoNode; index.d.ts and index.js updated. - WASM: WasmMom / WasmCmo / WasmTsi / WasmPmo via the scalar macro. - Wiki: Indicator-Mom/Cmo/Tsi/Pmo.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 262 core tests, 25 data tests and 37 doctests green. |
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kingchenc
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780a176072 |
F2: add ZLEMA, T3 and VWMA advanced moving averages
Completes the F2 family (Advanced MAs) end to end: - Rust core: zlema.rs (Zero-Lag EMA over the de-lagged series 2·price − price[lag]), t3.rs (Tillson's six-EMA cascade with the volume-factor polynomial), vwma.rs (volume-weighted rolling mean with a zero-volume fallback to the unweighted mean). Each with a full Indicator impl, runnable doctest and reference-value / warmup / reset / batch==streaming / non-finite tests. - Python: PyZlema / PyT3 / PyVwma PyO3 classes + module registration + .pyi stubs (T3 defaults v=0.7). - Node: ZlemaNode via the scalar macro, explicit T3Node and VwmaNode classes; index.d.ts and index.js updated. - WASM: WasmZlema / WasmT3 via the scalar macro, explicit WasmVwma. - Wiki: Indicator-Zlema.md, Indicator-T3.md, Indicator-Vwma.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 232 core tests, 25 data tests and 33 doctests green. |
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kingchenc
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ed7324115c |
F1: wire SMMA and TRIMA through every binding and the wiki
Completes the F1 family (Simple & Weighted MAs). The Rust core for both SMMA (Wilder's RMA) and TRIMA (triangular MA) already landed; this adds the remaining Definition-of-Done steps: - Python: PySmma / PyTrima PyO3 classes + module registration + .pyi stubs. - Node: SmmaNode / TrimaNode via the scalar-indicator macro; index.d.ts and index.js updated for the two new classes. - WASM: WasmSmma / WasmTrima via the scalar-indicator macro. - Wiki: Indicator-Smma.md and Indicator-Trima.md (full pages) plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 208 core tests, 25 data tests and 31 doctests green. |
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kingchenc
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41d52ec5be |
B9: raise ValueError instead of panicking on non-contiguous arrays
Every Python batch() did prices.as_slice().expect("contiguous"), so a
non-contiguous NumPy input (e.g. a strided view) aborted with a Rust
panic instead of a catchable exception. as_slice() failures now map to a
PyValueError pointing at np.ascontiguousarray; the scalar / MACD /
Bollinger batch methods that returned a bare array were lifted to
PyResult so the error can propagate. Adds input-validation tests
(non-contiguous arrays, unequal-length candle batches, ROC/TRIX
defaults). All 60 Python tests pass against the freshly built wheel.
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kingchenc
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4a9d27fb52 |
B7: give Python ROC and TRIX constructor defaults
Every other Python momentum indicator (RSI, CCI, ...) carries a #[pyo3(signature)] default, but ROC and TRIX required an explicit period. Both now default to the TA-Lib convention (ROC period=10, TRIX period=30), and the .pyi stubs reflect the defaults. Node and WASM constructors deliberately stay explicit-only -- napi-rs and wasm-bindgen do not support default arguments, and every constructor in those bindings is uniformly explicit. |
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kingchenc
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b4613a74c8 |
B3: guard candle batch methods against unequal-length arrays
Candle batch() methods that index parallel high/low/close/volume arrays without first checking their lengths panic on a length mismatch. Adds an equal-length guard returning a clean error to the 11 affected Node methods (Stochastic, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AO, Aroon), the 10 affected WASM methods, and the 8 affected Python methods (WilliamsR, ADX, MFI, PSAR, Keltner, VWAP, AO, Aroon) -- matching the guard ATR/OBV already had. Verified in Node: mismatched arrays now throw instead of crashing. |
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kingchenc
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3be267cb03 |
Wickra 0.1.0: streaming-first technical indicators
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
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