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Author SHA1 Message Date
kingchenc 4526278fa0 release: bump 0.6.4 -> 0.6.5 (#200)
Version bump for the **v0.6.5** release shipping the **B10 Ehlers / Cycle** family (#199): 452 -> 462 indicators. Bumps workspace + Python/Node/WASM package versions, lockfiles and CHANGELOG. No code changes.
2026-06-07 04:34:32 +02:00
kingchenc 80850c81f7 Add B10 Ehlers / Cycle deepening (10 indicators) (#199)
Deepens the **Ehlers / Cycle (DSP)** family (B10) with ten indicators (452 -> 462):

- **HighpassFilter**, **Reflex**, **Trendflex**, **CorrelationTrendIndicator**, **AdaptiveRsi**, **UniversalOscillator** — scalar (f64) Ehlers filters/oscillators.
- **AdaptiveCci** — efficiency-ratio-adaptive CCI on typical price (Candle input).
- **BandpassFilter**, **EvenBetterSinewave**, **AutocorrelationPeriodogram** — multi-arg scalar (hand-written bindings; the wasm variadic scalar macro covers wasm).

Verified locally: 3755 core lib + 420 doc tests, clippy clean, 537 node tests, 881 pytest, counter 462.
2026-06-07 04:25:16 +02:00
kingchenc 707f29e8e4 release: bump 0.6.3 -> 0.6.4 (#198)
Version bump for the **v0.6.4** release shipping the **B9 Price Statistics** family (#197): 447 -> 452 indicators. Bumps workspace + Python/Node/WASM package versions, lockfiles and CHANGELOG. No code changes.
2026-06-07 03:20:19 +02:00
kingchenc 389200f855 Add B9 Price Statistics deepening (5 indicators) (#197)
Deepens the **Price Statistics** family (B9) with five rolling-statistics indicators (447 -> 452):

- **ShannonEntropy** — Shannon entropy of a binned rolling value distribution.
- **SampleEntropy** — Richman-Moorman sample entropy (regularity/complexity of a window).
- **KendallTau** — Kendall rank correlation (tau-b) over paired observations (pairwise; distinct from Pearson/Spearman).
- **JarqueBera** — Jarque-Bera normality test statistic over a rolling window.
- **RollingMinMaxScaler** — maps the latest value to 0..1 over a rolling window.

All scalar f64 input except KendallTau (pairwise). Multi-arg scalars (Shannon/Sample entropy) use hand-written Python/Node bindings + the variadic wasm macro; KendallTau uses the pair macros. Verified locally: 3668 core lib + 410 doc tests, clippy clean, 527 node tests, 871 pytest, counter 452.
2026-06-07 03:08:53 +02:00
kingchenc 81406e7a1b release: bump 0.6.2 -> 0.6.3 (#196)
Version bump for the **v0.6.3** release shipping the **B8 Volume** family (#195): 440 -> 447 indicators. Bumps workspace + Python/Node/WASM package versions, lockfiles and CHANGELOG. No code changes.
2026-06-07 02:39:49 +02:00
kingchenc c78b84e186 Add B8 Volume family deepening (7 indicators) (#195)
Deepens the **Volume** family (B8) with seven indicators (440 -> 447):

- **VolumeRsi** — Wilder RSI computed on signed volume flow.
- **WilliamsAd** — Williams Accumulation/Distribution cumulative line (distinct from Chaikin A/D).
- **TwiggsMoneyFlow** — true-range volume accumulation with Wilder smoothing (distinct from CMF).
- **TradeVolumeIndex** — tick-direction volume accumulation past a min-tick threshold (distinct from TSV).
- **IntradayIntensity** — volume weighted by close position within the bar range.
- **BetterVolume** — VSA volume-vs-spread effort/result classifier.
- **VolumeWeightedMacd** — MACD computed on VWMA with signal line and histogram (struct output).

("Up/Down Volume Ratio" already ships from A2.) All Candle input; the six scalar stops emit f64, VolumeWeightedMacd a {macd, signal, histogram} struct. Hand-written Python/Node/WASM bindings for the volume signature. Verified locally: 3620 core lib + 405 doc tests, clippy clean, 522 node tests, 865 pytest, counter 447.
2026-06-07 02:30:56 +02:00
kingchenc fc6f3d80c2 release: bump 0.6.1 -> 0.6.2 (#194)
Version bump for the **v0.6.2** release shipping the **B7 Trailing Stops** family (#193): 434 -> 440 indicators.

Bumps workspace + Python/Node/WASM package versions, lockfiles and CHANGELOG (cuts the `[0.6.2]` section). No code changes.
2026-06-07 01:45:23 +02:00
kingchenc 2991ba411d Add B7 Trailing Stops family (6 indicators) (#193)
Adds the **Trailing Stops** family deepening (B7), six new indicators (434 -> 440):

- **KaseDevStop** — Cynthia Kase's volatility stop on the standard deviation of the two-bar true range.
- **ElderSafeZone** — Alexander Elder's stop offset by a multiple of average market noise.
- **AtrRatchet** — Kaufman ATR ratchet that tightens its multiple by a per-bar increment.
- **Nrtr** — Nick Rypock Trailing Reverse (percentage band).
- **TimeBasedStop** — exits after a fixed number of bars (scalar fraction of elapsed life).
- **ModifiedMaStop** — moving-average based trailing stop.

("Wilder Volatility System" is intentionally skipped — it overlaps the existing VoltyStop/Psar/SarExt.)

Each takes Candle input; the five band/structure stops emit a {value, direction} struct, TimeBasedStop a scalar. Wired across core, Python/Node/WASM bindings, fuzz target and tests. Verified locally: 3560 core lib + 398 doc tests, clippy clean, 515 node tests, 852 pytest, counter 440.
2026-06-07 01:32:15 +02:00
kingchenc 83e34c6f71 release: bump 0.6.0 -> 0.6.1 (#192)
Version bump for the v0.6.1 release shipping the B6 Bands & Channels family (#191): 429 -> 434 indicators.
2026-06-07 00:13:58 +02:00
kingchenc 67feec598a feat(indicators): add B6 Bands & Channels family (429 -> 434) (#191)
Adds the **B6 Bands & Channels** batch — five band/channel indicators, taking the catalogue from 429 to 434.

| Indicator | Input → Output | Summary |
|-----------|----------------|---------|
| `ProjectionBands` | `Candle` → `{upper,middle,lower}` | Widner forward-projected high/low regression envelope |
| `ProjectionOscillator` | `Candle` → `f64` | Close position inside the projection bands, scaled 0..100 |
| `QuartileBands` | `f64` → `{upper,middle,lower}` | Rolling 25th/50th/75th-percentile (Q1/median/Q3) envelope |
| `BomarBands` | `f64` → `{upper,middle,lower}` | Adaptive percentage bands containing a target coverage fraction of recent closes |
| `MedianChannel` | `f64` → `{upper,middle,lower}` | Robust median ± multiplier·MAD envelope |

All five are distinct from existing indicators (verified against the core: `LinRegChannel`, `StandardErrorBands`, `Donchian`, `RollingQuantile`, `HurstChannel`). SKIPped from the roadmap: Price Channel (= `Donchian`) and Moving-Average Channel (≈ `MaEnvelope`/`Keltner`).

Each ships:
- Core indicator with per-branch unit tests (Codecov-strict 100%).
- python / node / wasm bindings (struct outputs are hand-written; `ProjectionOscillator` uses the generated candle→f64 path).
- Fuzz drives, python (`MULTI`/`SCALAR_MULTI`/`CANDLE_SCALAR`) + node test registries, README + CHANGELOG counter bump to 434.

Verified locally: `cargo fmt`, `clippy --workspace --all-targets --all-features -D warnings` (clean), `wickra-core` 3511 lib + 392 doc tests, node 509 tests, pytest 840.
2026-06-07 00:03:02 +02:00
kingchenc 3dfbc415c5 release: bump 0.5.9 -> 0.6.0 (#190)
Version bump for the **v0.6.0** release (ships the B5 Volatility & Bands batch, #189 — 423 -> 429 indicators).

Bumps version strings across Cargo workspace, pyproject, node package.json + 6 platform packages, both package-lock.json files, and Cargo.lock; CHANGELOG `[Unreleased]` -> `[0.6.0]`. No code changes.

Versioning note: patch never reaches two digits — `0.5.9` rolls to the next minor `0.6.0` (not 0.5.10).
2026-06-06 22:48:56 +02:00
kingchenc 6b8c6a0e7f B5 volatility & bands batch (423 -> 429) (#189)
Adds six **Volatility & Bands** indicators (Part B5 of the expansion roadmap), 423 → 429.

| Indicator | Input → Output | Summary |
|-----------|----------------|---------|
| `EwmaVolatility` | `f64` → `f64` | RiskMetrics exponentially-weighted volatility (λ decay) |
| `Garch11` | `f64` → `f64` | GARCH(1,1) conditional volatility with a long-run-variance anchor |
| `BipowerVariation` | `f64` → `f64` | jump-robust realized bipower variation (π/2 · Σ\|rₜ\|\|rₜ₋₁\|) |
| `VolatilityRatio` | `Candle` → `f64` | Schwager's true range over the EMA of prior true ranges (>2 = wide-ranging day) |
| `VolatilityCone` | `Candle` → `VolatilityConeOutput` | current realized volatility within its min/median/max envelope + percentile |
| `VolatilityOfVolatility` | `f64` → `f64` | sample stddev of a rolling realized-volatility series |

### Notes
- Two B5 roadmap items were dropped as duplicates/by-construction: `RealizedVolatility` already ships (v0.5.4); `Downside Semi-Deviation` is internal to Sortino. `Bipower Variation` confirmed distinct from `JumpIndicator` (a ±1 flag, not a variance measure).
- `VolatilityRatio` implements the widely-charted EMA-of-true-range convention (denominator excludes the current bar so the 2.0 threshold means "twice typical"), distinct from the existing pairwise `variance_ratio`.
- `Garch11` mean-reverts to `ω/(1−β)` on a flat series (does not decay to 0 like EWMA) — pinned by a dedicated test.

### Coverage / verification
- Full core + Python/Node/WASM bindings, fuzz drivers (scalar + candle), registries, CHANGELOG, README + docs counter sync.
- 100% unit-test coverage per indicator (every branch).
- Green locally: `cargo clippy --workspace --all-targets --all-features -D warnings`, core lib (3479) + doc (387), node (504), python (830).

Deep-dive docs for all six are staged for `wickra-docs` and pushed after release (gated).
2026-06-06 22:38:34 +02:00
kingchenc db186b18d3 docs(readme): star-history chart + ci(sync-about): sync docs config count (#188)
Add a dark-mode star-history chart under the README footer thank-you line (all existing badges kept), and make sync-about also patch the indicator count into wickra-docs .vitepress/config.ts.
2026-06-06 21:32:35 +02:00
kingchenc 654da5722f release: bump 0.5.8 -> 0.5.9 (#187)
Patch release: streaming/batch perf (SMA, Bollinger, RSI, EMA, ATR; outputs unchanged), cross-library benchmark harness, honest tiered README. No new indicators, no API changes.
2026-06-06 21:09:05 +02:00
kingchenc aacb9280f1 Honest tiered cross-library benchmark + streaming/batch perf (#186)
## Summary

An honest, tiered cross-library benchmark — and the optimization pass it triggered.

### Performance (wickra-core, outputs unchanged)
Profiling against the other Rust TA crates exposed real inefficiencies. Each
benchmarked indicator is now **5–79% faster** in both streaming and batch:

- **SMA, Bollinger**: flat `Box<[f64]>` ring buffers replace `VecDeque` (−69…79%).
- **RSI**: `100·ag/(ag+al)` collapses three divisions into one; Wilder smoothing
  hoists `1/period` out of the hot path (−46%).
- **ATR**: reciprocal hoisted (−42%).
- **EMA/RSI/ATR**: per-tick `Option<f64>` hot state → bare `f64` + ready flag.

Net result vs `kand`: Wickra now wins **RSI, Bollinger and ATR** (streaming), and
ties `ta-rs` on SMA — up from losing every indicator 1.5–6× before.

### Benchmark harness
New `crates/wickra-bench` (publish=false): a Criterion benchmark comparing Wickra
against `kand`, `ta-rs` and `yata` on an identical BTCUSDT candle series, in
streaming and batch modes. Peer APIs were verified against their source, not
guessed. Wired into the nightly `cross-library-bench` workflow as a separate job.

### Honest README
The benchmark section is rewritten into three layered tables (Rust core vs Rust
crates; Python vs the Python ecosystem) that **show the losses as well as the
wins**. The "only library that combines…" claim is gone; the new framing is
breadth + multi-language reach + the deliberate safety trade-off that costs raw
speed. Added an origin/why-slower rationale and a star CTA.

### Python benchmark
Added `tulipy` runners and expanded per-tick streaming coverage to SMA/EMA/RSI/
MACD/Bollinger. `bench.in`/`bench.txt` now lock `TA-Lib` + `tulipy` (hash-pinned);
`pandas-ta` stays out (it requires Python ≥ 3.12, the bench runs on 3.11).

### Notes
- TA-Lib/tulipy numbers in the README Python table are marked ⧗ — they are
  produced by the CI Linux job (C extensions don't build cleanly on every
  desktop), not measured locally.
- The matching `wickra-docs` prose update is committed separately and will be
  pushed with the release, per the docs-don't-lead-the-registries rule.

Verified locally: `cargo fmt`, `cargo test --workspace --all-features` (3413 core
+ bindings), `cargo clippy --workspace --all-targets --all-features -D warnings`,
Node build + 498 tests, and pytest all green.
2026-06-06 20:57:31 +02:00
kingchenc d2bc000892 release: bump 0.5.7 -> 0.5.8 (#185)
Version bump for the B4 price oscillators release (#184): `TsfOscillator`, `MacdHistogram`, `PpoHistogram` — 420 → 423 indicators.

Bumps workspace + bindings (Cargo.toml/lock, pyproject, node package.json + 6 platform manifests + lockfiles) and rolls CHANGELOG `[Unreleased]` into `[0.5.8]`.
2026-06-04 19:47:22 +02:00
kingchenc 1f4bf9e3a6 feat(core): B4 price oscillators (TsfOscillator, MacdHistogram, PpoHistogram) (#184)
Adds three **Price Oscillators** family indicators (420 → 423).

## Indicators

- **TsfOscillator** — `100·(close − TSF)/close`, the percentage gap of the close to the **one-bar-ahead** time-series forecast. Close-relative companion to `Cfo`, which measures the same gap against the regression value at the *current* bar; the two differ by exactly the slope term `100·b/close`.
- **MacdHistogram** — the standalone `macd − signal` bar of MACD exposed as a plain `f64` series.
- **PpoHistogram** — the Percentage Price Oscillator with its 9-period signal EMA and the resulting scale-free, zero-centered histogram (PPO itself only emits the line).

All three are scalar `f64` indicators wrapping existing, already-tested building blocks (`MacdIndicator`, `Ppo` + `Ema`, `Tsf`).

## Scope notes (VORAB-CHECK)

The B4 roadmap listed six items; three were dropped to avoid duplicates:
- *Forecast Oscillator* already ships as `Cfo`.
- *Derivative Oscillator* already ships (`DerivativeOscillator`, B2).
- *Detrended Synthetic Price* deferred — no citable formula distinct from the existing `Apo`/`Dpo`.

## Touchpoints

Core (`tsf_oscillator.rs`, `macd_histogram.rs`, `ppo_histogram.rs`) with full per-branch unit tests, `mod.rs`/`lib.rs`, python/node/wasm bindings (wasm via typed-arg macro, python/node hand-written for the multi-arg histograms), fuzz drivers, python reference + streaming-vs-batch tests, node factories, README family row + counter, CHANGELOG.

Local verify: `cargo test --workspace` green, `clippy -D warnings` clean, node 498 tests, full python suite green.
2026-06-04 19:36:43 +02:00
kingchenc d36d514f56 fix(core): re-export GatorOscillatorOutput & KasePermissionStochasticOutput (#183)
The two market-profile struct-output indicators from the B3 batch (`GatorOscillator`, `KasePermissionStochastic`) exposed their public output structs from their own modules but did not re-export them from `indicators` / the crate root — unlike every other struct-output indicator (`ElderRayOutput`, `AlligatorOutput`, `QqeOutput`, …).

That left `wickra::GatorOscillatorOutput` / `wickra::KasePermissionStochasticOutput` un-nameable, so Rust callers could not annotate or store the `update` result by type. Surfaced by the wickra-docs Rust-snippet-compile check on the B3 deep-dives.

Re-export both alongside their structs. Both names end in `Output`, so the indicator counter strips them — catalog count stays **420**.
2026-06-04 18:43:26 +02:00
kingchenc 6e0464930e release: bump 0.5.6 -> 0.5.7 (#182)
Version bump 0.5.6 → 0.5.7 for the **B3 — Trend & Directional** batch (#181):
seven new indicators (`Qstick`, `TtmTrend`, `TrendStrengthIndex`,
`PolarizedFractalEfficiency`, `WavePm`, `GatorOscillator`,
`KasePermissionStochastic`), catalog 413 → 420.

Bumps: workspace `Cargo.toml` + `Cargo.lock`, `bindings/python/pyproject.toml`,
`bindings/node/package.json` + the six `npm/*/package.json` platform manifests,
both `package-lock.json` files, and the `CHANGELOG.md` `[Unreleased]` → `[0.5.7]`
roll with compare URLs.
2026-06-04 18:06:57 +02:00
kingchenc 13bc801f89 feat(indicators): B3 Trend & Directional batch (413 -> 420) (#181)
Adds the **B3 — Trend & Directional** batch: seven new indicators, taking the
catalog from 413 to 420 (Trend & Directional family).

| Indicator | Input → Output | Summary |
|-----------|----------------|---------|
| `Qstick` | candle → f64 | Chande's SMA of the candle body (close − open) |
| `TtmTrend` | candle → f64 (±1) | John Carter close-vs-median-SMA trend filter |
| `TrendStrengthIndex` | f64 → f64 | signed r² of an OLS regression of price vs time |
| `PolarizedFractalEfficiency` | f64 → f64 | Hannula directional trend efficiency |
| `WavePm` | f64 → f64 | Kase variance-normalised peak-momentum statistic (reconstruction) |
| `GatorOscillator` | candle → struct | Bill Williams Alligator convergence/divergence histogram |
| `KasePermissionStochastic` | candle → struct | double-smoothed stochastic permission filter |

Note: the roadmap's "Directional Indicator +DI/−DI" item is already covered by
the existing standalone `PlusDi` / `MinusDi` / `Dx`, so it is intentionally not
re-added.

All touchpoints wired: core (every-branch unit tests), Python/Node/WASM
bindings, fuzz drivers, Python test registries + reference tests, Node
factories, README/CHANGELOG counters.

Local verify: `cargo test -p wickra-core` (lib 3389 + doc 378), `cargo clippy
--workspace --all-targets --all-features -- -D warnings`, node build + 495
tests, maturin + 815 pytest, counter 420 == 420.
2026-06-04 17:57:24 +02:00
kingchenc ac8f6acf08 release: bump 0.5.5 -> 0.5.6 (#180)
Release bump `0.5.5 → 0.5.6` for the Momentum Oscillators family deepening
(#179): ten new indicators (DisparityIndex, FisherRsi, Rmi, DerivativeOscillator,
Rsx, DynamicMomentumIndex, IntradayMomentumIndex, StochasticCci, ElderRay, Qqe),
counter now 413.

Version strings only across all manifests + lockfiles; CHANGELOG `[Unreleased]`
rolled to `[0.5.6] - 2026-06-04` with the new compare links.
2026-06-04 15:35:41 +02:00
kingchenc 4f81222aed Deepen Momentum Oscillators family with ten additions (#179)
Deepens the **Momentum Oscillators** family with ten widely-used oscillators
(403 → 413 indicators), the second batch of Part B (family deepening).

| Indicator | Binding | Input → Output |
|-----------|---------|----------------|
| `DisparityIndex` | `DisparityIndex` | scalar → scalar |
| `FisherRsi` | `FisherRSI` | scalar → scalar |
| `Rmi` | `RMI` | scalar (period, momentum) → scalar |
| `DerivativeOscillator` | `DerivativeOscillator` | scalar (4 periods) → scalar |
| `Rsx` | `RSX` | scalar → scalar |
| `DynamicMomentumIndex` | `DynamicMomentumIndex` | scalar → scalar |
| `IntradayMomentumIndex` | `IMI` | candle (open+close) → scalar |
| `StochasticCci` | `StochasticCCI` | candle → scalar |
| `ElderRay` | `ElderRay` | candle → struct (bull/bear) |
| `Qqe` | `QQE` | scalar → struct (rsi_ma/trailing) |

LSMA was dropped from the planned set: it already ships as `LinearRegression`.

The single-period scalars use generated macro bindings; `Rmi` /
`DerivativeOscillator` use hand node/python bindings with the typed wasm macro;
`ElderRay`/`Qqe` use custom struct bindings; `IntradayMomentumIndex` uses custom
candle bindings carrying the open. Full coverage: core modules with per-branch
unit tests, mod/lib catalogue, FAMILIES + assert, README + docs counters,
CHANGELOG, all three bindings (regenerated `index.d.ts`/`index.js`), fuzz
drivers, and the python/node test registries.

Local verification: `cargo test -p wickra-core` (lib 3335 + doc 371),
`cargo clippy --workspace --all-targets --all-features -D warnings` clean,
node `npm run build && npm test` (488), python `pytest` (802).
2026-06-04 15:26:17 +02:00
kingchenc 0d2acad28d release: bump 0.5.4 -> 0.5.5 (#178)
Release bump `0.5.4 → 0.5.5` for the Moving Averages family deepening
(#177): seven new indicators (`SineWeightedMa`, `GeometricMa`, `Ehma`,
`MedianMa`, `AdaptiveLaguerreFilter`, `GeneralizedDema`, `HoltWinters`),
counter now 403.

Version strings only across all manifests + lockfiles; CHANGELOG `[Unreleased]`
rolled to `[0.5.5] - 2026-06-04` with the new compare links.
2026-06-04 13:55:26 +02:00
kingchenc b228a70d7d Deepen Moving Averages family with seven additions (#177)
Deepens the **Moving Averages** family with seven widely-used variants
(396 → 403 indicators), the first batch of Part B (family deepening).

All are scalar `f64 → f64`:

| Indicator | Binding | Notes |
|-----------|---------|-------|
| `SineWeightedMa` | `SWMA` | symmetric half-cycle sine-weighted window |
| `GeometricMa` | `GMA` | rolling geometric mean (log-space average) |
| `Ehma` | `EHMA` | exponential Hull MA (Hull construction over EMAs) |
| `MedianMa` | `MedianMA` | rolling median, robust to single outliers |
| `AdaptiveLaguerreFilter` | `AdaptiveLaguerre` | Ehlers' adaptive Laguerre filter (median-of-normalised-error γ) |
| `GeneralizedDema` | `GD` | Tillson's volume-factor double EMA; `v=1` is DEMA, `v=0` is EMA |
| `HoltWinters` | `HoltWinters` | Holt's linear double exponential smoothing (level + trend) |

LSMA was dropped from the planned set: it already ships as `LinearRegression`
(TA-Lib `LINEARREG`, the rolling least-squares endpoint).

The five single-period filters use the generated scalar macro bindings;
`GeneralizedDema` (period, v) and `HoltWinters` (alpha, beta) use hand-written
node/python bindings with the typed wasm macro (precedent `T3` / `Alma`).

Full coverage: core modules with per-branch unit tests (100% intent), mod/lib
catalogue, FAMILIES group + assert, README + docs counters, CHANGELOG, all three
bindings (regenerated `index.d.ts` / `index.js`), fuzz drivers, and the
python/node test registries.

Local verification: `cargo test -p wickra-core` (lib 3255 + doc 361),
`cargo clippy --workspace --all-targets --all-features -D warnings` clean,
node `npm run build && npm test` (478), python `pytest` (791).
2026-06-04 13:44:51 +02:00
kingchenc 8dc7158912 release: bump 0.5.3 -> 0.5.4 (#176)
Version bump 0.5.3 -> 0.5.4 for the release that ships the 19 external-feature-coverage indicators (#175, 377 -> 396).

Bumped: Cargo workspace + wickra-core dep, Cargo.lock (cargo build), pyproject.toml, node package.json (+6 optionalDependencies), 6 npm platform package.json, both package-lock.json, CHANGELOG ([Unreleased] -> [0.5.4]).

fmt/test/clippy green locally.
2026-06-04 12:14:29 +02:00
kingchenc fcb221ec03 feat: add 19 indicators for external feature-extractor coverage (377 -> 396) (#175)
Adds 19 streaming indicators so an external trading-bot feature extractor can replace its hand-built features with native, batch/streaming-equivalent ones. Each is a real gap (verified against the existing catalogue), production-only, with full Python/Node/WASM bindings, fuzz drivers, and tests. Five commits, one per family group; counter 377 -> 396.

## What's added

**Price Statistics (6)** — `LogReturn`, `RealizedVolatility` (raw quadratic variation, the un-annualised counterpart to `HistoricalVolatility`), `RollingQuantile`, `RollingIqr`, `RollingPercentileRank`, `SpreadAr1Coefficient` (pairwise AR(1) rho of the spread; complements `OuHalfLife`).

**Price Action (4)** — `CloseVsOpen`, `BodySizePct`, `WickRatio`, `HighLowRange` (stateless per-bar OHLC transforms).

**Regime / Trend / Jump labels (3)** — `TrendLabel` (sign of the rolling OLS slope), `JumpIndicator` (return outliers vs trailing volatility, measured as deviation from the trailing mean so steady drift is not flagged), `RegimeLabel` (volatility-quantile regime split).

**Risk / Performance (2)** — `WinRate`, `Expectancy` (R-multiple).

**Microstructure (4)** — `OrderFlowImbalance` (Cont-Kukanov-Stoikov OFI), `Vpin`, `AmihudIlliquidity`, `RollMeasure`. These reuse the existing `OrderBook` / `Trade` inputs (no new input type).

## Intentionally NOT added (already present, would be duplicates)

- **Population skew / kurtosis** — `skewness.rs` / `kurtosis.rs` are already population moments (divisor n).
- **Hurst R/S** — `hurst_exponent.rs` already uses rescaled-range (R/S) analysis.
- **Queue Imbalance** — exactly `OrderBookImbalanceTop1` ((bidSize - askSize) / (bidSize + askSize)).

## Verification

`cargo test -p wickra-core` (lib 3187 + doc 354), `cargo clippy --workspace --all-targets --all-features -D warnings` clean, node `npm run build && npm test` (471), python `pytest` (784). Counter consistent across `mod.rs`, lib block, README, and docs/README at 396.
2026-06-04 12:00:35 +02:00
kingchenc a93af60796 release: bump 0.5.2 -> 0.5.3 (#174)
Version bump publishing the **Fibonacci** family (10 tools across A5a + A5b, catalogue 377 indicators / twenty-four families):

`FibRetracement`, `FibExtension`, `FibProjection`, `AutoFib`, `GoldenPocket`, `FibConfluence`, `FibFan`, `FibArcs`, `FibChannel`, `FibTimeZones`.

Version strings + lockfiles only (Cargo.toml, pyproject.toml, package.json + 6 npm platform manifests, both package-lock.json, Cargo.lock); CHANGELOG `[0.5.3]` section + compare URLs.
2026-06-04 01:25:31 +02:00
kingchenc 5a1d607807 feat(indicators): A5b Fibonacci tools (geometric) (#172)
Completes the **Fibonacci** family with the four geometric/time tools (catalogue 373 -> 377). All extend the internal `pattern_swing` ZigZag tracker with a per-pivot bar index and a current-bar counter (additive — the chart/harmonic detectors are unaffected), and emit `Candle -> struct` outputs via custom Python/Node/WASM bindings.

| Tool | Output |
|------|--------|
| `FibFan` | three trendlines fanning from a swing start through its 38.2/50/61.8% retracement levels, extended to the current bar |
| `FibArcs` | semicircular retracement levels centred on the swing end, normalised by the leg's bar-width (chart-scale-free) |
| `FibChannel` | a sloped base trendline plus parallel lines at Fibonacci multiples of the channel width |
| `FibTimeZones` | markers at Fibonacci bar-distances (1/2/3/5/8/...) from the latest swing pivot |

The geometric tools are novel as streaming indicators; each normalises its geometry to the swing leg's bar-width so the output is chart-scale-free. Formulas are documented in each module and deep-dive.

Fully wired: core (100% unit-tested branches incl. the new `pattern_swing` bar tracking), Python/Node/WASM struct bindings, fuzz, reference + streaming-vs-batch tests.

Verification: `cargo test --workspace` green, clippy `-D warnings` clean, node 454 tests, python 768 tests.
2026-06-04 01:12:09 +02:00
kingchenc ea9da12d86 docs(governance): document continuity and succession plan (#173)
Add a Continuity and succession section to GOVERNANCE.md (trusted-contact emergency access to credentials enabling continuity within a week). Closes OpenSSF Silver access_continuity.
2026-06-04 01:09:52 +02:00
kingchenc 716eb40206 feat(indicators): A5a Fibonacci tools (price-level) (#171)
Adds the six price-level Fibonacci tools as a new **Fibonacci** family (catalogue 367 -> 373, twenty-four families). All build on the internal `pattern_swing` ZigZag tracker, are parameter-free (baked 5% swing threshold), and emit `Candle -> struct` outputs via custom Python/Node/WASM bindings.

| Tool | Output |
|------|--------|
| `FibRetracement` | seven levels (0/23.6/38.2/50/61.8/78.6/100%) of the last swing leg |
| `FibExtension` | five extension ratios (127.2/141.4/161.8/200/261.8%) projected beyond the leg |
| `FibProjection` | A-B-C measured-move target zone (61.8/100/161.8/261.8%) |
| `AutoFib` | retracement anchored on the dominant (largest-magnitude) recent leg |
| `GoldenPocket` | the 0.618-0.65 optimal-trade-entry band (low/mid/high) |
| `FibConfluence` | densest cluster of retracement levels across recent legs (price + strength) |

Fully wired: core (100% unit-tested branches), Python/Node/WASM struct bindings, fuzz driver, reference + streaming-vs-batch tests, README/docs counter. The four geometric/time tools (Fan, Arcs, Channel, Time Zones) follow in A5b.

Verification: `cargo test --workspace` green, clippy `-D warnings` clean, node 450 tests, python 760 tests.
2026-06-04 00:47:00 +02:00
kingchenc 8115d3b33d release: bump 0.5.1 -> 0.5.2 (#170)
Version bump to release the A4 Chart Patterns (#166) and Harmonic Patterns (#169) families (catalogue 351 -> 367).
2026-06-03 23:39:10 +02:00
kingchenc 4250ed99f4 feat(patterns): add the Harmonic Patterns family (8 XABCD detectors) (#169)
## Summary

Adds a new **Harmonic Patterns** indicator family (counter 359 → 367, families 22 → 23) — the second half of the A4 roadmap item, following the Chart Patterns family in #166.

Eight Fibonacci-ratio detectors built on the shared swing-pivot tracker (`indicators::pattern_swing`) plus two new helpers there — `xabcd` (reads the last five pivots as X-A-B-C-D) and `ratios_in` (checks a list of `(value, low, high)` Fibonacci windows in one expression, no multi-line `&&` coverage gaps). Each consumes candles and emits the uniform pattern sign convention — `+1.0` bullish (terminal point D a swing low), `-1.0` bearish (D a swing high), `0.0` otherwise, never `None`. Parameter-free, with the Fibonacci windows documented as constants per detector.

## Detectors

| Indicator | Defining ratio |
|-----------|----------------|
| `Abcd` | four-point AB=CD (BC retraces AB, CD ≈ AB) |
| `Gartley` | AD/XA ≈ 0.786 |
| `Butterfly` | AD/XA ∈ 1.27–1.618 (extended D) |
| `Bat` | AD/XA ≈ 0.886, shallow B |
| `Crab` | AD/XA ≈ 1.618 (deepest D) |
| `Shark` | expansion AB, AD/XA 0.886–1.13 |
| `Cypher` | BC on XA, CD/XC ≈ 0.786 |
| `ThreeDrives` | two symmetric extension drives |

## Touchpoints

Core modules + `FAMILIES` group/assert, crate root re-exports, Python/Node/WASM bindings via the candle-pattern macros (Node `index.d.ts`/`index.js` regenerated), the candle fuzz target (`// --- Harmonic Patterns ---` section), Python reference + `CANDLE_SCALAR` registry tests and the Node candle-scalar factory, README catalogue counter + banner cache-buster + family table row + family-count word, `docs/README.md` counter, and the changelog.

## Verification

- `cargo test -p wickra-core --lib` — 2966 passed
- `cargo test -p wickra-core --doc` — 335 passed
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean
- Node `npm run build && npm test` — 444 passed
- Python `maturin develop --release` + `pytest` — 748 passed

Every detector branch is unit-tested, including a bullish and a bearish match per pattern to cover both output arms, plus an out-of-ratio non-match. Fibonacci windows use standard harmonic-trading ranges with documented tolerance bands.
2026-06-03 23:24:25 +02:00
kingchenc 995f119010 feat(patterns): add the Chart Patterns family (8 swing-based detectors) (#166)
## Summary

Adds a new **Chart Patterns** indicator family (counter 351 → 359, families 21 → 22), the first half of the A4 roadmap item (the harmonic patterns follow in a second PR).

All eight detectors are built on a shared, non-repainting swing-pivot tracker — the internal, **uncounted** `indicators::pattern_swing` module (declared `pub(crate) mod`, re-exported nowhere). Each consumes candles and emits the uniform pattern sign convention already used by the candlestick family — `+1.0` bullish / `-1.0` bearish / `0.0` otherwise, never `None`. They are parameter-free, baking the swing threshold (5%) and level tolerance (3%) in as documented constants, mirroring how candlestick patterns bake in their geometric thresholds.

## Detectors

| Indicator | Signal |
|-----------|--------|
| `DoubleTopBottom` | twin-peak / twin-trough reversal |
| `TripleTopBottom` | three matching extremes (stronger reversal) |
| `HeadAndShoulders` | central head + matching shoulders + flat neckline (and inverse) |
| `Triangle` | ascending (+1) / descending (-1) / symmetrical |
| `Wedge` | rising wedge (-1) / falling wedge (+1) |
| `FlagPennant` | shallow consolidation against a pole → continuation |
| `RectangleRange` | flat support/resistance mean-reversion |
| `CupAndHandle` | rounded base + shallow handle (and inverse) |

## Touchpoints

Core modules + `FAMILIES` group and assert, crate root re-exports, Python/Node/WASM bindings via the candle-pattern macros (Node `index.d.ts`/`index.js` regenerated), the candle fuzz target, Python reference + `CANDLE_SCALAR` registry tests and the Node candle-scalar factory, README catalogue counter + banner cache-buster + family table row + family-count word, `docs/README.md` counter, and the changelog.

## Verification

- `cargo test -p wickra-core --lib` — 2915 passed
- `cargo test -p wickra-core --doc` — 335 passed
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean
- Node `npm run build && npm test` — 436 passed
- Python `maturin develop --release` + `pytest` — 732 passed

Every detector branch is unit-tested; multi-condition predicates were flattened to single-line precomputed booleans to keep patch coverage at 100%.
2026-06-03 22:55:36 +02:00
kingchenc 05d2e5dc61 ci(scorecard): pass a read-only PAT for the Branch-Protection check (#168)
Pass a read-only fine-grained PAT (SCORECARD_TOKEN) as repo_token so the OpenSSF Scorecard Branch-Protection check can read classic branch-protection rules instead of failing with an internal error.
2026-06-03 22:51:28 +02:00
kingchenc 404bcb040c docs: add threat model and security policies (#167)
Add THREAT_MODEL.md and SECURITY.md sections: secrets management, release verification, end-of-support, dependency/code-scanning remediation policy, and a VEX statement. Closes OSPS Baseline L3 documentation gaps (SA-03.02, BR-07.02, DO-03.01/03.02/05.01, VM-04.02/05.01/05.02/06.01). Additive only.
2026-06-03 22:40:56 +02:00
kingchenc 00ce899cc3 docs: add public ROADMAP (#165)
Add a public ROADMAP.md describing project direction and pointing to the issue tracker as the authoritative view. Closes the OpenSSF Silver documentation_roadmap gap.
2026-06-03 22:19:24 +02:00
kingchenc b6ead740e8 docs: add governance, support, DCO and security assurance case (#164)
Add GOVERNANCE.md, MAINTAINERS.md, SUPPORT.md, DCO; add a DCO sign-off requirement to CONTRIBUTING.md and a security assurance case to SECURITY.md. Closes OpenSSF Silver / OSPS Baseline documentation gaps. Additive only.
2026-06-03 22:16:03 +02:00
kingchenc 755f4aa0f6 docs: add OpenSSF Best Practices badge to README (#163)
Adds the OpenSSF Best Practices passing badge next to the OpenSSF Scorecard badge in the README header.

The project earned a passing badge: https://www.bestpractices.dev/projects/13094
2026-06-03 21:44:34 +02:00
kingchenc 4d602df8a3 release: bump 0.5.0 -> 0.5.1 (#162)
Version bump **0.5.0 → 0.5.1** for the Seasonality & Session family release (12 indicators, PR #161).

Bumped: `Cargo.toml` (workspace version + `wickra-core` dep), `Cargo.lock` (via `cargo build`), `bindings/python/pyproject.toml`, `bindings/node/package.json` (+ 6 `optionalDependencies`), the 6 `bindings/node/npm/<platform>/package.json`, both `package-lock.json` files, and `CHANGELOG.md` (`[Unreleased]` → `[0.5.1]` + compare URLs).

No code changes — version strings only.
2026-06-03 20:55:13 +02:00
kingchenc 3ab2d6ec2d feat(seasonality): add the Seasonality & Session family (12 indicators) (#161)
## Summary

Adds the **Seasonality & Session** family — the first family that reads the wall-clock fields of `Candle::timestamp`. A new private `calendar` module decomposes an epoch-millisecond instant (shifted by a per-indicator `utc_offset_minutes`) into civil fields via Howard Hinnant's branch-light `civil_from_days` algorithm. Session / day / month rollovers are detected automatically, so callers never have to invoke `reset()` at a boundary.

Indicator counter **339 → 351**; family count **20 → 21**.

## Indicators

| Shape | Indicators |
|-------|-----------|
| Scalar (`f64`) | `SessionVwap`, `AverageDailyRange`, `OvernightGap`, `TurnOfMonth`, `SeasonalZScore` |
| Struct | `SessionHighLow`, `SessionRange` (Asia/EU/US), `OvernightIntradayReturn` |
| Profile (`Vec<f64>`) | `TimeOfDayReturnProfile`, `DayOfWeekProfile`, `IntradayVolatilityProfile`, `VolumeByTimeProfile` |

## Bindings

The input is the **full** candle (`open, high, low, close, volume, timestamp`), not the `high/low/close` slice the value-indicator helper assumes, so the Python / Node / WASM bindings are custom full-candle implementations:

- **Python** — `update((o,h,l,c,v,ts))`; `batch(open, high, low, close, volume, timestamp)` → `PyArray1` (scalar) / `PyArray2` (struct & profile), warmup rows `NaN`.
- **Node** — `update(open, high, low, close, volume, timestamp)`; `batch(...)` → flat `Vec<f64>`; struct outputs as `#[napi(object)]` values.
- **WASM** — `update` only (multi-input precedent); profiles as `Float64Array`, structs as camelCase objects, `timestamp` as `BigInt`.

## Verification

- `wickra-core`: full per-branch unit tests, **100%** coverage target; 2852 lib tests + 334 doctests green.
- `cargo clippy --workspace --all-targets --all-features -- -D warnings`: clean.
- Node: 428 tests (dedicated `seasonality.test.js` streaming-vs-batch).
- Python: full suite + dedicated `test_seasonality.py` streaming-vs-batch.
- Counter check: mod-count == counted lib block == 351.
2026-06-03 20:31:32 +02:00
kingchenc 5e96d41916 chore: add REUSE-style LICENSES directory for license auto-detection (#160)
Adds a `LICENSES/` directory with SPDX-named copies of the existing license texts (`MIT.txt`, `Apache-2.0.txt`) per the [REUSE Specification](https://reuse.software/spec/).

## Why
Automated license scanners (the OpenSSF Best Practices BadgeApp, GitHub's license API, REUSE tooling) look for a top-level `LICENSE`/`COPYING` file or a `LICENSES/` directory with SPDX-named files. Our files are named `LICENSE-MIT` / `LICENSE-APACHE` (Rust convention), which these scanners do not recognize — so the BadgeApp's `license_location` check keeps auto-flipping to "Unmet".

## What
- New `LICENSES/MIT.txt` — byte-identical copy of `LICENSE-MIT`
- New `LICENSES/Apache-2.0.txt` — byte-identical copy of `LICENSE-APACHE`
- Existing `LICENSE-MIT` and `LICENSE-APACHE` are **unchanged**

The project remains dual-licensed under **MIT OR Apache-2.0**. This change is additive only.
2026-06-03 20:00:40 +02:00
kingchenc 099ae66b57 release: bump 0.4.7 -> 0.5.0 (#159)
Version bump for the 0.5.0 release, which ships the relicense to MIT OR Apache-2.0.

Stacked on #158 (base branch `chore/relicense-mit-apache`) so this PR's diff is the bump only. After #158 merges to main, GitHub retargets this PR to main; merge it, then tag `v0.5.0` to publish.

## Changes
- Bump 0.4.7 -> 0.5.0 across the Cargo workspace, Python `pyproject.toml`, Node `package.json` + 6 platform manifests + 2 lockfiles, and `Cargo.lock`.
- CHANGELOG: cut the [0.5.0] section (the relicense) and add compare URLs.
- SECURITY.md: supported versions 0.4.x -> 0.5.x.

Minor (not patch) bump: a relicense is a significant change. No code changes.

NOTE: do not tag/release until you give the go (irreversible publish to crates.io/PyPI/npm). Suggested merge order: #158 -> this -> tag `v0.5.0` -> then the downstream PRs.
2026-06-03 18:53:23 +02:00
kingchenc 11dd659b5f Relicense from PolyForm Noncommercial to MIT OR Apache-2.0 (#158)
Relicenses Wickra from PolyForm Noncommercial 1.0.0 to the dual, OSI-approved **MIT OR Apache-2.0** (the de-facto Rust convention). Wickra becomes permissive, commercial-use-permitted open source; users may choose either license.

## Changes
- Replace `LICENSE` (PolyForm) with `LICENSE-MIT` + `LICENSE-APACHE` (full texts).
- Cargo: workspace `license = "MIT OR Apache-2.0"` (SPDX) + all 7 sub-crates switched from `license-file.workspace` to `license.workspace`.
- `deny.toml`: drop PolyForm from the allowlist.
- Python: `pyproject.toml` PEP 639 SPDX expression; remove the non-commercial classifier (verified: sdist metadata emits `License-Expression: MIT OR Apache-2.0`).
- Node: `package.json`, the 6 platform manifests and both lockfiles.
- README + Python/Node/WASM binding READMEs, CONTRIBUTING, CITATION.cff, PR template, and the WASM `pkg.license` step in `release.yml`.
- SECURITY.md: refresh supported versions 0.1.x -> 0.4.x.
- CHANGELOG: note the relicense under [Unreleased].

## Notes
- No code changes; metadata/text only. `cargo build` and `cargo deny check licenses` pass locally.
- GitHub will auto-detect "MIT, Apache-2.0" once this lands (currently NOASSERTION).
- Matching downstream changes (org `.github` profile, webpage, docs) are in separate PRs; merge those together with the relicense release so the live sites and org profile do not claim MIT before the packages do.
2026-06-03 18:49:39 +02:00
kingchenc c096943bdf feat(breadth): complete the Market Breadth family (14 indicators) (#157)
Completes expansion-roadmap block **A2 — Market Breadth**: the 14 indicators that remained after the `AdvanceDecline` bootstrap, all built on the existing `CrossSection` input.

## Indicators (all scalar `Indicator<Input = CrossSection, Output = f64>`)

| Indicator | Reading |
|-----------|---------|
| `AdvanceDeclineRatio` | advancers / decliners |
| `AdVolumeLine` | cumulative net advancing volume |
| `McClellanOscillator` | 19/39 EMAs of ratio-adjusted net advances |
| `McClellanSummationIndex` | running total of the oscillator |
| `Trin` (Arms Index) | A/D ratio over up/down volume ratio |
| `BreadthThrust` (Zweig) | SMA of the advancing-issues share |
| `NewHighsNewLows` | new highs − new lows |
| `HighLowIndex` | SMA of the record-high percent |
| `PercentAboveMa` | % of the universe above its MA |
| `UpDownVolumeRatio` | advancing / declining volume |
| `BullishPercentIndex` | % on a point-and-figure buy signal |
| `CumulativeVolumeIndex` | volume-normalised cumulative net advancing volume |
| `AbsoluteBreadthIndex` | \|advancers − decliners\| |
| `TickIndex` | instantaneous net advancers − decliners |

## Input model

`AdVolumeLine` and `CumulativeVolumeIndex` are kept distinct (the latter normalises each tick's net advancing volume by total volume, so it stays comparable across volume regimes). `PercentAboveMa` and `BullishPercentIndex` need a per-symbol state signal that `Member` did not carry, so `Member` gains two additive flags (`above_ma`, `on_buy_signal`) via a new `Member::with_signals` constructor; the 4-arg `Member::new` leaves both cleared, so every existing caller and binding is unchanged. `CrossSection` gains volume / new-extreme / state aggregation helpers.

## Wiring

Fully wired across the Rust core, the python/node/wasm bindings, the cross-section fuzz target, the README + docs indicator counters (325 → 339), and dedicated python/node streaming-vs-batch tests. `fmt` / `test --workspace --all-features` / `clippy --workspace -D warnings` / node build+test / pytest all green locally.
2026-06-03 17:24:33 +02:00
kingchenc c44f625e69 release: bump 0.4.6 -> 0.4.7 (#156)
Routine patch release. Ships the 10 pairwise stat-arb indicators added to Price Statistics in #154 (Rolling Correlation, Rolling Covariance, OU Half-Life, Kalman Hedge Ratio, Variance Ratio, Spread Bollinger Bands, Spread Hurst, Distance SSD, Granger Causality, Beta-Neutral Spread) together with the new Market Breadth family and its `CrossSection` input type (AdvanceDecline).

Version strings bumped `0.4.6 -> 0.4.7` across:
- `Cargo.toml` (workspace + `wickra-core` dep), `Cargo.lock`
- `bindings/python/pyproject.toml`
- `bindings/node/package.json` (+ 6 optional platform deps) and the 6 `npm/<platform>/package.json`
- `bindings/node/package-lock.json`, `examples/node/package-lock.json`
- `CHANGELOG.md` — `[Unreleased]` rolled into `[0.4.7] - 2026-06-03` with refreshed compare links

No code changes. `fmt` / `test --workspace` / `clippy --workspace -D warnings` green locally.
2026-06-03 16:02:30 +02:00
kingchenc 46dc8f5a00 ci(sync-about): read-only PR counter check, no bot fix-up push (#155)
## Problem
On every indicator PR the `sync-about` workflow found `docs/README.md` lagging `lib.rs` (the wiring only bumped `README.md`) and pushed a `wickra-bot` *"sync indicator count"* commit onto the PR head. That push uses `GITHUB_TOKEN`, which **triggers no workflows**, so it moved the PR head onto a commit with no CI run — and the **Codecov patch status** (keyed to the PR head sha) stopped surfacing on the PR.

## Fix
- The indicator wiring (`ScriptHelpers/_common.py` `wire_readme_counter`) now bumps **both** `README.md` and `docs/README.md` in the author's code commit, so the counter is already correct when CI runs.
- This workflow's PR flow is reduced to a **read-only check** that fails loud (fork and same-repo PRs alike) if either counter is stale, and **never pushes**.
- The `GITHUB_TOKEN` job permission drops from `contents: write` back to `read` (OpenSSF Scorecard: Token-Permissions). The removed `ctx` step + push steps are gone.
- The `main`/tag outward syncs (About description, docs/webpage/wiki/org) are **unchanged** — they use the `ABOUT_SYNC_TOKEN` PAT, not `GITHUB_TOKEN`.

## Effect
Indicator PRs keep their head on the code commit → the Codecov patch status surfaces again. No functional change to merged-main state (the counts still land, now inside the squash-merged code commit).
2026-06-03 15:40:24 +02:00
kingchenc a3a1ae4dba Add 10 pairwise stat-arb indicators to Price Statistics (#154)
Adds ten pairwise `(f64, f64)` indicators to the **Price Statistics** family, completing the A1 stat-arb expansion block.

## Indicators

**Scalar output:**
- **RollingCorrelation** — rolling Pearson correlation of period-over-period *returns* (distinct from level-based `PearsonCorrelation`).
- **RollingCovariance** — rolling covariance of returns.
- **OuHalfLife** — Ornstein–Uhlenbeck half-life of mean reversion of the spread `a − b`.
- **SpreadHurst** — Hurst exponent of the spread (variance-of-lagged-differences fit) for regime detection.
- **DistanceSsd** — Gatev sum-of-squared-deviations between two start-normalised series.
- **BetaNeutralSpread** — rolling OLS regression residual `a − (α + β·b)`.
- **VarianceRatio** — Lo–MacKinlay variance-ratio test on the spread (two params: `period`, `q`).
- **GrangerCausality** — F-statistic for whether `b` predicts `a` (two params: `period`, `lag`).

**Struct output (custom bindings):**
- **KalmanHedgeRatio** — dynamic hedge ratio via a Kalman filter → `{ hedgeRatio, intercept, spread }`.
- **SpreadBollingerBands** — Bollinger bands on the spread → `{ middle, upper, lower, percentB }`.

## Notes
- No new traits or input families: all use the native `Indicator<Input = (f64, f64)>` (precedent `Beta`, `Cointegration`).
- Adds `Error::InvalidParameter` for floating-point constructor parameters (Kalman `delta`/`observation_var`, `num_std`).
- Full Python/Node/WASM bindings; the two struct-output indicators are hand-written, the rest use the pair macros.
- Indicator count 315 → 325; README, family rows, `__init__`, fuzz target, and CHANGELOG updated.

## Verification
- `cargo test --workspace --all-features` — green (2676 core lib + 308 doc).
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean.
- Node: `npm run build && npm test` — 410 passing (`index.d.ts`/`index.js` regenerated).
- Python: `pytest` — 684 passing.
2026-06-03 15:39:55 +02:00
kingchenc 53941b7b07 feat: add Market Breadth family with CrossSection input (#153)
## What

Adds a new indicator input type and family for **market-breadth** analysis — indicators that aggregate the state of an entire universe of symbols at each tick, rather than a single instrument's price. This is the last open input-type on the expansion roadmap (S10) and unblocks the remaining breadth indicators (McClellan, TRIN, High-Low Index, ...).

## Core

- **`CrossSection` input type** (`crates/wickra-core/src/cross_section.rs`) — one tick carrying the per-symbol state of the whole universe as a `Vec<Member>` + `timestamp`. Each `Member` precomputes a signed `change` (sign classifies advancing / declining / unchanged), a `volume`, and `new_high` / `new_low` extreme flags, so the breadth indicators stay stateless per tick. Both `Member` and `CrossSection` are `#[non_exhaustive]` for additive field growth. `CrossSection::new` validates the universe (non-empty, finite changes, finite non-negative volumes); `new_unchecked` skips validation for hot paths. `advancers()` / `decliners()` count by sign.
- **`Error::InvalidCrossSection`** variant for the validation failures.
- **`AdvanceDecline`** (`advance_decline.rs`) — the Advance/Decline Line: the running cumulative sum of net advancing-minus-declining issues. `Input = CrossSection`, `Output = f64`, ready after the first tick.
- New **"Market Breadth"** `FAMILIES` group; indicator count **314 → 315**, family count nineteen → twenty.

## Bindings

All custom (CrossSection is non-scalar, so no macros apply). The universe crosses each boundary as parallel arrays (`change`, `volume`, `new_high`, `new_low`):
- **Python / Node** expose `update` + `batch` (one array group per tick). Node satisfies the completeness contract (`update`/`batch`/`reset`/`isReady`/`warmupPeriod`).
- **WASM** exposes only `update` (the universe is ragged across ticks, matching the other multi-input wasm indicators) with numeric high/low flags.
- Python `map_err` gains the new error arm; `__init__.py` gets a `# Market Breadth` section in both the import and `__all__` blocks. `index.d.ts` / `index.js` regenerated.

## Tests / Fuzz

- Dedicated **streaming-vs-batch + reference-value + ragged-rejection** tests in Python (`test_new_indicators.py`) and Node (`indicators.test.js`) — kept out of the scalar/candle parametrize lists.
- Rust unit tests cover every reject branch (empty / non-finite change / negative & non-finite volume) and every indicator branch.
- New fuzz target `indicator_update_crosssection` drives `AdvanceDecline` over bounded ragged universes built with `new_unchecked`.

## Verify

- `cargo fmt --all` clean
- `cargo test -p wickra-core --lib` → 2593 passed; `--doc` → 298 passed
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` clean
- `cd bindings/node && npm run build && npm test` → 398 passed
- `maturin develop --release` + `pytest bindings/python/tests` → all passed
- counter check: mod-count 315 == lib-block 315
2026-06-03 04:11:10 +02:00
kingchenc 72ec65bbde fix: classify pairwise indicators into Price Statistics family (#152)
## What

The `FAMILIES` table in `crates/wickra-core/src/indicators/mod.rs` had drifted from the indicator count: `mod`-count was **314** but the FAMILIES total asserted **309**.

The five pairwise indicators `Cointegration`, `LeadLagCrossCorrelation`, `PairSpreadZScore`, `PairwiseBeta` and `RelativeStrengthAB` were exported via `pub use` but never assigned to a `FAMILIES` group — even though the README and docs already list them under **Price Statistics**. The "−5 offset" was therefore unclassified drift, not an intentional cross-asset offset.

## Change

- Add the five indicators to the `Price Statistics` group, next to the existing pairwise cluster (`PearsonCorrelation` / `Beta` / `SpearmanCorrelation`).
- Bump the drift assert `309 → 314` so the FAMILIES total now equals the `mod`-count exactly (offset 0).

No new indicators, no binding or doc changes — purely re-classification. The `mod`-count stays 314, so no counter bump.

## Verify

- `cargo fmt --all` clean
- `cargo test -p wickra-core --lib` → 2578 passed (incl. the FAMILIES drift test)
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` clean
2026-06-03 03:38:14 +02:00
kingchenc 82d1a4fe77 release: bump 0.4.5 -> 0.4.6 (#151)
Routine patch release. Ships the **19 TA-Lib parity indicators** (DM components, price transforms, ROC ratio forms, LinReg intercept / TSF, MACDFIX / MACDEXT / SAREXT, Hilbert phasor / DC-phase / trend-mode) added in #148, with the cold-path coverage fix from #150 — indicator count **314**, repo back at 100%.

Version strings bumped `0.4.5 -> 0.4.6` across:
- `Cargo.toml` (workspace + `wickra-core` dep), `Cargo.lock`
- `bindings/python/pyproject.toml`
- `bindings/node/package.json` (+ 6 optional platform deps) and the 6 `npm/<platform>/package.json`
- `bindings/node/package-lock.json`, `examples/node/package-lock.json`
- `CHANGELOG.md` — `[Unreleased]` rolled into `[0.4.6] - 2026-06-03` with refreshed compare links

No code changes. `fmt` / `clippy --workspace -D warnings` green locally.
2026-06-03 02:56:00 +02:00
kingchenc f71b3b6b49 test: cover the cold paths in the TA-Lib parity batch (100% patch) (#150)
PR #148 merged at **99.67%** patch coverage — `codecov/patch` flagged seven by-construction-rare lines in three of the new indicators that no test exercised. This brings the batch back to 100%.

**`ht_dcphase` / `ht_trendmode`** (6 lines) — the dominant-cycle phase recovery guards against a near-zero imaginary part (where `atan(real/imag)` is undefined) by collapsing to ±90° on the sign of the real part. That branch is unreachable with realistic price data. Extracted the phase-unwrap arithmetic into a private `compute_dc_phase(real, imag, smooth_period)` helper — a pure refactor with byte-identical output — and unit-tested it directly with crafted `(real, imag)` pairs, covering both the ±90 collapse and the normal `atan` path.

**`sar_ext`** (1 line) — `Accel::validate`'s non-finite guard was only ever hit for non-positive terms, never non-finite ones, despite the test comment claiming both. Added `NaN` / `infinity` cases on the long and short acceleration schedules.

No behaviour or public-API change. Locally: `cargo test -p wickra-core` (2578 + 297 doctests) and `clippy --workspace -D warnings` all green.
2026-06-03 02:48:11 +02:00
kingchenc d081cb9581 docs: list the 19 TA-Lib parity indicators in the README family rows (#149)
The TA-Lib parity batch (#148) bumped the indicator counter to 314, but `sync-about` only syncs the *number* — the family-table prose in the README still listed the pre-batch set. This fills the 19 new names into their existing family rows so the catalogue matches the count.

- **Momentum Oscillators**: ROC Percentage (ROCP), ROC Ratio (ROCR), ROC Ratio 100 (ROCR100)
- **Trend & Directional**: MACD Fixed (MACDFIX), MACD Extended (MACDEXT), Plus DM, Minus DM, Plus DI, Minus DI, DX
- **Trailing Stops**: Parabolic SAR Extended (SAREXT)
- **Price Statistics**: Mid Price, Mid Point, Average Price, Linear Regression Intercept, Time Series Forecast
- **Ehlers / Cycle (DSP)**: Hilbert Phasor, Hilbert DC Phase, Hilbert Trend Mode

No new family — the "nineteen families" wording and the 314 counter are untouched. Docs-only, no code changes.
2026-06-03 02:36:09 +02:00
kingchenc 9eb46f144a feat: TA-Lib parity — 19 standalone indicators (DM components, price transforms, ROC/LinReg/MACD/SAR variants, Hilbert outputs) (#148)
Closes the remaining TA-Lib function-name gap by shipping each missing or
bundled-only function as a real, standalone, fully-covered indicator. 19 new
indicators across 5 families; mod-count 295 -> 314.

### Trend & Directional — Directional Movement components
- `PlusDm` (`PLUS_DM`), `MinusDm` (`MINUS_DM`) — Wilder-smoothed ±DM.
- `PlusDi` (`PLUS_DI`), `MinusDi` (`MINUS_DI`) — `100·smoothed(±DM)/ATR`.
- `Dx` (`DX`) — `100·|+DI−−DI|/(+DI+−DI)`.

### Price Statistics
- `AvgPrice` (`AVGPRICE`) — `(O+H+L+C)/4`.
- `MidPoint` (`MIDPOINT`) — `(max+min)/2` of a scalar series over N.
- `MidPrice` (`MIDPRICE`) — `(highestHigh+lowestLow)/2` over N.
- `LinRegIntercept` (`LINEARREG_INTERCEPT`) — OLS intercept.
- `Tsf` (`TSF`) — time series forecast `a + b·period`.

### Momentum Oscillators
- `Rocp` (`ROCP`), `Rocr` (`ROCR`), `Rocr100` (`ROCR100`) — ROC ratio forms.

### Trailing Stops
- `SarExt` (`SAREXT`) — Parabolic SAR with start value, reversal offset,
  separate long/short acceleration, signed output.

### Trend & Directional — MACD variants
- `MacdFix` (`MACDFIX`) — MACD fixed 12/26.
- `MacdExt` (`MACDEXT`) — MACD with a selectable moving-average type per line
  (new public `MaType` enum: SMA/EMA/WMA/DEMA/TEMA/TRIMA).

### Ehlers / Cycle (DSP) — Hilbert transform outputs
- `HtPhasor` (`HT_PHASOR`) — in-phase / quadrature components.
- `HtDcPhase` (`HT_DCPHASE`) — dominant-cycle phase (degrees).
- `HtTrendMode` (`HT_TRENDMODE`) — trend (1) vs cycle (0) classification.

Each indicator ships the full chain: core + every-branch unit tests, Python /
Node / WASM bindings, fuzz coverage, README counter + family rows, CHANGELOG.
`cargo test`, doctests, `clippy -D warnings`, `npm test` and pytest all green
locally; mod-count == lib-block == README counter (314), FAMILIES total 309.
2026-06-03 02:26:38 +02:00
kingchenc 9a98e9bf55 release: bump 0.4.4 -> 0.4.5 (#147)
Version bump for 0.4.5: ships Anchored RSI, Volume Profile, TPO Profile and the Alt-Chart Bars family (Renko/Kagi/Point & Figure). Indicator count 289 -> 295.
2026-06-02 22:14:47 +02:00
kingchenc d4b3f9dbd1 feat: add Alt-Chart Bars (Renko, Kagi, Point & Figure) via a BarBuilder trait (#146)
Introduces a BarBuilder trait for price-driven chart constructors that emit a variable number of bars per candle (deliberately not Indicator). Adds Renko (box-size bricks, 2-box reversal), Kagi (reversal-amount segments) and Point & Figure (box-size X/O columns, N-box reversal) in a new Alt-Chart Bars family, with custom Python/Node/WASM bindings, a dedicated fuzz target, tests and docs. Indicator count 292 -> 295.
2026-06-02 21:56:00 +02:00
kingchenc f37eedd44e feat: add Volume Profile and TPO Profile to the market profile family (#145)
Volume Profile exposes the full per-bin volume histogram (price bounds plus raw distribution) that Value Area reduces to POC/VAH/VAL. TPO Profile is the volume-agnostic Time-Price-Opportunity letter count over a rolling window. Both candle-input, Vec-output, Market Profile family, with custom Python/Node/WASM bindings, fuzz, benches, tests and docs. Indicator count 290 -> 292.
2026-06-02 21:16:30 +02:00
kingchenc 93097db482 feat: add Anchored RSI to the momentum oscillators family (#144)
Cumulative Relative Strength Index whose averaging begins at a runtime-chosen anchor bar (set_anchor), the momentum counterpart to Anchored VWAP. Scalar f64 input, 0..=100 output; wired through core, Python, Node and WASM bindings, fuzz, benches, tests and docs. Indicator count 289 -> 290.
2026-06-02 20:50:56 +02:00
kingchenc 2f3a0b9149 release: bump 0.4.3 -> 0.4.4 (#143)
Release 0.4.4.

Version bump only — no code changes. Ships the 40 TA-Lib candlestick patterns
(parts 2–9, #132–#141, 249 → 289 indicators) plus the candlestick rejection-
guard coverage tests (#142) that landed on `main` since 0.4.3.

Bumped: workspace `Cargo.toml` (+ `wickra-core` dep) and `Cargo.lock`,
`bindings/python/pyproject.toml`, `bindings/node/package.json` (+ 6 platform
`optionalDependencies`), the 6 `bindings/node/npm/*/package.json`, both
`package-lock.json` files, and `CHANGELOG.md` ([Unreleased] → [0.4.4]).
2026-06-02 18:10:24 +02:00
kingchenc 49c0fd7dd5 ci: Dependabot cooldown + accept residual zizmor notes (#136)
Clears the remaining zizmor code-scanning findings on this repo.

**Fixed**
- `dependabot-cooldown` (5): a 7-day cooldown on every update ecosystem
  (cargo, npm, pip, ci-pip, github-actions) so Dependabot waits a week after a
  release before opening the bump PR.

**Accepted via `.github/zizmor.yml`** (no workflow code changed)
- `template-injection` (sync-about.yml): false positive — every expansion is the
  internal `grep -c` indicator count, not attacker-controllable.
- `use-trusted-publishing` (release.yml): OIDC migration tracked separately.
- `superfluous-actions` (release.yml): `softprops/action-gh-release` kept deliberately.

Verified with zizmor 1.25.2: 0 findings.
2026-06-02 17:59:46 +02:00
kingchenc 3d98592461 test: cover candlestick pattern rejection guards (#142)
Closes the coverage gaps in the candlestick-pattern family that landed across
PRs #132–#141. Codecov flagged 25 uncovered lines on `main` (99.93%) — all in
the new candlestick files, and all early-return rejection guards or unused
`Default` impls that the accept-path unit tests never exercised.

This PR adds focused white-box unit tests (one or two per affected file) that
drive each rejection branch through the public `update()` API:

- **Zero-range guards** — a flat bar (`high == low`) at the relevant window
  position: `DojiStar`, `InNeck`, `OnNeck`, `Thrusting`, `SeparatingLines`,
  `EveningDojiStar`, `MorningDojiStar`, `GapSideBySideWhite`,
  `FallingThreeMethods`, `RisingThreeMethods`, `MatHold`.
- **Too-short trigger body** — a wide-range bar with a tiny body that fails the
  "long body" check: the three-bar stars, the three-methods pair, `MatHold`,
  `SeparatingLines`.
- **Shape-specific guards** — `GapSideBySideWhite` body-size mismatch, `MatHold`
  bar-2 fails to gap up.
- **`Default` impls** — `LongLine` / `ShortLine` (`default()` was never called).

No production code changes; tests only. `cargo test -p wickra-core` and
`cargo clippy -p wickra-core --all-targets -- -D warnings` pass locally.
2026-06-02 17:52:04 +02:00
kingchenc 124efb4432 feat: TA-Lib candlestick patterns — tasuki-gap/unique-three-river/marubozu-pair/concealing-baby-swallow (part 9 of 9) (#141)
The final batch of the TA-Lib candlestick roadmap. Adds five patterns, each a streaming `Indicator<Input = Candle, Output = f64>` emitting the family's uniform `±1.0 / 0.0` sign convention, fully wired across the Rust core, Python / Node / WASM bindings, fuzz target and reference tests.

- **Tasuki Gap** (`CDLTASUKIGAP`) — a 3-bar continuation: two same-coloured candles gap in the trend direction, then an opposite candle opens within the second body and closes back into the gap without filling it; upside +1, downside -1.
- **Unique Three River** (`CDLUNIQUE3RIVER`) — a 3-bar bullish reversal: a long black candle, a black candle probing a new low with its body inside the first, then a small white candle held below it; bullish +1.
- **Closing Marubozu** (`CDLCLOSINGMARUBOZU`) — a single long-bodied candle with no shadow on the close end; +1 (white, closes at the high) or -1 (black, closes at the low).
- **Opening Marubozu** — a single long-bodied candle with no shadow on the open end; +1 (white, opens at the low) or -1 (black, opens at the high). No direct TA-Lib equivalent — completes the pair with the closing marubozu.
- **Concealing Baby Swallow** (`CDLCONCEALBABYSWALL`) — a rare 4-bar bullish capitulation: two black marubozu, a black candle gapping down with an upper shadow into the second, then a large black candle engulfing it entirely; bullish +1.

Body and shadow thresholds follow the geometric house style (fixed fractions of the bar range) rather than TA-Lib's rolling averages.

Counter 284 → 289 (mod-count == lib counted block; FAMILIES total 279 → 284).

Stacked on #140 (`feat/cdl-gap-methods`); base retargets to `main` as the stack merges down.
2026-06-02 17:27:39 +02:00
kingchenc 4d0bc08efd feat: TA-Lib candlestick patterns — gap-three-methods/stalled/stick-sandwich/takuri (part 8 of 9) (#140)
Adds five TA-Lib candlestick patterns, each a streaming `Indicator<Input = Candle, Output = f64>` emitting the family's uniform `±1.0 / 0.0` sign convention, fully wired across the Rust core, Python / Node / WASM bindings, fuzz target and reference tests.

- **Upside Gap Three Methods** (`CDLXSIDEGAP3METHODS`) — a 3-bar bullish continuation: two white candles gap up, then a black candle opens within the second body and closes within the first; bullish +1.
- **Downside Gap Three Methods** (`CDLXSIDEGAP3METHODS`) — the bearish mirror: two black candles gap down, then a white candle opens within the second body and closes within the first; bearish -1.
- **Stalled Pattern** (`CDLSTALLEDPATTERN`) — a 3-bar bearish reversal warning: two long white candles then a small white candle riding the shoulder, signalling the rally is stalling; bearish -1.
- **Stick Sandwich** (`CDLSTICKSANDWICH`) — a 3-bar bullish reversal: two black candles closing at the same level sandwich a white candle, marking a support floor; bullish +1.
- **Takuri** (`CDLTAKURI`) — a single-bar bullish reversal, a strict Dragonfly Doji with a negligible upper shadow and very long lower shadow; bullish +1.

Body and shadow thresholds follow the geometric house style (fixed fractions of the bar range) rather than TA-Lib's rolling averages. Upside / Downside Gap Three Methods share the `CDLXSIDEGAP3METHODS` code, so the second carries a manual CHANGELOG entry (as with Rising / Falling Three Methods).

Counter 279 → 284 (mod-count == lib counted block; FAMILIES total 274 → 279).

Stacked on #139 (`feat/cdl-lines`); base retargets to `main` once the predecessor merges.
2026-06-02 17:24:42 +02:00
kingchenc c2c85c7ecf feat: TA-Lib candlestick patterns — matching-low/lines/three-methods (part 7 of 9) (#139)
Adds five TA-Lib candlestick patterns, all `Input = Candle`, `Output = f64`
(`+1.0` bullish / `-1.0` bearish / `0.0` no pattern), wired across core,
Python/Node/WASM bindings, fuzz, and tests.

- **Matching Low** (`CDLMATCHINGLOW`) — 2-bar bullish reversal: two black candles in a decline share the same close, signalling selling pressure is exhausting; bullish +1.
- **Long Line** (`CDLLONGLINE`) — a candle whose range beats a rolling average of recent ranges with a body-dominated range; bullish +1 (white) / bearish -1 (black).
- **Short Line** (`CDLSHORTLINE`) — a compact candle whose range falls below the rolling average with a body-dominated range; bullish +1 (white) / bearish -1 (black).
- **Rising Three Methods** (`CDLRISEFALL3METHODS`) — 5-bar bullish continuation: a long white candle, three small bars holding within its range, then a white breakout to new highs; bullish +1.
- **Falling Three Methods** (`CDLRISEFALL3METHODS`) — the bearish mirror: a long black candle, three small bars within its range, then a black breakdown to new lows; bearish -1.

Counter 274 → 279 (mod-count == lib counted block; FAMILIES total 269 → 274).

Stacked on #138 (part 6 of 9); base retargets to `main` as the chain merges.
2026-06-02 17:16:15 +02:00
kingchenc 04ae145126 feat: TA-Lib candlestick patterns — separating/kicking/ladder/mat-hold (part 6 of 9) (#138) 2026-06-02 17:06:40 +02:00
kingchenc e4ca9c3f8f feat: TA-Lib candlestick patterns — hikkake-mod/pigeon/neck-lines (part 5 of 9) (#137)
* feat: add hikkake-modified, homing-pigeon and neck-line candlestick patterns

Five patterns, all `Input = Candle`, `Output = f64`:

- Modified Hikkake (CDLHIKKAKEMOD) — a close-confirmed Hikkake: an inside bar
  then a breakout that closes back inside the inside-bar range; bullish +1,
  bearish -1.
- Homing Pigeon (CDLHOMINGPIGEON) — two black candles, the second a small body
  inside the first, a bullish reversal; +1.
- On-Neck (CDLONNECK) — long black bar then a white bar closing at its low (the
  neckline), a bearish continuation; -1.
- In-Neck (CDLINNECK) — long black bar then a white bar closing just into its
  body, a bearish continuation; -1.
- Thrusting (CDLTHRUSTING) — long black bar then a white bar closing well into
  but below the midpoint of its body, a bearish continuation; -1.

Counter 264 -> 269 (mod-count == lib counted block; FAMILIES total 259 -> 264).

* chore: sync indicator count to 269

---------

Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
2026-06-02 17:03:49 +02:00
kingchenc d43bc9ddf3 feat: TA-Lib candlestick patterns — doji-star/gap/high-wave/hikkake (part 4 of 9) (#135)
* feat: add doji-star, gap, high-wave and hikkake candlestick patterns

Five patterns, all `Input = Candle`, `Output = f64`:

- Evening Doji Star (CDLEVENINGDOJISTAR) — bearish top reversal: long white bar,
  a doji gapping up, then a black bar closing deep into the first body; -1
  (penetration configurable, default 0.3).
- Morning Doji Star (CDLMORNINGDOJISTAR) — bullish bottom reversal mirror; +1.
- Gap Side-by-Side White (CDLGAPSIDESIDEWHITE) — two similar white candles
  opening side by side after a gap, a continuation; gap up +1, gap down -1.
- High-Wave (CDLHIGHWAVE) — a small body with very long shadows on both sides,
  an extreme indecision flag; +1 on detection.
- Hikkake (CDLHIKKAKE) — an inside bar followed by a failed breakout (a trap);
  bullish +1, bearish -1.

Counter 259 -> 264 (mod-count == lib counted block; FAMILIES total 254 -> 259).

* chore: sync indicator count to 264

---------

Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
2026-06-02 16:54:47 +02:00
kingchenc 244d754707 feat: TA-Lib candlestick patterns — Doji family (part 3 of 9) (#134)
* feat: add Doji-family candlestick patterns

Five single-/two-bar Doji patterns, all `Input = Candle`, `Output = f64`:

- Doji Star (CDLDOJISTAR) — a long body followed by a doji gapping away in the
  trend direction; bullish +1 (after a black bar), bearish -1 (after a white bar).
- Dragonfly Doji (CDLDRAGONFLYDOJI) — a doji opening and closing at the high with
  a long lower shadow; bullish +1.
- Gravestone Doji (CDLGRAVESTONEDOJI) — a doji opening and closing at the low with
  a long upper shadow; bearish -1.
- Long-Legged Doji (CDLLONGLEGGEDDOJI) — a doji with long shadows on both sides; a
  non-directional indecision flag, +1 on detection.
- Rickshaw Man (CDLRICKSHAWMAN) — a long-legged doji with the body centred in the
  range; a non-directional indecision flag, +1 on detection.

Counter 254 -> 259 (mod-count == lib counted block; FAMILIES total 249 -> 254).

* chore: sync indicator count to 259

---------

Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
2026-06-02 16:45:08 +02:00
kingchenc 03ceac1f3b feat: TA-Lib candlestick patterns — abandoned/advance/belt/break/counter (part 2 of 9) (#132)
* feat: add Abandoned Baby candlestick pattern (CDLABANDONEDBABY)

* feat: add Advance Block candlestick pattern (CDLADVANCEBLOCK)

* feat: add Belt Hold candlestick pattern (CDLBELTHOLD)

* feat: add Breakaway and Counterattack candlestick patterns (CDLBREAKAWAY, CDLCOUNTERATTACK)

Breakaway is a 5-bar reversal: a trend gaps away on the second bar, drifts
two more bars, then the fifth bar snaps back and closes inside the bar1/bar2
body gap (bullish +1, bearish -1). Counterattack is a 2-bar reversal where an
opposite-coloured long second bar closes level with the first (the counterattack
line; bullish +1, bearish -1).

Also suppress libtest's spanless `large_stack_arrays` false positive in
wickra-core test builds: the `#[test]` harness collects every test into a
compiler-generated array of references that crosses clippy's 16 KB threshold
once the suite passes ~2048 unit tests. The allow is scoped to `cfg(test)`, so
library code is still linted for genuinely large stack arrays.

* chore: sync indicator count to 254

---------

Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
2026-06-02 16:34:15 +02:00
kingchenc 73415cd2dc ci: zizmor security hardening (#133)
* ci: pass ref context through env in release tag step

zizmor flagged the "Resolve target tag" step in release.yml for
template-injection: github.event_name / github.ref / github.ref_name
were interpolated directly into the shell script. On a tag push the tag
name is attacker-influenceable, so a crafted tag could inject commands.

Move all three context values into the step env and reference them as
shell variables instead. Verified with zizmor 1.16.3: template-injection
findings on release.yml drop from 2 to 0.

* ci: accept release.yml build caches via zizmor config

The release pipeline restores Swatinem/rust-cache and actions/setup-node
caches as a deliberate optimisation. zizmor flags all eight under
cache-poisoning because release.yml publishes to crates.io / PyPI / npm.
The caches are maintainer-controlled and the restore speedup is kept on
purpose, so accept the finding via a zizmor config ignore for release.yml
rather than running cache-free release builds. (Six of the eight are
actions/setup-node, reported at Low confidence.)

Adds .github/zizmor.yml; release.yml now reports 0 high findings.

* ci: drop persisted checkout credentials on read-only jobs

zizmor's artipacked audit flags every actions/checkout that keeps the
default persisted credential: the token is written to the runner's
.git/config, where it can leak if a later step packs .git into an
uploaded artifact, or be read by another step in the same job.

Set persist-credentials: false on the 20 checkouts whose jobs never push
or authenticate to git (build/test/clippy/msrv/coverage/supply-chain/
fuzz/python/wasm/node in ci.yml, plus bench.yml, codeql.yml, the seven
release.yml build/publish jobs, and sync-metadata.yml). The publish and
release jobs authenticate to crates.io / npm / PyPI / the GitHub API with
their own tokens, not persisted git credentials, so this is safe.

sync-about.yml genuinely pushes the indicator-count fix-up to the PR
branch, so it keeps its credential and is accepted via .github/zizmor.yml.
zizmor artipacked for the repo drops to 0 (0 high, 0 medium remaining).
2026-06-02 02:08:40 +02:00
kingchenc ad51dbc1a3 fix: keep docs/README indicator count in sync-about (#131)
* fix: keep docs/README indicator count in sync-about

The docs/README.md pointer prose names the indicator count
("**N indicators**") but was never part of the sync-about counter
pipeline, so it drifted to 214 while the real count (lib.rs) is 249.

Add docs/README.md to the PR-flow gate check, the patch sed, and the
fix-up commit so future count changes keep it in sync, and correct the
current stale value to 249.

* docs: fix stale Wiki reference in CONTRIBUTING layout table

The project-layout table still described docs/ as a "Pointer to the
project Wiki" even though the wiki was retired and the docs moved to
docs.wickra.org (wickra-lib/wickra-docs). Align the row with the
already-correct doc-site section further down the same file.
2026-06-01 23:35:03 +02:00
kingchenc f09057aaf1 feat: TA-Lib candlestick patterns — crows & three-line (part 1 of 9) (#130)
* feat: add Two Crows candlestick pattern (CDL2CROWS)

* feat: add Upside Gap Two Crows candlestick pattern (CDLUPSIDEGAP2CROWS)

* feat: add Identical Three Crows candlestick pattern (CDLIDENTICAL3CROWS)

* feat: add Three Line Strike candlestick pattern (CDL3LINESTRIKE)

* feat: add Three Stars in the South candlestick pattern (CDL3STARSINSOUTH)
2026-06-01 23:20:10 +02:00
kingchenc 458ef2385e ci: add zizmor GitHub Actions security scanning (#129) 2026-06-01 22:34:03 +02:00
kingchenc 2d140419bb feat: derivatives basis & calendar-spread indicators (part 3 of 3) (#128)
* feat(derivatives): TermStructureBasis indicator (core)

* feat(derivatives): CalendarSpread indicator (core)

* feat(derivatives): Python, Node and WASM bindings for basis & calendar-spread indicators

* test(derivatives): Python and Node tests for basis & calendar-spread indicators

* docs(derivatives): README row + counter 242->244, CHANGELOG part 3; fuzz basis indicators
2026-06-01 22:07:35 +02:00
kingchenc 8e5bfd07ce feat: derivatives open-interest, flow & liquidation indicators (part 2 of 3) (#127)
* feat(derivatives): OIPriceDivergence indicator (core)

* feat(derivatives): OIWeighted indicator (core)

* feat(derivatives): LongShortRatio indicator (core)

* feat(derivatives): TakerBuySellRatio indicator (core)

* feat(derivatives): LiquidationFeatures multi-output indicator (core)

* feat(derivatives): Python, Node and WASM bindings for OI, flow & liquidation indicators

* test(derivatives): Python and Node tests for OI, flow & liquidation indicators

* fuzz(derivatives): drive OI, flow & liquidation indicators in derivatives target

* docs(derivatives): README row + counter 237->242, CHANGELOG part 2
2026-06-01 21:50:35 +02:00
kingchenc 5eb820a9c7 feat: derivatives funding & open-interest indicators (part 1 of 3) (#126)
* feat(derivatives): DerivativesTick input type + InvalidDerivatives error

* feat(derivatives): FundingRate indicator (core)

* feat(derivatives): FundingRateMean indicator (core)

* feat(derivatives): FundingRateZScore indicator (core)

* feat(derivatives): FundingBasis indicator (core)

* feat(derivatives): OpenInterestDelta indicator (core)

* feat(derivatives): Python, Node and WASM bindings for funding & OI-delta indicators

* test(derivatives): Python and Node tests for funding & OI-delta indicators

* bench(derivatives): synthetic-tick bench + derivatives fuzz target

* docs(derivatives): README family row + counter 232->237, CHANGELOG entry
2026-06-01 21:26:37 +02:00
kingchenc fae60e0d54 release: bump 0.4.2 -> 0.4.3 (#125) 2026-06-01 20:49:11 +02:00
kingchenc 433b06367f ci: bump actions/checkout v4.3.1 -> v6.0.2 in codeql & scorecard (#124) 2026-06-01 20:24:20 +02:00
kingchenc 3dd7010129 feat: footprint microstructure indicator (part 4 of 4) (#123) 2026-06-01 20:00:58 +02:00
kingchenc 4f11df0e33 feat: microstructure price-impact & depth indicators (part 3 of 4) (#122)
* feat: effective spread microstructure indicator (part 3 of 4)

* feat: realized spread microstructure indicator (part 3 of 4)

* feat: kyle's lambda microstructure indicator (part 3 of 4)

* feat: depth slope microstructure indicator (part 3 of 4)
2026-06-01 19:45:38 +02:00
kingchenc b5d9e47a2e release: bump 0.4.1 -> 0.4.2 (#121) 2026-06-01 18:29:32 +02:00
kingchenc 511d3a27f7 ci: self-updating README banner + fix webpage count sync (#119)
* ci: fix webpage count sync crashing on removed public/hero.svg

The webpage indicator-count sync sed'd index.md, .vitepress/config.ts and
public/hero.svg, but hero.svg was removed from wickra-lib/webpage (the count is
now baked into og-banner.webp at build time from index.md). Under 'bash -e' the
missing file made sed exit 2 before the commit/push, so the count never reached
the webpage repo and its OG banner stayed stale — masked green by the step's
continue-on-error. Drops public/hero.svg from the sed and git add; index.md and
.vitepress/config.ts (both still carry the count) remain.

* docs: point README banner at the self-updating org profile image

Switches the top README banner from https://wickra.org/og-banner.webp (baked at
webpage deploy time) to the org profile banner that wickra-lib/.github's
banner.yml regenerates from the indicator count on every sync
(raw.githubusercontent.com/wickra-lib/.github/main/profile/wickra-banner.webp).
The ?v=227 query busts GitHub's Camo image cache; the next commit teaches
sync-about to bump it with the count.

* ci: bump README banner cache-buster alongside the indicator count

Extends the PR-head README counter patch to also rewrite wickra-banner.webp?v=N
to the current count. The banner now points at the org profile image, whose
content changes when .github/banner.yml regenerates it; bumping the ?v query
busts GitHub's Camo cache so the README shows the new banner immediately. Rides
in the existing count-sync commit, so no extra commit lands on main.

* docs: changelog entry for self-updating README banner and sync fix
2026-06-01 18:19:23 +02:00
kingchenc 5b23b36261 ci: pin CI dependency installs by hash (Scorecard PinnedDependencies) (#114)
* ci: use npm ci instead of npm install for reproducible installs

Pins the node binding dependency install to the committed package-lock.json
integrity hashes (OpenSSF Scorecard PinnedDependencies). npm ci installs
strictly from the lockfile; npm install could resolve newer patch versions.
Covers ci.yml and both release.yml node steps.

* ci: hash-pin Python dev tooling in ci.yml (Scorecard #19)

Replaces the unpinned 'pip install maturin pytest numpy hypothesis' with a
hash-locked '--require-hashes -r' install (OpenSSF Scorecard PinnedDependencies).

Two lock files are needed because numpy publishes no single release with wheels
for both cp39 and cp313 (<=2.0.2 has cp39 only, >=2.1 drops cp39):
  ci-dev-py39.txt  numpy 2.0.2  (Python 3.9, + tomli/exceptiongroup)
  ci-dev-py3.txt   numpy 2.4.6  (Python 3.10+)

The step selects the file by matrix.python-version under shell: bash. Both are
generated from ci-dev.in via uv (scripts/update-lockfiles.sh, added next).

* ci: hash-pin Python deps in bench.yml (Scorecard #16)

Replaces the unpinned 'pip install maturin numpy pandas talipp finta' with a
hash-locked '--require-hashes -r .github/requirements/bench.txt' install.
bench.yml runs on a single Python version (3.11), so one lock file (generated
from bench.in via uv) is sufficient.

* build: add scripts/update-lockfiles.sh to regenerate all lockfiles

One command refreshes every committed lockfile across languages: Cargo.lock and
fuzz/Cargo.lock (cargo update), the Node binding package-lock.json, and the
hash-pinned Python requirements under .github/requirements/ (uv pip compile
--generate-hashes). Uses uv for the Python locks so a target Python version's
hashed transitive closure can be resolved without that interpreter installed
(needed for the numpy cp39/cp313 split); bootstraps uv if absent.

.gitattributes pins *.sh to LF so the script stays runnable on Linux/macOS.

* ci: split ci-dev requirements per Python version + Dependabot rehash

Splits the single ci-dev.in into ci-dev-py39.in (numpy <2.1, the last series
with cp39 wheels) and ci-dev-py3.in (3.10+), giving a 1:1 .in->.txt layout.
The cap keeps Python 3.9 permanently installable and stops Dependabot from
proposing 3.9-breaking numpy bumps.

Adds a Dependabot pip entry on /.github/requirements so the hash-locked tooling
is kept current automatically; the canonical manual refresh stays
scripts/update-lockfiles.sh. Only the '# via -r' provenance lines in the .txt
change; no package versions or hashes move.

* ci: cache pip and npm downloads in the PR-loop jobs

Adds setup-python cache: pip (ci.yml python matrix + bench.yml, keyed on the
hash-locked requirements) and setup-node cache: npm (ci.yml node job, keyed on
bindings/node/package-lock.json), on both the primary and retry setup steps.

Scoped to jobs that actually install dependencies; the examples-smoke and
clippy-bindings jobs install nothing and are left uncached. release.yml is
intentionally left out: it runs only on tag push (not the PR loop) and is the
publish-critical path, so no caching is added there.

* docs: document hash-pinned requirements and update-lockfiles.sh

Updates the lockfile-policy table: the bindings/python row no longer claims CI
installs tooling unpinned, and a new .github/requirements row documents the
hash-locked CI/bench tooling and the per-Python-version ci-dev split. Adds a
paragraph pointing contributors at scripts/update-lockfiles.sh (uv-based,
self-bootstrapping) as the canonical lockfile refresh.

* docs: changelog entry for hash-pinned CI dependency installs
2026-06-01 17:58:31 +02:00
kingchenc 5867f71450 feat: trade-flow microstructure indicators (part 2 of 4) (#113)
* feat(core): add 3 trade-flow microstructure indicators

SignedVolume (per-trade size signed by aggressor), CumulativeVolumeDelta
(running signed-volume total), and TradeImbalance (rolling buy/sell volume
imbalance over a trade window). All consume the Trade type, with full unit
coverage. Extends the Microstructure family.

* feat(bindings): expose trade-flow microstructure indicators

Python, Node and WASM bindings for SignedVolume, CumulativeVolumeDelta and
TradeImbalance. Each takes a trade via update(price, size, is_buy); Python and
Node expose a batch over three parallel arrays, WASM exposes per-trade update.
Regenerates node index.d.ts/.js.

* test(bindings,fuzz,bench): cover trade-flow microstructure indicators

Python and Node: reference values, streaming-vs-batch, lifecycle/repr and input
validation (zero window, negative size, non-positive price, mismatched batch
lengths). New indicator_update_trade fuzz target. Synthetic trade-tape benches
(signed_volume cheapest, trade_imbalance windowed/expensive).

* docs: add trade-flow indicators + bump counter to 227

README Microstructure family row gains signed volume / CVD / trade imbalance and
the counter goes 224 -> 227; CHANGELOG records the trade-flow indicators.
2026-06-01 16:38:48 +02:00
kingchenc 2be21df803 feat: order-book microstructure indicators (part 1 of 4) (#112)
* feat(core): add microstructure input types (OrderBook, Trade, TradeQuote)

New non-OHLCV value types for the order-book / trade-flow indicator family:
Level, OrderBook (sorted, uncrossed depth snapshot), Side, Trade (with
aggressor side), and TradeQuote (trade paired with prevailing mid). Each has a
validating constructor plus a new_unchecked hot-path constructor, with full
unit coverage. Adds InvalidOrderBook / InvalidTrade error variants.

* feat(core): add 5 order-book microstructure indicators

OrderBookImbalanceTop1/TopN/Full (signed depth imbalance), Microprice
(size-weighted fair value), and QuotedSpread (top-of-book spread in bps). All
consume the OrderBook snapshot type, emit f64, are stateless and ready after
the first snapshot, with full unit coverage. Registers a new Microstructure
family in the taxonomy.

* feat(bindings): expose order-book microstructure indicators

Python, Node, and WASM bindings for OrderBookImbalanceTop1/TopN/Full,
Microprice and QuotedSpread. Each takes a depth snapshot via four equal-length
(bid_px, bid_sz, ask_px, ask_sz) arrays. Python and Node expose a batch over a
list of snapshots; WASM exposes per-snapshot update (the streaming model that
fits a browser book feed). Regenerates node index.d.ts/.js and registers the
new InvalidOrderBook/InvalidTrade arms in the Python error mapping.

* test(bindings,fuzz): cover order-book microstructure indicators

Python: smoke, reference values, streaming-vs-batch, lifecycle/repr and input
validation (mismatched lengths, crossed book, misordered levels, zero levels)
for all five order-book indicators. Node: reference values, streaming-vs-batch,
and rejection cases. Adds an indicator_update_orderbook fuzz target driving
every order-book indicator over arbitrary (incl. degenerate) snapshots.

* bench(microstructure): synthetic order-book benchmarks

Add a bench_orderbook_input harness and synthesise a five-level book around
each candle close (no order-book dataset ships with the repo). Benches the
cheapest (top-of-book imbalance) and most-expensive (full-depth imbalance) plus
microprice, matching the curated cheapest/expensive-per-family approach.

* docs: add Microstructure family + bump indicator counter to 224

README gains the Microstructure family row (order-book imbalance, microprice,
quoted spread) and the indicator counter goes 219 -> 224 across seventeen
families; CHANGELOG records the new order-book indicators and value types.
2026-06-01 16:06:22 +02:00
kingchenc 498b74a5ae chore(license): point package metadata at the modified license file
The LICENSE now carries an Additional Permissions section on top of
PolyForm Noncommercial 1.0.0, so the bare SPDX id no longer describes
it exactly. Update the package manifests to reference the actual file
instead of claiming the unmodified standard:

- Cargo (workspace + all crates): license -> license-file = "LICENSE"
- npm (main + 6 platform packages): LicenseRef-Wickra-Noncommercial-1.0.0
- PyPI: license text notes the additional personal-account permissions
2026-06-01 15:28:18 +02:00
kingchenc 921a250715 docs(license): grant personal-account trading use to match README
Append an Additional Permissions section to the PolyForm Noncommercial
License 1.0.0. The base PolyForm text is unmodified; the section only
broadens the grant. It explicitly permits a natural person to use the
software for their own personal account, including running a trading
bot on their own capital for profit, matching the README's plain-English
summary (hobby trading bots: all fine). Commercial sale of the software
or of services built around it still requires a commercial license.
2026-06-01 15:05:03 +02:00
kingchenc 0479191b66 feat: signed candlestick directional ±1 encoding (Doji signed mode) (#111)
* feat(core): add signed dragonfly/gravestone encoding to Doji

Doji gains an opt-in `.signed()` mode that classifies a detected Doji by the
position of its body within the bar range: dragonfly (long lower shadow) emits
+1.0 (bullish), gravestone (long upper shadow) emits -1.0 (bearish), and a
long-legged/standard Doji emits 0.0. The default detection-flag behaviour
(+1.0/0.0) is unchanged, so existing callers are unaffected.

The other 14 candlestick patterns already emit the uniform +1 bull / -1 bear /
0 none convention; document that explicitly with a "Signed +-1 encoding"
section on each so the whole family is a consistent drop-in ML feature.

* feat(bindings): expose Doji signed mode in python, node, wasm

Hand-write the Doji binding in all three language bindings (instead of the
shared candle-pattern macro) so it accepts an opt-in `signed` flag and exposes
an `is_signed`/`isSigned` accessor:

- Python: `Doji(signed=False)` keyword argument
- Node: `new Doji(signed?)` optional constructor argument (index.d.ts/.js
  regenerated via napi build)
- WASM: `new Doji(signed?)` optional constructor argument

The default construction is unchanged, so existing callers keep the
direction-less +1/0 detection flag.

* test(bindings,fuzz): cover Doji signed dragonfly/gravestone encoding

- python: dragonfly(+1)/gravestone(-1)/neutral(0) and default-flag cases in
  test_known_values
- node: equivalent signed/default assertions in indicators.test.js
- fuzz: drive a signed Doji alongside the default in indicator_update_candle

* docs: document signed candlestick convention and Doji signed mode

README gains a candlestick sign-convention note; CHANGELOG records the new
opt-in Doji signed dragonfly/gravestone encoding under [Unreleased].
2026-06-01 14:37:20 +02:00
kingchenc 4631519885 release: bump 0.4.0 -> 0.4.1 (#110)
Releases the cross-asset / pairwise indicator family (PR #109):
PairwiseBeta, PairSpreadZScore, LeadLagCrossCorrelation, Cointegration,
RelativeStrengthAB. Indicator count 214 -> 219.

Bumps workspace + binding versions and the CHANGELOG ([Unreleased] ->
[0.4.1]) with the new compare URL.
2026-06-01 13:58:50 +02:00
kingchenc 0b85142ad1 feat: cross-asset / pairwise indicators (5 new) (#109)
* feat(core): add PairwiseBeta cross-asset indicator

Rolling OLS slope of one asset's log-returns on another's. Unlike Beta,
which regresses the raw inputs it is fed, PairwiseBeta differences
consecutive prices into log-returns internally -- the conventional way to
measure cross-asset beta, where a beta on price levels would be dominated
by the shared trend.

Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with unit/known-value/streaming tests and a pair fuzz target.

* feat(core): add PairSpreadZScore cross-asset indicator

Standardised log-spread ln(a) - beta*ln(b) of a pair, where beta is a
rolling-OLS hedge ratio and the spread is z-scored over its own look-back.
The canonical mean-reversion / statistical-arbitrage entry signal, with
independent beta_period and z_period windows.

Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with sign/known-value/streaming tests and a pair fuzz target.

* feat(core): add LeadLagCrossCorrelation cross-asset indicator

Reports the integer offset k in [-max_lag, max_lag] that maximises
|corr(a[t], b[t+k])|, answering which of two assets leads the other and by
how many bars. A positive lag means a leads b. Fully causal: a's window is
held centred while b's window slides across the buffered history, so every
lag is evaluated only against data already seen.

Struct output { lag, correlation }, exposed in Rust, Python, Node and WASM
with lead-detection/streaming tests and a pair fuzz driver.

* feat(core): add Cointegration (Engle-Granger + ADF) indicator

Rolling pairs-trading screen: an OLS hedge ratio of a on b, the spread
(residual) a - (alpha + beta*b), and an augmented Dickey-Fuller t-statistic
on the spread with configurable lags. A strongly negative statistic flags a
mean-reverting, tradeable spread. Includes a small Gaussian-elimination
solver for the augmented regression.

Struct output { hedge_ratio, spread, adf_stat }, exposed in Rust, Python,
Node and WASM with stationarity/hedge-ratio/streaming tests and a pair fuzz
driver.

* feat(core): add RelativeStrengthAB cross-asset indicator

Comparative relative strength of two assets: the ratio line a/b together
with its moving average and its RSI, the classic asset-vs-asset /
asset-vs-index rotation screen. Composes the existing Sma and Rsi over the
ratio; a zero denominator or non-finite price is skipped.

Struct output { ratio, ratio_ma, ratio_rsi }, exposed in Rust, Python, Node
and WASM with flat/rising-ratio/streaming tests and a pair fuzz driver.

* test(cointegration): cover ADF guard branches

The ADF helper's short-series and degrees-of-freedom guards and the
zero-dispersion (perfect AR) path are unreachable through the public
Cointegration API (period >= 2*adf_lags + 4), so exercise them with direct
unit tests on adf_no_constant. The second linear solve cannot be singular
once the coefficient solve on the same matrix has succeeded, so it now uses
expect() instead of a dead error branch.
2026-06-01 13:45:21 +02:00
kingchenc 1ab9bc70d1 ci(release): make the release immutability-ready (draft then publish) (#108)
GitHub release immutability locks a release's assets at publish time. The
current flow publishes the release in github-release and only afterwards uploads
the Sigstore provenance bundle (P21.1e) via 'gh release upload', which
immutability would reject (actions/attest-build-provenance#734).

Reorder to draft -> attach everything -> publish:
- github-release now creates the release as a draft (draft: true) with all build
  artefacts.
- attestations attaches the provenance bundle to the draft (gh release upload
  works on drafts), unchanged otherwise.
- a new publish-release job flips the draft to published + latest, gated on
  'always() && needs.github-release.result == success' so a Sigstore hiccup in
  attestations costs only the provenance asset, never the release — the same
  isolation as before. A skipped github-release (failed publish) skips this too.

Correct with immutability off (today: release ends published with every asset)
and on (later, user toggle: all assets present before the lock). No behaviour
removed; nothing deleted.
2026-06-01 12:06:20 +02:00
kingchenc 2ab578bee8 docs: surface docs.wickra.org + keep the wiki pointer count in sync (#107)
* docs(readme): surface the documentation site (docs.wickra.org)

The README never linked the canonical docs at docs.wickra.org — visitors had
no path from the repo to the per-indicator deep dives, quickstarts, and
guides. Add a docs badge, a Documentation section mirroring the binding
READMEs and docs/README.md, and an Indicators-Overview link in the indicator
section.

* ci(sync-about): keep the wiki pointer page's indicator count in sync

The GitHub wiki was collapsed to a single Home.md that points at
docs.wickra.org but still names the indicator count ('… for all N indicators').
Add a count-sync step mirroring the docs/webpage steps — clone wickra.wiki into
its own dir, sed Home.md, commit as wickra-bot, push — with the same
continue-on-error soft-skip so a missing PAT scope never fails the run.
2026-06-01 04:10:00 +02:00
kingchenc 2be39b8b98 ci(release): attach Sigstore provenance bundle as a release asset (P21.1e) (#106)
OpenSSF Scorecard's Signed-Releases check scans the GitHub Release *assets* for
signed/provenance files (`*.intoto.jsonl`, `*.sig`, ...). It does not look at
GitHub's separate attestations store, so although the attestations job has signed
the published bytes since v0.4.0, the v0.4.0 release assets carried no provenance
file and the check stayed at 0.

Attach the Sigstore provenance bundle (already produced by
actions/attest-build-provenance) to the release as `wickra-<tag>.provenance.intoto.jsonl`:

- github-release now exposes its resolved tag as a job output.
- attestations `needs: github-release` (so the Release already exists), gains
  `contents: write`, gives the attest step an id, and uploads the bundle with
  `gh release upload --clobber` (idempotent on re-runs).

Publishes stay fully isolated — cargo/PyPI/npm all run upstream of github-release,
so a Sigstore hiccup here can never block or corrupt a publish; at worst the
release just lacks the provenance asset. Signed-Releases climbs over the next
releases as each tag carries the bundle.
2026-06-01 04:09:09 +02:00
kingchenc 99af5f8ee1 ci: retry transient registry/DNS flakes at the cargo/npm/pip tool level (#105)
The v0.4.0-era CI failure was a runner network blip — `napi build` invokes cargo,
whose fetch of index.crates.io hit "Could not resolve host: index.crates.io" and
failed the Node-on-macOS job, forcing a manual re-run. The earlier flake-hardening
(setup-node/setup-python + rust-cache retries) only covered toolchain download and
cache restore, not the registry fetches inside the actual build/publish steps.

Set tool-level network retries as workflow env so every cargo/napi/maturin/
wasm-pack/npm/pip invocation in every job inherits them — including the nested
cargo calls inside napi/maturin/wasm-pack:

- CARGO_NET_RETRY=10 (default 3): cargo classes DNS-resolve / connect / timeout
  errors as spurious and retries with backoff; 10 attempts ride out a transient
  blip instead of failing the job.
- CARGO_NET_GIT_FETCH_WITH_CLI=true: more robust git-dep fetches.
- npm_config_fetch_retries=5 / maxtimeout=120s: npm ci/install registry retries.
- PIP_RETRIES=5 / PIP_DEFAULT_TIMEOUT=120: pip install resilience.

Applied to ci.yml, release.yml and bench.yml (the workflows that build). No more
manual re-runs for transient registry flakes.
2026-06-01 04:08:18 +02:00
kingchenc bff1148d20 ci(sync-about): fix docs version-sync clone collision + webpage npm race (#104)
* ci(sync-about): fix docs version-sync clone collision + webpage npm race

Two real release-time bugs surfaced by the v0.4.0 release, where the docs
"Published versions" table never updated and the marketing-site Cloudflare
build failed:

1. docs version sync never ran. The "Sync docs version (wickra-docs)" step
   cloned into a directory literally named `docs`, but on a tag push the job
   checks out the wickra repo at the workspace root, which already contains a
   top-level `docs/` directory. `git clone … docs` therefore failed with
   "destination path 'docs' already exists", silenced by `2>/dev/null` and
   misreported as a missing-token warning, so the docs version table stayed at
   the previous release. Clone into `docs-ver` instead (mirrors the `docs-count`
   dir the count step already uses); it collides with nothing in the repo.

2. webpage build broke on a version race. The "Sync webpage version" step bumps
   package.json's `wickra-wasm` pin to the released version and pushes
   immediately, but release.yml publishes wickra-wasm to npm in parallel on the
   same tag and finishes minutes later. Cloudflare's `npm clean-install` then
   hit `ETARGET: No matching version found for wickra-wasm@^0.4.0`. Poll npm for
   wickra-wasm@<version> (up to ~15 min) before committing; if it never appears
   the step skips with a warning rather than pushing a build-breaking commit.

Both steps were designed to mirror each other across docs/webpage; these fixes
restore that symmetry so every release self-heals both sites.

* ci(sync-about): regenerate webpage package-lock on version bump

Third v0.4.0 release-sync defect: the webpage version step seds package.json's
wickra-wasm pin but never touched package-lock.json, so even after wickra-wasm
went live on npm the Cloudflare build still failed with
`npm ci` EUSAGE: "lock file's wickra-wasm@0.3.1 does not satisfy
wickra-wasm@0.4.0".

After the package.json sed, run `npm install --package-lock-only` so the lockfile
(version + resolved + integrity) matches the new pin; commit package-lock.json
alongside package.json. The earlier npm-wait already guarantees the version is
resolvable. Guarded: if the regen fails the step skips the whole commit rather
than push a package.json/lock mismatch.

The live site was unblocked out-of-band by a matching lockfile commit on the
webpage repo; this makes it self-heal on every future release.
2026-06-01 02:02:19 +02:00
kingchenc ebddc5e376 ci: set least-privilege top-level token permissions (P21.1b) (#103)
The auto-injected GITHUB_TOKEN defaulted to write-all in ci.yml, bench.yml
and release.yml (no top-level permissions block), and codeql.yml declared
its scopes only at job level. Add a top-level `permissions: contents: read`
to all four so the token starts read-only and only the jobs that genuinely
write through it raise the scope:

- release.yml: github-release keeps contents: write; node-/wasm-publish and
  attestations keep their id-token / attestations: write blocks. The
  cargo/python/node publish jobs push to crates.io/PyPI/npm via their own
  registry secrets, not the GITHUB_TOKEN, so read-only is correct for them.
- codeql.yml: analyze keeps security-events: write (job level).
- ci.yml / bench.yml: no job writes back to the repo (coverage uploads via
  CODECOV_TOKEN; bench only uploads an artifact), so no job override is needed.

sync-about.yml already had a top-level block but at contents: write; demote
the top level to read and move contents: write down to the single `sync` job
(the PR-head counter push is the only GITHUB_TOKEN write). The cross-repo
About/docs/webpage/org writes are unaffected — they run through the
fine-grained ABOUT_SYNC_TOKEN, which the permissions key does not govern.

Raises OpenSSF Scorecard Token-Permissions from 0 toward 10.
2026-06-01 02:01:31 +02:00
kingchenc eab2649f1c release: bump 0.3.1 -> 0.4.0 (#89)
Minor release. The headline user-facing change is the Node binding now
rejecting invalid indicator periods instead of silently clamping period 0
to 1 (matches Python/WASM/core); plus per-ecosystem binding READMEs and a
corrected MSRV statement in CONTRIBUTING. See CHANGELOG [0.4.0].

- Cargo.toml (workspace.package + wickra-core dep) + Cargo.lock (6 members)
- bindings/python/pyproject.toml
- bindings/node/package.json (version + 6 optionalDependencies) + package-lock.json
- bindings/node/npm/*/package.json (6 platform subpackages)
- examples/node/package-lock.json (wickra-* platform pins)
- CHANGELOG: [Unreleased] -> [0.4.0] - 2026-05-31 + compare URL

No tag pushed (release publish is a separate, user-confirmed step).
2026-06-01 01:28:53 +02:00
kingchenc f7f947e048 docs(changelog): record provenance attestations + CI security tooling (Unreleased) (#102) 2026-06-01 01:08:14 +02:00
kingchenc 01dd08714b ci: harden workflows against network/CDN flakes (resilient cache + setup retries) (#101) 2026-06-01 01:07:56 +02:00
kingchenc 0fa70c9882 ci: pin remaining GitHub Actions to commit SHAs (P21.1c) (#100) 2026-06-01 01:07:34 +02:00
kingchenc debe4523d5 ci: pass untrusted workflow contexts via env to prevent shell injection (P21.1a) (#99) 2026-06-01 00:32:24 +02:00
kingchenc a046c441e5 docs(readme): show the Wickra banner; drop the redundant heading (#98)
Embed the brand banner at the top of the README (linked to wickra.org) and
drop the now-redundant "# Wickra" heading — the banner shows the wordmark.

The image is served from https://wickra.org/og-banner.webp, which the webpage
build regenerates on every deploy from the current indicator count (4K WebP),
so the README banner stays current with no committed binary in this repo and
renders on GitHub, crates.io and docs.rs alike.
2026-06-01 00:07:36 +02:00
kingchenc f7b91f6fa5 chore: use support@wickra.org for contact/author email; drop dead sponsor link (#97)
Now that the wickra.org catch-all mailbox exists, move the project contact +
package-author email off the personal gmail to support@wickra.org across all
surfaces: CODE_OF_CONDUCT, SECURITY, CITATION.cff, Cargo.toml, the npm + PyPI
author fields, the release.yml npm author, and repo-metadata.toml. (The
package-author changes take effect on the next published release.)

repo-metadata.toml's [audit].forbidden still pins kingchencp@gmail.com (the
private commit email) as a banned substring — unchanged.

Also remove the FUNDING.yml custom "https://wickra.org/sponsor" entry: that
page 404s, so the Sponsor button linked to a dead URL. The GitHub Sponsors
entry (github: [kingchenc]) stays.
2026-05-31 23:22:57 +02:00
kingchenc 5030360a0c ci: CodeQL SAST workflow + badge (P13.6) (#96)
* ci: add OpenSSF Scorecard workflow + badge (P13.1)

* ci(release): attest build provenance for crates + Python artifacts (P13.2)

* docs(readme): add GitHub release badge (P13.4)

* ci: add CodeQL SAST workflow + badge (P13.6)
2026-05-31 22:34:00 +02:00
kingchenc 1dd487fabc docs(readme): GitHub release badge (P13.4) (#94)
* ci: add OpenSSF Scorecard workflow + badge (P13.1)

* ci(release): attest build provenance for crates + Python artifacts (P13.2)

* docs(readme): add GitHub release badge (P13.4)
2026-05-31 22:21:05 +02:00
kingchenc cee174c0de ci(release): build-provenance attestations for crates + Python (P13.2) (#91)
* ci: add OpenSSF Scorecard workflow + badge (P13.1)

* ci(release): attest build provenance for crates + Python artifacts (P13.2)
2026-05-31 22:13:06 +02:00
kingchenc c8e5d8a658 ci: add OpenSSF Scorecard workflow + badge (P13.1) (#90) 2026-05-31 22:05:00 +02:00
kingchenc dc2e19e762 ci(sync-about): self-update the marketing site count + version (P12.1) (#95)
* ci(sync-about): auto-sync the published version to wickra-docs on release

* ci(sync-about): retarget indicator-count sync from wiki to wickra-docs + point About homepage at docs.wickra.org (P8.3)

* ci(sync-about): self-update the marketing site (webpage) count + version (P12.1)
2026-05-31 22:04:32 +02:00
kingchenc d8212beff6 ci(sync-about): retarget count sync to wickra-docs + point About at docs.wickra.org (P8.3) (#88)
* ci(sync-about): auto-sync the published version to wickra-docs on release

* ci(sync-about): retarget indicator-count sync from wiki to wickra-docs + point About homepage at docs.wickra.org (P8.3)
2026-05-31 21:57:42 +02:00
kingchenc 86a595d505 ci(sync-about): auto-sync the published version to wickra-docs on release (#87) 2026-05-31 21:48:53 +02:00
kingchenc 9309bf9d60 docs(P6.4): repoint all doc links from the GitHub wiki to docs.wickra.org (#86)
The documentation now lives in the wickra-lib/wickra-docs VitePress repo and
will deploy to docs.wickra.org. Rewrite every tracked, user-facing wiki link
in the main repo to the new canonical site:

- docs/README.md, CONTRIBUTING.md, PULL_REQUEST_TEMPLATE.md
- bindings/{node,python,wasm}/README.md, examples/wasm/README.md
- repo-metadata.toml: wiki_url/wiki_git -> docs_url/docs_git

Page paths map 1:1 to VitePress clean URLs (e.g. /wiki/Quickstart-Rust.md ->
docs.wickra.org/Quickstart-Rust). The sync-about.yml wiki-sync step is left
untouched on purpose: it stays until the docs site is live (tracked as P8.3).
The GitHub wiki itself is not deleted yet for the same reason.
2026-05-31 21:48:24 +02:00
kingchenc 3f05342f72 docs(P7): per-ecosystem binding READMEs + correct MSRV documentation (#85)
* docs(contributing): correct MSRV to 1.86/1.88 and document the dep-forced floor (P7.1)

* docs(bindings): trim binding READMEs to per-ecosystem install + links (P7.2)

* docs(changelog): note per-ecosystem binding READMEs + MSRV doc fix (P7.1/P7.2)
2026-05-31 05:30:34 +02:00
kingchenc eb4454ab27 P5: track index.d.ts + reject invalid periods in the Node binding (#83)
* build(node): track the generated index.d.ts (P5.1)

index.js was committed but index.d.ts was gitignored, an inconsistency that
also contradicts CONTRIBUTING ('regenerate both .d.ts/.js when a binding API
changes'). Track index.d.ts too so the repo carries the TypeScript types as a
matched pair with index.js. Generated by napi build; ~214 indicator classes.

* fix(node): reject invalid periods instead of clamping them (P5.4)

The Node scalar-indicator macro clamped period 0 to 1 (via clamp_period + must)
and the multi-parameter constructors did the same, silently swallowing the
core's PeriodZero validation. The core rejects period 0 (Error::PeriodZero),
and the Python and WASM bindings already propagate it — Node was the outlier,
masking caller mistakes. Make the macro constructor fallible and let every
constructor propagate the core error via map_err, removing clamp_period/must.
Update the smoke test (period 0 now throws, matching core/Python/WASM).

* docs(changelog): note the Node period-validation change (P5.4)

Behavior change per CONTRIBUTING: Node constructors now reject invalid periods
instead of clamping. Add an [Unreleased] Changed entry.

* chore: drop accidentally committed scratch log
2026-05-31 05:22:56 +02:00
kingchenc 3093f194a2 docs: document the repo-wide lockfile policy (P4) (#82)
The .gitignore comment claimed package-lock.json is committed only under
bindings/node/, but examples/node/package-lock.json has also been tracked
since #80. Correct the comment and add a CONTRIBUTING 'Lockfile policy'
section spelling out every component: Cargo.lock + the two Node package-locks
are tracked; fuzz/Cargo.lock is ignored (cargo-fuzz default); the Python
package has no lockfile by PyO3 convention (pinned via Cargo.lock); the
ghost-ignored site keeps its lockfile local.
2026-05-31 05:11:08 +02:00
kingchenc 21c86f348f examples + bindings: Node/WASM strategy parity + test/benchmark parity (P2 + P3) (#81)
* examples(node): add RSI mean-reversion strategy

Node counterpart of strategy_rsi_mean_reversion.{py,rs}: RSI(14) < 30 long,
> 70 exit, 0.1% fees, hourly BTCUSDT. Output verified byte-identical to the
Rust reference (37 trades W24/L13, -17.84% return, 46.89% max drawdown).

* examples(node): add MACD + ADX trend-filter strategy

Node counterpart of strategy_macd_adx.{py,rs}: MACD(12,26,9) histogram
crossover entries gated by ADX(14) > 20, hourly BTCUSDT, 0.1% fees. Output
verified byte-identical to the Rust reference (246 trades W90/L156, -47.19%
return, 53.75% max drawdown).

* examples(node): add Bollinger-squeeze breakout strategy

Node counterpart of strategy_bollinger_squeeze.{py,rs}: enter on a fresh
180-bar Bollinger-bandwidth low + close above the upper band, exit on a
2*ATR(14) stop or upper-band collapse, daily BTCUSDT, 0.1% fees. Output
verified byte-identical to the Rust reference (1 trade, -7.82% return,
13.01% max drawdown).

* examples(wasm): add RSI mean-reversion strategy demo

Browser counterpart of strategy_rsi_mean_reversion.{py,js,rs}: RSI(14) < 30
long, > 70 exit, 0.1% fees, summary table. Same signal/fill/PnL/equity loop as
the runtime-verified Node example; loads via the established wickra_wasm.js
init + fetch-CSV pattern. (wasm32 build runs in CI.)

* examples(wasm): add MACD + ADX trend-filter strategy demo

Browser counterpart of strategy_macd_adx.{py,js,rs}: MACD(12,26,9) histogram
crossover gated by ADX(14) > 20, hourly BTCUSDT, 0.1% fees. Logic identical to
the runtime-verified Node example; standard wickra_wasm.js init + fetch-CSV
loader. (wasm32 build runs in CI.)

* examples(wasm): add Bollinger-squeeze breakout strategy demo

Browser counterpart of strategy_bollinger_squeeze.{py,js,rs}: fresh 180-bar
Bollinger-bandwidth low + upper-band breakout, 2*ATR(14) stop, daily BTCUSDT,
0.1% fees. Logic identical to the runtime-verified Node example; standard
wickra_wasm.js init + fetch-CSV loader. (wasm32 build runs in CI.)

* ci: add examples syntax-smoke job (P2.3)

The Rust examples are built by 'cargo build -p wickra-examples --bins'; the
Node, browser-WASM and Python examples had no build gate. New job parse-checks
every examples/{node,wasm}/*.js, extracts and node --checks each WASM .html
module script, and python -m py_compile's every examples/python/*.py — so a
broken example edit fails CI instead of landing silently.

* docs(examples): list the new Node + WASM strategy examples

Add the three Node strategy scripts and three WASM strategy demos to the
examples README tables, bringing Node and WASM to parity with the existing
Rust and Python strategy rows.

* chore(examples): refresh examples/node lockfile for the linked wickra binding

npm install rewrote the file: dependency snapshot of the local wickra binding
that the examples link against (version 0.1.4 -> 0.3.1, license + engines
fields), which had gone stale in the committed lockfile.

* test(node): add input-validation suite

Node counterpart of bindings/python/tests/test_input_validation.py: invalid
constructor parameters (ATR zero period, MACD non-increasing fast/slow,
BollingerBands negative multiplier, PSAR step > max, ValueArea period/pct,
InitialBalance/OpeningRange zero period, Ichimoku non-increasing periods,
Ehlers-family ordering) and unequal-length candle/ValueArea batch inputs all
throw a JS Error. Validated against the built binding.

* test(node): add indicator completeness contract

Introspects every exported indicator class and asserts the full interface
(update / batch / reset / isReady / warmupPeriod) plus the pre-warmup contract
for zero-arg indicators, and guards that the full catalogue (>= 200 classes)
is exported. Catches a new indicator wired without the standard methods, or a
stale/partial native build dropping exports, with no per-indicator boilerplate.

* test(wasm): broaden scalar streaming-vs-batch coverage

Extend the inline wasm-bindgen-test suite with a streaming==batch check across
~70 scalar indicators spanning moving averages, momentum, volatility,
statistics/regression, Ehlers/cycle and risk/performance families (previously
only EMA + the candle-input group were covered per-indicator), plus four more
invalid-constructor assertions. Constructor args mirror the CI-passing Node
factories. Host-compiles (cargo test -p wickra-wasm --no-run); executed in CI
via wasm-pack test --node.

* bench(node): add indicator throughput benchmark

Node counterpart of the Rust criterion benches / Python compare_libraries:
measures streaming (per-tick update) and batch throughput in Mupd/s across a
representative indicator set over a synthetic OHLCV series (--bars, default
200k). Dependency-free; wired as 'npm run bench'.

* docs(wasm): list strategy demos + document the benchmark story

Add the three new strategy demos to the WASM examples table and a Performance
section: parallel_assets.html is the in-browser benchmark, with raw throughput
covered by the Rust criterion / Python / Node benchmarks (the WASM engine is the
same core compiled to wasm32).
2026-05-31 05:09:26 +02:00
kingchenc a2ff35f5f9 ci(sync-about): auto-sync repo homepage + org profile from the indicator count (#84)
Extend the count-sync workflow to drive three more surfaces from the same
single count, so the org page never drifts again:

- Repo About **homepage** URL — enforced unconditionally via
  `gh repo edit --homepage` in the existing About step (fixes the stale
  kingchenc URL and self-heals). Uses the same Administration-write
  permission as --description, so NO extra token scope.
- Org **profile README** count (wickra-lib/.github, profile/README.md) —
  clone + sed + bot commit/push, same pattern as the Sync Wiki step.
- Org **description** field ('… N indicators, install-free.') — gh api PATCH.

The two org steps need ABOUT_SYNC_TOKEN scope the main-repo syncs do not
(write on wickra-lib/.github; admin:org for the description PATCH). Both are
written to soft-skip with a ::warning:: and continue-on-error, so the run
stays green until the scope is granted — then they sync with no further
change. Homepage swap-point to a docs domain is a one-line change.

Refs findings P9 / P10.
2026-05-31 03:02:32 +02:00
kingchenc 01aeb965d1 release: bump 0.3.0 -> 0.3.1 (#80)
* chore: remove ROADMAP.md from the public repo

ROADMAP is kept as a local-only draft (ghost-ignored via
.git/info/exclude); it is not part of the published package surface.

* release: bump 0.3.0 -> 0.3.1

CI-only patch: fixes the release.yml CycloneDX SBOM step (cargo-cyclonedx
has no -p flag, see #79) that skipped the GitHub Release attach-assets job
on 0.3.0. No library changes — republishes the same code with a working
release pipeline.

- Cargo.toml (workspace.package + wickra-core dep) + Cargo.lock
- bindings/python/pyproject.toml
- bindings/node/package.json (version + 6 optionalDependencies) + package-lock.json
- bindings/node/npm/*/package.json (6 platform subpackages)
- CHANGELOG: finalize [0.3.0] (was still under [Unreleased]), add [0.3.1]

* chore: track examples/node/package-lock.json

Since the global package-lock ignore rule was dropped (#68) this file was
left untracked. Commit it for reproducible example installs, consistent
with bindings/node (findings P4.1).
2026-05-30 19:50:45 +02:00
kingchenc f1fed6cdd5 ci(release): fix CycloneDX SBOM generation (cargo-cyclonedx has no -p flag) (#79)
cargo-cyclonedx 0.5.9 walks the whole workspace in a single pass and
writes a <package>.cdx.json next to each member's Cargo.toml; it has no
-p/--package selector. The previous three 'cargo cyclonedx ... -p <crate>'
invocations aborted with 'error: unexpected argument -p found', failing
the cargo-publish job after the crates were already published and, in
turn, skipping the github-release attach-assets job (it needs all four
publish jobs). v0.3.0 published to every registry but got no GitHub
Release page or SBOM assets as a result.

Replace the three invalid calls with a single workspace pass and copy
the three crates.io crate SBOMs into the upload dir.
2026-05-30 19:26:30 +02:00
kingchenc 70e9cbb397 release: bump 0.2.7 -> 0.3.0 (supersedes PR #61) (#69)
Minor bump (not patch) because the [Unreleased] section since 0.2.7 has
accumulated a sweep of additive changes that justify a new minor:

- Family 9-16 indicator catalogue expansion (Bands & Channels, Trailing
  Stops, Volume, Statistics, Ehlers/Cycle DSP, Pivots, DeMark, Ichimoku,
  Candlestick Patterns, Market Profile, Risk/Performance) — roughly
  100+ new indicators since 0.2.7 across all four bindings.
- New `wickra_core::FAMILIES` const + family-taxonomy guard tests.
- GitHub org migration (kingchenc -> wickra-lib) and new maintainer
  email (wickra.lib@gmail.com).
- New `repo-metadata.toml` + `sync-metadata.yml` audit workflow.
- WASM CI tests now run on every PR (existing tests had been
  manually-only).
- CycloneDX SBOMs + npm provenance attestations attached to releases.
- Three end-to-end strategy examples.
- Governance polish: ARCHITECTURE / ROADMAP / CITATION / FUNDING /
  .editorconfig.
- Curated benchmark suite (~33 representative indicators).
- Three cold-path coverage fixes (mama, rsi, sine_wave).
- bindings/node/package-lock.json now committed.

Workspace + bindings (Rust crate, Python wheel, Node main + 6 platform
sub-packages, WASM) all step to 0.3.0. CHANGELOG opens the [0.3.0]
section dated 2026-05-28 with the full Changed / Added inventory.
Compare-URL block adds the v0.2.7...v0.3.0 line under [Unreleased] and
points [Unreleased] at v0.3.0...HEAD using the new wickra-lib org.

**Supersedes PR #61** (0.2.7 -> 0.2.8 patch bump). Close #61 when this
one merges. Merge ordering remains: #59 (org migration) + #60
(family-api) + the polish PRs first, rebase this PR on top of the new
main, then merge.

Tag-push `v0.3.0` is a SEPARATE, manual step after merge — it triggers
release.yml's irreversible publish to crates.io / PyPI / npm.
2026-05-30 19:06:48 +02:00
dependabot[bot] 37e5e19b57 deps(actions): bump taiki-e/install-action from 2.79.5 to 2.79.15 (#76)
Bumps [taiki-e/install-action](https://github.com/taiki-e/install-action) from 2.79.5 to 2.79.15.
- [Release notes](https://github.com/taiki-e/install-action/releases)
- [Changelog](https://github.com/taiki-e/install-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/taiki-e/install-action/compare/6c1f7cf125e42770ff087ea443901b487cc5471a...0fd46367812ee04360509b4169d9f659d6892bb2)

---
updated-dependencies:
- dependency-name: taiki-e/install-action
  dependency-version: 2.79.15
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-30 18:54:31 +02:00
dependabot[bot] c189075491 deps(actions): bump EmbarkStudios/cargo-deny-action (#74)
Bumps [EmbarkStudios/cargo-deny-action](https://github.com/embarkstudios/cargo-deny-action) from 2.0.19 to 2.0.20.
- [Release notes](https://github.com/embarkstudios/cargo-deny-action/releases)
- [Commits](https://github.com/embarkstudios/cargo-deny-action/compare/a531616d8ce3b9177443e48a1159bc945a099823...bb137d7af7e4fb67e5f82a49c4fce4fad40782fe)

---
updated-dependencies:
- dependency-name: EmbarkStudios/cargo-deny-action
  dependency-version: 2.0.20
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-30 18:52:23 +02:00
dependabot[bot] 74dd82a867 deps(actions): bump actions/checkout from 4 to 6 (#75)
Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 6.
- [Release notes](https://github.com/actions/checkout/releases)
- [Commits](https://github.com/actions/checkout/compare/v4...v6)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-30 18:52:15 +02:00
kingchenc b654db312e docs: fix unresolved and private intra-doc links (#78)
A full `cargo doc --workspace` (and docs.rs, which builds with
`-D rustdoc::all`) emitted five broken intra-doc links. docs.rs treats
these as hard errors, so any 0.2.x doc build was at risk of aborting.

- mama.rs: `[`Fama`]` -> `[`crate::Fama`]` (Fama lives in another module)
- standard_error.rs: `[`crate::Bollinger`]` -> `[`crate::BollingerBands`]`
  (the public type is `BollingerBands`, not `Bollinger`)
- aggregator.rs: drop the link to the private `OpenBar::into_candle`,
  keep it as plain code text
- resample.rs: drop the link to the private `RolledBar::into_candle`
- csv.rs: `CandleReader::with_timestamp_parser` never existed; reword to
  state plainly that ISO/RFC timestamps must be converted to integers

Verified with `RUSTDOCFLAGS="-D rustdoc::broken_intra_doc_links \
-D rustdoc::private_intra_doc_links" cargo doc --workspace --no-deps`:
clean, zero warnings.
2026-05-30 18:46:49 +02:00
kingchenc 88f119109d chore(node): commit bindings/node/package-lock.json for reproducible installs (#68)
Until now `package-lock.json` was globally ignored. Two practical
consequences for the Node binding:

- A fresh `git clone && cd bindings/node && npm install` resolved
  `@napi-rs/cli` and any transitive deps to whatever the npm registry
  currently considered the latest matching the package.json semver
  ranges. Contributors could get different dep graphs on different days.
- No protection against transitive-dep tampering at install time
  (lockfile records resolved versions + integrity hashes, npm verifies
  on subsequent installs).

Drop the global `package-lock.json` ignore and commit the freshly
generated `bindings/node/package-lock.json` (140 lines, only a couple
of direct deps because the binding is small). The `.gitignore` comment
notes that we still don't expect lockfiles at the workspace root.

No CI workflow changes — the existing `npm install` in the Node-test job
will now consume the committed lockfile, which is exactly what we want.
2026-05-30 18:24:46 +02:00
kingchenc 2945b47e1a feat(release): add CycloneDX SBOMs and npm provenance attestations (#66)
Two modern supply-chain-trust additions to the release pipeline,
neither of which changes what gets published — only adds verifiable
signals attached to existing releases.

1. **CycloneDX SBOMs.** `cargo-cyclonedx` is installed in the
   cargo-publish job after the .crate files are built, and runs once
   per published crate (`wickra-core`, `wickra-data`, `wickra`). The
   resulting `*.cdx.json` files are uploaded as the `sboms` artifact,
   then attached to the GitHub Release alongside the existing wheels,
   tarballs, .node binaries and .crate files. Future security advisories
   can answer "is my version of crate X transitive in wickra Y.Z?" by
   reading the SBOM directly instead of resolving the lockfile.

2. **npm `--provenance` flag** on every npm publish call:
   - main `wickra` package (node-publish, first + retry)
   - per-platform `wickra-<triple>` subpackages (node-publish loop)
   - `wickra-wasm` (wasm-publish)

   Provenance attestations are generated server-side by npm from the
   GitHub Actions OIDC token. The publishing jobs gain
   `permissions: id-token: write` so the runner can exchange that
   token. The npm page for each published version will then carry the
   "Verified provenance" badge, which proves the tarball was built by
   *this* workflow run and not by an arbitrary local laptop with the
   NPM_TOKEN.

Skipped deliberately (to keep this PR focused, possibly follow-ups):
- Sigstore cosign signing of artefacts (different audit story; can be
  layered on after npm-provenance lands).
- SLSA build-provenance attestations via `actions/attest-build-provenance`
  (would target every artefact uniformly; the npm-provenance flag is
  the more pragmatic first cut).

YAML structure validated (8 jobs intact). No production code touched.
This PR conflicts with PR #59 only in line-by-line URL substitutions
on release.yml — rebase after #59 should be clean.
2026-05-30 18:23:47 +02:00
kingchenc c212f91256 docs(examples): add 3 end-to-end strategy examples (Rust + Python) (#65)
Wires real indicators into complete signal -> fill -> PnL -> equity
loops over the checked-in BTCUSDT datasets, with per-trade Sharpe and
max-drawdown reported on stdout. Closes the gap where existing
examples showed only the mechanics of calling `update`/`batch` but not
how Wickra plugs into a trading-system shape.

Three strategies, each in Rust + Python (six files total):

- strategy_rsi_mean_reversion — RSI(14) thresholds (30/70) on 1h
  BTCUSDT. Binary position, 0.1% per-trade fee.
- strategy_macd_adx — MACD crossover entries gated by ADX(14) > 20 on
  1h BTCUSDT. Trend-follower demo of multi-indicator gating.
- strategy_bollinger_squeeze — Bollinger-bandwidth 180-day-low squeeze
  + upper-band breakout entry, ATR(14) * 2 stop. On 1d BTCUSDT for
  interpretable lookback.

Each file is self-contained — print_summary is inlined per script so
the example stays a single-file read. Every script prints a
NOT-financial-advice notice next to its results.

examples/README.md updated to list the new bins/scripts.
2026-05-30 18:23:03 +02:00
kingchenc 6c2ddf319f feat(wasm): wire wasm-bindgen-test into CI and expand coverage (#64)
* feat(wasm): wire wasm-bindgen-test into CI and expand binding coverage

The WASM binding already had 8 wasm_bindgen_test cases checked in but
they ran only when someone manually invoked `wasm-pack test` locally —
CI never executed them, so a breakage between the Rust core API and the
JS surface would only surface in user reports.

Two changes:

1. **New CI job `wasm-test`** in `.github/workflows/ci.yml` runs
   `wasm-pack test --node bindings/wasm` on every push and pull-request.
   Uses Node-runtime mode (no headless browser needed), pins
   `taiki-e/install-action` for `wasm-pack`, installs the
   `wasm32-unknown-unknown` target.

2. **10 new `#[wasm_bindgen_test]` cases** in `bindings/wasm/src/lib.rs`
   covering families that the existing 8 tests did not touch:
   - Family 4 — Macd multi-output batch shape (3-component packing)
   - Family 5 — Bollinger {upper, middle, lower} ordering invariant
   - Family 10 — FisherTransform streaming roundtrip (recursive DSP)
   - Family 16 — SharpeRatio reset semantics, MaxDrawdown monotone-
     uptrend invariant, ValueAtRisk constructor validation
   - Cross-family — reset returns indicator to warmup (3 shapes),
     warmup_period parity with wickra-core, NaN input rejection
     does not advance warmup counter

No production code changed; additive tests only. Total wasm test count
goes from 8 to 18.

* fix(wasm-tests): WasmAtr's hand-coded wrapper has no is_ready accessor

The new `reset_returns_indicator_to_warmup` test in this branch called
`atr.is_ready()` but `WasmAtr` is hand-coded (not generated by the
`wasm_scalar_indicator!` macro) and does not expose `is_ready` on the
JS surface — that pattern is reserved for macro-generated scalar
wrappers.

Replace the second leg of the test with `WasmEma` so the test exercises
two macro-generated scalars whose `is_ready`/`reset` semantics are
guaranteed by the same code path. The candle-input lifecycle is
already covered by `candle_input_streaming_matches_batch_and_lifecycle`.

Workspace clippy locally green:
- cargo clippy -p wickra-wasm --all-targets -- -D warnings
- cargo check -p wickra-wasm --target wasm32-unknown-unknown --tests

* fix(wasm-tests): Bollinger batch layout is 4 floats per bar, not 3

The new `bollinger_batch_orders_upper_mid_lower` test indexed the batch
output as `[u0, m0, l0, u1, m1, l1, ...]` (3 floats per bar) but the
actual WasmBb::batch layout is `[u0, m0, l0, sd0, u1, m1, l1, sd1, ...]`
— four floats per bar including stddev. The assertion read an upper
band against a middle from the previous bar, which can violate the
expected upper >= middle ordering and made the test fail intermittently.

Switch the stride from `* 3` to `* 4` so we read upper / middle / lower
from the same bar. Also add an `is_finite()` precondition so the test
fails with a clear "warmup positions unexpectedly NaN" message rather
than a confusing NaN-comparison if the dataset ever shrinks.

* fix(wasm-tests): drop redundant wasm-test job; existing WASM build covers it

The CI workflow already had a `WASM build` job that runs
`wasm-pack test --node bindings/wasm` after building. The
`wasm-test` job I added in cc961b3 duplicated that step and was
the only one whose `taiki-e/install-action` SHA was wrong, so it
also kept failing on a non-resolvable action reference.

Removing the duplicate keeps the test coverage (it lives in the
existing WASM build job) and gets rid of the unresolvable SHA.
2026-05-30 18:21:36 +02:00
kingchenc 9db1ff8023 chore(bench): curate benchmarks to ~30 representative indicators (#63)
Previously every push of `cargo bench -p wickra` ran 114 indicators at
three workload sizes each (1k / 10k / 50k candles), which inflated bench
runtime past ten minutes for diminishing signal — most family members
are linear scalings of the same hot loop, so a regression in any one of
them shows up identically in the cheapest member.

This commit replaces the exhaustive list with a curated selection: the
cheapest baseline and the most expensive representative from each of
the sixteen families, totalling ~33 indicators across scalar, candle,
and multi-output APIs.

If you need to profile a specific indicator that is not in the curated
set, add it temporarily and run `cargo bench -- <name>` to target just
that bench; it does not need to be committed.

Fixed in passing:
- `Cci` is `Indicator<Input = Candle>`, not f64; corrected the bench
  selector to `bench_candle_input`.
- `Psar::new` takes (af_start, af_step, af_max) — supplied all three.
- `TdSequential` uses `::classic` for the textbook (4, 9, 2, 13)
  parameters; the old call was missing arguments.
2026-05-30 18:21:19 +02:00
kingchenc fb6eae7fe2 docs: add ARCHITECTURE, ROADMAP, CITATION, FUNDING, editorconfig (#62)
Five governance + onboarding files that collectively close the
"no high-level documentation outside of README" gap.

- ARCHITECTURE.md: workspace layout, Indicator trait contract,
  per-indicator file conventions, numerical-stability notes,
  cross-crate flow diagram, navigation cheatsheet, deliberate
  non-goals, performance characteristics, stability commitments.
- ROADMAP.md: north star, 0.3 / 0.4 / 0.5 release windows,
  indicator-wishlist intake rules, explicit non-goals, versioning policy.
- CITATION.cff: GitHub-renders as "Cite this repository" button;
  enables academic adoption.
- .github/FUNDING.yml: surfaces Sponsor button on the repo page.
- .editorconfig: normalises indent/EOL across IDEs (Rust/Python 4 spaces,
  JS/TS/JSON/YAML/MD 2 spaces, Makefile tabs).

Touches no Rust source, no CI workflows, no behaviour. Additive only.
URLs in ROADMAP/CITATION already reference the post-transfer
`wickra-lib/wickra` org so the files stay valid once the org migration
in PR #59 lands; merge this PR only after #59 to keep main consistent.
2026-05-30 18:20:52 +02:00
kingchenc 0edb9f4857 fix(bindings): clear clippy pedantic lints and lint bindings in CI (#77)
* fix(bindings): clear clippy pedantic lints and lint bindings in CI

Resolve the pedantic lints that only surfaced under a full
`cargo clippy --workspace` (the CI clippy job covered only the core
crates, so the Python/Node bindings drifted):

- manual_midpoint: `(a + b) / 2.0` -> `f64::midpoint(a, b)` (node + python)
- new_without_default: add `Default` impls for the six no-arg Node nodes
- type_complexity: factor the pivot/Ichimoku return tuples into
  `PivotLevels` / `WoodieLevels` / `IchimokuLines` aliases (python)
- many_single_char_names: allow at crate level — OHLCV batch helpers bind
  the conventional o/h/l/c/v column names

Add a dedicated `clippy-bindings` CI job (ubuntu-only, with Python + Node
toolchains) so future binding lints fail CI instead of slipping through.

* ci: lint Python and Node bindings in a dedicated clippy job

The main `rust` job's clippy step only covers wickra-core/wickra/
wickra-data/wickra-wasm, so pedantic lints in the PyO3/napi bindings
slipped through. Add an ubuntu-only `clippy-bindings` job that
provisions Python + Node (needed by the build scripts) and runs
`cargo clippy -p wickra-node -p wickra-python --all-targets -- -D warnings`.
2026-05-30 18:16:21 +02:00
kingchenc ea684e0d48 feat(family-api): add FAMILIES const and family-taxonomy tests (#60)
* feat(family-api): add FAMILIES const and family-taxonomy tests

Introduce `wickra_core::FAMILIES`, a `&'static [(&str, &[&str])]` mapping
every built-in indicator to one of 16 families (Moving Averages, Momentum
Oscillators, Trend & Directional, Price Oscillators, Volatility & Bands,
Bands & Channels, Trailing Stops, Volume, Price Statistics, Ehlers /
Cycle (DSP), Pivots & S/R, DeMark, Ichimoku & Charts, Candlestick
Patterns, Market Profile, Risk / Performance).

Two compile-time-anchored guards make sure the const stays trustworthy:
- `no_duplicates_across_families`: no indicator is listed in two families
- `total_count_matches_expected`: hard-coded total bumps in lockstep with
  the indicator catalogue (214 today), so any new indicator added without
  being filed under a family trips the test.

Also corrects the module doc comment which falsely claimed
indicators were grouped by category internally; the `mod` block is
alphabetical, and the canonical taxonomy now lives in `FAMILIES`.

* chore: sync indicator count to 214

---------

Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
2026-05-30 14:58:16 +02:00
kingchenc 62ab84c472 chore(migration): switch org to wickra-lib and maintainer email to wickra.lib@gmail.com (#59)
Introduce repo-metadata.toml as single source of truth for repo identity
(org slug, maintainer email, canonical URLs) and add sync-metadata.yml
workflow with a Python audit script that fails CI if any tracked file
drifts back to pre-migration values.

Bulk-replace across 24 tracked files:
- kingchenc/wickra -> wickra-lib/wickra (URL segment)
- kingchencp@gmail.com -> wickra.lib@gmail.com (maintainer email)
- @kingchenc -> @wickra-lib (CODEOWNERS mention only)

Person-name credits are preserved: LICENSE copyright holder, Cargo.toml
authors handle, and CHANGELOG historical @kingchenc reference all remain
unchanged. Crate / PyPI / npm package names also untouched.

Merge this PR only after the kingchenc/wickra -> wickra-lib/wickra org
transfer has happened on the GitHub side, otherwise all badges and
repository links 404 until the transfer is performed.
2026-05-30 12:18:10 +02:00
kingchenc 0f2ff9c3c7 chore(sync-about): count public indicator types, not module files (#70)
* chore(sync-about): count public indicator types, not module files

The sync-about workflow counted `mod xxx;` lines in
crates/wickra-core/src/indicators/mod.rs to derive the indicator count
that gets propagated to README, the GitHub About description, and the
wiki. That under-reported by one because `vwap.rs` exports two public
types — `Vwap` (cumulative) and `RollingVwap` (finite window) — from
the same module. The bindings reach both, so users see 214 indicators
even though there are only 213 source files.

Fix the count by parsing the canonical `pub use indicators::{ ... }`
block in lib.rs, dropping the `FAMILIES` constant and any `*Output`
companion structs, and counting the remaining public types. Pure-shell
implementation so the workflow doesn't grow a python dependency.

Sync README to the corrected count (213 -> 214) in the same commit so
the PR validates cleanly through the workflow's PR-flow.

* docs(bindings): sync per-binding READMEs and docs/ pointer to 214 / 16

The bindings/{node,python,wasm}/README.md files (which ship to
npm / PyPI / npm again) and docs/README.md (the pointer to the
wiki) all carried the stale '71 streaming-first indicators across
eight families' header from before the family expansion. Pull the
canonical 16-family table out of the main README into all three
binding READMEs and update docs/README.md to list every family by
name, so a user landing on PyPI / npm sees the current catalogue
shape.
2026-05-30 00:40:13 +02:00
kingchenc e85334a2e9 test(cold-paths): cover 3 lines in mama/rsi/sine_wave (#58)
* test(cold-paths): cover phase fallback in mama/sine_wave and rsi naive helper saturation

* fix(rsi): drop redundant closure in test helper
2026-05-26 21:08:50 +02:00
kingchenc 4e3c41ea80 feat(family-15): add 17 risk/performance metrics (#54)
* feat(family-15): add 17 risk/performance metrics

Implements Family 15 pragmatically as standard `Indicator`s instead of a
separate `wickra-metrics` crate. Input is scalar `f64` per bar — period
return, equity sample, or per-trade P&L depending on the metric.

Scalar `Indicator<f64>` (14):
- SharpeRatio(period, risk_free)
- SortinoRatio(period, mar)
- CalmarRatio(period)
- OmegaRatio(period, threshold)
- MaxDrawdown(period)          — rolling, peak-to-trough
- AverageDrawdown(period)
- DrawdownDuration             — cumulative, bars under water (u32 output)
- PainIndex(period)
- ValueAtRisk(period, confidence)
- ConditionalValueAtRisk(period, confidence)
- ProfitFactor(period)
- GainLossRatio(period)
- RecoveryFactor               — cumulative, net return / max drawdown
- KellyCriterion(period)

Two-series `Indicator<(f64, f64)>` for (asset, benchmark) returns (3):
- TreynorRatio(period, risk_free)
- InformationRatio(period)
- Alpha(period, risk_free)     — Jensen / CAPM

Touchpoints:
- 17 new files under `crates/wickra-core/src/indicators/`.
- `mod.rs` + `lib.rs` re-exports.
- Python bindings (`bindings/python/src/lib.rs`, `__init__.py`).
- Node bindings (`bindings/node/src/lib.rs`, `index.js`).
- WASM bindings (`bindings/wasm/src/lib.rs`).
- Fuzz: scalar metrics appended to `indicator_update.rs`; new
  `indicator_update_pair.rs` fuzz target for `(f64, f64)` indicators.
- Python tests: SCALAR + new PAIR parameter lists in `test_new_indicators.py`,
  reference-value cases in `test_known_values.py`.
- Node tests: scalar factories + new pair-factory block in
  `bindings/node/__tests__/indicators.test.js`.
- Benches: 5 Family-15 benches added in `crates/wickra/benches/indicators.rs`.
- Docs: README family-table row + counter (71 -> 88), CHANGELOG entry under
  [Unreleased].

Note: Family 12 (statistik-regression, PR #51) introduces
`node_pair_indicator!` and `wasm_pair_indicator!` macros for Pearson /
Beta / Spearman. Family 15 needs the same pair-input pattern but Family 12
is not yet in main, so the three pair wrappers below are written by hand
in this PR. When PR #51 lands, the trivial merge-conflict is resolved by
keeping the macros from Family 12 and re-using them for Treynor / IR /
Alpha (drop the three handwritten wrappers).

cargo check --workspace --all-features: green.

* fix(family-15): satisfy clippy doc_markdown / if_not_else / digit_grouping

* fix(family-15): unused TreynorRatio import, duplicate pairFactories, _eq_nan inf handling

* fix(family-15): node eq() handles matching infinities for ratio indicators

* test(family-15): cover cold paths flagged by codecov patch
2026-05-26 20:44:21 +02:00
kingchenc 55284a3042 feat(family-14): add 15 candlestick patterns (#53)
* feat(family-14): add 15 candlestick patterns

Introduces the Candlestick Patterns family (block A of the family-14 spec)
as scalar f64 indicators on Candle inputs. Each detector emits +1.0 for a
bullish reading, -1.0 for a bearish reading, and 0.0 when no pattern is
present. Doji is direction-less and emits +1.0 / 0.0 only.

New indicators (15):

- Doji
- Hammer
- InvertedHammer
- HangingMan
- ShootingStar
- Engulfing
- Harami
- MorningEveningStar (signed: +1.0 morning star, -1.0 evening star)
- ThreeSoldiersOrCrows (signed: +1.0 soldiers, -1.0 crows)
- PiercingDarkCloud (signed: +1.0 piercing, -1.0 dark cloud)
- Marubozu (signed: +1.0 bullish, -1.0 bearish, 5 percent shadow tolerance default)
- Tweezer (signed: +1.0 bottom, -1.0 top, 10 bps relative tolerance default)
- SpinningTop (direction-signed indecision)
- ThreeInside (confirmed Harami)
- ThreeOutside (confirmed Engulfing)

MVP scope notes:

- Pattern-shape check only, no trend filter applied. Caller combines with a
  trend indicator for actionable signals. Documented in every doc comment.
- Block B (Harmonic patterns) and block C (Chart patterns) remain
  out-of-scope and will follow when the pattern-detection framework (pivot
  detector, multi-bar state machines) lands.

Touched across all bindings: Python, Node, WASM. Fuzz target, Python tests
(streaming-vs-batch + reference values), Node tests (streaming-vs-batch +
reference values), and a representative bench subset (1-, 2- and 3-bar
patterns) added. README family table + indicator counter (71 -> 86, eight
-> nine families) and CHANGELOG [Unreleased] updated.

* fix(family-14): unpack MULTI values with *_ to handle 3-element tuples

* cov(family-14): cover Default impl cold paths and MorningEveningStar guard branches
2026-05-26 00:54:11 +02:00
kingchenc 9b8e1346ed feat(family-16): add ValueArea + InitialBalance + OpeningRange (#52)
* feat(family-16): add ValueArea + InitialBalance + OpeningRange

Opens family #16 (Market Profile) with the three OHLCV-compatible scalar /
multi-output indicators:

- ValueArea(period, bin_count, value_area_pct) -> {poc, vah, val}.
  Rolling bin-approximation volume profile over the last `period`
  candles. Each candle's volume is spread uniformly across [low, high];
  POC is the bin with highest cumulative volume; the value area expands
  symmetrically from POC and always absorbs the higher-volume neighbour
  next, until `value_area_pct` (default 0.70) of total volume is
  enclosed. Defaults (20, 50, 0.70).

- InitialBalance(period) -> {high, low}. Tracks session-opening high
  and low over the first `period` bars, then locks. Default period = 12
  (one-hour IB on 5-minute bars for US equities). Callers MUST invoke
  reset() at every session boundary, otherwise IB stays fixed for the
  lifetime of the instance.

- OpeningRange(period) -> {high, low, breakout_distance}. Same
  lock-after-N-bars semantics as IB with a shorter default period
  (6 = 30 min on 5-minute bars) and a third output that tracks
  close - or_mid (positive above the range mid, negative below).

Histogram-output Market Profile variants (Volume Profile, VPVR,
Composite Profile) are deferred because they need a new histogram
output API layer rather than fixed-arity scalars. Tick-data-only
variants (TPO Profile, Single Print, Order Flow Delta, Cumulative
Delta, Volume-Weighted Open) are out of scope because `wickra-data`
does not currently expose tick / L2 data.

All four bindings (Rust core, Python, Node, WASM) ship the new
indicators with parity tests; benches added; fuzz target extended.
Counter 71 -> 74 across 8 -> 9 families. cargo check --workspace
--all-features green.

* fix(family-16): cover cold paths in InitialBalance + ValueArea

InitialBalance::value() public getter had no test covering the post-update
Some(...) branch — extended accessors_and_metadata to call value() after one
update. ValueArea single-print bar path (c.high == c.low) was unreachable in
existing tests since the only single-print test used a uniform 100-price
window which exits early via the span == 0 guard; added a mixed-window test
that triggers the c.high <= c.low branch directly. The (None, None) arm of
the expansion match was by-construction unreachable (the loop condition
already requires at least one neighbour) and has been folded into an
if/else.
2026-05-26 00:14:30 +02:00
kingchenc 05fcdd9a5e feat(family-12): add 13 Statistik/Regression indicators (#51)
* feat(family-12): add 13 Statistik/Regression indicators

Brings the Price Statistics family to 20 indicators (7 → 20) and the
total catalogue to 84 (71 → 84). Every indicator ships in the Rust
core plus Python, Node, and WASM bindings with full streaming ↔ batch
parity, fuzz coverage, and benches.

Scalar (f64 → f64):
- Variance, CoefficientOfVariation: rolling population variance and
  its dimensionless ratio with the mean. O(1) updates.
- Skewness, Kurtosis: rolling Pearson skewness and excess kurtosis,
  derived from running sums of x, x², x³, x⁴ via the binomial
  identities — also O(1) per bar.
- StandardError, DetrendedStdDev: standard error of estimate (n − 2)
  and population StdDev (n) of OLS residuals, sharing the LinReg
  O(1) sliding sums.
- RSquared: coefficient of determination of the rolling OLS fit; the
  trend-quality filter, clamped to [0, 1].
- MedianAbsoluteDeviation: robust dispersion estimator; O(period log
  period) per emission via two in-place sorts of a reusable scratch
  buffer.
- Autocorrelation(period, lag): rolling lag-k Pearson autocorrelation.
- HurstExponent(period, chunks): R/S-analysis trend-persistence
  estimator clamped to [0, 1].

Pair indicators (Input = (f64, f64)):
- PearsonCorrelation: rolling cross-series Pearson, O(1).
- Beta: rolling OLS slope of asset vs. benchmark (CAPM).
- SpearmanCorrelation: rolling rank correlation with mid-rank tie
  handling; O(period log period).

Touchpoints:
- crates/wickra-core: 13 new indicator modules + mod.rs / lib.rs
  re-exports.
- bindings/python: pyclasses + add_class registration + __init__.py
  import & __all__ updates. The pair indicators expose
  update(x, y) and batch(x, y) over two equally-sized numpy arrays.
- bindings/node: scalar indicators via node_scalar_indicator! macro;
  pair indicators via new node_pair_indicator! macro; explicit
  structs for Autocorrelation and HurstExponent (two-arg ctors).
  index.js extended with the new exports.
- bindings/wasm: scalar wrappers via wasm_scalar_indicator!; pair
  wrappers via new wasm_pair_indicator! macro.
- fuzz: every scalar drove through the generic helper; pair
  indicators stress-tested by pairing adjacent samples of the fuzz
  input.
- Python tests (test_new_indicators.py): added to SCALAR
  parametrisation, plus algebraic reference values
  (variance of [2,4,6] = 8/3, MAD ignoring outlier = 0, monotone
  non-linear Spearman = 1, two-to-one Beta = 2, etc.) and a
  streaming-vs-batch test for the pair indicators.
- Node tests (indicators.test.js): extended the scalar factories
  map and added a pair-indicator section with the same algebraic
  reference values.
- crates/wickra/benches: bench_scalar entries for all 10 single-
  input new indicators.
- README: counter 71 → 84; Price Statistics family-table row
  expanded with the 13 new indicators.
- CHANGELOG: Unreleased section documents the family addition.

Wiki drafts (ghost-ignored, manual sync to wickra.wiki at release
time): indicator-ideas/families/wiki/family-12-statistik-regression/
contains 13 deep-dive pages plus _Sidebar / Indicators-Overview /
Warmup-Periods / Home fragments for the curator merge.

cargo check --workspace --all-features: clean.

* fix(family-12): remove unreachable defensive guards in hurst_exponent

The three guards (m < 2 continue, end > buf.len() break, denom == 0.0
return) are by-construction unreachable given the constructor invariant
period >= 2 * chunks: m = period / k for k in 1..=chunks always
satisfies m >= 2 and end = (c+1) * m <= k * m <= period = buf.len(),
and m_1 = period and m_2 = period / 2 are always distinct so the slope
denominator is strictly positive. Removing them brings codecov/patch
back to 100%.
2026-05-25 23:42:05 +02:00
kingchenc 5aa0949bce feat(family-13): add Ichimoku + Heikin-Ashi (#50)
Two new indicators in a brand-new "Ichimoku & alternative charts"
family:

- `Ichimoku` (Ichimoku Kinko Hyo): the full five-line cloud system
  (Tenkan-sen, Kijun-sen, Senkou Span A/B, Chikou Span). Classic
  (9, 26, 52, 26) defaults; configurable. Forward displacement is
  handled in an O(1) ring buffer so the visible Senkou A/B at bar n
  are the values computed at bar n-displacement.
- `HeikinAshi`: recursive candle smoothing transform emitting a
  four-field synthetic candle. Seeds ha_open from (open+close)/2 on
  the first bar.

Touchpoints: core + unit tests, mod.rs/lib.rs re-exports, Python +
Node + WASM bindings (multi-output via PyArray2 / interleaved Vec<f64>
/ Object+Float64Array), Python tests across smoke/new-indicators/
input-validation, Node parity tests, fuzz target (Candle), benches,
README family table + counter (71 -> 73, 8 -> 9 families), CHANGELOG.

Note: Renko, Kagi, and Point & Figure from the family-13 ideas list
are intentionally skipped. They are bar generators (the bar boundary
is defined by price moves, not by a fixed time interval) rather than
indicators that consume a candle stream, and belong in wickra-data
as candle/tick transforms alongside the existing tick-to-candle
aggregator and resampler.
2026-05-25 23:02:29 +02:00
kingchenc b971e671b4 test(family-10): cover cold paths flagged by codecov (#57)
Add tests that exercise the protective fallbacks reachable via flat or
zero-valued input:

- `CenterOfGravity::zero_window_uses_zero_fallback` — den == 0 branch.
- `EhlersStochastic::flat_window_emits_zero` — range == 0 branch.
- `Mama::flat_input_uses_phase_fallback` and the matching
  `SineWave` variant — `i1` collapses to zero on a constant series.
- `Fama::new_with_valid_limits_constructs_via_mama` exercises the
  `Ok(Self { inner: Mama::new(..)? })` arm that no other test reaches.

Three branches were genuinely unreachable by construction, so the dead
code is removed rather than masked with an attribute:

- `Mama` clamped `alpha > fast_limit` after the lower-bound clamp; the
  upper bound is implied by `delta_phase >= 1` and `alpha = fast / delta_phase`.
- `CyberneticCycle` had a `0.0` fallback after the warmup gate that the
  3-slot ring buffers preclude (`count >= 7` => all five `Some`s).
- `DecyclerOscillator` used a `let-else { return None }` over a pair of
  `Decycler::update` calls that always emit `Some` from the first bar.
2026-05-25 22:32:00 +02:00
kingchenc 7a18a26daf feat(family-10): add 16 Ehlers / Cycle (DSP) indicators (#49)
Implements Family 10 (Ehlers / Cycle) end-to-end across Rust core,
Python / Node / WASM bindings, fuzz, tests, benches and docs. This
is an entirely new family covering John Ehlers' digital-signal-
processing school of cycle analytics — a strong differentiator
versus TA-Lib and pandas-ta, which ship only fragments.

Indicators:
- MAMA (Mesa Adaptive MA) — multi-output { mama, fama }
- FAMA (Following Adaptive MA) — scalar wrapper around MAMA's slow line
- Fisher Transform — Gaussian-normalising price transform
- Inverse Fisher Transform — bounded oscillator (tanh-based)
- SuperSmoother — 2-pole Butterworth lowpass
- Roofing Filter — high-pass + SuperSmoother bandpass
- Decycler — price minus 2-pole high-pass (lag-free trend)
- Decycler Oscillator — fast / slow Decycler difference (MACD-like)
- Hilbert Dominant Cycle — phase-derived period estimator [6, 50]
- Sine Wave Indicator — sin(phase) with 45° lead companion
- Adaptive Cycle Indicator — half-period driver for adaptive oscillators
- Center of Gravity Oscillator — weighted-mass momentum
- Cybernetic Cycle Component — EasyLanguage classic
- Empirical Mode Decomposition — bandpass + envelope mean
- Ehlers Stochastic — Stochastic on Roofing Filter input, [-1, +1]
- Instantaneous Trendline — Ehlers 2-pole lag-free trend

Indicator count rises 71 -> 87 across nine families (was eight).

All sixteen pass batch == streaming equivalence, expose the standard
Indicator surface (update / batch / reset / is_ready / warmup_period
/ name), are fuzz-tested, benchmarked against the checked-in BTCUSDT
1-minute dataset and reach across all four bindings.

Wiki deep-dive drafts for every indicator + Sidebar / Overview /
Home / Warmup updates are staged under indicator-ideas/families/
wiki/family-10-ehlers-cycle/ in the main repo (ghost-ignored) for
the maintainer to publish to the wiki repo manually.
2026-05-25 22:14:27 +02:00
kingchenc 4f9ed34884 feat(family-11): add DeMark suite (TD Setup, Sequential, DeMarker, REI, Pressure) (#48)
* feat(family-11): add DeMark suite (TD Setup, Sequential, DeMarker, REI, Pressure)

Family 11 (DeMark) was previously empty; this PR adds five
streaming-first DeMark indicators in one batch.

- **TD Setup** (`TdSetup`): parameterised buy/sell setup counter.
  Counts consecutive bars whose close is less-than (buy) or
  greater-than (sell) the close `lookback` bars earlier, saturating
  at `target`. Emits a signed `f64` so callers read direction from
  the sign and run length from the magnitude. Classic config:
  `lookback = 4`, `target = 9`.

- **TD Sequential** (`TdSequential`): the canonical Setup + Countdown
  exhaustion pattern. Output struct `{ setup, countdown, direction }`
  exposes both phase counts as signed numbers plus the active
  countdown direction (+1 buy / -1 sell / 0 none). Countdown
  activates when a setup completes and tracks the close-vs-high/low
  comparison `countdown_lookback` bars back, capped at
  `countdown_target`. Classic: 4/9/2/13.

- **TD DeMarker** (`TdDeMarker`): bounded [0, 1] oscillator from the
  rolling average of upward high expansion (DeMax) and downward low
  expansion (DeMin). Falls back to the neutral 0.5 on a flat market
  (denominator zero).

- **TD REI** (`TdRei`): Range Expansion Index, bounded [-100, 100].
  Per-bar numerator gated on a range-overlap condition vs the bars
  5 and 6 back, normalised by a `period`-bar sum of absolute moves.
  Classic period = 5. Saturates at +100 in a slow steady uptrend
  and at -100 in the mirror downtrend; emits 0 on a flat market.

- **TD Pressure** (`TdPressure`): volume-weighted buying / selling
  pressure normalised to [-100, 100]. Per-bar pressure is the
  intra-bar close-vs-open ratio scaled by volume; the output is the
  rolling mean divided by the rolling mean volume. Zero-range bars
  contribute zero (avoid the undefined ratio) and a flat zero-volume
  window falls back to 0.

Bindings: all five exposed in Python (`ta.TDSetup`, `ta.TDSequential`,
`ta.TDDeMarker`, `ta.TDREI`, `ta.TDPressure`), Node (`wickra.TDSetup`
etc.), and WASM. Multi-output classes (`TDSequential`) return either
a struct `{ setup, countdown, direction }` per bar (streaming) or a
flat interleaved Float64Array of length `3 * n` (batch).

Tests: 47 unit tests across the five new core files (pure-trend
saturation, flat-market neutral fallback, batch-equals-streaming,
zero-parameter rejection, reset semantics, accessors). Python
test_new_indicators.py picks up all five plus a multi-output TD
Sequential block. Node indicators.test.js picks up all five.
Reference values added to test_known_values.py.

Fuzz: candle fuzz target sweeps all five DeMark indicators with the
existing `Vec<f64>` -> `Vec<Candle>` driver.

Benches: BTCUSDT 1-minute dataset benches for each DeMark indicator
in `crates/wickra/benches/indicators.rs`.

Docs: README family table gains a "DeMark" row; indicator counter
bumped 71 -> 76. CHANGELOG entry added under [Unreleased]. Wiki
drafts (deep-dive pages + Sidebar / Overview / Warmup-Periods / Home
deltas) live under `indicator-ideas/families/wiki/family-11-demark/`
for manual merge into the wiki repo.

* feat(family-11): add 7 missing DeMark indicators

Complete the DeMark suite (family 11) with the seven indicators not
covered by the first commit: TD Combo, TD Countdown, TD Lines (TDST),
TD Range Projection, TD Differential, TD Open, and TD Risk Level.

- TdCombo: aggressive countdown variant with three strictness rules
  on top of the classic close-vs-low/high lookback rule (monotone
  low/high, monotone close vs prior bar).
- TdCountdown: standalone 13-bar countdown packaging only the signed
  countdown count (the setup machine runs internally).
- TdLines: TDST horizontal support/resistance levels from the
  highest-high / lowest-low bars of the most-recently-completed
  setup, exposed as a multi-output struct.
- TdRangeProjection: DeMark X-projection of the next bar's high and
  low from the current bar's OHLC via an open-vs-close-weighted
  pivot (three branches: close<open, close>open, close==open).
- TdDifferential: two-bar buying-pressure vs selling-pressure
  reversal pattern emitting +1/-1/0.
- TdOpen: gap-and-fade reversal pattern (open outside prior range
  with subsequent recovery into it) emitting +1/-1/0.
- TdRiskLevel: protective stop levels derived from the setup
  extreme bar +/- its true range.

All seven are wired through Rust core, Python, Node and WASM
bindings, registered in the candle-stream fuzz target, given
benchmark entries on the BTCUSDT 1-minute dataset, and covered by
streaming-vs-batch equivalence, reference-value, lifecycle and
input-validation tests on the Python and Node sides. README counter
moves 76 -> 83 and the CHANGELOG "family 11" entry is extended to
list all twelve indicators.

* fix(td_risk_level tests): check first emission at idx 12, not last bar

TdRiskLevel re-ratchets the sell-risk level on each subsequent setup
completion, so a strictly rising series produces 22.0 at idx 19 (latest
setup) rather than 15.0 (first setup). The test comment already named
idx 12 as the reference; switch the assertion from out[-1] to out[12]
to match the reference computation.

* test(family-11): cover buy-direction branches in TD indicators

Add downtrend tests to TdSequential, TdCombo and TdCountdown so the
buy-side countdown/combo increment branches are exercised; remove an
empty `if buy_countdown == target {}` block in TdSequential whose
behavior is already enforced by the outer strict `<` guard.

Closes codecov/patch gaps reported on PR #48 (10 missed lines across
the three files).
2026-05-25 20:36:36 +02:00
kingchenc 7e1e988596 feat(family-08): Pivots & Support/Resistance (7 indicators) (#47)
* feat(family-08): add Classic, Fibonacci, Camarilla, Woodie and DeMark pivots + Williams Fractals + ZigZag

Seven new indicators land the previously empty Pivots & S/R family
(family 08), each implemented in wickra-core with the full Indicator
trait surface (update / reset / warmup_period / is_ready / name),
exposed across Python (PyO3), Node (napi-rs) and WASM (wasm-bindgen)
with the standard streaming + batch APIs, and covered by Rust unit
tests, Python streaming-vs-batch + reference-value tests, Node
streaming-vs-batch tests, the candle-input fuzz target and Rust
microbenchmarks.

- ClassicPivots (7 levels): PP = (H+L+C)/3, three R/S tiers per the
  floor-trader formulas.
- FibonacciPivots (7 levels): PP plus R/S spaced by 0.382 / 0.618 /
  1.000 of the prior range.
- Camarilla (9 levels): Nick Stott's four-tier `C +/- (H - L) * 1.1 /
  {12, 6, 4, 2}` levels.
- WoodiePivots (5 levels): close-weighted PP = (H + L + 2*C) / 4 plus
  two R/S tiers.
- DemarkPivots (3 levels): conditional X sum based on the previous
  bar's open-vs-close relationship.
- WilliamsFractals: five-bar swing detector emitting optional up/down
  fractal prices at the centre of each window.
- ZigZag: percent-threshold swing tracker, non-repainting; emits the
  just-completed extreme and direction on confirmed reversals only.

README family table updated to nine families / 78 indicators;
CHANGELOG records the family-08 addition under [Unreleased].

* fix(family-08 tests): unify MULTI dict to 3-tuple (factory, batch_call, k)

The HEAD-side family-08 test parametrised MULTI[name] as
`(factory, batch_call, output_arity)` so that pivots with arity 3/5/7/9
fit the same harness. Main's entries arrived as 2-tuples; convert them
all to the 3-tuple shape so `make, batch_call, k = MULTI[name]` unpacks
cleanly. Lifecycle test now indexes the tuple instead of destructuring.

* test(zig_zag): tighten flat-oscillation test (drop dead counter branch)

The previous version of `small_oscillations_yield_no_swings` counted
emitted swings, but the assertion proves the counter never increments
so codecov flagged `emitted += 1` as uncovered. Switch to a per-bar
`assert!(...is_none())` — same coverage of the no-swing path, no dead
branch.
2026-05-25 20:06:46 +02:00
kingchenc f10b8c2e2d feat(family-09): add 7 trailing stops (HiLo, Volty, Yo-Yo, Donchian, Pct, Step, Renko) (#46)
* feat(family-09): add 7 trailing stops (HiLo, Volty, Yo-Yo, Donchian, Pct, Step, Renko)

Rounds out the Trailing Stops family from 5 to 12 indicators:

- HiLoActivator (Crabel): SMA-of-high/SMA-of-low trail with a one-bar
  lag; emits the opposite-side SMA as the trailing stop.
- VoltyStop (Cynthia Kase): ATR trail anchored on the extreme close
  since the trade was opened — tighter than AtrTrailingStop on
  pullbacks.
- YoyoExit: long-only ATR trail with an explicit re-entry trigger at
  trail + multiplier*ATR; exposes an in_trade flag.
- DonchianStop (Turtle): lowest low / highest high over the window;
  multi-output {stop_long, stop_short}.
- PercentageTrailingStop: fixed-percent trail that scales across
  instruments without per-asset tuning.
- StepTrailingStop: snaps to a step_size-aligned grid; mirrors
  discretionary stop-by-hand workflow.
- RenkoTrailingStop: block-anchored trail; only moves on full-block
  advances, ignores intra-block noise.

All seven are wired into wickra-core, the Python / Node / WASM
bindings, the indicator_update + indicator_update_candle fuzz targets,
the wickra bench harness, and the Python + Node test suites. README
counter bumps from 71 to 78; CHANGELOG entry under [Unreleased].

* fix(family-09): satisfy pedantic clippy lints

- hilo_activator: rewrite match-Some/None as if-let-else (single_match_else),
  add backticks around the HiLo identifier in module/struct doc (doc_markdown).
- percentage / step / renko trailing stop tests: use f64::from(i32) instead
  of `as f64` (cast_lossless).
- bench `benches()` is now >100 lines after Family 09 was wired in; allow
  too_many_lines (matches the python pymodule fn).
2026-05-25 19:36:14 +02:00
kingchenc 880a0e7430 feat: Family 07 Volume - 6 new volume-flow indicators (#45)
* feat(kvo): add Klinger Volume Oscillator

Stephen J. Klinger's trend-aware volume-force MACD. Each bar produces a 'volume force' (vf) signed by the local trend (+1 / -1 / carry) and scaled by the ratio of the current accumulation horizon to its previous trend. KVO = EMA(vf, fast) - EMA(vf, slow), classic (34, 55).

Rust core (Kvo) with 7 unit tests (rejects zero / fast>=slow, accessors, constant series collapses to 0, warmup lands at slow+1, batch == streaming, reset clears state), plus Python (PyKvo + KVO export), Node (KvoNode), and WASM (WasmKvo) bindings. Fuzz target adds Kvo to the candle-input sweep, bench adds the candle-input KVO benchmark, README counter 71 -> 72 + family table row, CHANGELOG [Unreleased].

* feat(volume-oscillator): add Volume Oscillator (VO)

Percent difference between a fast and a slow SMA of the bar volume: 100 * (SMA(vol, fast) - SMA(vol, slow)) / SMA(vol, slow). Default (14, 28). The line stays near zero in stable conditions; positive readings show rising short-term participation, negative readings show waning interest.

Rust core (VolumeOscillator) with 8 unit tests (period validation, accessors, constant volume == 0, zero-volume window defensive branch, two reference values verified algebraically, batch == streaming, reset), plus Python (PyVolumeOscillator + VolumeOscillator export), Node (VolumeOscillatorNode), and WASM (WasmVolumeOscillator) bindings. Fuzz target adds VolumeOscillator to the candle-input sweep, bench adds the volume_oscillator benchmark, README counter 72 -> 73 + family table row, CHANGELOG [Unreleased].

* feat(nvi-pvi): add Negative & Positive Volume Index

Paul Dysart's cumulative volume-flow indices, popularised by Norman Fosback in 'Stock Market Logic'. Both run from a 1000.0 baseline and only update on a specific direction of volume change:

- NVI updates on volume-contraction bars (volume_t < volume_{t-1}), absorbing the percent close change. Tracks the 'smart money' leg per Fosback.
- PVI updates on volume-expansion bars (volume_t > volume_{t-1}). Tracks the 'crowd' leg.

Both expose with_baseline(f64) for custom starting indexes. The NVI/PVI pair is listed as a single line in indicator-ideas/families/07-volume.md and shares the same lifecycle/test/binding surface, so they ship as one commit.

Rust core (Nvi, Pvi) with 9 unit tests each (accessors, baseline seed, volume direction branches, zero-prev-close guard, custom baseline, batch == streaming, reset), plus Python (PyNvi/PyPvi + NVI/PVI exports), Node (NviNode/PviNode), and WASM (WasmNvi/WasmPvi) bindings. Fuzz target adds Nvi+Pvi to the candle-input sweep, bench adds nvi+pvi entries, README counter 73 -> 75 + family table row, CHANGELOG [Unreleased].

* feat(family-07): add Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index

Finishes the volume-flow family with the remaining (new) entries from
indicator-ideas/families/07-volume.md.

Indicators added:

- Williams A/D (`WilliamsAD`): Larry Williams' volume-less cumulative
  accumulation/distribution line. Anchors each bar's contribution to
  the previous close via true-high/true-low (gap-aware).
- Anchored VWAP (`AnchoredVwap`): cumulative VWAP whose accumulation
  starts at a user-chosen anchor bar. Exposes `set_anchor()` (queued
  to the next `update`) for click-to-anchor workflows. Reset clears
  both state and pending-anchor flag.
- Demand Index (`DemandIndex`): James Sibbet's smoothed buying-vs-
  selling pressure, in the streaming-friendly textbook form
  `EMA(volume * close-return * (1 + range/close), period)`.
- Time Segmented Volume (`Tsv`): Don Worden's rolling window-sum of
  `(close_t - close_{t-1}) * volume_t`. Default `period = 18`.
- Volume Zone Oscillator (`Vzo`): Walid Khalil's normalised volume-flow
  oscillator bounded in `[-100, +100]`, defined as
  `100 * EMA(signed_volume) / EMA(volume)`.
- Market Facilitation Index (`MarketFacilitationIndex`): Bill Williams'
  per-bar `(high - low) / volume`. Returns `None` on zero-volume bars.

All six indicators ship with unit tests (`rejects_zero_period` where
applicable, `accessors_and_metadata`, constant-series behaviour,
batch == streaming equivalence, reset semantics, and reference-value
or saturation-extreme tests), Python / Node / WASM bindings, fuzz
coverage in `indicator_update_candle`, a `bench_candle_input` line per
indicator, README + CHANGELOG entries, and Python reference-value
tests in `test_new_indicators.py`.

The README indicator counter advances 75 -> 81.

* test(family-07): cover defensive cold paths + Default impls

- ad_oscillator: exercise `value()` after first emission.
- kvo: cover the `cm == 0.0` zero-OHLC defensive branch.
- nvi / pvi: exercise the Default impls.
2026-05-25 19:15:22 +02:00
kingchenc 6287bd48c1 feat: Family 06 Trend-Strength - 5 new directional/random-walk indicators (#44)
* feat(adxr): add Wilder Average Directional Movement Index Rating

ADXR is the trend-strength smoother Wilder published alongside ADX in
*New Concepts in Technical Trading Systems* (1978):

    ADXR_t = (ADX_t + ADX_{t - (period - 1)}) / 2

The lookback length is the same period that feeds the underlying ADX.
Because the older ADX is period - 1 bars stale, ADXR responds more
slowly than ADX and is the canonical metric for comparing
trend-strength across instruments.

Implementation reuses the existing wickra_core::Adx engine plus a
period-length ring of past ADX values; warmup is 3 * period - 1
(41 for period = 14). Bindings: Python PyAdxr (PyArray1 batch),
Node AdxrNode (number scalar), WASM WasmAdxr. Fuzz target covers
the candle-input path. Python + Node streaming-vs-batch tests
parametrised, plus a pure-uptrend reference value (ADXR == 100
when ADX saturates at 100). Criterion bench added under crates/
wickra/benches/indicators.rs.

README family table and indicator counter updated (71 -> 72).

* feat(rwi): add Mike Poulos Random Walk Index

RWI compares actual price displacement to what a random walk would
produce over the same horizon: for each lookback i in [2, period],

    RWI_High_t(i) = (high_t - low_{t-i+1}) / (ATR_i(t) * sqrt(i))
    RWI_Low_t(i)  = (high_{t-i+1} - low_t) / (ATR_i(t) * sqrt(i))

Per-bar output is the maximum across lookbacks for each direction;
a reading > 1 means the trend beats random-walk noise, > 2 is the
typical strong-trend threshold. Multi-output (high, low). period
must be >= 2 (the shortest meaningful lookback); period < 2 returns
InvalidPeriod. Warmup = period (e.g. 14 for the standard default).

Bindings: Python PyRwi (PyArray2 shape (n, 2)), Node RwiNode +
RwiValue struct, WASM WasmRwi (Object/Reflect for update,
Float64Array interleaved for batch). Fuzz target adds the candle
input case. Python parametric streaming-vs-batch test and pure
uptrend reference test (RWI_High dominates RWI_Low and exceeds 1).
Node parametric streaming-vs-interleaved-batch test. Criterion
bench under crates/wickra/benches/indicators.rs.

README family table and indicator counter updated (72 -> 73).

* feat(tii): add M.H. Pee Trend Intensity Index

TII is a [0, 100] oscillator that asks 'what fraction of the recent
SMA deviations are positive?'. The construction is

    dev_t  = close_t - SMA(close, sma_period)_t
    SD_pos = sum of positive dev_t over the last dev_period bars
    SD_neg = sum of |negative dev_t| over the last dev_period bars
    TII    = 100 * SD_pos / (SD_pos + SD_neg)

Saturates at 100 on a pure uptrend (every close above the lagging
SMA), at 0 on a pure downtrend, and returns the neutral mid-point 50
on a perfectly flat window. The output is clamped to [0, 100] as
the rolling-sum subtraction loop can accumulate a few ULP of error
on long histories. Canonical Pee parameters (sma_period=60,
dev_period=30) wired as Python defaults; warmup is
sma_period + dev_period - 1 (89 for the defaults).

Bindings: Python PyTii (PyArray1 batch), Node TiiNode (scalar
update + batch), WASM WasmTii via the two-arg wasm_scalar_indicator!
macro. Fuzz target adds the scalar path. Python parametric
streaming-vs-batch test plus pure-uptrend (TII == 100) and
flat-market (TII == 50) reference tests. Node parametric
streaming-vs-batch test. Criterion bench under crates/wickra/
benches/indicators.rs.

README family table and indicator counter updated (73 -> 74).

* feat(kst): add Pring Know Sure Thing oscillator

KST is Martin Pring's long-horizon momentum gauge: four smoothed
rate-of-change components combined with fixed weights (1, 2, 3, 4),
plus an SMA signal line.

    RCMA_i = SMA(ROC(close, roc_i), sma_i)        for i in 1..=4
    KST    = 1*RCMA_1 + 2*RCMA_2 + 3*RCMA_3 + 4*RCMA_4
    Signal = SMA(KST, signal_period)

Kst::classic() exposes Pring's recommended parameter set
(roc = (10, 15, 20, 30), sma = (10, 10, 10, 15), signal = 9);
warmup = max(roc_i + sma_i) + signal_period - 1 (53 for the classic
parameters). All four parallel branches are fed unconditionally so
they warm in lock-step.

Bindings: Python PyKst (PyArray2 shape (n, 2)) with a KST.classic()
staticmethod, Node KstNode + KstValue with a KST.classic() factory,
WASM WasmKst with both new(...) and classic() constructors plus
Object/Reflect for update and Float64Array for batch. Fuzz target
adds the scalar multi-output path. Python tests gain a new
MULTI_SCALAR section parametric over scalar-input/multi-output
indicators, plus a classic-on-constant-series reference test. Node
tests gain a KST entry in the multi-output section. Criterion
benchmark added under crates/wickra/benches/indicators.rs.

README family table and indicator counter updated (74 -> 75).

* feat(wave-trend): add LazyBear Wave Trend Oscillator

Two-line mean-reverting momentum gauge built from the typical price
and three cascaded EMAs:

    ap   = (high + low + close) / 3
    esa  = EMA(ap, channel_period)
    d    = EMA(|ap - esa|, channel_period)
    ci   = (ap - esa) / (0.015 * d)
    wt1  = EMA(ci, average_period)
    wt2  = SMA(wt1, signal_period)

WaveTrend::classic() exposes LazyBear's defaults
(channel = 10, average = 21, signal = 4); warmup is
2 * channel_period + average_period + signal_period - 3 (42 for the
classic defaults). On a perfectly flat market the SMA-seeded EMA
introduces a single-ULP drift between ap and esa, which on a tiny d
would make the ratio explode to -1/0.015 = -66.67; a price-scaled
flat-tolerance guard (d <= 16 * EPSILON * max(|esa|, 1)) collapses
the channel index to 0 in that regime so both lines remain at zero.

Bindings: Python PyWaveTrend (PyArray2 shape (n, 2)) with a
WaveTrend.classic() staticmethod, Node WaveTrendNode + WaveTrendValue
with a WaveTrend.classic() factory, WASM WasmWaveTrend with both
new(...) and classic() constructors. Fuzz target adds the candle
multi-output path (sorted alphabetically). Python parametric
streaming-vs-batch test plus a flat-market reference test. Node
parametric streaming-vs-interleaved-batch test. Criterion bench
under crates/wickra/benches/indicators.rs.

README family table and indicator counter updated (75 -> 76).

* fix(family-06): re-add KST::classic() factory + drop dup fuzz block

Family-06 PR's tests call ta.KST.classic() / wickra.KST.classic() — main's
KST binding shipped without the static factory. Add classic() in Python
(staticmethod) and Node (napi factory); WASM already had it. Also drop the
duplicate Kst::classic().unwrap() block in fuzz/indicator_update.rs that
the merge left behind (main's API no longer returns Result).

* test(rwi): drop dead count==0 guard

The loop `for i in 2..=period` makes `count = tr_end - tr_start = i - 1`
which is always >= 1, so the `if count == 0 { continue; }` branch was
unreachable defensive code that codecov flagged on the family-06 PR.
2026-05-25 19:00:13 +02:00
kingchenc 54194a4ff8 feat: Family 05 Bands & Channels - 11 new price-envelope indicators (#43)
* feat(bands-channels): add Family 05 with 11 indicators

Eleven price-envelope overlays organised into a new "Bands & Channels"
family, exposed across all four bindings (Rust core, Python, Node, WASM)
plus fuzz/test/bench/docs coverage:

- MaEnvelope - SMA centerline with fixed-percent envelope (the oldest
  band overlay still in regular use).
- AccelerationBands (Price Headley) - momentum-biased bands that widen
  with the bar's relative range (H - L) / (H + L).
- StarcBands (Stoller Average Range Channel) - SMA(close) +/- k*ATR;
  Keltner's SMA-centerline sibling.
- AtrBands - close-anchored envelope of width k*ATR; the standard
  volatility-targeting stop/target band.
- HurstChannel - SMA centerline wrapped by the rolling high-low range
  (Brian Millard / Hurst-cycle channel).
- LinRegChannel - rolling OLS endpoint +/- k * population stddev of the
  residuals; dispersion about the trend rather than the mean.
- StandardErrorBands - regression line +/- k * OLS standard error
  (denominator n - 2) for prediction-interval bands.
- DoubleBollinger (Kathy Lien) - two concentric BB envelopes
  (typically +/- 1 sigma and +/- 2 sigma) for the zone-partition setup.
- TtmSqueeze (John Carter) - BB-inside-KC squeeze flag paired with a
  detrended-close linear-regression momentum reading.
- FractalChaosBands - Bill Williams 5-bar fractal high/low envelope.
- VwapStdDevBands - cumulative VWAP with volume-weighted population
  standard deviation bands.

Each indicator ships:
- Core impl with the full Indicator trait, classic() where applicable,
  and unit tests (rejects_zero_period / multiplier, accessors, flat
  market, monotonic ordering, batch == streaming, reset, plus
  algebraically verifiable reference values).
- Python PyO3 binding with multi-column NumPy batch (PyArray2).
- Node napi binding with #[napi(object)] struct + interleaved flat
  batch.
- WASM wasm-bindgen binding via Object/Reflect for update +
  Float64Array for batch.
- Fuzz coverage in fuzz_targets/indicator_update{,_candle}.rs.
- Python streaming-vs-batch parametric test + reference test.
- Node streaming-vs-interleaved-batch test + reference test.
- Criterion microbench under crates/wickra/benches/indicators.rs.

README family table, README indicator-count line, and CHANGELOG
Unreleased entry updated: indicator total rises from 71 to 82 across
nine families. Wiki pages are updated in a separate commit in the
wickra.wiki repo.

* test(acceleration-bands): cover sum_hl==0 zero-price guard

Exercises line 104 (`0.0` branch of the `sum_hl == 0.0` guard) which
was the last patch-coverage miss on the family-05 PR. `Candle::new`
accepts a fully-zero bar so the branch is reachable in principle —
add a degenerate-candle unit test to hit it.
2026-05-25 18:37:12 +02:00
kingchenc 3ea0f12b7a feat: Family 04 Volatility — RVI / Parkinson / Garman-Klass / Rogers-Satchell / Yang-Zhang (#42)
* feat(rvi): add Relative Volatility Index

Donald Dorsey's RSI-shaped volatility gauge. Partitions the rolling
population standard deviation of close into "up" samples (close rose
since the previous bar) and "down" samples (close fell), Wilder-smooths
each side, and reports 100 * AvgUp / (AvgUp + AvgDown). Output bounded
on [0, 100]; saturates at 100 in pure uptrends, 0 in pure downtrends,
and falls back to 50 on a completely flat series (same undefined-RS
convention as RSI).

Single period parameter (default 10) drives both the stddev window and
the Wilder smoothing constant. First emit lands at index 2*period - 2
(2*period - 1 bars are needed: period to fill the stddev window plus
period - 1 to seed the Wilder averages, overlapping by one bar).

Touchpoints: rvi.rs + mod.rs + lib.rs re-export, PyRvi + __init__.py +
test_new_indicators SCALAR + test_known_values uptrend reference,
RviNode + index.d.ts/index.js + indicators.test.js factory +
reference, WasmRvi via scalar macro, scalar-fuzz target, bench_scalar
entry, README + CHANGELOG.

* feat(parkinson): add Parkinson Volatility

Michael Parkinson's (1980) high-low realised volatility estimator.
Under a driftless Geometric-Brownian-Motion assumption, the extreme
range of a bar carries roughly 5x the variance information of the
close-to-close estimator, so for a given statistical efficiency
Parkinson needs five times fewer samples.

Formula:
    sigma^2 = (1 / (4n * ln 2)) * Sum_{i=1..n} (ln(H_i / L_i))^2
    out     = sqrt(sigma^2) * sqrt(trading_periods) * 100

The output is annualised to a percent in the same style as
HistoricalVolatility (pass `trading_periods = 1` for the raw per-bar
sigma * 100 figure). Two parameters: `period` (default 20) for the
rolling window, `trading_periods` (default 252) for the annualisation
factor. First emit at index `period - 1`.

Touchpoints: parkinson.rs + mod.rs + lib.rs re-export,
PyParkinsonVolatility + __init__.py + test_new_indicators CANDLE_SCALAR
+ test_known_values zero-range reference, ParkinsonVolatilityNode +
index.d.ts/index.js + indicators.test.js factory + reference,
WasmParkinsonVolatility hand-rolled, candle-fuzz target,
bench_candle_input entry, README + CHANGELOG.

* feat(garman-klass): add Garman-Klass Volatility

Garman & Klass (1980) OHLC realised-volatility estimator. Extends
Parkinson's high-low estimator with an open-to-close term, lifting
statistical efficiency from ~5x to ~7.4x relative to close-to-close
stddev under driftless Geometric Brownian Motion.

Formula (per bar):
    s_t  = 0.5 * (ln(H_t / L_t))^2 - (2*ln(2) - 1) * (ln(C_t / O_t))^2
    out  = sqrt(max(mean(s_t over `period`), 0)) * sqrt(trading_periods) * 100

The per-bar sample can be marginally negative when the bar has a small
range relative to its open-to-close move; a max(., 0) clamp on the
rolling mean absorbs that and the FP cancellation noise before the
square root.

Still biased on data with meaningful overnight drift -- use Yang-Zhang
when gaps matter. Defaults: `period = 20`, `trading_periods = 252`
(annualised percent, same convention as HistoricalVolatility).

Touchpoints: garman_klass.rs + mod.rs + lib.rs re-export,
PyGarmanKlassVolatility + __init__.py + test_new_indicators
CANDLE_SCALAR + test_known_values zero-movement reference,
GarmanKlassVolatilityNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmGarmanKlassVolatility hand-rolled,
candle-fuzz target, bench_candle_input entry, README + CHANGELOG.

* feat(rogers-satchell): add Rogers-Satchell Volatility

Rogers, Satchell & Yoon (1994) OHLC realised-volatility estimator.
Unlike Garman-Klass, the per-bar sample is exact under arbitrary
Brownian drift -- the drift component cancels algebraically.

Formula (per bar):
    s_t  = ln(H_t / C_t) * ln(H_t / O_t) + ln(L_t / C_t) * ln(L_t / O_t)
    out  = sqrt(max(mean(s_t over `period`), 0)) * sqrt(trading_periods) * 100

Each per-bar sample is also non-negative by construction: with
`Candle::new` guaranteeing H >= max(O, L, C) and L <= min(O, H, C), the
four log factors have predictable signs (ln(H/.) >= 0, ln(L/.) <= 0),
so both products contribute >= 0. The max(., 0) clamp on the rolling
mean is only there to absorb FP cancellation.

Defaults: `period = 20`, `trading_periods = 252` (annualised percent,
same convention as HistoricalVolatility / Parkinson / Garman-Klass).

Touchpoints: rogers_satchell.rs + mod.rs + lib.rs re-export,
PyRogersSatchellVolatility + __init__.py + test_new_indicators
CANDLE_SCALAR + test_known_values zero-movement reference,
RogersSatchellVolatilityNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmRogersSatchellVolatility hand-rolled,
candle-fuzz target, bench_candle_input entry, README + CHANGELOG.

* feat(yang-zhang): add Yang-Zhang Volatility

Yang & Zhang (2000) drift- and gap-robust OHLC realised-volatility
estimator. Combines three independent components into a single estimate
with minimum variance:

    overnight    = sample_var(ln(O_t / C_{t-1}))   over n bars  (close-to-open)
    open_close   = sample_var(ln(C_t / O_t))       over n bars
    rs           = mean(ln(H/C)*ln(H/O) + ln(L/C)*ln(L/O)) over n bars
    sigma^2_YZ   = overnight + k*open_close + (1-k)*rs
    k            = 0.34 / (1.34 + (n+1)/(n-1))
    out          = sqrt(max(sigma^2_YZ, 0)) * sqrt(trading_periods) * 100

The overnight and open-to-close variances use Bessel's correction (the
sample estimator, divisor n-1), same convention as
HistoricalVolatility. The blending factor `k` is the one that
minimises estimator variance under driftless Geometric Brownian Motion
with overnight gaps.

This is the gold-standard OHLC estimator for assets with both
close-to-open gaps and intraday drift: equities, futures, and any
market that does not trade continuously. For pure intraday data (where
O_t == C_{t-1} and the open-to-close return is constant), the
overnight and open-close terms vanish and the estimator collapses to
(1-k) * Rogers-Satchell -- this is the indicator's
intraday_data_collapses_to_rs_only unit test.

Period >= 2 (Bessel correction needs >= 2 samples). First emit at
index `period` (the (period+1)-th bar): one bar seeds prev_close, the
next `period` fill the rolling windows. Defaults: `period = 20`,
`trading_periods = 252`.

Touchpoints: yang_zhang.rs + mod.rs + lib.rs re-export,
PyYangZhangVolatility + __init__.py + test_new_indicators
CANDLE_SCALAR + test_known_values zero-movement reference,
YangZhangVolatilityNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmYangZhangVolatility hand-rolled, candle-fuzz
target, bench_candle_input entry, README + CHANGELOG.

* fix(rvi): rename to RviVolatility to avoid clash with family-02 RVI

Family 02 (PR #40) ships a separate `Rvi` struct for Relative Vigor
Index. The two indicators have nothing to do with each other beyond
sharing the acronym, so disambiguate by giving the volatility one a
longer name everywhere:

- Rust crate: `Rvi`        -> `RviVolatility`
- Rust file:  `rvi.rs`     -> `rvi_volatility.rs`
- Python:     `RVI`        -> `RVIVolatility`
- Node:       `RVI`        -> `RVIVolatility`
- WASM:       `RVI`        -> `RVIVolatility`

Once the two PRs are both merged, callers get `wickra::Rvi` for Vigor
and `wickra::RviVolatility` for Volatility. The shorter `RVI` acronym
stays with the Momentum family per the existing wiki pages and the
implementation that shipped first.

Updates: rvi_volatility.rs (renamed), mod.rs, lib.rs re-export,
bindings/python/src/lib.rs + __init__.py + tests, bindings/node/src/lib.rs
+ index.d.ts + index.js + __tests__, bindings/wasm/src/lib.rs,
fuzz/fuzz_targets/indicator_update.rs, crates/wickra/benches/indicators.rs,
README family-table label, CHANGELOG entry.

* test(volatility): Rename test_rvi -> test_rvi_volatility + drop dead match arms

The Python test test_rvi_pure_uptrend_saturates_at_one_hundred was
calling ta.RVI() expecting the volatility version, but ta.RVI now
means Family 02's Relative Vigor Index (candle input). Renamed to
ta.RVIVolatility to match the binding rename done at merge time.

In all four OHLC volatility tests, the existing `match (r, a) { ...,
_ => panic!() }` arm is dead in passing runs (every aligned pair is
either (None, None) or (Some, Some)). Codecov flagged it as a patch
miss on each of parkinson / garman_klass / rogers_satchell /
yang_zhang. Refactored per CLAUDE.md cold-path guidance to
`assert_eq!(r.is_some(), a.is_some()); if let (Some, Some) ...`.
2026-05-25 18:18:20 +02:00
kingchenc d9d3ad18aa feat: Family 03 MACD & Price Oscillators — APO / AO-Hist / CFO / Zero-Lag MACD / Elder Impulse / STC (#41)
* 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 from ec269d8

The 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 from 733afd9

* 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.
2026-05-25 17:26:46 +02:00
kingchenc 7f1a6df202 ci(sync-about): Push counter fix to PR branch instead of main (#56)
Previously the workflow patched README.md on main after every push,
producing an unsigned 'chore: sync indicator count' commit per merge.
Now the README counter is kept in sync on the PR side instead: on
every pull_request event, the workflow checks out the PR's head ref,
compares grep -c '^mod ' to the README counter, and if they differ,
pushes a fix-up commit back onto the PR branch using the default
GITHUB_TOKEN.

When the PR is squash-merged, that fix-up commit is folded into the
single web-flow-signed merge commit on main — so main's history never
shows a separate bot commit. About description and Wiki sync still
run on push to main / v* tags via the existing PAT, since both reach
outside the main repo (Administration:write and the .wiki repo).

For PRs from forks the workflow cannot push back; it emits a hard
::error:: pointing at README.md so the contributor can fix the
counter manually.

Pushes via GITHUB_TOKEN do not re-trigger downstream workflows
(GitHub's anti-recursion policy), so the fix-up commit costs zero
extra CI minutes — only sync-about itself re-runs on the next
synchronize event and no-ops once the counter matches.
2026-05-25 17:22:38 +02:00
wickra-bot 1ea05fb2a1 chore: sync indicator count to 85 [skip ci] 2026-05-25 13:29:06 +00:00
kingchenc 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.
2026-05-25 15:28:56 +02:00
wickra-bot a39adb9dae chore: sync indicator count to 78 [skip ci] 2026-05-25 13:08:09 +00:00
kingchenc 1cd5d1d8da fix(ci): Drop site/index.md from sync-about workflow (#55)
site/ is local-only (listed in .git/info/exclude), so the sed call in
the Patch step aborted the workflow on every push to main with
"sed: can't read site/index.md: No such file or directory". That kept
the README counter from being committed and skipped the wiki sync.

Patches README only now. site/ stays out of CI until the marketing
site is promoted.
2026-05-25 15:08:01 +02:00
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 178fbfd68e ci: Add sync-about workflow to auto-update indicator count (#38)
Counts `mod xxx;` declarations in crates/wickra-core/src/indicators/mod.rs
on every push to main, every PR, and every v* tag push. On non-PR runs
it syncs the count into:

- GitHub repo About description (via `gh repo edit`)
- README.md + site/index.md (commit with [skip ci] back to main)
- Wiki: Home.md, FAQ.md, Streaming-vs-Batch.md

Requires a `ABOUT_SYNC_TOKEN` secret (classic PAT with `repo` scope, or
fine-grained PAT with Administration+Contents write on the wickra repo).
PR runs are read-only: count is logged but nothing is mutated, so forks
cannot trigger writes.
2026-05-25 14:52:58 +02:00
kingchenc e30b3c6b35 release: 0.2.7 (Windows ARM64 restored + CPU label fix) (#37)
* chore(docs): rename benchmark CPU from 7950X3D to 9950X

The "Reproduced on" line in the umbrella + binding READMEs and the
benchmark page on the site listed the wrong AMD CPU. The benchmarks
were actually produced on a Ryzen 9 9950X, not a 7950X3D. Same
column for absolute µs values applies — the speedup ratios in the
tables are unchanged either way because they're relative across
libraries on the same machine.

The performance-regression issue template's CPU example also
updated for consistency (it was a generic placeholder, but matching
the canonical machine makes the example concrete).

* chore(npm): restore Windows ARM64 sub-package + napi matrix entry

npm Support unblocked the `wickra-win32-arm64-msvc` package name and
transferred write access to @kingchenc (placeholder 0.0.1-security
was published from their side; we ship our first real version on
top of that). This re-enables every change 8aa74cb temporarily
backed out for 0.2.1:

- bindings/node/package.json: re-add `aarch64-pc-windows-msvc` to
  napi.triples.additional and `wickra-win32-arm64-msvc` to
  optionalDependencies.
- bindings/node/npm/win32-arm64-msvc/package.json: restored — name,
  cpu = arm64, os = win32, version pinned to the workspace.
- .github/workflows/release.yml: re-enable the
  `windows-11-arm / aarch64-pc-windows-msvc` row in the node-build
  matrix and drop the "temporarily skipped" comment block.

After the next tag-push this binding will be published alongside
the other five platforms and `npm install wickra` on Windows ARM64
will resolve to a native build instead of failing the loader's
optional-dep lookup.

* release: bump workspace + bindings to 0.2.7

Workspace, every binding (Python, Node, six platform stubs incl. the
restored win32-arm64-msvc), and the CHANGELOG all move together to
0.2.7. wickra-win32-arm64-msvc is now part of the standard publish
matrix and will land on npm alongside the other five binaries.

The 0.2.7 CHANGELOG entry consolidates the two changes this cycle:
- Windows ARM64 binding restored (npm Support unblocked the name).
- Benchmark CPU label corrected (Ryzen 9 9950X, not 7950X3D).
2026-05-24 11:46:49 +02:00
kingchenc 070be2eb27 release: 0.2.6 (docs.rs fix + README table reordering) (#36)
* fix(docs-rs): rename `doc_auto_cfg` to `doc_cfg` after Rust 1.92 merge

`doc_auto_cfg` was removed in Rust 1.92.0 and folded back into
`doc_cfg` (rust-lang/rust#138907). docs.rs builds with the latest
nightly and sets `--cfg docsrs`, so the previous

    #![cfg_attr(docsrs, feature(doc_auto_cfg))]

aborts compilation with E0557 on every published 0.2.x. GitHub CI
never tripped this — stable rustc ignores the line because nothing
sets the `docsrs` cfg there.

Switch all three published library crates (`wickra`, `wickra-core`,
`wickra-data`) to the merged-into `doc_cfg` gate. Same intent, same
on-docs.rs output, builds again on nightly.

* docs(readme): float Wickra to the top of the comparison tables

Reorders the "Why Wickra exists" library-comparison table and the two
benchmark headers so Wickra is the first row (with a ★ marker) instead
of the last. The previous order placed Wickra at the bottom, which
buries the only row a reader landing on the README is here to compare
against. Same column data, same ★/winner annotations, just the row
order flipped and a ★ prefix on the Wickra label.

Mirrored across the umbrella README and every binding README so the
crates.io / PyPI / npm landing pages stay in sync.

* release: bump workspace + bindings to 0.2.6

Workspace, every binding (Python, Node, Node platform stubs), the
release.yml comment and the CHANGELOG all move together to 0.2.6 so
the next tagged release lines every artefact up.

0.2.6 carries two changes from the [0.2.6] CHANGELOG entry:
- fix(docs-rs): swap the now-removed `doc_auto_cfg` feature gate for
  the merged-into `doc_cfg` so docs.rs nightly builds resume.
- docs(readme): float ★ Wickra to the top of every comparison table
  across the umbrella + binding READMEs.

wickra-win32-arm64-msvc stays excluded for this release with the same
npm spam-filter rationale that held for 0.2.5.
2026-05-24 03:20:13 +02:00
524 changed files with 159706 additions and 1421 deletions
+34
View File
@@ -0,0 +1,34 @@
# EditorConfig: https://editorconfig.org
# Keeps indentation and line-endings consistent across IDEs.
root = true
[*]
charset = utf-8
end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
indent_style = space
# Rust + Python + most config files use 4-space indents.
[*.{rs,py,toml}]
indent_size = 4
# JS / TS / JSON / YAML / Markdown use 2-space indents per ecosystem conventions.
[*.{js,ts,jsx,tsx,json,yml,yaml,md}]
indent_size = 2
# Markdown allows trailing whitespace as a hard line break — keep it intact.
[*.md]
trim_trailing_whitespace = false
# Makefiles must use tabs.
[Makefile]
indent_style = tab
# Generated files are not authored by humans; leave them alone.
[bindings/node/index.{js,d.ts}]
indent_style = unset
indent_size = unset
trim_trailing_whitespace = unset
insert_final_newline = unset
+3
View File
@@ -0,0 +1,3 @@
# Shell scripts must keep LF line endings so they run on Linux/macOS CI and
# local shells regardless of the committer's platform autocrlf setting.
*.sh text eol=lf
+1 -1
View File
@@ -3,4 +3,4 @@
# The owner listed here is requested for review automatically on every pull
# request. See https://docs.github.com/articles/about-code-owners.
* @kingchenc
* @wickra-lib
+5
View File
@@ -0,0 +1,5 @@
# Funding sources surfaced on the repository "Sponsor" button.
# Each platform's value is the username/handle on that platform.
# Leave a key empty (e.g. patreon:) to skip a platform.
github: [kingchenc]
+2 -2
View File
@@ -1,8 +1,8 @@
blank_issues_enabled: false
contact_links:
- name: Security vulnerability
url: https://github.com/kingchenc/wickra/security/advisories/new
url: https://github.com/wickra-lib/wickra/security/advisories/new
about: Report security issues privately — do not open a public issue.
- name: Question or discussion
url: https://github.com/kingchenc/wickra/discussions
url: https://github.com/wickra-lib/wickra/discussions
about: Ask usage questions and discuss ideas here.
@@ -44,7 +44,7 @@ ema/update time: [38.5 ns 38.7 ns 38.9 ns]
| Field | Value |
| ------------ | -------------------------------------- |
| CPU | `e.g. Ryzen 9 7950X, AVX2 + AVX512` |
| CPU | `e.g. Ryzen 9 9950X, AVX2 + AVX512` |
| OS / arch | `e.g. Linux 6.8 x86_64` |
| Toolchain | `rustc 1.x.y` |
| Build flags | `RUSTFLAGS=...`, `--release`, profile |
+3 -3
View File
@@ -24,9 +24,9 @@
- [ ] New behaviour has tests; bug fixes have a regression test.
- [ ] Public API changes are mirrored in the Python / Node / WASM bindings
and their type stubs (If applicable).
- [ ] The relevant page on the [project Wiki](https://github.com/kingchenc/wickra/wiki)
and the `README.md` are updated (If applicable). Wiki edits go to a
separate repository: `https://github.com/kingchenc/wickra.wiki.git`.
- [ ] The relevant page on the [documentation site](https://docs.wickra.org)
and the `README.md` are updated (If applicable). Docs edits go to a
separate repository: `https://github.com/wickra-lib/wickra-docs`.
- [ ] An entry was added under `## [Unreleased]` in `CHANGELOG.md`.
## Notes for reviewers
+1 -1
View File
@@ -60,7 +60,7 @@ Closes #
- [ ] Public API changes are reflected in `CHANGELOG.md`
- [ ] Public API changes are reflected in rustdoc / README / examples
- [ ] No `todo*.md` or other local-only notes are staged
- [ ] License header / `LICENSE` reference unchanged (PolyForm-NC-1.0.0)
- [ ] License header / `LICENSE` reference unchanged (MIT OR Apache-2.0)
## Notes for reviewers
+23
View File
@@ -6,6 +6,8 @@ updates:
schedule:
interval: weekly
open-pull-requests-limit: 10
cooldown:
default-days: 7
commit-message:
prefix: "deps(cargo)"
@@ -15,6 +17,8 @@ updates:
schedule:
interval: weekly
open-pull-requests-limit: 10
cooldown:
default-days: 7
commit-message:
prefix: "deps(npm)"
@@ -24,9 +28,26 @@ updates:
schedule:
interval: weekly
open-pull-requests-limit: 10
cooldown:
default-days: 7
commit-message:
prefix: "deps(pip)"
# Hash-pinned CI/bench Python tooling under .github/requirements/. Each
# <name>.in is the loose source; the matching hash-locked <name>.txt is the
# output regenerated by scripts/update-lockfiles.sh (uv). Dependabot keeps the
# pins fresh; ci-dev-py39.in caps numpy <2.1 so 3.9 stays installable. Any
# bump that breaks a matrix row surfaces in the PR's CI run.
- package-ecosystem: pip
directory: "/.github/requirements"
schedule:
interval: weekly
open-pull-requests-limit: 10
cooldown:
default-days: 7
commit-message:
prefix: "deps(ci-pip)"
# GitHub Actions — keeps the SHA-pinned actions current (Dependabot reads
# the version comment after each pinned SHA and bumps both together).
- package-ecosystem: github-actions
@@ -34,5 +55,7 @@ updates:
schedule:
interval: weekly
open-pull-requests-limit: 10
cooldown:
default-days: 7
commit-message:
prefix: "deps(actions)"
+11
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@@ -0,0 +1,11 @@
# Python deps + peer TA libraries for the bench.yml cross-library benchmark.
# Loose source spec — the pinned, hash-locked output is generated from this:
# bench.txt (Python 3.11) via scripts/update-lockfiles.sh
# bench.yml runs on a single Python version (3.11), so one output suffices.
maturin
numpy
pandas
TA-Lib
tulipy
talipp
finta
+247
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@@ -0,0 +1,247 @@
# This file was autogenerated by uv via the following command:
# ./scripts/update-lockfiles.sh
build==1.5.0 \
--hash=sha256:13f3eecb844759ab66efec90ca17639bbf14dc06cb2fdf37a9010322d9c50a6f \
--hash=sha256:302c22c3ba2a0fd5f3911918651341ebb3896176cbdec15bd421f80b1afc7647
# via ta-lib
colorama==0.4.6 \
--hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
--hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
# via build
finta==1.3 \
--hash=sha256:b94b94df311c18bf5402eb2fe8fd2db5e1bdaff08baf58a7367d05c7abdd10d3 \
--hash=sha256:f2fa0673748f4be8f57e57cf6d5c00a4d44bc6071ea69dbb9a1d329d045cbba2
# via -r .github/requirements/bench.in
maturin==1.13.3 \
--hash=sha256:0ef257e692cc756c87af5bea95ddfe7d3ac49d3376a7a87f728d63f06e7b6f8b \
--hash=sha256:1cc0a110b224ca90406b668a3e3c1f5a515062e59e26292f6dbaf5fd4909c6f3 \
--hash=sha256:2389fe92d017cea9d94e521fa0175314a4c52f79a1057b901fbc9f8686ef7d0b \
--hash=sha256:3cc13929ca82aefa4adbf0f2c35419369796213c6fb0eb24e914945f50ef5d8c \
--hash=sha256:3db93337ed97e60ffc878aa8b493cd7ae44d3a5e1a37256db3a4491f57565018 \
--hash=sha256:4667ef609ab446c1b5e0bfe4f9fb99699ab6d8548433f8d1a684256e0b67217f \
--hash=sha256:49fd6ab08da28098ccf37afca24cdba72376ba9c1eedf9dd25ff82ed771961ff \
--hash=sha256:4cd478e6e4c56251e48ed079b8efd55b30bc5c09cf695a1bdafaeb582ee735a0 \
--hash=sha256:53b08bd075649ce96513ad9abf241a43cb685ed6e9e7790f8dbc2d66e95d8323 \
--hash=sha256:771e1e9e71a278e56db01552e0d1acfd1464259f9575b6e72842f893cd299079 \
--hash=sha256:a2675e25f313034ae6f57388cf14818f87d8961c4a96795287f3e155f59beb11 \
--hash=sha256:b6741d7bf4af97da937528fd1e523c6ab54f53d9a21870fa735d6e67fd88e273 \
--hash=sha256:c00ea6428dea17bf616fe93770837634454b28c2de1a876e42ef8036c616079a \
--hash=sha256:def4a435ea9d2ee93b18ba579dc8c9cf898889a66f312cd379b5e374ec3e3ad6
# via -r .github/requirements/bench.in
numpy==2.4.6 \
--hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \
--hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \
--hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \
--hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \
--hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \
--hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \
--hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \
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--hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \
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--hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \
--hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \
--hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \
--hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \
--hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \
--hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \
--hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \
--hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \
--hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \
--hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \
--hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \
--hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20
# via
# -r .github/requirements/bench.in
# finta
# pandas
# ta-lib
# tulipy
packaging==26.2 \
--hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
--hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661
# via build
pandas==3.0.3 \
--hash=sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c \
--hash=sha256:05f1f1752b8533ea03f7f39a9c15b1a058d067bb48f4748948e7a8691e0510f2 \
--hash=sha256:08d789b41f87e0905880e293cedf6197ce71fe67cc081358b1e148a491b9bd13 \
--hash=sha256:0d589105b3c14645af1738ff279b2995102d8f7a03b0a66dc8d95550eb513e04 \
--hash=sha256:13fc1e853d9e04743d11ba75a985ccbc2a317fe07d8af61e445a6fd24dacd6a6 \
--hash=sha256:14da8316da4d0c5a77618425996bfb1248ca87fc2c1486e6fde4652bd18b5824 \
--hash=sha256:1928e07221f82db493cd4af1e23c1bfca524a19a4699887975bff68f49a72bfb \
--hash=sha256:261e308dfb22448384b7580cf719d2f998fe2966c92893c3e77d14008af1f066 \
--hash=sha256:275c14e0fce14a2ec20eee474aecd305478ea3c1e6f6a9d8fe219a165542717e \
--hash=sha256:335f62418ed562cfc3c49e9e196375c28b729dcef8543abf4f9438e381bf3c76 \
--hash=sha256:3650109c0f22879df8bd6179ab9ee3d7f1d1d4e7e0094a3f0032d9f51e2e64ac \
--hash=sha256:39436b377d56d2a2e52d0395bdbee171f01068e99af5250509aceeb929f765c7 \
--hash=sha256:3c20a521bbb85902f79f7270c80a59e1b5452d96d170c034f207181870f97ac5 \
--hash=sha256:3e91cec1879ada0624fc3dc9953c5cbd60208e59c0db28f540c5d6d47502422f \
--hash=sha256:455f6f8139d4282188f526868dbc3c828470e88a3d9d59a891bd46a455f21b98 \
--hash=sha256:46997386d528eb40376ecd6b033cf4a8a1e5282580f68f43de875b78cba2199d \
--hash=sha256:4db8c527972a821cf5286b40ccc57642a39bc62e62022b42f99f8a67fca8c3a1 \
--hash=sha256:4e15135e2ee5df1063313e2425ceef8ac0f4ae775893815b0923651b806a5639 \
--hash=sha256:51b1fe551acb77dac643c6fda86084d8d446c10fe64b06a9cc29c4cc8540e7f2 \
--hash=sha256:557409bc4178e70ee8d9ddb494798e51ebf6ea59330f6be22c51bab2a7db6c49 \
--hash=sha256:5cc09a68b3120e0f54870dede8287a7bb1fa463907e4fcec1ea77cab6179bf7a \
--hash=sha256:60ae316d3fd75d1858d450d0db0103ea2be3e7d4a95ec2f064f7e2ae63f7b028 \
--hash=sha256:6674ab18ad8c57802867264b00e15e7bb904700cdd9046e3b2fa1fce237439ea \
--hash=sha256:67b3b64c11910cfa29f4e94a14d3bff9ee693b6fc76055e7cad549cee0aec5fa \
--hash=sha256:696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc \
--hash=sha256:6dc0b3fd2169c9157deed50b4d519553a3655c8c6a96027136d654592be973a9 \
--hash=sha256:7e65d5407dc0b394f509699650e4a2ec01c0514f21850f453fa60f3be79a5dbf \
--hash=sha256:819959dab7bbd0049c15623fbac4e29a191b9528160a61fb1032242d8ced2d9c \
--hash=sha256:8a1e45c80cceb3b4a21bc5939d52e8cbd8d9b7305309219d59e9754d9ce09e27 \
--hash=sha256:9c39be2d709d01fa972a0cabc522389fceca4f3969332ba25a7d6c5802cf976a \
--hash=sha256:9d71c63ae4ebdbf70209742096f1fc46a83a0613c99d4b23766cced9ff8cd62a \
--hash=sha256:a2d2dff8a04f3917b55ab3910c32990f8ddf7eceba114947838cefa976a68977 \
--hash=sha256:a4eeb6830daf35a71cc09649bd823e2b542dac246cdee9614c6e4bd65028cd6a \
--hash=sha256:a55066a0505dae0ba2b50a46637db34b46f9094c65c5d4800794ef6335010938 \
--hash=sha256:a82d532a3351d435432cd913edbccaf8b8e01d4dd0e5ced5a8d2e8ecd94c7e44 \
--hash=sha256:b168fc218fd80a6cbdbdbc1a97ddc7889ed057d7eb45f50d866ceab5f39904c4 \
--hash=sha256:b2c95f8bfc1ee412bf482605d7bfd30c12d1d26bd59fdd91efeef1d4718decb1 \
--hash=sha256:ba7e08b9ac1d54569cd1e256e3668975ed624d6826f7b68df0342b012007bddb \
--hash=sha256:bab900348131a7db1f69a7309ef141fd5680f1487094193bcbbb61791573bf8f \
--hash=sha256:bd3a518890b400d32f9023722dc9a9a5c969f00b415419a3c06c043f09bb5d7d \
--hash=sha256:c7be265b62cef88e253a941e4698604973736dcfe242fdb5198f0f7bc473cdcc \
--hash=sha256:d26cbe1fcfc12e8fd900e2454163e466b2d3af84f7c75481df7683ffc073d870 \
--hash=sha256:d4be06d68f9ddcfc645b87534911da79a8fbffc7573c80e0edcf42a5020624d8 \
--hash=sha256:d72828c20c6d6e83e1e22a6a3b47b326b71664112fa9705dcbccfd7a39b62085 \
--hash=sha256:dd1a5d1def6a46002e964510bdc67c368aa0951df5d1d9f8365336f5a1f490cd \
--hash=sha256:e3a2ec42c98ffa2565a67e08e218d06d72576d758d90facb7c00805194d8f360 \
--hash=sha256:f8894dc474d648fe7b6ff0ca9b0bd73950d19952bc1a6534540762c5d79d305c \
--hash=sha256:fed2ff7fd9779120e388e285fc029bd5cf9490cdd2e4166a9ee22c0e49a9ab09
# via
# -r .github/requirements/bench.in
# finta
pyproject-hooks==1.2.0 \
--hash=sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8 \
--hash=sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913
# via build
python-dateutil==2.9.0.post0 \
--hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
--hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
# via pandas
six==1.17.0 \
--hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
--hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
# via python-dateutil
ta-lib==0.6.8 \
--hash=sha256:02388054c059945e5f02625f5075bac20a1803573cb43e7d096091027511961f \
--hash=sha256:094677b279a59c3f01c3aca8a889fda3523fd641a3805f69a2d642121b72e55e \
--hash=sha256:0a08a29690a922ba92a6cf42902a8a93c6fbda4cfed62c3c5b0471560ef60135 \
--hash=sha256:0ccd478ff5735831bf2a61d653466bfda8afadc26ad58ca6b1edb9e7521cc674 \
--hash=sha256:0e371d14b49e70caa973a234c8823341dd446f5c5d7acc826868bb42b272bdc0 \
--hash=sha256:11a373c9308eae3bac2d56d37017f9ab63968cc074a8b95be879aae3d13133aa \
--hash=sha256:128ec92e6a0e9ff7a38edef80e3b74f15bb2ed1c531d5d3252c8dca22677651b \
--hash=sha256:1fb4028437201e19014e4e374272b739867c8a3eb655da46675ef4c2ff14b616 \
--hash=sha256:282e49c766b5952dd8796f77d7ed3ae412cdd88e31f845b1fbbb86ac6cb7bebf \
--hash=sha256:2b369cabb48485fbf444beb3f5a878075367b99c2c86db2f796afeabebc749e0 \
--hash=sha256:2bf714333788bf5175f2512b86d2ed129e89ae6f6c2923e8a297a1e3395e13b5 \
--hash=sha256:30de46b55873b51be945a09edf486afcc190dc47eff9fb5d2b12c9f7e3d743da \
--hash=sha256:34e3b12407ddf99f6627435aa8a165f094339bb7dc33de92e1d7472e9f237304 \
--hash=sha256:36b2a516fce57309840f5ef3fa2fd0c4449293fc72536a0400d2e1e26b414da8 \
--hash=sha256:3a9195299df9d7d2a6e9d16bebd6b706b0ea99e4b871864c4b034c2577e21a77 \
--hash=sha256:3c32fc0f546ceecc47dd45f33d72ab4a1e341b80d9081c2d77b100add5d49104 \
--hash=sha256:3d7333e907bff3e3997e54f89733ffa8d619842a3e1cd962bca34bdc11944c28 \
--hash=sha256:4795e93d130c9b7fb661f0cead49752ae6a980437df74b99d5918026c212443e \
--hash=sha256:490e19a45cd3cdd6dfe6b46019f7ffe1103500750b41b51996a870e7c1c5f066 \
--hash=sha256:4aa0fe08383f3e5fc7d2f8cf9b42ac778f4d53fd75bcd2799a858225954eab89 \
--hash=sha256:559326d8f3d904cd4aa61f6a392d5626f35eec6a9f6cc83bcddb0abf88c40516 \
--hash=sha256:5929c83bd8cb7572d1c17ffdbf0eac235bf3c4d53cde1950cf89d944eaf97525 \
--hash=sha256:5bfd21b6acb32e20d4e279c34405a34e63da345be4b2b6eabd683e1a88857406 \
--hash=sha256:613cf06313331f49dd7b85a5a24fbddb1156c9723b6921a231906241726e5aee \
--hash=sha256:66a8e1c1e899d15a2f7510e43527fba22d895e7f6058d027db3e3837d88a69de \
--hash=sha256:691a62926ba09f2653ec0908554b3635497efb7751c5d46b916cd1ebbb1d3c25 \
--hash=sha256:6c1fd18e45c39d5a4be4b0d6a20c141e43fe46daeb1b2e2f304ebae7015ab6e6 \
--hash=sha256:6c6a1e8f98de92e817491b50aa4d01d69a1b41a4ed3173747e8f16f0d4cf81cc \
--hash=sha256:6cf029b886cfb28a2701503b7c602b811f2daa45276bd6459b0c71e051deb497 \
--hash=sha256:71506116eac0d3e3598d6325b4b818c3a0f6acb3222b24d30ad726e8c4bf7ea8 \
--hash=sha256:7993164e8e9f78ec31d38c47850ca6ba5451788b5b49a8a2dbb3322b36b5693b \
--hash=sha256:7a5cc6bf60791d8274edfdfe2dd7cec3f00f656dcc92e2b0a9af06c8b18ce6a6 \
--hash=sha256:87c1cc1057d903b78a8257a7c5f497db6fd5284f5080392bd57b66031d7389a3 \
--hash=sha256:98376c75bd6c103c74396953084a5e0798ffe476aecbfcc51ec6d100a685ac38 \
--hash=sha256:a395524b0fafa10446d11e11acb4742e919523de58aac03b791f26d7a783bcf0 \
--hash=sha256:a5100a4be91b7d4b7c8fe16a3600bd0951e10205eb1066b6873afd3996b51ee4 \
--hash=sha256:a63a52221f8c73f82f4e00493351d987f594931198589287aee96f8da673cfd5 \
--hash=sha256:a89734a7bcb2ea3b6fd600a74d6fbcdb8d3fa3f7917dbd978e039710b5509c9c \
--hash=sha256:b165f5e6de1ccc964e863bd2035807a4d3bad3e0481f9db2dc52034d6ad4f9de \
--hash=sha256:b3845e4c2fa32963fb7f384ebbaa2761b0e6b96145239bf80e956d4aff4b071c \
--hash=sha256:b3b017d9103e7a7372a146773be32b184ff7330bd708d40b1f56f06a686756ed \
--hash=sha256:b6c6e4858d8c3f88e19b7aa94b6a7619108f0bee51da9fa67b0785a8b59955f9 \
--hash=sha256:bfad1202fb1f9140e3810cc607058395f59032d9128cc0d716900c78bea5f337 \
--hash=sha256:c01809fb602e2fefc8cbfb3b603bb59d2a2eaee8708410896d48a835ba00e7c5 \
--hash=sha256:cce8de9d48289927ed18aaa420740efd52b2cd9289da32e3799afbb3a02822e8 \
--hash=sha256:ce2bc1ea01200b6d8130ab917296d05d77a1a571ec6c1ee25cfca6d55cd5db4a \
--hash=sha256:d4601e2a8b46ffbf540601a4926fd6cc5aae8a13b36fdd467f1040f01f9edaed \
--hash=sha256:d556d1c256b3700b60b6b061664a667b2e49d599c2772d46a9f2348f2dc4ab5c \
--hash=sha256:ddf7453acd03b966624ebefdb38169b5bbbeea1a1a58c90b095667247f9de327 \
--hash=sha256:e781eeb65b2007af553389c8a7fb7bc53cb856118b0fcffb2c26b0f49561c686 \
--hash=sha256:e920c272cd9e70a6b10eae9203cc96845da142e1dd4482de9343dda3738a9862 \
--hash=sha256:f5b6174bf4bf9152e368561dff410203c6921e4dd2afbcda3283a95957158112 \
--hash=sha256:f69bd42fd2515060af69b120668213121264bb7976b113954b6f9db327727c65 \
--hash=sha256:f823d0f6b04a6797fbe253bcf91666e71a6b63c290683819650c68b2468ebe64 \
--hash=sha256:fa7e9f2e80a9535f9692e113d02b4268b5f88675a730d1b0ef0abeb74c9a4e80
# via -r .github/requirements/bench.in
talipp==2.7.0 \
--hash=sha256:567f59ad74366cb59a14a00d350f35fd9d22e6924d6228bad581e6dcf1de2205 \
--hash=sha256:f749f22b9ad615605e71faf26457bb7f5e3fe16f04d3287f4ca54fd16bc3d4eb
# via -r .github/requirements/bench.in
tulipy==0.4.0 \
--hash=sha256:540704956b5b940a5f6306aa393a37536a6d7c3cbc07efe47512f3496e5203ab \
--hash=sha256:95542e40537afdd345d875baf37485eac993c6a819d00c51432e9de8df21eba8 \
--hash=sha256:fbc31727ef7657c93ad910bfdce65fecc6aaa7a5e961fe00240718e7a3fc79d8
# via -r .github/requirements/bench.in
tzdata==2026.2 \
--hash=sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10 \
--hash=sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7
# via pandas
+7
View File
@@ -0,0 +1,7 @@
# Python 3.10+ dev/test tooling for the ci.yml binding test job
# (covers the 3.11 / 3.12 / 3.13 matrix rows). Locked output: ci-dev-py3.txt
# Refresh via scripts/update-lockfiles.sh.
maturin
pytest
numpy
hypothesis
+124
View File
@@ -0,0 +1,124 @@
# This file was autogenerated by uv via the following command:
# ./scripts/update-lockfiles.sh
colorama==0.4.6 \
--hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
--hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
# via pytest
hypothesis==6.155.1 \
--hash=sha256:07c102031612b98d7c1be15ca3608c43e1234d9d07e3a190a53fa01536700196 \
--hash=sha256:2753f469df3ba3c483b08e0c37dbcbc41d8316ebb921abcc07493ee9c8a7d187
# via -r .github/requirements/ci-dev-py3.in
iniconfig==2.3.0 \
--hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \
--hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12
# via pytest
maturin==1.13.3 \
--hash=sha256:0ef257e692cc756c87af5bea95ddfe7d3ac49d3376a7a87f728d63f06e7b6f8b \
--hash=sha256:1cc0a110b224ca90406b668a3e3c1f5a515062e59e26292f6dbaf5fd4909c6f3 \
--hash=sha256:2389fe92d017cea9d94e521fa0175314a4c52f79a1057b901fbc9f8686ef7d0b \
--hash=sha256:3cc13929ca82aefa4adbf0f2c35419369796213c6fb0eb24e914945f50ef5d8c \
--hash=sha256:3db93337ed97e60ffc878aa8b493cd7ae44d3a5e1a37256db3a4491f57565018 \
--hash=sha256:4667ef609ab446c1b5e0bfe4f9fb99699ab6d8548433f8d1a684256e0b67217f \
--hash=sha256:49fd6ab08da28098ccf37afca24cdba72376ba9c1eedf9dd25ff82ed771961ff \
--hash=sha256:4cd478e6e4c56251e48ed079b8efd55b30bc5c09cf695a1bdafaeb582ee735a0 \
--hash=sha256:53b08bd075649ce96513ad9abf241a43cb685ed6e9e7790f8dbc2d66e95d8323 \
--hash=sha256:771e1e9e71a278e56db01552e0d1acfd1464259f9575b6e72842f893cd299079 \
--hash=sha256:a2675e25f313034ae6f57388cf14818f87d8961c4a96795287f3e155f59beb11 \
--hash=sha256:b6741d7bf4af97da937528fd1e523c6ab54f53d9a21870fa735d6e67fd88e273 \
--hash=sha256:c00ea6428dea17bf616fe93770837634454b28c2de1a876e42ef8036c616079a \
--hash=sha256:def4a435ea9d2ee93b18ba579dc8c9cf898889a66f312cd379b5e374ec3e3ad6
# via -r .github/requirements/ci-dev-py3.in
numpy==2.4.6 \
--hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \
--hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \
--hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \
--hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \
--hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \
--hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \
--hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \
--hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \
--hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \
--hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \
--hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \
--hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \
--hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \
--hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \
--hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \
--hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \
--hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \
--hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \
--hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \
--hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \
--hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \
--hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \
--hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \
--hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \
--hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \
--hash=sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662 \
--hash=sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91 \
--hash=sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0 \
--hash=sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f \
--hash=sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3 \
--hash=sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f \
--hash=sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67 \
--hash=sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6 \
--hash=sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997 \
--hash=sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b \
--hash=sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e \
--hash=sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538 \
--hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \
--hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \
--hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \
--hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \
--hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \
--hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \
--hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \
--hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \
--hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \
--hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \
--hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \
--hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \
--hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \
--hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \
--hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \
--hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \
--hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \
--hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \
--hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \
--hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \
--hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \
--hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \
--hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \
--hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \
--hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \
--hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \
--hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \
--hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \
--hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \
--hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \
--hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \
--hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \
--hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \
--hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \
--hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20
# via -r .github/requirements/ci-dev-py3.in
packaging==26.2 \
--hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
--hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661
# via pytest
pluggy==1.6.0 \
--hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \
--hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746
# via pytest
pygments==2.20.0 \
--hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \
--hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176
# via pytest
pytest==9.0.3 \
--hash=sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9 \
--hash=sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c
# via -r .github/requirements/ci-dev-py3.in
sortedcontainers==2.4.0 \
--hash=sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88 \
--hash=sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0
# via hypothesis
+8
View File
@@ -0,0 +1,8 @@
# Python 3.9 dev/test tooling for the ci.yml binding test job.
# numpy is capped <2.1 because that is the last series shipping cp39 wheels
# (>=2.1 dropped Python 3.9). Locked output: ci-dev-py39.txt
# Refresh via scripts/update-lockfiles.sh.
maturin
pytest
numpy<2.1
hypothesis
+162
View File
@@ -0,0 +1,162 @@
# This file was autogenerated by uv via the following command:
# ./scripts/update-lockfiles.sh
attrs==26.1.0 \
--hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \
--hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32
# via hypothesis
colorama==0.4.6 \
--hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
--hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
# via pytest
exceptiongroup==1.3.1 \
--hash=sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219 \
--hash=sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598
# via
# hypothesis
# pytest
hypothesis==6.141.1 \
--hash=sha256:8ef356e1e18fbeaa8015aab3c805303b7fe4b868e5b506e87ad83c0bf951f46f \
--hash=sha256:a5b3c39c16d98b7b4c3c5c8d4262e511e3b2255e6814ced8023af49087ad60b3
# via -r .github/requirements/ci-dev-py39.in
iniconfig==2.1.0 \
--hash=sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7 \
--hash=sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760
# via pytest
maturin==1.13.3 \
--hash=sha256:0ef257e692cc756c87af5bea95ddfe7d3ac49d3376a7a87f728d63f06e7b6f8b \
--hash=sha256:1cc0a110b224ca90406b668a3e3c1f5a515062e59e26292f6dbaf5fd4909c6f3 \
--hash=sha256:2389fe92d017cea9d94e521fa0175314a4c52f79a1057b901fbc9f8686ef7d0b \
--hash=sha256:3cc13929ca82aefa4adbf0f2c35419369796213c6fb0eb24e914945f50ef5d8c \
--hash=sha256:3db93337ed97e60ffc878aa8b493cd7ae44d3a5e1a37256db3a4491f57565018 \
--hash=sha256:4667ef609ab446c1b5e0bfe4f9fb99699ab6d8548433f8d1a684256e0b67217f \
--hash=sha256:49fd6ab08da28098ccf37afca24cdba72376ba9c1eedf9dd25ff82ed771961ff \
--hash=sha256:4cd478e6e4c56251e48ed079b8efd55b30bc5c09cf695a1bdafaeb582ee735a0 \
--hash=sha256:53b08bd075649ce96513ad9abf241a43cb685ed6e9e7790f8dbc2d66e95d8323 \
--hash=sha256:771e1e9e71a278e56db01552e0d1acfd1464259f9575b6e72842f893cd299079 \
--hash=sha256:a2675e25f313034ae6f57388cf14818f87d8961c4a96795287f3e155f59beb11 \
--hash=sha256:b6741d7bf4af97da937528fd1e523c6ab54f53d9a21870fa735d6e67fd88e273 \
--hash=sha256:c00ea6428dea17bf616fe93770837634454b28c2de1a876e42ef8036c616079a \
--hash=sha256:def4a435ea9d2ee93b18ba579dc8c9cf898889a66f312cd379b5e374ec3e3ad6
# via -r .github/requirements/ci-dev-py39.in
numpy==2.0.2 \
--hash=sha256:0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a \
--hash=sha256:11a76c372d1d37437857280aa142086476136a8c0f373b2e648ab2c8f18fb195 \
--hash=sha256:13e689d772146140a252c3a28501da66dfecd77490b498b168b501835041f951 \
--hash=sha256:1e795a8be3ddbac43274f18588329c72939870a16cae810c2b73461c40718ab1 \
--hash=sha256:26df23238872200f63518dd2aa984cfca675d82469535dc7162dc2ee52d9dd5c \
--hash=sha256:286cd40ce2b7d652a6f22efdfc6d1edf879440e53e76a75955bc0c826c7e64dc \
--hash=sha256:2b2955fa6f11907cf7a70dab0d0755159bca87755e831e47932367fc8f2f2d0b \
--hash=sha256:2da5960c3cf0df7eafefd806d4e612c5e19358de82cb3c343631188991566ccd \
--hash=sha256:312950fdd060354350ed123c0e25a71327d3711584beaef30cdaa93320c392d4 \
--hash=sha256:423e89b23490805d2a5a96fe40ec507407b8ee786d66f7328be214f9679df6dd \
--hash=sha256:496f71341824ed9f3d2fd36cf3ac57ae2e0165c143b55c3a035ee219413f3318 \
--hash=sha256:49ca4decb342d66018b01932139c0961a8f9ddc7589611158cb3c27cbcf76448 \
--hash=sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece \
--hash=sha256:5fec9451a7789926bcf7c2b8d187292c9f93ea30284802a0ab3f5be8ab36865d \
--hash=sha256:671bec6496f83202ed2d3c8fdc486a8fc86942f2e69ff0e986140339a63bcbe5 \
--hash=sha256:7f0a0c6f12e07fa94133c8a67404322845220c06a9e80e85999afe727f7438b8 \
--hash=sha256:807ec44583fd708a21d4a11d94aedf2f4f3c3719035c76a2bbe1fe8e217bdc57 \
--hash=sha256:883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78 \
--hash=sha256:8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66 \
--hash=sha256:8cafab480740e22f8d833acefed5cc87ce276f4ece12fdaa2e8903db2f82897a \
--hash=sha256:8df823f570d9adf0978347d1f926b2a867d5608f434a7cff7f7908c6570dcf5e \
--hash=sha256:9059e10581ce4093f735ed23f3b9d283b9d517ff46009ddd485f1747eb22653c \
--hash=sha256:905d16e0c60200656500c95b6b8dca5d109e23cb24abc701d41c02d74c6b3afa \
--hash=sha256:9189427407d88ff25ecf8f12469d4d39d35bee1db5d39fc5c168c6f088a6956d \
--hash=sha256:96a55f64139912d61de9137f11bf39a55ec8faec288c75a54f93dfd39f7eb40c \
--hash=sha256:97032a27bd9d8988b9a97a8c4d2c9f2c15a81f61e2f21404d7e8ef00cb5be729 \
--hash=sha256:984d96121c9f9616cd33fbd0618b7f08e0cfc9600a7ee1d6fd9b239186d19d97 \
--hash=sha256:9a92ae5c14811e390f3767053ff54eaee3bf84576d99a2456391401323f4ec2c \
--hash=sha256:9ea91dfb7c3d1c56a0e55657c0afb38cf1eeae4544c208dc465c3c9f3a7c09f9 \
--hash=sha256:a15f476a45e6e5a3a79d8a14e62161d27ad897381fecfa4a09ed5322f2085669 \
--hash=sha256:a392a68bd329eafac5817e5aefeb39038c48b671afd242710b451e76090e81f4 \
--hash=sha256:a3f4ab0caa7f053f6797fcd4e1e25caee367db3112ef2b6ef82d749530768c73 \
--hash=sha256:a46288ec55ebbd58947d31d72be2c63cbf839f0a63b49cb755022310792a3385 \
--hash=sha256:a61ec659f68ae254e4d237816e33171497e978140353c0c2038d46e63282d0c8 \
--hash=sha256:a842d573724391493a97a62ebbb8e731f8a5dcc5d285dfc99141ca15a3302d0c \
--hash=sha256:becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b \
--hash=sha256:c05e238064fc0610c840d1cf6a13bf63d7e391717d247f1bf0318172e759e692 \
--hash=sha256:c1c9307701fec8f3f7a1e6711f9089c06e6284b3afbbcd259f7791282d660a15 \
--hash=sha256:c7b0be4ef08607dd04da4092faee0b86607f111d5ae68036f16cc787e250a131 \
--hash=sha256:cfd41e13fdc257aa5778496b8caa5e856dc4896d4ccf01841daee1d96465467a \
--hash=sha256:d731a1c6116ba289c1e9ee714b08a8ff882944d4ad631fd411106a30f083c326 \
--hash=sha256:df55d490dea7934f330006d0f81e8551ba6010a5bf035a249ef61a94f21c500b \
--hash=sha256:ec9852fb39354b5a45a80bdab5ac02dd02b15f44b3804e9f00c556bf24b4bded \
--hash=sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04 \
--hash=sha256:f26b258c385842546006213344c50655ff1555a9338e2e5e02a0756dc3e803dd
# via -r .github/requirements/ci-dev-py39.in
packaging==26.2 \
--hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
--hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661
# via pytest
pluggy==1.6.0 \
--hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \
--hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746
# via pytest
pygments==2.20.0 \
--hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \
--hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176
# via pytest
pytest==8.4.2 \
--hash=sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01 \
--hash=sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79
# via -r .github/requirements/ci-dev-py39.in
sortedcontainers==2.4.0 \
--hash=sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88 \
--hash=sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0
# via hypothesis
tomli==2.4.1 \
--hash=sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853 \
--hash=sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe \
--hash=sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5 \
--hash=sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d \
--hash=sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd \
--hash=sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26 \
--hash=sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54 \
--hash=sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6 \
--hash=sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c \
--hash=sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a \
--hash=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \
--hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \
--hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \
--hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \
--hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \
--hash=sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9 \
--hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \
--hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \
--hash=sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e \
--hash=sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c \
--hash=sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41 \
--hash=sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f \
--hash=sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085 \
--hash=sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15 \
--hash=sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7 \
--hash=sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c \
--hash=sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36 \
--hash=sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076 \
--hash=sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \
--hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \
--hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \
--hash=sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece \
--hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \
--hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \
--hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \
--hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \
--hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \
--hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \
--hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \
--hash=sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc \
--hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \
--hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \
--hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \
--hash=sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30 \
--hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \
--hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 \
--hash=sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049
# via
# maturin
# pytest
typing-extensions==4.15.0 \
--hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \
--hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
# via exceptiongroup
+113
View File
@@ -0,0 +1,113 @@
"""Audit that no file in the repo contains the pre-migration org slug or
maintainer email. Driven by `repo-metadata.toml` at the repo root.
This is the read-only side of the metadata pipeline. It does not patch any
files — it just fails CI when drift sneaks in. Pair with a future
`--write` mode (auto-fix + signed commit on main) once the migration has
settled.
"""
from __future__ import annotations
import argparse
import os
import subprocess
import sys
import tomllib
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parents[2]
METADATA_PATH = REPO_ROOT / "repo-metadata.toml"
def load_metadata() -> dict:
with METADATA_PATH.open("rb") as f:
return tomllib.load(f)
def is_allowlisted(rel_path: str, allowlist: list[str]) -> bool:
norm = rel_path.replace(os.sep, "/")
for entry in allowlist:
entry_norm = entry.replace(os.sep, "/")
if entry_norm.endswith("/"):
if norm.startswith(entry_norm):
return True
else:
if norm == entry_norm:
return True
return False
def tracked_files() -> list[str]:
"""List git-tracked files relative to the repo root."""
out = subprocess.run(
["git", "ls-files"],
cwd=REPO_ROOT,
check=True,
capture_output=True,
text=True,
)
return [line for line in out.stdout.splitlines() if line]
def scan(forbidden: list[str], allowlist: list[str]) -> list[tuple[str, int, str, str]]:
"""Return a list of (rel_path, line_no, needle, line_text) findings.
Only git-tracked files are scanned, so local-only ghost-ignored files
(`.claude/`, drafts) never trigger false positives.
"""
findings: list[tuple[str, int, str, str]] = []
for rel_path in tracked_files():
if is_allowlisted(rel_path, allowlist):
continue
abs_path = REPO_ROOT / rel_path
if not abs_path.is_file():
continue
try:
lines = abs_path.read_text(encoding="utf-8", errors="replace").splitlines()
except (OSError, UnicodeDecodeError):
continue
for lineno, line in enumerate(lines, start=1):
for needle in forbidden:
if needle in line:
findings.append((rel_path, lineno, needle, line.strip()))
return findings
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true", help="audit-only (default)")
args = parser.parse_args()
_ = args # currently only --check is supported
meta = load_metadata()
audit = meta.get("audit", {})
forbidden: list[str] = list(audit.get("forbidden", []))
allowlist: list[str] = list(audit.get("allowlist", []))
if not forbidden:
print("repo-metadata.toml [audit].forbidden is empty — nothing to scan.")
return 0
findings = scan(forbidden, allowlist)
if findings:
print(f"sync-metadata: {len(findings)} forbidden-substring hits:", file=sys.stderr)
for rel_path, lineno, needle, text in findings:
print(f" {rel_path}:{lineno}: matched {needle!r}", file=sys.stderr)
print(f" {text}", file=sys.stderr)
print(
"\nUpdate the offending lines to use the values from repo-metadata.toml,",
"or add the path to [audit].allowlist if the reference is intentional",
"(e.g. historical CHANGELOG entries).",
file=sys.stderr,
)
return 1
org = meta["repo"]["org"]
email = meta["maintainer"]["email"]
print(f"sync-metadata: clean. org={org!r} email={email!r}")
return 0
if __name__ == "__main__":
sys.exit(main())
+78 -3
View File
@@ -22,8 +22,26 @@ on:
required: false
default: "10"
# Least-privilege default for the auto-injected GITHUB_TOKEN. The single job
# only builds and uploads an artifact (upload-artifact uses the artifact
# storage API, not the contents scope), so it never needs repo write (OpenSSF
# Scorecard: Token-Permissions).
permissions:
contents: read
env:
CARGO_TERM_COLOR: always
# Network-flake resilience: retry transient registry/DNS failures at the tool
# level so a blip fetching crates.io / PyPI inside any build step (cargo,
# maturin, pip) retries automatically instead of failing the job. Cargo treats
# "couldn't resolve host" / connect / timeout as spurious and retries with
# backoff; 10 attempts ride out a transient DNS blip on a runner.
CARGO_NET_RETRY: "10"
CARGO_NET_GIT_FETCH_WITH_CLI: "true"
npm_config_fetch_retries: "5"
npm_config_fetch_retry_maxtimeout: "120000"
PIP_RETRIES: "5"
PIP_DEFAULT_TIMEOUT: "120"
jobs:
cross-library-bench:
@@ -31,20 +49,45 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
- uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Set up Python
id: setup_python
continue-on-error: true
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
cache: pip
cache-dependency-path: .github/requirements/bench.txt
- name: Wait before Python retry
if: steps.setup_python.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-python failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Python (retry)
if: steps.setup_python.outcome == 'failure'
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
cache: pip
cache-dependency-path: .github/requirements/bench.txt
- name: Install Python deps + peer libs
run: |
python -m pip install --upgrade pip
python -m pip install maturin numpy pandas talipp finta
# Hash-locked deps (OpenSSF Scorecard PinnedDependencies). bench.yml
# runs on a single Python version (3.11), so one lock file suffices.
python -m pip install --require-hashes -r .github/requirements/bench.txt
- name: Build Wickra wheel
working-directory: bindings/python
@@ -56,10 +99,16 @@ jobs:
- name: Run cross-library benchmark
working-directory: bindings/python
# workflow_dispatch inputs are untrusted; pass them through the
# environment and quote them rather than interpolating into the shell
# command (OpenSSF Scorecard: Dangerous-Workflow).
env:
BENCH_SIZE: ${{ github.event.inputs.size || '20000' }}
BENCH_ITERATIONS: ${{ github.event.inputs.iterations || '10' }}
run: |
python -m benchmarks.compare_libraries \
--size ${{ github.event.inputs.size || '20000' }} \
--iterations ${{ github.event.inputs.iterations || '10' }} \
--size "$BENCH_SIZE" \
--iterations "$BENCH_ITERATIONS" \
--streaming-window 5000 --streaming-iterations 2 \
| tee benchmark.txt
@@ -68,3 +117,29 @@ jobs:
with:
name: cross-library-bench
path: bindings/python/benchmark.txt
rust-cross-bench:
name: Rust cross-library benchmark report
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
- uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
# Wickra vs the other Rust TA crates (kand, ta-rs, yata) on an identical
# candle series — the like-for-like engine comparison with no binding
# overhead. Streaming + batch, in crates/wickra-bench/benches/cross_lib.rs.
- name: Run Rust cross-library benchmark
run: cargo bench -p wickra-bench --bench cross_lib | tee rust_cross_bench.txt
- name: Upload Rust report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: rust-cross-bench
path: rust_cross_bench.txt
+276 -7
View File
@@ -6,9 +6,29 @@ on:
pull_request:
branches: [main]
# Least-privilege default for the auto-injected GITHUB_TOKEN. None of the CI
# jobs write back to the repo — coverage uploads via CODECOV_TOKEN, everything
# else is build/test/lint — so a read-only token is sufficient (OpenSSF
# Scorecard: Token-Permissions).
permissions:
contents: read
env:
CARGO_TERM_COLOR: always
RUSTFLAGS: "-D warnings"
# Network-flake resilience: retry transient registry/DNS failures at the tool
# level so a blip fetching crates.io / npm / PyPI inside any build step (cargo,
# napi, maturin, wasm-pack, npm ci, pip) retries automatically instead of
# failing the job and needing a manual re-run. Cargo treats "couldn't resolve
# host" / connect / timeout as spurious and retries with backoff; 10 attempts
# ride out a transient DNS blip on a runner. Complements the setup-action /
# cache retries (which only covered toolchain download + cache restore).
CARGO_NET_RETRY: "10"
CARGO_NET_GIT_FETCH_WITH_CLI: "true"
npm_config_fetch_retries: "5"
npm_config_fetch_retry_maxtimeout: "120000"
PIP_RETRIES: "5"
PIP_DEFAULT_TIMEOUT: "120"
jobs:
rust:
@@ -20,6 +40,8 @@ jobs:
os: [ubuntu-latest, macos-latest, windows-latest]
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
@@ -28,6 +50,8 @@ jobs:
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Format check
run: cargo fmt --all -- --check
@@ -55,6 +79,162 @@ jobs:
# streaming.
run: cargo build -p wickra-examples --bins
# Syntax/parse smoke for the non-Rust examples. The Rust examples are built
# in the `rust` job above (`cargo build -p wickra-examples --bins`); the Node,
# browser-WASM and Python examples otherwise have no build gate, so a broken
# edit could land unnoticed. This is a parse-only smoke — actually running the
# examples needs the built native binding / wasm module / wheel, which the
# binding jobs provide separately.
examples-smoke:
name: Examples (syntax smoke)
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
- name: Set up Python
id: setup_python
continue-on-error: true
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.12"
- name: Wait before Python retry
if: steps.setup_python.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-python failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Python (retry)
if: steps.setup_python.outcome == 'failure'
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.12"
- name: Node examples — syntax check
run: |
shopt -s nullglob
count=0
for f in examples/node/*.js examples/wasm/*.js; do
echo "node --check $f"
node --check "$f"
count=$((count + 1))
done
echo "checked $count Node/WASM .js files"
- name: WASM demo module scripts — syntax check
# The .html demos embed an ES module; extract it and parse-check so a
# broken edit to the in-page strategy logic fails CI.
run: |
shopt -s nullglob
count=0
for f in examples/wasm/*.html; do
node -e 'const fs=require("fs");const h=fs.readFileSync(process.argv[1],"utf8");const m=h.match(/<script type="module">([\s\S]*?)<\/script>/);if(!m){console.error("no <script type=module> in "+process.argv[1]);process.exit(1);}fs.writeFileSync("module-check.mjs",m[1]);' "$f"
echo "node --check (module of) $f"
node --check module-check.mjs
count=$((count + 1))
done
rm -f module-check.mjs
echo "checked $count WASM .html module scripts"
- name: Python examples — byte-compile
run: |
shopt -s nullglob
count=0
for f in examples/python/*.py; do
echo "py_compile $f"
python -m py_compile "$f"
count=$((count + 1))
done
echo "compiled $count Python files"
# Clippy for the Python and Node bindings. These are kept out of the main
# `rust` job because PyO3 / napi build scripts need a Python interpreter and
# a Node toolchain on PATH, which the 3-OS matrix job does not provision.
# Ubuntu-only is sufficient: the lints are platform-independent.
clippy-bindings:
name: Clippy bindings
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
with:
components: clippy
- name: Set up Python
id: setup_python
continue-on-error: true
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Wait before Python retry
if: steps.setup_python.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-python failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Python (retry)
if: steps.setup_python.outcome == 'failure'
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Clippy (bindings, all targets)
run: cargo clippy -p wickra-node -p wickra-python --all-targets -- -D warnings
# Verify the crates still build and test on their declared minimum supported
# Rust version. The workspace pins rust-version = "1.86" — that floor is
# set by criterion 0.8.2 (the bench dev-dep), which itself rolled past the
@@ -79,6 +259,8 @@ jobs:
packages: "-p wickra-node"
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust ${{ matrix.toolchain }}
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
@@ -87,6 +269,8 @@ jobs:
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Build on MSRV
run: cargo build ${{ matrix.packages }} --verbose
@@ -100,6 +284,8 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
@@ -108,9 +294,12 @@ jobs:
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Install cargo-llvm-cov
uses: taiki-e/install-action@6c1f7cf125e42770ff087ea443901b487cc5471a # v2.79.5
uses: taiki-e/install-action@0fd46367812ee04360509b4169d9f659d6892bb2 # v2.79.15
timeout-minutes: 10 # fail fast on a stuck download instead of hanging the job
with:
tool: cargo-llvm-cov
@@ -136,9 +325,11 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: cargo-deny
uses: EmbarkStudios/cargo-deny-action@a531616d8ce3b9177443e48a1159bc945a099823 # v2.0.19
uses: EmbarkStudios/cargo-deny-action@bb137d7af7e4fb67e5f82a49c4fce4fad40782fe # v2.0.20
with:
command: check
@@ -152,6 +343,8 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install nightly Rust
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
@@ -160,6 +353,8 @@ jobs:
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
with:
workspaces: fuzz
@@ -171,7 +366,8 @@ jobs:
# attributes the modern nightly compiler rejects, so the install
# never gets off the ground. The prebuilt binary avoids the entire
# transitive-dep compile.
uses: taiki-e/install-action@6c1f7cf125e42770ff087ea443901b487cc5471a # v2.79.5
uses: taiki-e/install-action@0fd46367812ee04360509b4169d9f659d6892bb2 # v2.79.15
timeout-minutes: 10 # fail fast on a stuck download instead of hanging the job
with:
tool: cargo-fuzz
@@ -205,22 +401,58 @@ jobs:
python-version: ["3.9", "3.11", "3.12", "3.13"]
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
# setup-python downloads the interpreter from the Actions tool cache /
# nodejs CDN and occasionally hangs or 5xx's on the Windows runners.
# Run it with continue-on-error, then retry once after a backoff so a
# single CDN flake does not fail the whole job (see also: GitHub
# Actions runner-images#7061).
- name: Set up Python
id: setup_python
continue-on-error: true
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: .github/requirements/ci-dev-*.txt
- name: Wait before Python retry
if: steps.setup_python.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-python failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Python (retry)
if: steps.setup_python.outcome == 'failure'
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: .github/requirements/ci-dev-*.txt
- name: Install Python dev dependencies
shell: bash
run: |
python -m pip install --upgrade pip
python -m pip install maturin pytest numpy hypothesis
# Hash-locked dev tooling (OpenSSF Scorecard PinnedDependencies).
# Split by Python version: numpy ships no single release with wheels
# for both cp39 and cp313 (<=2.0.2 has cp39 only, >=2.1 drops cp39).
if [ "${{ matrix.python-version }}" = "3.9" ]; then
python -m pip install --require-hashes -r .github/requirements/ci-dev-py39.txt
else
python -m pip install --require-hashes -r .github/requirements/ci-dev-py3.txt
fi
- name: Build wheel
working-directory: bindings/python
@@ -229,7 +461,12 @@ jobs:
- name: Install wheel
shell: bash
working-directory: bindings/python
run: python -m pip install --find-links dist --force-reinstall wickra
# --no-index forces pip to ignore PyPI; --no-deps skips re-resolving
# numpy (already installed in the previous step). Without --no-index
# pip prefers the PyPI 0.2.x wheel over our freshly built one when
# platform tags overlap (e.g. macOS arm64), so tests would run
# against the released package and miss any new symbols the PR adds.
run: python -m pip install --no-index --find-links dist --force-reinstall --no-deps wickra
- name: Run Python tests
working-directory: bindings/python
@@ -240,6 +477,8 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain (with wasm target)
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
@@ -248,6 +487,8 @@ jobs:
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Install wasm-pack
# jetli/wasm-pack-action@v0.4.0 with no `version:` input installs an
@@ -257,7 +498,8 @@ jobs:
# same taiki-e prebuilt-binary installer we already use for
# cargo-llvm-cov and cargo-fuzz; it tracks the latest wasm-pack
# release, which has `--features` as a top-level flag (since 0.12).
uses: taiki-e/install-action@6c1f7cf125e42770ff087ea443901b487cc5471a # v2.79.5
uses: taiki-e/install-action@0fd46367812ee04360509b4169d9f659d6892bb2 # v2.79.15
timeout-minutes: 10 # fail fast on a stuck download instead of hanging the job
with:
tool: wasm-pack
@@ -283,21 +525,48 @@ jobs:
node-version: ["18", "20"]
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
- name: Cache cargo
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
# setup-node downloads Node from nodejs.org and we've seen it fail on
# Windows runners with "Attempting to download 18..." followed by a
# silent hang or curl error. Retry once after a backoff so a single
# CDN flake does not fail the whole job.
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: ${{ matrix.node-version }}
cache: npm
cache-dependency-path: bindings/node/package-lock.json
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: ${{ matrix.node-version }}
cache: npm
cache-dependency-path: bindings/node/package-lock.json
- name: Install Node dependencies
working-directory: bindings/node
run: npm install
run: npm ci
- name: Build native module
working-directory: bindings/node
+56
View File
@@ -0,0 +1,56 @@
name: CodeQL
# Static analysis security testing (findings P13.x). Analyses the Rust core and
# the Python / JavaScript binding surfaces with GitHub's CodeQL engine. Results
# appear under Security → Code scanning. `build-mode: none` analyses source
# directly — no compilation step — for every language here.
on:
push:
branches: [main]
pull_request:
branches: [main]
schedule:
- cron: '31 3 * * 0' # Sundays 03:31 UTC
# Least-privilege default for the auto-injected GITHUB_TOKEN. The analyze job
# raises exactly the scopes CodeQL needs (security-events: write to upload
# results) in its own job-level block below; this top-level read-only default
# covers any future job (OpenSSF Scorecard: Token-Permissions).
permissions:
contents: read
jobs:
analyze:
name: Analyze (${{ matrix.language }})
runs-on: ubuntu-latest
permissions:
security-events: write # upload CodeQL results to code-scanning
packages: read
actions: read
contents: read
strategy:
fail-fast: false
matrix:
include:
- language: rust
build-mode: none
- language: python
build-mode: none
- language: javascript-typescript
build-mode: none
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Initialize CodeQL
uses: github/codeql-action/init@03e4368ac7daa2bd82b3e85262f3bf87ee112f57 # v3.36.0
with:
languages: ${{ matrix.language }}
build-mode: ${{ matrix.build-mode }}
- name: Perform CodeQL analysis
uses: github/codeql-action/analyze@03e4368ac7daa2bd82b3e85262f3bf87ee112f57 # v3.36.0
with:
category: "/language:${{ matrix.language }}"
+302 -31
View File
@@ -5,8 +5,30 @@ on:
tags: ["v*"]
workflow_dispatch:
# Least-privilege default for the auto-injected GITHUB_TOKEN. The publish jobs
# (cargo/python/node) push to external registries via their own secrets
# (CARGO_REGISTRY_TOKEN / PYPI_API_TOKEN / NPM_TOKEN), not the GITHUB_TOKEN, so
# they need no repo write. The jobs that genuinely write through the
# GITHUB_TOKEN — github-release (contents: write), node-/wasm-publish and
# attestations (id-token / attestations: write) — declare those rights in their
# own job-level permissions blocks, which override this default (OpenSSF
# Scorecard: Token-Permissions).
permissions:
contents: read
env:
CARGO_TERM_COLOR: always
# Network-flake resilience: retry transient registry/DNS failures at the tool
# level so a blip fetching crates.io / npm inside any build or publish step
# (cargo, napi, maturin, wasm-pack, npm) retries automatically instead of
# failing the job. Cargo treats "couldn't resolve host" / connect / timeout as
# spurious and retries with backoff; 10 attempts ride out a transient DNS blip.
CARGO_NET_RETRY: "10"
CARGO_NET_GIT_FETCH_WITH_CLI: "true"
npm_config_fetch_retries: "5"
npm_config_fetch_retry_maxtimeout: "120000"
PIP_RETRIES: "5"
PIP_DEFAULT_TIMEOUT: "120"
jobs:
# --------------------------------------------------------------------------
@@ -24,8 +46,12 @@ jobs:
environment: release
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
- uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
# Idempotent publishing: if the version is already on crates.io we
# treat that as success so re-runs of the workflow don't fail.
@@ -79,6 +105,35 @@ jobs:
name: crate-files
path: target/package/*.crate
# CycloneDX SBOM per published crate. Attached to the GitHub Release
# alongside the .crate / .whl / .tgz artefacts so downstream
# consumers can audit the published dependency tree without
# re-resolving Cargo.lock.
- name: Install cargo-cyclonedx
uses: taiki-e/install-action@0fd46367812ee04360509b4169d9f659d6892bb2 # v2.79.15
timeout-minutes: 10 # fail fast on a stuck download instead of hanging the job
with:
tool: cargo-cyclonedx
- name: Generate CycloneDX SBOMs
run: |
# cargo-cyclonedx walks the whole workspace in a single pass and
# writes a <package>.cdx.json next to each member's Cargo.toml; it
# has no -p/--package selector. Collect the three crates.io crates
# (the .crate files published by this job) into the upload dir.
cargo cyclonedx --format json --top-level
mkdir -p sboms
cp crates/wickra-core/wickra-core.cdx.json sboms/
cp crates/wickra-data/wickra-data.cdx.json sboms/
cp crates/wickra/wickra.cdx.json sboms/
ls -lh sboms/
- name: Upload SBOMs
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: sboms
path: sboms/*.cdx.json
# --------------------------------------------------------------------------
# PyPI: cross-platform wheels + sdist
# --------------------------------------------------------------------------
@@ -103,7 +158,23 @@ jobs:
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
persist-credentials: false
- name: Set up Python
id: setup_python
continue-on-error: true
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
- name: Wait before Python retry
if: steps.setup_python.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-python failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Python (retry)
if: steps.setup_python.outcome == 'failure'
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
- name: Sync root README into bindings/python so it ships with the wheel
@@ -127,6 +198,8 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Sync root README into bindings/python so it ships in the sdist
run: cp README.md bindings/python/README.md
- uses: PyO3/maturin-action@e83996d129638aa358a18fbd1dfb82f0b0fb5d3b # v1.51.0
@@ -173,20 +246,30 @@ jobs:
- { host: macos-latest, target: x86_64-apple-darwin }
- { host: macos-latest, target: aarch64-apple-darwin }
- { host: windows-latest, target: x86_64-pc-windows-msvc }
# NOTE: aarch64-pc-windows-msvc is temporarily skipped for 0.2.5.
# The wickra-win32-arm64-msvc npm subpackage name is blocked by the
# npm spam-detection filter for new accounts (same situation that
# affected wickra-win32-x64-msvc through 0.1.4 until npm Support
# unblocked it). A support ticket is open; once the new arm64 name
# is unblocked this matrix entry will be restored alongside the
# corresponding optionalDependencies / napi.triples / npm/<target>
# entries in a follow-up release.
# - { host: windows-11-arm, target: aarch64-pc-windows-msvc }
- { host: windows-11-arm, target: aarch64-pc-windows-msvc }
runs-on: ${{ matrix.host }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
@@ -195,10 +278,12 @@ jobs:
targets: ${{ matrix.target }}
- uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
timeout-minutes: 6
- name: Install Node deps
working-directory: bindings/node
run: npm install
run: npm ci
- name: Build native module
working-directory: bindings/node
@@ -216,17 +301,44 @@ jobs:
needs: node-build
runs-on: ubuntu-latest
environment: release
# `id-token: write` lets npm publish embed a Sigstore provenance
# attestation generated from the GitHub Actions OIDC token. The npm
# registry then shows a "Verified provenance" badge and lets
# consumers verify the package was built from this exact workflow
# run.
permissions:
contents: read
id-token: write
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
registry-url: "https://registry.npmjs.org"
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
registry-url: "https://registry.npmjs.org"
- name: Install Node deps
working-directory: bindings/node
run: npm install
run: npm ci
- name: Download all platform binaries
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
@@ -274,13 +386,13 @@ jobs:
# scripts during publish (npm runs prepublishOnly/prepare/etc. from
# the package being published — a malicious or stray script would
# execute with the npm token in the environment).
(cd "$dir" && npm publish --access public --ignore-scripts)
(cd "$dir" && npm publish --access public --ignore-scripts --provenance)
local rc=$?
echo "::endgroup::"
if [ "$rc" -ne 0 ]; then
echo "::warning::first attempt of $pkgname failed (rc=$rc); retrying after 30s"
sleep 30
(cd "$dir" && npm publish --access public --ignore-scripts)
(cd "$dir" && npm publish --access public --ignore-scripts --provenance)
rc=$?
fi
if [ "$rc" -ne 0 ]; then
@@ -321,12 +433,12 @@ jobs:
# --ignore-scripts so any leftover prepublish hooks (which would
# otherwise try to republish the already-published platform
# subpackages) can't sabotage the main publish.
npm publish --access public --ignore-scripts
npm publish --access public --ignore-scripts --provenance
rc=$?
if [ "$rc" -ne 0 ]; then
echo "::warning::first attempt failed (rc=$rc); retrying after 30s"
sleep 30
npm publish --access public --ignore-scripts
npm publish --access public --ignore-scripts --provenance
rc=$?
fi
exit $rc
@@ -354,14 +466,41 @@ jobs:
# --------------------------------------------------------------------------
# WASM: wasm-pack build + npm publish (as `wickra-wasm`)
# --------------------------------------------------------------------------
# Note: this job's npm publish call uses `--provenance` (see below),
# which requires the `id-token: write` permission set at the job level.
wasm-publish:
name: Publish wickra-wasm to npm
runs-on: ubuntu-latest
environment: release
# `id-token: write` lets npm publish embed a Sigstore provenance
# attestation generated from the GitHub Actions OIDC token (same
# mechanism as the node-publish job above).
permissions:
contents: read
id-token: write
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
- name: Set up Node
id: setup_node
continue-on-error: true
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
registry-url: "https://registry.npmjs.org"
- name: Wait before Node retry
if: steps.setup_node.outcome == 'failure'
shell: bash
run: |
echo "::warning::setup-node failed (likely CDN flake), waiting 30s before retry..."
sleep 30
- name: Set up Node (retry)
if: steps.setup_node.outcome == 'failure'
uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: "20"
registry-url: "https://registry.npmjs.org"
@@ -373,7 +512,8 @@ jobs:
- name: Install wasm-pack (latest, via prebuilt binary)
# See the matching note in ci.yml: jetli's default installs an old
# 0.10.x wasm-pack whose build subcommand rejects --features.
uses: taiki-e/install-action@6c1f7cf125e42770ff087ea443901b487cc5471a # v2.79.5
uses: taiki-e/install-action@0fd46367812ee04360509b4169d9f659d6892bb2 # v2.79.15
timeout-minutes: 10 # fail fast on a stuck download instead of hanging the job
with:
tool: wasm-pack
@@ -392,11 +532,11 @@ jobs:
node -e "
const fs = require('fs');
const pkg = JSON.parse(fs.readFileSync('package.json'));
pkg.author = 'kingchenc <kingchencp@gmail.com>';
pkg.repository = { type: 'git', url: 'https://github.com/kingchenc/wickra' };
pkg.homepage = 'https://github.com/kingchenc/wickra';
pkg.bugs = { url: 'https://github.com/kingchenc/wickra/issues' };
pkg.license = 'PolyForm-Noncommercial-1.0.0';
pkg.author = 'kingchenc <support@wickra.org>';
pkg.repository = { type: 'git', url: 'https://github.com/wickra-lib/wickra' };
pkg.homepage = 'https://github.com/wickra-lib/wickra';
pkg.bugs = { url: 'https://github.com/wickra-lib/wickra/issues' };
pkg.license = 'MIT OR Apache-2.0';
fs.writeFileSync('package.json', JSON.stringify(pkg, null, 2));
"
@@ -413,7 +553,7 @@ jobs:
- name: Publish wickra-wasm to npm (idempotent)
working-directory: bindings/wasm/pkg
run: |
out=$(npm publish --access public 2>&1) && echo "$out" \
out=$(npm publish --access public --provenance 2>&1) && echo "$out" \
|| (echo "$out" | grep -q "You cannot publish over" && echo "skip: version already on npm" \
|| (echo "$out"; exit 1))
env:
@@ -421,23 +561,43 @@ jobs:
# --------------------------------------------------------------------------
# GitHub Release: attach every built artefact to the tag's release page.
#
# The release is created as a DRAFT here and only flipped to published by the
# downstream publish-release job, after the provenance bundle is attached. That
# ordering (draft -> attach everything -> publish) makes the pipeline compatible
# with GitHub release immutability, which locks assets at publish time (P24):
# the old "publish, then upload provenance" order would have the provenance
# upload rejected once immutability is enabled.
# --------------------------------------------------------------------------
github-release:
name: Attach assets to the GitHub Release
name: Attach assets to the draft GitHub Release
needs: [cargo-publish, python-publish, node-publish, wasm-publish]
runs-on: ubuntu-latest
permissions:
contents: write
# Expose the resolved tag so the attestations job can attach the provenance
# bundle to this same release without re-resolving it.
outputs:
tag: ${{ steps.tag.outputs.tag }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
fetch-depth: 0
- name: Resolve target tag
id: tag
# Pass the (potentially attacker-influenceable on a tag push) ref context
# through the environment instead of interpolating it into the shell
# script, so a crafted tag name cannot inject commands (zizmor:
# template-injection).
env:
EVENT_NAME: ${{ github.event_name }}
REF: ${{ github.ref }}
REF_NAME: ${{ github.ref_name }}
run: |
if [ "${{ github.event_name }}" = "push" ] && [[ "${{ github.ref }}" == refs/tags/* ]]; then
tag="${{ github.ref_name }}"
if [ "$EVENT_NAME" = "push" ] && [[ "$REF" == refs/tags/* ]]; then
tag="$REF_NAME"
else
# workflow_dispatch / non-tag push: attach to the latest v* tag.
tag=$(git tag --list 'v*' --sort=-v:refname | head -n1)
@@ -467,10 +627,12 @@ jobs:
find artifacts -type f -name "wickra-*.tgz" -exec cp {} release-assets/ \;
# Cargo .crate files (one per workspace member).
find artifacts -type f -name "*.crate" -exec cp {} release-assets/ \;
# CycloneDX SBOMs (one per published crate).
find artifacts -type f -name "*.cdx.json" -exec cp {} release-assets/ \;
ls -lh release-assets/
echo "asset-count=$(ls release-assets/ | wc -l)"
- name: Create / update GitHub Release with assets
- name: Create / update the draft GitHub Release with assets
uses: softprops/action-gh-release@b4309332981a82ec1c5618f44dd2e27cc8bfbfda # v3.0.0
with:
tag_name: ${{ steps.tag.outputs.tag }}
@@ -478,6 +640,9 @@ jobs:
files: release-assets/*
generate_release_notes: true
fail_on_unmatched_files: false
# Created as a draft; publish-release flips it to published + latest once
# the provenance bundle is attached (P24, immutability-ready).
draft: true
body: |
Wickra ${{ github.ref_name }} — streaming-first technical indicators across 4 language registries.
@@ -503,4 +668,110 @@ jobs:
### Auto-generated changelog
See below; GitHub computes it from the commits since the previous tag.
See below; GitHub computes it from the commits since the previous tag.
# --------------------------------------------------------------------------
# Build provenance attestations (findings P13.2)
# --------------------------------------------------------------------------
attestations:
name: Attest build provenance
needs: [cargo-publish, python-wheels, python-sdist, github-release]
runs-on: ubuntu-latest
# Signed SLSA build-provenance attestations for the published crates and
# Python wheels/sdist. npm tarballs already carry inline Sigstore provenance
# from `npm publish --provenance`, so they are covered there.
#
# The job stays isolated from the *publishes*: cargo/PyPI/npm all run upstream
# of github-release, so a Sigstore hiccup here can never block or corrupt a
# publish (the isolation the SBOM step lacked before #79). It additionally
# `needs: github-release` so the (still-draft) GitHub Release already exists
# when it attaches the provenance bundle as a release asset (P21.1e) — OpenSSF
# Scorecard's Signed-Releases check scans release *assets* (*.intoto.jsonl),
# not GitHub's separate attestations store, so the bundle has to live on the
# release. The release is published afterwards by the publish-release job
# whether or not this attestation succeeds (P24), so a failure here still only
# costs the provenance asset, never the release.
permissions:
id-token: write # OIDC for keyless Sigstore signing
attestations: write # write the attestations to this repo
contents: write # upload the provenance bundle as a release asset
steps:
- name: Download crate files
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
with:
name: crate-files
path: artifacts/crates
- name: Download wheels + sdist
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
with:
pattern: wheels-*
path: artifacts/python
merge-multiple: true
- name: Attest build provenance
id: attest
uses: actions/attest-build-provenance@a2bbfa25375fe432b6a289bc6b6cd05ecd0c4c32 # v4.1.0
with:
subject-path: |
artifacts/crates/*.crate
artifacts/python/*.whl
artifacts/python/*.tar.gz
# Attach the Sigstore provenance bundle to the GitHub Release as a
# `*.intoto.jsonl` asset so OpenSSF Scorecard's Signed-Releases check finds
# signed provenance on the release itself (P21.1e). attest-build-provenance
# writes a single JSONL bundle covering every subject above; copy it to a
# `.intoto.jsonl`-suffixed name and upload with --clobber so re-runs are
# idempotent. github.token has contents: write here, which is all gh needs.
- name: Attach provenance bundle to the GitHub Release
env:
GH_TOKEN: ${{ github.token }}
TAG: ${{ needs.github-release.outputs.tag }}
BUNDLE: ${{ steps.attest.outputs.bundle-path }}
run: |
if [ -z "$TAG" ]; then
echo "::error::no tag resolved from github-release; cannot attach provenance."
exit 1
fi
if [ -z "$BUNDLE" ] || [ ! -f "$BUNDLE" ]; then
echo "::error::attestation bundle not found at '$BUNDLE'."
exit 1
fi
dest="wickra-${TAG}.provenance.intoto.jsonl"
cp "$BUNDLE" "$dest"
echo "Uploading $dest to release $TAG"
gh release upload "$TAG" "$dest" --clobber --repo "${{ github.repository }}"
# --------------------------------------------------------------------------
# Publish the drafted release LAST (P24 — immutability-ready).
#
# github-release creates the release as a draft and attestations attaches the
# provenance bundle to it; only now, with every asset in place, is it flipped to
# published + latest. With GitHub release immutability enabled, assets lock at
# this publish step — so the provenance bundle and every build artefact are
# already present and never need a (rejected) post-publish upload.
#
# `if: always() && needs.github-release.result == 'success'` preserves the old
# robustness: the release is published whenever the draft was created, even if
# the attestations job hit a Sigstore hiccup — that only costs the provenance
# asset, exactly as before. If github-release was skipped (a publish job failed)
# there is no draft, so this is skipped too and no release is published.
# --------------------------------------------------------------------------
publish-release:
name: Publish the GitHub Release
needs: [github-release, attestations]
if: always() && needs.github-release.result == 'success'
runs-on: ubuntu-latest
permissions:
contents: write # flip the draft release to published
steps:
- name: Flip the draft release to published (latest)
env:
GH_TOKEN: ${{ github.token }}
TAG: ${{ needs.github-release.outputs.tag }}
run: |
if [ -z "$TAG" ]; then
echo "::error::no tag resolved from github-release; cannot publish."
exit 1
fi
echo "::notice::publishing release $TAG (draft -> published, latest)"
gh release edit "$TAG" --draft=false --latest=true --repo "${{ github.repository }}"
+56
View File
@@ -0,0 +1,56 @@
name: OpenSSF Scorecard
# Supply-chain / security-posture analysis (findings P13.1). Runs on a weekly
# schedule, on branch-protection changes, and on push to main. `publish_results`
# uploads the score to the public OpenSSF API so the README badge resolves, and
# the SARIF is surfaced under the repo's Security → Code scanning tab.
on:
branch_protection_rule:
schedule:
- cron: '27 7 * * 2' # Tuesdays 07:27 UTC
push:
branches: [main]
workflow_dispatch:
# Read-only by default; the analysis job widens to exactly what it needs.
permissions: read-all
jobs:
analysis:
name: Scorecard analysis
runs-on: ubuntu-latest
permissions:
security-events: write # upload the SARIF result to code-scanning
id-token: write # OIDC token to publish results to the OpenSSF API
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Run Scorecard analysis
uses: ossf/scorecard-action@4eaacf0543bb3f2c246792bd56e8cdeffafb205a # v2.4.3
with:
results_file: results.sarif
results_format: sarif
# The default GITHUB_TOKEN cannot read classic branch-protection
# rules, so the Branch-Protection check fails with an internal error
# and scores -1. A read-only fine-grained PAT (Administration: read,
# Contents: read, Metadata: read) supplied as SCORECARD_TOKEN lets the
# check read the protection settings. See
# https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
repo_token: ${{ secrets.SCORECARD_TOKEN }}
# Publish to the public OpenSSF endpoint that backs the README badge.
publish_results: true
- name: Upload SARIF artifact
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: SARIF file
path: results.sarif
retention-days: 5
- name: Upload SARIF to code-scanning
uses: github/codeql-action/upload-sarif@03e4368ac7daa2bd82b3e85262f3bf87ee112f57 # v3.36.0
with:
sarif_file: results.sarif
+451
View File
@@ -0,0 +1,451 @@
name: Sync indicator count
# Indicator count appears in four places that must stay in sync with
# the number of public indicator types exported from
# crates/wickra-core/src/lib.rs (the `pub use indicators::{ ... }` block,
# minus the `FAMILIES` constant and any `*Output` companion structs):
#
# 1. README.md prose — synced on PR branches (this workflow)
# 2. GitHub repo "About" description — synced on push to main / v* tag
# 3. Docs site: index.md / overview.md / Indicators-Overview.md
# (wickra-lib/wickra-docs) — synced on push to main / v* tag*
# 4. Marketing site count (wickra-lib/webpage: index.md /
# .vitepress/config.ts) — push to main / v* tag*
# 5. org profile README count (wickra-lib/.github, profile/README.md)
# — synced on push to main / v* tag*
# 6. org description ("… N indicators, install-free.")
# — synced on push to main / v* tag*
# 7. docs site published version (wickra-lib/wickra-docs: the
# "Published versions" table in overview.md + the Rust quickstart prose)
# — synced on v* tag only*
# 8. Marketing site version (wickra-lib/webpage: api/*.md "Latest" lines, the
# nav version label, and the wickra-wasm dep) — synced on v* tag only*
# 9. Wiki pointer page count (wickra-lib/wickra.wiki, Home.md — the wiki was
# collapsed to a single page that points at docs.wickra.org but still names
# the count) — synced on push to main / v* tag*
#
# *Surfaces 3 + 7 need the ABOUT_SYNC_TOKEN to have write on
# wickra-lib/wickra-docs, surfaces 4 + 8 on wickra-lib/webpage; surfaces 5 + 6
# need write on wickra-lib/.github and admin:org for the org-description PATCH;
# surface 9 needs write on wickra-lib/wickra (the wiki rides on the parent
# repo's permission).
# Until that scope is granted these steps emit a ::warning:: and soft-skip —
# they never fail the run. The repo "About" homepage URL is also enforced in
# step 2 (constant value, no extra scope); it points at docs.wickra.org.
#
# Note: surface 7 carries the release *version*, not the indicator count, so
# it is driven by the v* tag (which is the version) rather than the count.
#
# We count public types (not `mod xxx;` lines) because some modules export
# more than one indicator — e.g. `vwap.rs` exposes both `Vwap` and
# `RollingVwap`, so the mod-count under-reports by one. lib.rs is the
# single source of truth for what the bindings reach.
#
# Design: on PRs this workflow is a READ-ONLY check. The indicator wiring
# (ScriptHelpers/_common.py wire_readme_counter) bumps both README.md and
# docs/README.md inside the author's code commit, so the counter is already
# correct by the time CI runs. If it is not, the check below fails loud and
# asks the author to re-run the wiring — it never pushes a fix-up commit.
#
# (An earlier version pushed a GITHUB_TOKEN "sync indicator count" commit to
# the PR head. Because GITHUB_TOKEN pushes trigger no workflows, that commit
# moved the PR head onto a commit with no CI run, which hid the Codecov patch
# status — keyed to the PR head sha — from the PR. Keeping the counter in the
# code commit avoids that entirely.)
on:
push:
branches: [main]
tags: ['v*']
pull_request:
types: [opened, synchronize, reopened]
workflow_dispatch:
# Least-privilege default for the auto-injected GITHUB_TOKEN. The `contents:
# write` the workflow needs — to push the counter fix-up commit to the PR head
# branch — is raised at the job level below, not here, so the top-level default
# stays read-only (OpenSSF Scorecard: Token-Permissions). The wider About /
# docs / webpage / org writes still go through the fine-grained PAT
# (ABOUT_SYNC_TOKEN), which the `permissions:` key does not govern at all.
permissions:
contents: read
pull-requests: read
jobs:
sync:
runs-on: ubuntu-latest
# This workflow never writes to wickra-lib/wickra with GITHUB_TOKEN: the PR
# flow is a read-only check, and the main/tag flow writes only to other
# repos (About metadata, docs, webpage, wiki, org) through the fine-grained
# ABOUT_SYNC_TOKEN PAT. So GITHUB_TOKEN stays read-only (OpenSSF Scorecard:
# Token-Permissions).
permissions:
contents: read
pull-requests: read
steps:
# On PRs we check out the PR *head* commit (the author's code, not the
# merge ref) so the counter check validates exactly what will land. On
# push events we check out the default ref. No push is made, so a shallow
# checkout is enough.
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 1
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.ref || github.ref }}
repository: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.repo.full_name || github.repository }}
- name: Count indicators
id: count
run: |
# Parse the `pub use indicators::{ ... }` block from lib.rs, strip
# the FAMILIES constant and any `*Output` companion structs, count
# the remaining identifiers. Pure-shell so the workflow doesn't
# require a python runtime.
n=$(sed -n '/^pub use indicators::{/,/^};/p' crates/wickra-core/src/lib.rs \
| tr ',{}' '\n' \
| sed 's/[[:space:]]//g' \
| grep -E '^[A-Z][A-Za-z0-9_]*$' \
| grep -vE '^FAMILIES$|Output$' \
| sort -u | wc -l)
echo "count=$n" >> "$GITHUB_OUTPUT"
echo "Indicator count: $n"
# ----- PR flow ---------------------------------------------------
- name: Check README counter (PR, read-only)
if: github.event_name == 'pull_request'
run: |
n="${{ steps.count.outputs.count }}"
ok=true
if ! grep -qE "^${n} streaming-first indicators" README.md; then
echo "::error::README.md does not say '${n} streaming-first indicators' — lib.rs exports ${n}. Re-run the indicator wiring (it bumps README.md), then push again."
ok=false
fi
if ! grep -qE "\*\*${n} indicators\*\*" docs/README.md; then
echo "::error::docs/README.md does not say '**${n} indicators**' — lib.rs exports ${n}. Re-run the indicator wiring (it bumps docs/README.md), then push again."
ok=false
fi
if [ "$ok" = "true" ]; then
echo "README.md + docs/README.md counter already at ${n}; nothing to do."
else
exit 1
fi
# ----- main / tag flow ------------------------------------------
#
# After a PR squash-merges, this workflow runs again on the push to main.
# README.md / docs/README.md are already correct (the indicator wiring
# bumped them in the merged code commit); the only outward syncs left are
# the GitHub About description (repo metadata, not a commit) and the docs /
# webpage / wiki / org repos (separate repos, no main history pollution).
# The wickra repo's own README is not touched on main any more.
- name: Update GitHub About (description + homepage)
if: github.event_name != 'pull_request'
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
# Canonical homepage — the docs site (P8.3). This is enforced on every
# run, so it must only point at docs.wickra.org once that domain is
# actually live (Cloudflare Pages, P8.1); merging this PR is therefore
# gated on the domain resolving, otherwise the About link would 404.
homepage="https://docs.wickra.org"
desc="Streaming-first technical indicators with a Rust core and Python, Node.js, and WebAssembly bindings. ${n} indicators, O(1) per-tick updates, no system dependencies. Drop-in TA-Lib replacement."
# Enforce the homepage unconditionally — it is a constant, so this both
# corrects the stale kingchenc URL and self-heals any future drift.
# Same Administration-write permission as --description (no extra scope).
gh repo edit --homepage "$homepage"
current=$(gh repo view --json description -q .description)
if [ "$current" = "$desc" ]; then
echo "About description unchanged; homepage enforced."
else
gh repo edit --description "$desc"
echo "About description + homepage updated."
fi
# Counter sync target moved from the retired GitHub wiki to the docs site
# repo (wickra-lib/wickra-docs). The count appears in index.md (hero),
# overview.md prose, and Indicators-Overview.md prose. Soft-skips like the
# org steps so a token/scope gap never fails the run. Uses its own clone
# dir (docs-count) so it cannot collide with the tag-only version step
# below, which clones the same repo into `docs`.
- name: Sync docs indicator count (wickra-docs)
if: github.event_name != 'pull_request'
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/wickra-docs.git" docs-count 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/wickra-docs — ABOUT_SYNC_TOKEN likely lacks write on that repo (findings P10.0a). Skipping docs count sync."
exit 0
fi
cd docs-count
sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" index.md overview.md Indicators-Overview.md .vitepress/config.ts
if git diff --quiet; then
echo "Docs indicator count unchanged."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add index.md overview.md Indicators-Overview.md .vitepress/config.ts
git commit -m "chore: sync indicator count to ${n}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/wickra-docs failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."
else
echo "Docs indicator count synced to ${n}."
fi
# The GitHub wiki (wickra-lib/wickra.wiki) was collapsed to a single
# Home.md pointer page that sends visitors to docs.wickra.org, but that
# page still names the count ("… for all N indicators"), so keep it in
# sync here too. Mirrors the docs/webpage count steps: own clone dir
# (wiki-count) and the same soft-skip contract. Wiki write rides on the
# parent repo's permission, so the PAT needs write on wickra-lib/wickra;
# the wiki has no signing gate, so a plain wickra-bot commit is fine.
- name: Sync wiki pointer indicator count (wickra.wiki)
if: github.event_name != 'pull_request'
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/wickra.wiki.git" wiki-count 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/wickra.wiki — ABOUT_SYNC_TOKEN likely lacks write on the wiki. Skipping wiki count sync."
exit 0
fi
cd wiki-count
sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" Home.md
if git diff --quiet; then
echo "Wiki pointer indicator count unchanged."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add Home.md
git commit -m "chore: sync indicator count to ${n}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/wickra.wiki failed — ABOUT_SYNC_TOKEN likely lacks write on the wiki."
else
echo "Wiki pointer indicator count synced to ${n}."
fi
# ----- org-profile sync (soft-skip until PAT scope lands) -------
#
# These two steps keep the org page (github.com/wickra-lib) in sync
# with the same count. They need ABOUT_SYNC_TOKEN scope the main-repo
# syncs do not: write on wickra-lib/.github, and admin:org for the org
# description PATCH. Both are written to soft-skip with a ::warning::
# (never fail the run) so this workflow stays green before the scope is
# granted — once it is, they start syncing with no further code change.
- name: Sync org profile README count
if: github.event_name != 'pull_request'
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/.github.git" orgprofile 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/.github — ABOUT_SYNC_TOKEN likely lacks write on that repo (findings P10.0a). Skipping org profile sync."
exit 0
fi
cd orgprofile
sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" profile/README.md
if git diff --quiet; then
echo "Org profile README count unchanged."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add profile/README.md
git commit -m "chore: sync indicator count to ${n}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/.github failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."
else
echo "Org profile README synced to ${n}."
fi
- name: Sync org description
if: github.event_name != 'pull_request'
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
org="wickra-lib"
# Reading the org description is public; the PATCH needs admin:org.
current=$(gh api "orgs/${org}" --jq '.description // ""' 2>/dev/null || true)
if [ -z "$current" ]; then
echo "::warning::could not read org description (network/PAT?). Skipping."
exit 0
fi
updated=$(printf '%s' "$current" | sed -E "s/[0-9]+ indicators/${n} indicators/")
if [ "$current" = "$updated" ]; then
echo "Org description count unchanged."
exit 0
fi
if gh api -X PATCH "orgs/${org}" -f description="$updated" >/dev/null 2>&1; then
echo "Org description synced to ${n}."
else
echo "::warning::org description PATCH failed — ABOUT_SYNC_TOKEN likely lacks admin:org (findings P10.0b)."
fi
# ----- docs version sync (tag-only, soft-skip until PAT scope lands) -----
#
# Surface 7: the docs site (wickra-lib/wickra-docs) carries the published
# version in the "Published versions" table (overview.md) and the Rust
# quickstart prose. Unlike the indicator count these change only on a
# release, so this step runs on v* tag pushes only and takes the version
# straight from the tag. It needs ABOUT_SYNC_TOKEN to have write on
# wickra-lib/wickra-docs (findings P10.0a). Until that scope is granted it
# soft-skips with a ::warning:: and never fails the run; once granted, every
# release self-heals the docs version with no code change (replaces the old
# manual P0.5 post-release wiki bump).
- name: Sync docs version (wickra-docs)
if: startsWith(github.ref, 'refs/tags/v')
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
version="${GITHUB_REF#refs/tags/v}"
if ! printf '%s' "$version" | grep -qE '^[0-9]+\.[0-9]+\.[0-9]+$'; then
echo "::warning::tag '${GITHUB_REF}' is not a plain vMAJOR.MINOR.PATCH release; skipping docs version sync."
exit 0
fi
# Clone into `docs-ver`, NOT `docs`: on a tag push this job checks out
# the wickra repo at the workspace root, which already contains a
# top-level `docs/` directory, so `git clone … docs` fails with
# "destination path 'docs' already exists" — silently, because of the
# 2>/dev/null below — and the version sync never runs (this is exactly
# why v0.4.0 did not bump the docs table). `docs-ver` mirrors the
# `docs-count` dir used by the count step above and collides with
# nothing in the repo.
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/wickra-docs.git" docs-ver 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/wickra-docs — ABOUT_SYNC_TOKEN likely lacks write on that repo (findings P10.0a). Skipping docs version sync."
exit 0
fi
cd docs-ver
# Published-versions table rows (crates.io / PyPI / npm): replace only the
# version number, leaving the trailing padding + pipe intact. The '.' in
# the quickstart pattern matches the literal backtick around the version
# without needing a backtick in this shell string. Historical "since
# X.Y.Z" references contain no such anchor and are never matched.
sed -i -E "s/^(\| (crates\.io|PyPI|npm) .*\| )[0-9]+\.[0-9]+\.[0-9]+/\1${version}/" overview.md
sed -i -E "s/(published crate is at version .)[0-9]+\.[0-9]+\.[0-9]+/\1${version}/" Quickstart-Rust.md
if git diff --quiet; then
echo "Docs version already at ${version}."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add overview.md Quickstart-Rust.md
git commit -m "chore: sync published version to ${version}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/wickra-docs failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."
else
echo "Docs version synced to ${version}."
fi
# ----- webpage (marketing site) self-update (findings P12.1) ------------
#
# The marketing site (wickra-lib/webpage) carries the same indicator count
# and published version as the docs. Mirrors the docs steps above: the
# count syncs on push-to-main + tag, the version syncs on v* tags only.
# Distinct clone dirs (webpage-count / webpage-ver) avoid any collision on
# a tag run. Soft-skips with a ::warning:: if the token can't reach the
# repo, so the run never fails.
- name: Sync webpage indicator count (wickra-lib/webpage)
if: github.event_name != 'pull_request'
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
n="${{ steps.count.outputs.count }}"
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/webpage.git" webpage-count 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/webpage — ABOUT_SYNC_TOKEN likely lacks write on that repo (findings P10.0a). Skipping webpage count sync."
exit 0
fi
cd webpage-count
sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" index.md .vitepress/config.ts
if git diff --quiet; then
echo "Webpage indicator count unchanged."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add index.md .vitepress/config.ts
git commit -m "chore: sync indicator count to ${n}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/webpage failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."
else
echo "Webpage indicator count synced to ${n}."
fi
- name: Sync webpage version (wickra-lib/webpage)
if: startsWith(github.ref, 'refs/tags/v')
continue-on-error: true
env:
GH_TOKEN: ${{ secrets.ABOUT_SYNC_TOKEN }}
run: |
version="${GITHUB_REF#refs/tags/v}"
if ! printf '%s' "$version" | grep -qE '^[0-9]+\.[0-9]+\.[0-9]+$'; then
echo "::warning::tag '${GITHUB_REF}' is not a plain vMAJOR.MINOR.PATCH release; skipping webpage version sync."
exit 0
fi
# The webpage pins wickra-wasm to the released version in package.json,
# and its Cloudflare Pages build runs `npm clean-install`. release.yml
# publishes wickra-wasm to npm in parallel on this same tag and finishes
# minutes later, so committing the bump immediately would point the site
# at a version npm cannot resolve yet (ETARGET) and break the build —
# exactly what happened on v0.4.0. Wait until wickra-wasm@$version is
# actually live on npm before committing; if it never appears (the wasm
# publish failed), skip rather than push a build-breaking commit.
echo "Waiting for wickra-wasm@${version} on npm before bumping the webpage..."
attempts=0
until npm view "wickra-wasm@${version}" version >/dev/null 2>&1; do
attempts=$((attempts + 1))
if [ "$attempts" -ge 30 ]; then
echo "::warning::wickra-wasm@${version} not on npm after ~15 min; skipping webpage version sync to avoid a broken Cloudflare build."
exit 0
fi
echo " not on npm yet (attempt ${attempts}/30); waiting 30s..."
sleep 30
done
echo "wickra-wasm@${version} is live on npm; proceeding with the webpage version bump."
if ! git clone "https://x-access-token:${GH_TOKEN}@github.com/wickra-lib/webpage.git" webpage-ver 2>/dev/null; then
echo "::warning::cannot clone wickra-lib/webpage — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a). Skipping webpage version sync."
exit 0
fi
cd webpage-ver
# api/*.md "Latest" lines, the nav version label, and the wickra-wasm
# dep pin. The '.' anchors match the backtick / quote / caret without a
# literal in this shell string; historical "Since X.Y.Z" prose has no
# such anchor and is never matched.
sed -i -E "s/(Latest:\*\* \[.wickra(-wasm)? )[0-9]+\.[0-9]+\.[0-9]+/\1${version}/" api/*.md
sed -i -E "s/(text: .v)[0-9]+\.[0-9]+\.[0-9]+/\1${version}/" .vitepress/config.ts
sed -i -E "s/(.wickra-wasm.: .\^)[0-9]+\.[0-9]+\.[0-9]+/\1${version}/" package.json
# Keep package-lock.json in sync with the package.json bump. The site's
# Cloudflare build runs `npm clean-install` (npm ci), which hard-fails
# with EUSAGE if the lockfile still pins the previous wickra-wasm —
# editing package.json alone is not enough. The npm-wait above already
# proved wickra-wasm@$version is resolvable, so --package-lock-only
# regenerates the lock (version + resolved + integrity) without fetching
# node_modules. Guard it: if the regen fails, skip the whole commit so we
# never push a package.json/lock mismatch that would break the build.
if ! npm install --package-lock-only --no-audit --no-fund; then
echo "::warning::could not regenerate package-lock.json for wickra-wasm@${version}; skipping webpage version sync to avoid a lockfile-drift build break."
exit 0
fi
if git diff --quiet; then
echo "Webpage version already at ${version}."
exit 0
fi
git config user.name "wickra-bot"
git config user.email "wickra-bot@users.noreply.github.com"
git add api/*.md .vitepress/config.ts package.json package-lock.json
git commit -m "chore: sync published version to ${version}"
if ! git push 2>/dev/null; then
echo "::warning::push to wickra-lib/webpage failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."
else
echo "Webpage version synced to ${version}."
fi
+24
View File
@@ -0,0 +1,24 @@
name: sync-metadata
on:
push:
branches: [main]
pull_request:
workflow_dispatch:
permissions:
contents: read
jobs:
audit:
name: metadata audit
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Audit repo-metadata.toml drift
run: python .github/scripts/sync-metadata.py --check
+40
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@@ -0,0 +1,40 @@
name: zizmor
# Static analysis of the GitHub Actions workflows themselves — the surface the
# CodeQL pass does not cover. zizmor flags template injection, overly broad
# GITHUB_TOKEN permissions, unpinned actions, cache poisoning, and dangerous
# triggers. Findings appear under Security -> Code scanning alongside CodeQL.
#
# Report-only: with `advanced-security: true` the action runs zizmor in SARIF
# mode, which exits 0 regardless of findings, so this job never blocks CI —
# triage happens in the Security tab. Switch to gating later (e.g. a
# `min-severity` input) once the existing findings are triaged.
on:
push:
branches: [main]
pull_request:
branches: [main]
schedule:
- cron: '17 4 * * 1' # Mondays 04:17 UTC
# Least-privilege default for the auto-injected GITHUB_TOKEN; the job raises
# exactly the scopes it needs below (matches codeql.yml's pattern).
permissions:
contents: read
jobs:
zizmor:
name: Audit workflows
runs-on: ubuntu-latest
permissions:
security-events: write # upload SARIF to code-scanning
contents: read # checkout
actions: read # online audits resolve referenced actions
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Run zizmor
uses: zizmorcore/zizmor-action@5f14fd08f7cf1cb1609c1e344975f152c7ee938d # v0.5.6
+49
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@@ -0,0 +1,49 @@
# zizmor configuration — https://docs.zizmor.sh/configuration/
#
# cache-poisoning (release.yml):
# The release pipeline restores build caches (Swatinem/rust-cache for the Rust
# compilation, actions/setup-node) as a deliberate, accepted optimisation.
# zizmor flags these under cache-poisoning because release.yml publishes
# artifacts to crates.io / PyPI / npm, so a poisoned cache could in theory
# reach a released build. Our caches are maintainer-controlled and the
# restore speedup is kept on purpose; we accept this risk rather than running
# cache-free release builds. (Six of the eight hits are actions/setup-node,
# which zizmor reports at "Low" confidence.)
#
# artipacked (sync-about.yml):
# The sync-about job checks out with persisted credentials on purpose: it
# pushes the indicator-count fix-up back to the PR head branch (git commit +
# git push), which needs the token in the runner's git config. It uploads no
# artifacts, so the persisted token is never packaged or leaked; accept it.
#
# template-injection (sync-about.yml):
# False positive. Every flagged expansion is steps.count.outputs.count, the
# indicator count produced by an internal `grep -c` over lib.rs. It is not
# attacker-controllable, so there is nothing to inject.
#
# use-trusted-publishing (release.yml):
# Informational suggestion to use OIDC trusted publishing for PyPI / npm
# instead of long-lived tokens. A worthwhile migration, but it reconfigures
# the live publish pipeline on the registry side; tracked separately rather
# than blocking on it here.
#
# superfluous-actions (release.yml):
# The GitHub release step uses softprops/action-gh-release. The runner ships
# `gh`, so this is replaceable by a script step, but the action is stable and
# battle-tested; we keep it deliberately.
rules:
cache-poisoning:
ignore:
- release.yml
artipacked:
ignore:
- sync-about.yml
template-injection:
ignore:
- sync-about.yml
use-trusted-publishing:
ignore:
- release.yml
superfluous-actions:
ignore:
- release.yml
+7 -2
View File
@@ -44,9 +44,14 @@ tarpaulin-report.html
# Node binding artifacts
**/node_modules/
bindings/node/*.node
bindings/node/index.d.ts
bindings/node/npm-debug.log*
package-lock.json
# index.js + index.d.ts are generated by `napi build` but committed (a matched
# pair) so consumers and the repo get TypeScript types; CONTRIBUTING requires
# regenerating both when a binding's public API changes.
# package-lock.json is committed for the tracked Node packages — bindings/node/
# and examples/node/ — so contributors get reproducible npm installs. There is
# no top-level npm package, and the ghost-ignored site/ keeps its lockfile local.
# See CONTRIBUTING.md "Lockfile policy" for the full per-component breakdown.
# WASM build output
bindings/wasm/pkg/
+321
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@@ -0,0 +1,321 @@
# Architecture
A walkthrough of how Wickra is organised internally — written for new
contributors who want to know **where the code lives, why it's split that
way, and which invariants they must not break**. Pair it with [`CONTRIBUTING.md`](CONTRIBUTING.md)
for the day-to-day workflow.
## Workspace layout
Wickra is a Cargo workspace of three Rust crates plus three binding crates.
The split is deliberate: every concern that one user might want to disable
or replace lives behind a separate crate boundary.
```
┌────────────────────────────────────────────────────────────────────┐
│ wickra (facade) │
│ re-exports wickra-core::* + wickra-data::* │
└──────────────┬──────────────────────────────────┬──────────────────┘
│ │
┌───────────▼──────────┐ ┌──────────▼─────────┐
│ wickra-core │ │ wickra-data │
│ indicator engine │ │ i/o + aggregation │
│ • 214 indicators │ │ • CSV reader │
│ • Indicator trait │ │ • Tick aggregator │
│ • BatchExt impl │ │ • Resampler │
│ • OHLCV / Candle │ │ • Live feeds │
│ no I/O, no deps │ │ optional features │
└──────────────────────┘ └────────────────────┘
│ (every binding wraps the same core)
┌────────────┴───────────┬─────────────────────┐
│ │ │
┌──▼──────┐ ┌───────▼──────┐ ┌───────▼────────┐
│ Python │ │ Node │ │ WASM │
│ (PyO3) │ │ (napi-rs) │ │ (wasm-bindgen) │
└─────────┘ └──────────────┘ └────────────────┘
```
| Crate | Path | What it owns | Public deps |
|---|---|---|---|
| `wickra-core` | `crates/wickra-core` | every indicator, the `Indicator` trait, `BatchExt`, `Candle`/`Tick` types, `Error` | `thiserror`, `rayon` (parallel batch) |
| `wickra` | `crates/wickra` | thin facade — re-exports everything user-facing from `wickra-core` and `wickra-data` | both internal crates |
| `wickra-data` | `crates/wickra-data` | CSV reader, tick aggregator, resampler, live exchange feeds (feature-gated) | `tokio`, `tokio-tungstenite` (live), `serde_json` |
| `wickra-python` | `bindings/python` | `_wickra` PyO3 module + Python package | `pyo3`, `numpy`, depends on `wickra-core` |
| `wickra-node` | `bindings/node` | NAPI-RS native binding | `napi`, depends on `wickra-core` |
| `wickra-wasm` | `bindings/wasm` | WebAssembly binding | `wasm-bindgen`, depends on `wickra-core` |
| `wickra-examples` | `examples/rust` | runnable binary examples | depends on `wickra`, `wickra-data` |
The `fuzz/` directory is **excluded** from the workspace (it has its own
`Cargo.toml`) because the libfuzzer-sys harness requires a nightly
toolchain, which would otherwise infect the stable workspace lints.
## The `Indicator` trait
Every indicator in Wickra implements one trait, defined in
`crates/wickra-core/src/traits.rs`:
```rust
pub trait Indicator {
type Input;
type Output;
fn update(&mut self, input: Self::Input) -> Option<Self::Output>;
fn reset(&mut self);
fn warmup_period(&self) -> usize;
fn is_ready(&self) -> bool;
fn name(&self) -> &'static str;
}
```
Four design choices that are non-negotiable:
1. **Streaming-first.** `update` is the only computation entry point. Each
call must be O(1) amortised — no replays over history, no `clone`s of
the input window unless absolutely necessary.
2. **`Option<Output>` warmup.** A new indicator returns `None` until it has
ingested `warmup_period()` inputs. After that it returns `Some(value)`
on every call. The `None``Some` transition happens exactly once per
`reset()`.
3. **Reset is mandatory.** Calling `reset()` returns the indicator to the
state of a newly constructed one. Tests verify this for every indicator.
4. **No interior mutability across `update` calls.** Indicators may hold
`VecDeque` / array state, but no `Cell`/`RefCell`/`Mutex` should be
needed — `&mut self` is the only mutation channel.
### Batch is free
`BatchExt` is a blanket impl over `Indicator`:
```rust
impl<I: Indicator> BatchExt for I {
fn batch<'a>(&mut self, input: &'a [I::Input]) -> Vec<Option<I::Output>>
where I::Input: Copy
{
input.iter().map(|x| self.update(*x)).collect()
}
fn batch_parallel(...) // rayon-based for multi-asset processing
}
```
Consequence: **every indicator gets batch and parallel-batch for free** as
soon as `Indicator` is implemented. Tests verify `batch == streaming`
equivalence on every indicator — this is the `batch_equals_streaming` test
that appears in every indicator module.
## Indicator-module convention
Each indicator lives in its own file under
`crates/wickra-core/src/indicators/`. Naming: snake-case of the struct,
e.g. `Sma``sma.rs`, `MacdIndicator``macd.rs`.
Layout inside an indicator file is uniform:
```rust
//! Doc-comment with the formula and one-line summary.
use std::collections::VecDeque;
use crate::error::{Error, Result};
use crate::traits::Indicator;
/// Public struct + rustdoc with mathematical definition + a runnable example.
#[derive(Debug, Clone)]
pub struct Foo { /* state fields */ }
impl Foo {
/// Constructor with parameter validation.
pub fn new(period: usize, ...) -> Result<Self> { ... }
/// Const accessors for configured params.
pub const fn period(&self) -> usize { ... }
}
impl Indicator for Foo {
type Input = f64; // or (f64, f64), or Candle
type Output = f64; // or FooOutput { ... }
fn update(...) -> ... { ... }
fn reset(...) { ... }
fn warmup_period(...) -> usize { ... }
fn is_ready(...) -> bool { ... }
fn name(...) -> &'static str { "Foo" }
}
#[cfg(test)]
mod tests {
// mandatory tests (every indicator):
// - rejects_invalid_params
// - accessors_and_metadata
// - reference_value (vs TA-Lib / pandas-ta / hand-calculated)
// - ignores_non_finite_input
// - reset_clears_state
// - batch_equals_streaming
// plus indicator-specific edge cases
}
```
The `FAMILIES` constant in `mod.rs` (introduced in PR #60) is the
machine-readable index of which family every indicator belongs to. It is
the canonical taxonomy; README and Wiki tables should be derived from it.
## Input types
| Input | Used for | Examples |
|---|---|---|
| `f64` | Scalar inputs — usually a price or a return | SMA, EMA, RSI, ROC |
| `Candle` | OHLCV bar — `{open, high, low, close, volume, timestamp}` | ATR, Bollinger, Ichimoku, all candlestick patterns |
| `(f64, f64)` | Two-series indicators — `(asset, benchmark)` or `(x, y)` | PearsonCorrelation, Beta, Alpha, TreynorRatio |
The `Candle` type lives in `wickra-core::ohlcv` and is the binding
contract across bindings — Python's `Candle` namedtuple, Node's
`Candle` object, and WASM's `Candle` JS class all map 1:1.
## Output types
Most indicators emit `f64`. Multi-output indicators emit a dedicated
struct in the same module, named `FooOutput`:
```rust
pub struct BollingerOutput {
pub upper: f64,
pub middle: f64,
pub lower: f64,
}
```
Bindings flatten these into matrix outputs (NumPy 2-D array for Python,
typed object arrays for Node/WASM).
## Numerical-stability notes
A handful of indicators need care beyond naive accumulation:
- **Welford's online variance** is used in `StdDev`, `Variance`, `ZScore`,
`BollingerBands`, and several others. Standard sum-of-squares is
catastrophically lossy for low-variance inputs; Welford's recurrence
keeps O(eps) error.
- **Kahan summation** is used wherever rolling sums could span > 1e6
elements without resetting — currently only Hurst-exponent's R/S
chunks. Most rolling sums are bounded by the window size and don't need
it.
- **Logarithm bases** matter for some indicators (Hurst, MFI). Wickra
uses natural log everywhere unless the reference math explicitly
requires `log10` or `log2` — and then it documents the choice in the
rustdoc.
- **NaN / infinity guards.** Every indicator's `update` rejects
non-finite input early (returns `None` without state mutation). Tests
cover this with `ignores_non_finite_input`.
## Cross-crate flow
A typical full-stack call sequence for a Python live-trading example:
```
[ Python: live_trading.py ]
[ binance.AsyncClient WebSocket ] ──── wickra_data live feed ───┐
┌──────────────────┘
[ Candle struct conversion ]
[ PyRsi.update(close) ]
wraps │
[ wickra_core::Rsi::update(f64) ] <-- the only place math runs
[ Option<f64> -> Py<PyFloat> ]
[ Python user code ]
```
The same call sequence happens identically for Node (via NAPI),
WASM (via wasm-bindgen → JS), and Rust (no FFI overhead, just direct
calls).
## What lives where — the navigation cheat sheet
| You want to … | Look in |
|---|---|
| add a new indicator | `crates/wickra-core/src/indicators/<name>.rs` + add to `mod.rs` + add to `FAMILIES` + re-export in `lib.rs` |
| change the `Indicator` trait surface | `crates/wickra-core/src/traits.rs` — this affects every indicator, treat as breaking |
| add a new Candle field | `crates/wickra-core/src/ohlcv.rs` — also propagates to every binding's `Candle` mapping |
| add a new exchange / data source | `crates/wickra-data/src/live/<exchange>.rs`, feature-gated under `live-<exchange>` |
| expose a new binding | new crate under `bindings/` + macro-driven boilerplate in `bindings/<lang>/src/lib.rs` |
| change benchmark coverage | `crates/wickra/benches/indicators.rs` |
| add a new fuzz target | `fuzz/fuzz_targets/<name>.rs` + register in `fuzz/Cargo.toml` |
| change CI matrix | `.github/workflows/ci.yml` |
| change release pipeline | `.github/workflows/release.yml` (irreversible on `v*` tag — test on a throwaway tag first) |
## What is **deliberately** not in this repo
- **Backtest framework.** Wickra is an indicator library, not a backtester.
Strategy + PnL + fills logic is for the user (see `examples/` for
illustrative scripts).
- **Multi-exchange aggregation.** Binance is the demo feed; full
exchange-agnostic aggregation is `ccxt`'s job. Wickra's
`wickra-data::live` is intentionally minimal.
- **Order-book / L2 data.** Wickra works on OHLCV bars and ticks, not
full depth. Tick-data variants (cumulative delta, single print) are on
the roadmap but require new input types.
- **Charting / visualization.** Out of scope for the Rust core. The
WASM examples include a `lightweight-charts` integration as a
starting point, but no charting code lives in the published packages.
- **GPU / SIMD optimisation.** Indicators are O(1) per update — the
bottleneck is not vector throughput. SIMD would only help large-batch
workloads, which already saturate memory bandwidth via the cache-
friendly `VecDeque` window.
## Performance characteristics
Every indicator is amortised O(1) per `update`. The constant factor
varies:
| Class | Indicators | Per-`update` cost (approx) |
|---|---|---|
| Simple rolling | SMA, EMA, WMA, Mom | 1-2 floating-point ops |
| Recursive smoothers | KAMA, FRAMA, VIDYA, JMA | 5-15 ops |
| Window-sort | OmegaRatio, percentile-based VaR | O(period · log period) per update |
| Multi-buffer DSP | MAMA, HilbertDominantCycle, EmpiricalModeDecomposition | 30-80 ops |
| Multi-component | MacdIndicator, TtmSqueeze, Alligator | sum of components |
Benchmarks against real BTCUSDT 1-minute data live in
`crates/wickra/benches/indicators.rs`. Cross-library comparison vs
TA-Lib / pandas-ta / talipp / finta lives in
`bindings/python/benchmarks/compare_libraries.py`.
## Stability commitments
- **MSRV.** Workspace: Rust 1.86. Node binding: 1.88 (NAPI-RS pins it).
- **`Indicator` trait surface.** Breaking changes here are major-version
events. Adding a new method with a default impl is minor.
- **Indicator removal.** Once an indicator ships in a release, it stays
callable. Renames go through a deprecation period of at least one
minor version.
- **Output structs.** Adding a field to a `FooOutput` is non-breaking
because the binding contracts go through serde and accept extra keys.
## Open questions / known sharp edges
These are documented for contributors so you don't waste time
re-discovering them.
- **`Rvi`** (Relative Vigor Index) and `RviVolatility` (Relative
Volatility Index) are different indicators with the same short
acronym — make sure you import the right one.
- **Fuzz coverage of pair indicators** uses `indicator_update_pair.rs`,
which is small because pair indicators are simpler — but coverage
should grow as more pair indicators land.
- **`FAMILIES` (from PR #60) is hand-maintained.** Adding a new
indicator requires a separate entry in `FAMILIES`. The
`total_count_matches_expected` test will fail if you forget.
- **WASM does not have automated tests yet.** Smoke-validated only
through the manual examples. Adding `wasm-bindgen-test` coverage is
on the roadmap.
For the high-level project goals see [`ROADMAP.md`](ROADMAP.md); for
day-to-day contribution mechanics see [`CONTRIBUTING.md`](CONTRIBUTING.md).
+1029 -9
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+31
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@@ -0,0 +1,31 @@
cff-version: 1.2.0
title: Wickra
message: >-
If you use Wickra in academic work, please cite it using the metadata
below.
type: software
authors:
- alias: kingchenc
email: support@wickra.org
repository-code: "https://github.com/wickra-lib/wickra"
url: "https://wickra.org"
abstract: >-
Wickra is a streaming-first technical-analysis library implemented in
Rust with bindings for Python, Node.js and WebAssembly. Each indicator
is a state machine that updates in constant time per new input, so
identical code paths serve live-trading workloads and historical
back-testing. The library covers 214 indicators across 16 families
(moving averages, momentum, volatility, volume, statistics, Ehlers
digital-signal-processing cycles, pivots, DeMark, Ichimoku, candlestick
patterns, market profile, and risk/performance metrics).
keywords:
- technical-analysis
- technical-indicators
- streaming
- algorithmic-trading
- quantitative-finance
- rust
- time-series
license:
- MIT
- Apache-2.0
+1 -1
View File
@@ -33,7 +33,7 @@ project in public spaces.
## Enforcement
Instances of unacceptable behaviour may be reported to the project maintainer
at **kingchencp@gmail.com**. All reports will be reviewed and investigated
at **support@wickra.org**. All reports will be reviewed and investigated
promptly and fairly, and the maintainer will respect the privacy and security
of the reporter.
+69 -11
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@@ -5,11 +5,11 @@ build the project, the standards a change must meet, and how to get it merged.
## License of contributions
Wickra is licensed under the **PolyForm Noncommercial License 1.0.0** (see
[`LICENSE`](LICENSE)). By submitting a contribution you agree that it is
licensed to the project under those same terms. The Noncommercial license
permits use for any purpose **other than** a commercial one; keep that in mind
when proposing features or depending on Wickra elsewhere.
Wickra is dual-licensed under the [MIT](LICENSE-MIT) and
[Apache-2.0](LICENSE-APACHE) licenses; users may choose either. Unless you
explicitly state otherwise, any contribution you intentionally submit for
inclusion in the work, as defined in the Apache-2.0 license, shall be dual
licensed as above, without any additional terms or conditions.
## Project layout
@@ -22,7 +22,7 @@ when proposing features or depending on Wickra elsewhere.
| `bindings/node` | napi-rs bindings (`wickra` on npm). |
| `bindings/wasm` | wasm-bindgen bindings (`wickra-wasm` on npm). |
| `examples/` | Runnable examples. |
| `docs/` | Pointer to the project Wiki, which holds all documentation. |
| `docs/` | Pointer to the documentation site (docs.wickra.org); the docs live in the `wickra-lib/wickra-docs` repo. |
## Building and testing
@@ -35,8 +35,13 @@ cargo test --workspace
cargo test -p wickra-data --features live-binance
```
The minimum supported Rust version is **1.75** for the workspace crates and
**1.77** for `bindings/node`; the `msrv` CI job enforces both.
The minimum supported Rust version is **1.86** for the workspace crates and
**1.88** for `bindings/node`; the `msrv` CI job enforces both. These floors are
not chosen freely — they are the lowest versions our dependencies allow
(criterion 0.8.2, the bench dev-dependency, requires 1.86; napi-build 2.3.2
requires 1.88). We keep the MSRV at that dependency-forced floor on purpose so
the library builds for the widest possible audience; please don't raise it
without a dependency that actually requires it.
### Python
@@ -63,6 +68,29 @@ wasm-pack build bindings/wasm --target web --release --features panic-hook
wasm-pack test --node bindings/wasm
```
## Lockfile policy
| Component | Lockfile | Tracked? | Why |
| --- | --- | --- | --- |
| Workspace (Rust) | `Cargo.lock` | **yes** | The workspace ships binaries (examples, fuzz harness) and CI builds, so the dependency graph is pinned for reproducible builds. |
| `bindings/node` | `package-lock.json` | **yes** | Reproducible `npm install` for the native binding. |
| `examples/node` | `package-lock.json` | **yes** | Same — the runnable Node examples link the binding via a `file:` dependency. |
| `bindings/python` | — | n/a (no lockfile) | The published package pins only `numpy>=1.22` at runtime; its native code is pinned through the workspace `Cargo.lock`. The CI/bench dev tooling it installs is hash-locked separately — see the `.github/requirements` row. |
| `.github/requirements` | `*.txt` (hash-pinned) | **yes** | CI/bench Python tooling, locked with `uv pip compile --generate-hashes` (OpenSSF Scorecard PinnedDependencies). `ci-dev` is split per Python version — `ci-dev-py39.txt` and `ci-dev-py3.txt` — because numpy ships no single release with wheels for both cp39 and cp313; `bench.txt` covers the single-version bench job. |
| `fuzz` | `fuzz/Cargo.lock` | **no** (ignored) | `fuzz/` is a detached crate; `cargo-fuzz init` generates `fuzz/.gitignore` which ignores its `Cargo.lock`. The fuzz smoke job resolves dependencies fresh, so the lock is not needed for reproducibility here. |
| `site` (marketing) | `package-lock.json` | **no** (ghost-ignored) | The VitePress site is a local-only project excluded via `.git/info/exclude`; its lockfile stays local. |
When adding a new committed Node package, commit its `package-lock.json` too and
remove any matching ignore rule. Do **not** add a top-level `package-lock.json`
the repository root is not an npm package.
To refresh every committed lockfile in the workspace — `Cargo.lock`,
`fuzz/Cargo.lock`, the Node binding lock, and the hash-pinned Python
requirements — run `./scripts/update-lockfiles.sh`. It uses `uv` for the Python
locks (and bootstraps it on Linux/macOS if absent) so each target Python
version's hashed transitive closure can be regenerated without that interpreter
installed. Dependabot also keeps the `.github/requirements` pins current.
## Standards for a change
- **Formatting & lints.** `cargo fmt` must leave the tree unchanged and
@@ -76,9 +104,9 @@ wasm-pack test --node bindings/wasm
- **Bindings.** A change to a public indicator API must be mirrored across the
Python, Node, and WASM bindings, including their type stubs / `.d.ts`.
- **Docs.** Update the relevant page on the
[project Wiki](https://github.com/kingchenc/wickra/wiki) and the
`README.md` when behaviour or the public API changes. The Wiki lives in
a separate git repository: `https://github.com/kingchenc/wickra.wiki.git`.
[documentation site](https://docs.wickra.org) and the
`README.md` when behaviour or the public API changes. The docs live in
a separate git repository: `https://github.com/wickra-lib/wickra-docs`.
- **Changelog.** Add an entry under `## [Unreleased]` in `CHANGELOG.md`.
## Commit and pull-request workflow
@@ -94,3 +122,33 @@ wasm-pack test --node bindings/wasm
Use the issue templates under
[`.github/ISSUE_TEMPLATE`](.github/ISSUE_TEMPLATE). For security-sensitive
reports, follow [`SECURITY.md`](SECURITY.md) instead of opening a public issue.
## Developer Certificate of Origin (DCO)
All contributions to Wickra are made under the [Developer Certificate of
Origin (DCO) 1.1](DCO). By signing off on your commits you certify that you
wrote the patch, or otherwise have the right to submit it under the project's
`MIT OR Apache-2.0` license.
Sign off every commit by adding a `Signed-off-by` trailer with your real name
and email — Git adds it automatically with the `-s` flag:
```bash
git commit -s -m "your message"
```
This produces a trailer of the form:
```
Signed-off-by: Your Name <you@example.com>
```
The name and email must match the commit author. Commits without a valid
sign-off line cannot be merged. To sign off a commit you already made, amend it
with `git commit -s --amend`, or sign off a range with an interactive rebase.
## Governance
Wickra's decision-making and maintainership are described in
[`GOVERNANCE.md`](GOVERNANCE.md); the current maintainers are listed in
[`MAINTAINERS.md`](MAINTAINERS.md).
Generated
+114 -7
View File
@@ -702,6 +702,16 @@ dependencies = [
"wasm-bindgen",
]
[[package]]
name = "kand"
version = "0.2.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "af1f41590bd014ef6c3dd815b45f07deb4c3198e355a4319bb7521b6a3a6aeb5"
dependencies = [
"num_enum",
"thiserror",
]
[[package]]
name = "leb128fmt"
version = "0.1.0"
@@ -911,6 +921,28 @@ dependencies = [
"libm",
]
[[package]]
name = "num_enum"
version = "0.7.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5d0bca838442ec211fa11de3a8b0e0e8f3a4522575b5c4c06ed722e005036f26"
dependencies = [
"num_enum_derive",
"rustversion",
]
[[package]]
name = "num_enum_derive"
version = "0.7.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "680998035259dcfcafe653688bf2aa6d3e2dc05e98be6ab46afb089dc84f1df8"
dependencies = [
"proc-macro-crate",
"proc-macro2",
"quote",
"syn",
]
[[package]]
name = "numpy"
version = "0.28.0"
@@ -1081,6 +1113,15 @@ dependencies = [
"syn",
]
[[package]]
name = "proc-macro-crate"
version = "3.5.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e67ba7e9b2b56446f1d419b1d807906278ffa1a658a8a5d8a39dcb1f5a78614f"
dependencies = [
"toml_edit",
]
[[package]]
name = "proc-macro2"
version = "1.0.106"
@@ -1498,6 +1539,12 @@ dependencies = [
"syn",
]
[[package]]
name = "ta"
version = "0.5.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "609409d472a0a7d8d4dd9e19891bbdef546b9dce670c3057d0e02192dc541226"
[[package]]
name = "target-lexicon"
version = "0.13.5"
@@ -1607,6 +1654,36 @@ dependencies = [
"tungstenite",
]
[[package]]
name = "toml_datetime"
version = "1.1.1+spec-1.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3165f65f62e28e0115a00b2ebdd37eb6f3b641855f9d636d3cd4103767159ad7"
dependencies = [
"serde_core",
]
[[package]]
name = "toml_edit"
version = "0.25.12+spec-1.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "d2153edc6955a6c354fad8f5efd38b6a8769bdccf9fe50f8e1329f81b0baa5d7"
dependencies = [
"indexmap",
"toml_datetime",
"toml_parser",
"winnow",
]
[[package]]
name = "toml_parser"
version = "1.1.2+spec-1.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a2abe9b86193656635d2411dc43050282ca48aa31c2451210f4202550afb7526"
dependencies = [
"winnow",
]
[[package]]
name = "tungstenite"
version = "0.29.0"
@@ -1867,7 +1944,7 @@ dependencies = [
[[package]]
name = "wickra"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"approx",
"criterion",
@@ -1876,9 +1953,21 @@ dependencies = [
"wickra-data",
]
[[package]]
name = "wickra-bench"
version = "0.6.5"
dependencies = [
"criterion",
"kand",
"ta",
"wickra",
"wickra-data",
"yata",
]
[[package]]
name = "wickra-core"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"approx",
"proptest",
@@ -1888,7 +1977,7 @@ dependencies = [
[[package]]
name = "wickra-data"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"approx",
"csv",
@@ -1905,7 +1994,7 @@ dependencies = [
[[package]]
name = "wickra-examples"
version = "0.0.0"
version = "0.6.5"
dependencies = [
"serde_json",
"tokio",
@@ -1915,7 +2004,7 @@ dependencies = [
[[package]]
name = "wickra-node"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"napi",
"napi-build",
@@ -1925,7 +2014,7 @@ dependencies = [
[[package]]
name = "wickra-python"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"numpy",
"pyo3",
@@ -1934,7 +2023,7 @@ dependencies = [
[[package]]
name = "wickra-wasm"
version = "0.2.5"
version = "0.6.5"
dependencies = [
"console_error_panic_hook",
"js-sys",
@@ -1991,6 +2080,15 @@ dependencies = [
"windows-link",
]
[[package]]
name = "winnow"
version = "1.0.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0592e1c9d151f854e6fd382574c3a0855250e1d9b2f99d9281c6e6391af352f1"
dependencies = [
"memchr",
]
[[package]]
name = "wit-bindgen"
version = "0.51.0"
@@ -2091,6 +2189,15 @@ version = "0.6.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1ffae5123b2d3fc086436f8834ae3ab053a283cfac8fe0a0b8eaae044768a4c4"
[[package]]
name = "yata"
version = "0.7.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6b4ef8ddfa3ccd93454262c0e60a43a2bbf403d404174e1815f7581d5028229f"
dependencies = [
"serde",
]
[[package]]
name = "yoke"
version = "0.8.2"
+7 -6
View File
@@ -8,23 +8,24 @@ members = [
"bindings/wasm",
"bindings/node",
"examples/rust",
"crates/wickra-bench",
]
exclude = ["fuzz"]
[workspace.package]
version = "0.2.5"
authors = ["kingchenc <kingchencp@gmail.com>"]
version = "0.6.5"
authors = ["kingchenc <support@wickra.org>"]
edition = "2021"
rust-version = "1.86"
license = "PolyForm-Noncommercial-1.0.0"
repository = "https://github.com/kingchenc/wickra"
homepage = "https://github.com/kingchenc/wickra"
license = "MIT OR Apache-2.0"
repository = "https://github.com/wickra-lib/wickra"
homepage = "https://github.com/wickra-lib/wickra"
readme = "README.md"
keywords = ["finance", "trading", "indicators", "technical-analysis", "ta"]
categories = ["finance", "mathematics", "science"]
[workspace.dependencies]
wickra-core = { path = "crates/wickra-core", version = "0.2.5" }
wickra-core = { path = "crates/wickra-core", version = "0.6.5" }
thiserror = "2"
rayon = "1.10"
+34
View File
@@ -0,0 +1,34 @@
Developer Certificate of Origin
Version 1.1
Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
Developer's Certificate of Origin 1.1
By making a contribution to this project, I certify that:
(a) The contribution was created in whole or in part by me and I
have the right to submit it under the open source license
indicated in the file; or
(b) The contribution is based upon previous work that, to the best
of my knowledge, is covered under an appropriate open source
license and I have the right under that license to submit that
work with modifications, whether created in whole or in part
by me, under the same open source license (unless I am
permitted to submit under a different license), as indicated
in the file; or
(c) The contribution was provided directly to me by some other
person who certified (a), (b) or (c) and I have not modified
it.
(d) I understand and agree that this project and the contribution
are public and that a record of the contribution (including all
personal information I submit with it, including my sign-off) is
maintained indefinitely and may be redistributed consistent with
this project or the open source license(s) involved.
+71
View File
@@ -0,0 +1,71 @@
# Governance
Wickra is an open-source project maintained under a **single-maintainer
("BDFL") model**. This document describes how decisions are made and how the
project is run, so contributors know what to expect.
## Roles
- **Maintainer.** The maintainer (see [`MAINTAINERS.md`](MAINTAINERS.md)) is
responsible for the project's direction, reviews and merges changes, cuts
releases, and has final say on all technical and project decisions.
- **Contributors.** Anyone who proposes changes via pull requests, files
issues, improves documentation, or otherwise participates. Contributors do
not need any special status to take part.
## Decision-making
- Day-to-day technical decisions (APIs, indicator implementations, refactors)
are made by the maintainer, informed by discussion on issues and pull
requests.
- Proposals are raised as GitHub issues or pull requests. Significant or
breaking changes should be opened as an issue first to agree on the approach
before implementation.
- The maintainer aims to act transparently: rationale for non-trivial decisions
is recorded in the relevant issue, pull request, or commit message.
## Contribution flow
All changes — including the maintainer's own — go through pull requests so that
CI (tests, linting, static analysis) runs against them, and so the change
history is reviewable. Contribution requirements are documented in
[`CONTRIBUTING.md`](CONTRIBUTING.md), including the Developer Certificate of
Origin sign-off that every commit must carry.
## Becoming a maintainer
The project currently has one maintainer. Maintainership may be extended to
contributors who have demonstrated sustained, high-quality involvement, at the
current maintainer's discretion. If the project grows to multiple maintainers,
this document will be updated to describe shared decision-making.
## Continuity and succession
The project is designed to survive the loss of any single individual, so that
issues can be triaged, proposed changes accepted, and releases published within
one week of confirmed loss of the maintainer:
- **Credentials.** All credentials required to operate the project — the
`wickra-lib` GitHub organization, the publishing tokens for crates.io, PyPI
and npm, and the `wickra.org` domain registrar — are stored in a password
manager. A trusted contact (a family member) holds **emergency access** to
that password manager and can obtain these credentials if the maintainer can
no longer continue.
- **Continuity actions.** With that access, the trusted contact (or a delegate
they appoint) can create and close issues, accept pull requests, and publish
releases through the existing CI/CD workflows.
- **Account recovery.** The maintainer's GitHub account has recovery configured,
and ownership of the `wickra-lib` organization can be transferred to a new
maintainer.
- **Legal rights.** Legal rights to the project name and DNS are covered by the
maintainer's estate arrangements.
## Code of conduct
All participants are expected to follow the
[Code of Conduct](CODE_OF_CONDUCT.md).
## Changes to this document
This governance model may evolve as the project grows. Changes are made via
pull request and take effect once merged.
-136
View File
@@ -1,136 +0,0 @@
# PolyForm Noncommercial License 1.0.0
<https://polyformproject.org/licenses/noncommercial/1.0.0>
## Acceptance
In order to get any license under these terms, you must agree
to them as both strict obligations and conditions to all
your licenses.
## Copyright License
The licensor grants you a copyright license for the
software to do everything you might do with the software
that would otherwise infringe the licensor's copyright
in it for any permitted purpose. However, you may
only distribute the software according to [Distribution
License](#distribution-license) and make changes or new works
based on the software according to [Changes and New Works
License](#changes-and-new-works-license).
## Distribution License
The licensor grants you an additional copyright license
to distribute copies of the software. Your license to
distribute covers distributing the software with changes
and new works permitted by [Changes and New Works
License](#changes-and-new-works-license).
## Notices
You must ensure that anyone who gets a copy of any part of
the software from you also gets a copy of these terms or the
URL for them above, as well as copies of any plain-text lines
beginning with `Required Notice:` that the licensor provided
with the software. For example:
> Required Notice: Copyright 2026 kingchenc (https://github.com/kingchenc/wickra)
## Changes and New Works License
The licensor grants you an additional copyright license
to make changes and new works based on the software for any
permitted purpose.
## Patent License
The licensor grants you a patent license for the software that
covers patent claims the licensor can license, or becomes able
to license, that you would infringe by using the software.
## Noncommercial Purposes
Any noncommercial purpose is a permitted purpose.
## Personal Uses
Personal use for research, experiment, and testing for
the benefit of public knowledge, personal study, private
entertainment, hobby projects, amateur pursuits, or religious
observance, without any anticipated commercial application,
is use for a permitted purpose.
## Noncommercial Organizations
Use by any charitable organization, educational institution,
public research organization, public safety or health
organization, environmental protection organization, or
government institution is use for a permitted purpose regardless
of the source of funding or obligations resulting from the
funding.
## Fair Use
You may have "fair use" rights for the software under the
law. These terms do not limit them.
## No Other Rights
These terms do not allow you to sublicense or transfer any of
your licenses to anyone else, or prevent the licensor from
granting licenses to anyone else. These terms do not imply
any other licenses.
## Patent Defense
If you make any written claim that the software infringes or
contributes to infringement of any patent, your patent license
for the software granted under these terms ends immediately. If
your company makes such a claim, your patent license ends
immediately for work on behalf of your company.
## Violations
The first time you are notified in writing that you have
violated any of these terms, or done anything with the software
not covered by your licenses, your licenses can nonetheless
continue if you come into full compliance with these terms,
and take practical steps to correct past violations, within 32
days of receiving notice. Otherwise, all your licenses end
immediately.
## No Liability
***As far as the law allows, the software comes as is, without
any warranty or condition, and the licensor will not be liable
to you for any damages arising out of these terms or the use
or nature of the software, under any kind of legal claim.***
## Definitions
The **licensor** is the individual or entity offering these
terms, and the **software** is the software the licensor makes
available under these terms.
**You** refers to the individual or entity agreeing to these
terms.
**Your company** is any legal entity, sole proprietorship,
or other kind of organization that you work for, plus all
organizations that have control over, are under the control
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**Control** means ownership of substantially all the assets
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direct or indirect.
**Your licenses** are all the licenses granted to you for the
software under these terms.
**Use** means anything you do with the software requiring one
of your licenses.
---
Required Notice: Copyright 2026 kingchenc (https://github.com/kingchenc/wickra)
+201
View File
@@ -0,0 +1,201 @@
Apache License
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http://www.apache.org/licenses/
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Copyright 2026 kingchenc and the Wickra contributors
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+21
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MIT License
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APPENDIX: How to apply the Apache License to your work.
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Copyright 2026 kingchenc and the Wickra contributors
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MIT License
Copyright (c) 2026 kingchenc and the Wickra contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
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# Maintainers
This file lists the current maintainers of Wickra. See
[`GOVERNANCE.md`](GOVERNANCE.md) for what the role entails and how the project
is run.
| Maintainer | GitHub | Areas |
| --- | --- | --- |
| kingchenc | [@kingchenc](https://github.com/kingchenc) | All (core, bindings, CI/release, docs) |
## Contacting the maintainers
- General questions and support: see [`SUPPORT.md`](SUPPORT.md).
- Bug reports and feature requests: open an issue using the
[issue templates](.github/ISSUE_TEMPLATE).
- Security reports: follow [`SECURITY.md`](SECURITY.md) — do **not** open a
public issue.
+206 -87
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@@ -1,11 +1,19 @@
# Wickra
<p align="center">
<a href="https://wickra.org"><img src="https://raw.githubusercontent.com/wickra-lib/.github/main/profile/wickra-banner.webp?v=462" alt="Wickra — streaming-first technical indicators" width="100%"></a>
</p>
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**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
@@ -31,98 +39,192 @@ for price in live_feed:
print("overbought")
```
## Documentation
Full documentation lives at **[docs.wickra.org](https://docs.wickra.org)**:
- **Quickstarts** — [Rust](https://docs.wickra.org/Quickstart-Rust),
[Python](https://docs.wickra.org/Quickstart-Python),
[Node](https://docs.wickra.org/Quickstart-Node),
[WASM](https://docs.wickra.org/Quickstart-WASM).
- **Indicators** — a per-indicator deep dive (formula, parameters, warmup) for
every one of the 462 indicators; start at the
[indicators overview](https://docs.wickra.org/Indicators-Overview).
- **Reference** — [warmup periods](https://docs.wickra.org/Warmup-Periods),
[streaming vs batch](https://docs.wickra.org/Streaming-vs-Batch),
[indicator chaining](https://docs.wickra.org/Indicator-Chaining), the
[data layer](https://docs.wickra.org/Data-Layer).
- **Guides** — [Cookbook](https://docs.wickra.org/Cookbook),
[TA-Lib migration](https://docs.wickra.org/TA-Lib-Migration),
[FAQ](https://docs.wickra.org/FAQ).
## Why Wickra exists
The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
talipp, tulipy — and every one of them shares the same blind spot:
Wickra started as a personal itch. The existing TA libraries never quite fit the
projects I was building, so I decided to build one from the ground up — partly to
learn, partly because I genuinely enjoy taking something that already exists and
trying to do it differently (and, ideally, better). It's open source because the
useful version of that itch is the one other people can build on too.
| Library | Install pain | Streaming | Multi-language | Active |
|--------------------|-----------------|-----------|----------------|--------|
| TA-Lib (Python) | yes (C deps) | no | no | barely |
| pandas-ta | clean | no | no | slow |
| finta | clean | no | no | stale |
| ta-lib-python | yes (C deps) | no | no | barely |
| talipp | clean | yes | no | yes |
| Tulip Indicators | yes (C deps) | no | partial | stale |
| ooples (C#) | clean | no | C# only | yes |
| **Wickra** | **clean** | **yes** | **Python+Node+WASM+Rust** | **yes** |
Plenty of TA libraries are fast. Each one forces a trade-off Wickra does not:
Wickra is the only library that combines all of: clean install, streaming,
multi-language reach, and active maintenance.
| Library | Install | Streaming | Languages | Indicators | Active |
|------------------|-------------|-------------|-----------------------------|-----------:|--------|
| **★&nbsp;Wickra**| **clean** | **yes, O(1)** | **Python · Node · WASM · Rust** | **423** | **yes** |
| kand | clean | yes | Python · WASM · Rust | ~60 | yes |
| ta-rs | clean | yes | Rust only | ~30 | stale |
| yata | clean | partial | Rust only | ~35 | yes |
| TA-Lib | yes (C deps)| no | many bindings | ~150 | barely |
| pandas-ta | clean | no | Python | ~130 | slow |
| finta | clean | no | Python | ~80 | stale |
| talipp | clean | yes | Python | ~40 | yes |
## Benchmark: how much faster is "streaming-first"?
Wickra's edge is **breadth with reach**: 462 indicators that all update in O(1)
per tick and ship natively to Python, Node.js, WebAssembly and Rust from a
single engine.
The numbers below were measured on a single developer workstation and are not
guaranteed to reproduce identically on different hardware — absolute µs values
depend on CPU, memory clock and OS scheduler. Read them as **relative
speedups** between libraries on identical input, not as a universal
performance contract.
**On speed — and why Wickra isn't the fastest.** It deliberately isn't. The
leaner Rust crates (kand, ta-rs) win several of the micro-benchmarks below, and
those losses are shown rather than hidden. The gap is a *choice*, not a ceiling:
every `update` validates its input, runs a real warmup before it emits a value,
and returns an `Option` so a single bad tick can't silently poison the state.
ta-rs, by contrast, hands back a bare `f64` from the first tick with no
validation. If Wickra threw all of that away — raw `f64` out, no checks, no
warmup contract — it would match or beat the leanest crate on every row. It
keeps the guarantees instead, and still wins RSI, Bollinger and ATR against kand.
What no other library matches is the *combination*: catalogue size, native O(1)
streaming, NaN-safety, and four first-class language targets at once.
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 7950X3D, 64 GB DDR5,
Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
Python 3.12, Node 20.
- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries`. The script auto-detects every
installed peer library and runs them on the same generated inputs as
Wickra. The CI job `cross-library-bench` runs the same script on every
push and uploads the raw report as a build artefact.
## Benchmarks
Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
Three comparisons, split by layer and mode. Read them as **relative** speedups
on identical input — absolute µs depend on CPU, memory clock and OS scheduler,
not a universal contract.
### Batch — single full pass over a 20 000-bar series
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
Rust 1.92 (release: `lto = "fat"`, `codegen-units = 1`), Python 3.12.
- **Reproduce yourself:**
- Rust core vs Rust crates: `cargo bench -p wickra-bench`
- Python vs Python libs: `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries` (auto-detects installed peers).
Reading the table: each cell shows that library's runtime, plus how many times
slower it is than Wickra in parentheses. **★** marks the winner per row.
### 1. Rust core vs the other Rust TA crates
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
Like-for-like, no language-binding overhead, over a 50 000-bar series (µs for
the whole series, lower = faster). This is the honest engine comparison —
Wickra wins some and loses some, and both are shown.
### Streaming — per-tick latency after seeding with 5 000 historical bars
**Streaming** (one value fed per `update`):
A batch-only library has to re-run its full indicator over the entire history on
every new tick; Wickra updates state in O(1).
| Indicator | **★&nbsp;Wickra** | kand | ta-rs | yata |
|------------------|------------------:|-----:|------:|-----:|
| SMA(20) | 50 | 38 | 47 | 38 |
| EMA(20) | 154 | 69 | 56 | 69 |
| RSI(14) | 164 | 216 | 74 | — |
| MACD(12, 26, 9) | 275 | 143 | 66 | — |
| Bollinger(20, 2) | **128 ★** | 248 | 168 | — |
| ATR(14) | 152 | 166 | 61 | — |
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
**Batch** (whole series at once). Only Wickra and kand expose a batch API;
ta-rs and yata are streaming-only.
> TA-Lib and pandas-ta are not included here because both fail to install
> cleanly on Windows without C build tooling — which is precisely the install
> pain Wickra was built to remove. The benchmark script auto-detects every
> peer library it can find and runs them on the same inputs as Wickra; install
> them in your environment to see those rows light up too.
| Indicator | **★&nbsp;Wickra** | kand |
|------------------|------------------:|-----:|
| SMA(20) | 82 | 42 |
| EMA(20) | 159 | 74 |
| RSI(14) | **253 ★** | 274 |
| MACD(12, 26, 9) | 681 | 283 |
| Bollinger(20, 2) | **445 ★** | 462 |
| ATR(14) | 175 | 173 |
ta-rs is the per-indicator speed champion on almost every row — it returns a
bare `f64` with no warmup state and no input validation, trading away the
`None`-warmup and NaN-safety semantics Wickra keeps. Against kand, Wickra wins
streaming RSI, Bollinger and ATR (and batch RSI + Bollinger); Bollinger is the
one row where Wickra is the outright fastest of all four. The leaner crates
still win the pure recurrences (EMA, MACD) and SMA. yata exposes only SMA/EMA as
raw-value methods, so its other rows are omitted rather than faked.
### 2. Python vs the Python TA ecosystem — batch
Full pass over a 20 000-bar series, µs/op (lower = faster). **★** per row.
| Indicator | **★&nbsp;Wickra** | finta | TA-Lib | tulipy |
|------------------|------------------:|---------------------|--------|--------|
| SMA(20) | **59.6 ★** | 354.2 (5.9× slower) | ⧗ | ⧗ |
| EMA(20) | **88.4 ★** | 309.3 (3.5× slower) | ⧗ | ⧗ |
| RSI(14) | **77.3 ★** | 1 283 (16.6× slower)| ⧗ | ⧗ |
| MACD(12, 26, 9) | **116.4 ★** | 529.5 (4.6× slower) | ⧗ | ⧗ |
| Bollinger(20, 2) | **146.0 ★** | 1 246 (8.5× slower) | ⧗ | ⧗ |
| ATR(14) | **135.8 ★** | 3 812 (28× slower) | ⧗ | ⧗ |
> ⧗ = published by the CI Linux job. TA-Lib and tulipy ship C extensions that
> don't build cleanly on every desktop, so their canonical numbers come from the
> `cross-library-bench` workflow rather than this local table. pandas-ta needs
> Python ≥ 3.12 and isn't in the 3.11 CI matrix. The script auto-detects
> whichever peers are installed in your environment.
### 3. Python — streaming (per-tick latency)
Seed 5 000 bars, then feed ticks one at a time. talipp is the only Python peer
with a true incremental API; batch-only libraries like TA-Lib must recompute the
entire history on every tick — Wickra updates in O(1).
| Indicator | **★&nbsp;Wickra (per tick)** | talipp (per tick) |
|------------------|------------------------------:|-------------------------|
| SMA(20) | **0.067 µs ★** | 0.63 µs (9.4× slower) |
| EMA(20) | **0.051 µs ★** | 0.63 µs (12.2× slower) |
| RSI(14) | **0.053 µs ★** | 1.00 µs (19.1× slower) |
| MACD(12, 26, 9) | **0.071 µs ★** | 3.64 µs (51.5× slower) |
| Bollinger(20, 2) | **0.085 µs ★** | 4.87 µs (57.2× slower) |
Run the suite yourself:
```bash
pip install -e bindings/python[bench]
cargo bench -p wickra-bench # Rust core vs kand / ta-rs / yata
pip install -e bindings/python[bench] # Python peers
python -m benchmarks.compare_libraries
```
## Indicators
71 streaming-first indicators across eight families. Every one passes the
462 streaming-first indicators across twenty-four families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
semantics tests. Each has a per-indicator deep dive (formula, parameters,
warmup) at [docs.wickra.org](https://docs.wickra.org/Indicators-Overview).
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator |
| Trend & Directional | MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility |
| Trailing Stops | Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle |
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA, SWMA, GMA, EHMA, Median MA, Adaptive Laguerre, GD, Holt-Winters |
| Momentum Oscillators | RSI (Wilder), Anchored RSI, Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia, ROC Percentage (ROCP), ROC Ratio (ROCR), ROC Ratio 100 (ROCR100), Disparity Index, Fisher RSI, RSX, Dynamic Momentum Index, Stochastic CCI, RMI, Derivative Oscillator, Elder Ray, Intraday Momentum Index, QQE |
| Trend & Directional | MACD, MACD Fixed (MACDFIX), MACD Extended (MACDEXT), ADX (+DI/-DI), ADXR, Aroon, TRIX, Aroon Oscillator, Vortex, Random Walk Index, Trend Intensity Index, Wave Trend Oscillator, Mass Index, Choppiness Index, Vertical Horizontal Filter, Plus DM, Minus DM, Plus DI, Minus DI, DX, TTM Trend, Trend Strength Index, Qstick, Polarized Fractal Efficiency, Wave PM, Gator Oscillator, Kase Permission Stochastic |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC, TSF Oscillator, MACD Histogram, PPO Histogram |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility, RVI (Relative Volatility Index), Parkinson Volatility, Garman-Klass Volatility, Rogers-Satchell Volatility, Yang-Zhang Volatility, Volatility Cone |
| Bands & Channels | MA Envelope, Acceleration Bands, STARC Bands, ATR Bands, Hurst Channel, LinReg Channel, Standard Error Bands, Double Bollinger Bands, TTM Squeeze, Fractal Chaos Bands, VWAP StdDev Bands, Quartile Bands, Bomar Bands, Median Channel, Projection Bands, Projection Oscillator |
| Trailing Stops | Parabolic SAR, Parabolic SAR Extended (SAREXT), SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop, HiLo Activator, Volty Stop, Yo-Yo Exit, Donchian Channel Stop, Percentage Trailing Stop, Step Trailing Stop, Renko Trailing Stop, Kase DevStop, Elder SafeZone, ATR Ratchet, NRTR, Time-Based Stop, Modified MA Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement, Klinger Volume Oscillator, Volume Oscillator, NVI, PVI, Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index, Volume RSI, Williams Accumulation/Distribution, Twiggs Money Flow, Trade Volume Index, Intraday Intensity Index, Better Volume, Volume-Weighted MACD |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle, Variance, Coefficient of Variation, Skewness, Kurtosis, Standard Error, Detrended StdDev, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Pairwise Beta, Pair Spread Z-Score, Lead-Lag Cross-Correlation, Cointegration, Relative Strength A-vs-B, Spearman Correlation, Mid Price, Mid Point, Average Price, Linear Regression Intercept, Time Series Forecast, Rolling Correlation, Rolling Covariance, OU Half-Life, Spread Hurst, Distance SSD, Beta-Neutral Spread, Variance Ratio, Granger Causality, Kalman Hedge Ratio, Spread Bollinger Bands, Spread AR(1) Coefficient, Jarque-Bera, Rolling Min-Max Scaler, Shannon Entropy, Sample Entropy, Kendall Tau |
| Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, Hilbert Phasor, Hilbert DC Phase, Hilbert Trend Mode, Sine Wave, Decycler, Decycler Oscillator, Roofing Filter, Center of Gravity, Cybernetic Cycle, Adaptive Cycle, Empirical Mode Decomposition, Ehlers Stochastic, Instantaneous Trendline, Highpass Filter, Reflex, Trendflex, Correlation Trend Indicator, Adaptive RSI, Universal Oscillator, Adaptive CCI, Bandpass Filter, Even Better Sinewave, Autocorrelation Periodogram |
| Pivots & S/R | Classic Pivots, Fibonacci Pivots, Camarilla, Woodie Pivots, DeMark Pivots, Williams Fractals, ZigZag |
| DeMark | TD Setup, TD Sequential, TD DeMarker, TD REI, TD Pressure, TD Combo, TD Countdown, TD Lines, TD Range Projection, TD Differential, TD Open, TD Risk Level |
| Ichimoku & Charts | Ichimoku Kinko Hyo (Tenkan, Kijun, Senkou A/B, Chikou), Heikin-Ashi |
| Alt-Chart Bars | Renko (box-size bricks), Kagi (reversal-amount lines), Point & Figure (X/O columns) |
| Candlestick Patterns | Doji, Hammer, Inverted Hammer, Hanging Man, Shooting Star, Engulfing, Harami, Morning/Evening Star, Three White Soldiers/Black Crows, Piercing Line/Dark Cloud Cover, Marubozu, Tweezer, Spinning Top, Three Inside Up/Down, Three Outside Up/Down, Two Crows, Upside Gap Two Crows, Identical Three Crows, Three Line Strike, Three Stars in the South, Abandoned Baby, Advance Block, Belt-hold, Breakaway, Counterattack, Doji Star, Dragonfly Doji, Gravestone Doji, Long-Legged Doji, Rickshaw Man, Evening Doji Star, Morning Doji Star, Gap Side-by-Side White, High-Wave, Hikkake, Modified Hikkake, Homing Pigeon, On-Neck, In-Neck, Thrusting, Separating Lines, Kicking, Kicking by Length, Ladder Bottom, Mat Hold, Matching Low, Long Line, Short Line, Rising Three Methods, Falling Three Methods, Upside Gap Three Methods, Downside Gap Three Methods, Stalled Pattern, Stick Sandwich, Takuri, Closing Marubozu, Opening Marubozu, Tasuki Gap, Unique Three River, Concealing Baby Swallow |
| Chart Patterns | Double Top / Bottom, Triple Top / Bottom, Head and Shoulders, Triangle (asc/desc/sym), Wedge (rising/falling), Flag / Pennant, Rectangle / Range, Cup and Handle |
| Harmonic Patterns | AB=CD, Gartley, Butterfly, Bat, Crab, Shark, Cypher, Three Drives |
| Fibonacci | Fibonacci Retracement, Fibonacci Extension, Fibonacci Projection, Auto-Fibonacci, Golden Pocket, Fibonacci Confluence, Fibonacci Fan, Fibonacci Arcs, Fibonacci Channel, Fibonacci Time Zones |
| Microstructure | Order-Book Imbalance (Top-1 / Top-N / Full), Microprice, Quoted Spread, Depth Slope, Signed Volume, Cumulative Volume Delta, Trade Imbalance, Effective Spread, Realized Spread, Kyle's Lambda, Footprint, Order Flow Imbalance, VPIN, Amihud Illiquidity, Roll Measure |
| Derivatives | Funding Rate, Funding Rate Mean, Funding Rate Z-Score, Funding Basis, Open-Interest Delta, OI / Price Divergence, OI-Weighted Price, Long/Short Ratio, Taker Buy/Sell Ratio, Liquidation Features, Term-Structure Basis, Calendar Spread |
| Market Profile | Value Area (POC / VAH / VAL), Volume Profile (histogram), TPO Profile, Initial Balance, Opening Range |
| Market Breadth | Advance/Decline Line, Advance/Decline Ratio, Advance/Decline Volume Line, McClellan Oscillator, McClellan Summation Index, TRIN / Arms Index, Breadth Thrust, New Highs - New Lows, High-Low Index, Percent Above Moving Average, Up/Down Volume Ratio, Bullish Percent Index, Cumulative Volume Index, Absolute Breadth Index, TICK Index |
| Risk / Performance | Sharpe Ratio, Sortino Ratio, Calmar Ratio, Omega Ratio, Max Drawdown, Average Drawdown, Drawdown Duration, Pain Index, Value at Risk, Conditional Value at Risk (CVaR), Profit Factor, Gain/Loss Ratio, Recovery Factor, Kelly Criterion, Treynor Ratio, Information Ratio, Alpha (Jensen) |
| Seasonality & Session | Session VWAP, Session High/Low, Session Range, Average Daily Range, Overnight Gap, Overnight/Intraday Return, Turn-of-Month, Seasonal Z-Score, Time-of-Day Return Profile, Day-of-Week Profile, Intraday Volatility Profile, Volume-by-Time Profile |
Every candlestick pattern emits a signed per-bar value — `+1.0` bullish,
`1.0` bearish, `0.0` none — so the family drops straight into a feature matrix
as one column each. `Doji` is direction-less by default (`+1.0` / `0.0`);
construct it in signed mode (`Doji::new().signed()`, `Doji(signed=True)`,
`new Doji(true)`) for a dragonfly / gravestone `±1` reading.
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
@@ -195,9 +297,10 @@ A Python live-trading example using the public `websockets` package lives at
```
wickra/
├── crates/
│ ├── wickra-core/ core engine + all 71 indicators
│ ├── wickra-core/ core engine + all 462 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
── wickra-data/ CSV reader, tick aggregator, live exchange feeds
── wickra-data/ CSV reader, tick aggregator, live exchange feeds
│ └── wickra-bench/ internal cross-library benchmark harness (not published)
├── bindings/
│ ├── python/ PyO3 + maturin (publishes on PyPI)
│ ├── node/ napi-rs (publishes on npm)
@@ -211,9 +314,10 @@ wickra/
└── .github/workflows/ CI and release pipelines
```
Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
in the workspace member crate at `examples/rust/`. There is no top-level
`benches/` directory.
Wickra's own regression benchmarks live in `crates/wickra/benches/`; the
cross-library comparison against kand, ta-rs and yata lives in the internal
`crates/wickra-bench/` crate. Runnable Rust examples live in the workspace member
crate at `examples/rust/`. There is no top-level `benches/` directory.
## Building everything from source
@@ -221,7 +325,8 @@ in the workspace member crate at `examples/rust/`. There is no top-level
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
cargo bench -p wickra # Wickra's own regression benchmarks
cargo bench -p wickra-bench # cross-library comparison (kand, ta-rs, yata)
# Python binding (requires Rust toolchain + maturin)
cd bindings/python
@@ -256,7 +361,7 @@ Every layer is covered; run the suites with the commands in
## Contributing
Contributions are very welcome — issues, bug reports, ideas, and pull requests
all land in the same place: <https://github.com/kingchenc/wickra>.
all land in the same place: <https://github.com/wickra-lib/wickra>.
A short orientation for first-time contributors:
@@ -279,13 +384,20 @@ shape together before you invest the time.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](LICENSE).
Licensed under either of
In plain English: use it, fork it, modify it, redistribute it, file issues, send
pull requests — all welcome. Personal projects, research, education, non-profits,
government, hobby trading bots: all fine. The one thing that's not allowed is
commercial sale of the software or of services built around it. If you want to
use Wickra commercially, get in touch about a license.
- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or
<http://www.apache.org/licenses/LICENSE-2.0>)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or <http://opensource.org/licenses/MIT>)
at your option. Use it, fork it, modify it, redistribute it — commercially or
not — file issues, send pull requests; all welcome.
### Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be
dual licensed as above, without any additional terms or conditions.
## Disclaimer
@@ -300,17 +412,24 @@ The library is provided **as is**, without warranty of any kind; see
---
<p align="center">
<a href="https://github.com/kingchenc/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
<a href="https://github.com/wickra-lib/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/kingchenc/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
<a href="https://github.com/wickra-lib/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/kingchenc/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
<a href="https://github.com/wickra-lib/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
<p align="center">
<a href="https://star-history.com/#wickra-lib/wickra&Date">
<img alt="Wickra star history" width="640"
src="https://api.star-history.com/svg?repos=wickra-lib/wickra&type=Date&theme=dark">
</a>
</p>
+36
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@@ -0,0 +1,36 @@
# Roadmap
This roadmap describes the project's direction at a high level. It is
intentionally non-binding: priorities shift with feedback and available time,
and the authoritative, up-to-date view of planned work is the
[issue tracker](https://github.com/wickra-lib/wickra/issues). Shipped changes
are recorded in [`CHANGELOG.md`](CHANGELOG.md).
## Status
Wickra is **pre-1.0**. The public API is largely stable but may still change in
minor releases; breaking changes are called out in the changelog.
## Themes
- **Indicator coverage.** Continue broadening the indicator catalogue across
families (trend, momentum, volatility, volume, statistics, market profile,
and more), each with the same streaming/batch parity and test guarantees.
- **API stabilization toward 1.0.** Settle the public `Indicator` and
`BarBuilder` traits and the binding surfaces, then commit to semantic
versioning stability for a 1.0 release.
- **Performance.** Keep per-tick updates O(1) and maintain the benchmark suite;
investigate further allocation and cache improvements.
- **Bindings parity.** Keep the Python, Node.js and WebAssembly bindings in
lockstep with the Rust core, including type stubs and platform coverage.
- **Documentation.** Maintain a deep-dive page per indicator on
<https://docs.wickra.org>, plus quickstarts and cookbook material.
- **Project health.** Maintain test coverage, static and dynamic analysis,
signed releases, and supply-chain monitoring.
## How to influence the roadmap
Open or comment on an issue, or start with the
[feature-request template](.github/ISSUE_TEMPLATE/feature_request.md).
Well-scoped proposals and pull requests are the most effective way to move an
item forward.
+101 -5
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@@ -2,13 +2,13 @@
## Supported versions
Wickra is pre-1.0. Security fixes are applied to the latest released `0.1.x`
Wickra is pre-1.0. Security fixes are applied to the latest released `0.5.x`
version only; please upgrade to the newest release before reporting an issue.
| Version | Supported |
| --- | --- |
| 0.1.x (latest) | :white_check_mark: |
| older 0.1.x | :x: |
| 0.5.x (latest) | :white_check_mark: |
| older 0.5.x | :x: |
## Reporting a vulnerability
@@ -16,9 +16,9 @@ version only; please upgrade to the newest release before reporting an issue.
Report it privately through one of:
- GitHub's [private vulnerability reporting](https://github.com/kingchenc/wickra/security/advisories/new)
- GitHub's [private vulnerability reporting](https://github.com/wickra-lib/wickra/security/advisories/new)
("Report a vulnerability" under the repository's *Security* tab), or
- email to **kingchencp@gmail.com** with a subject line starting with
- email to **support@wickra.org** with a subject line starting with
`[wickra security]`.
Please include:
@@ -41,3 +41,99 @@ PyPI/npm packages, and the build/release workflows in `.github/workflows/`.
Out of scope: vulnerabilities in third-party dependencies (report those
upstream; we track them via Dependabot and `cargo-deny`).
## Security assurance case
This is a short, evidence-backed argument for why Wickra can be used safely.
**Security requirements.** Wickra is a computational library: it ingests
numeric market data and produces indicator values. It stores no user
credentials, authenticates no external users, and implements no cryptography of
its own. The requirements are therefore: (1) memory safety and freedom from
undefined behaviour, (2) robust handling of untrusted/degenerate numeric input
without panics or unbounded resource use, (3) integrity of the published
artifacts, and (4) a healthy dependency supply chain.
**How the requirements are met.**
- *Memory safety* — the core and all bindings are written in Rust. The crates
forbid or minimise `unsafe`, so the compiler guarantees memory and thread
safety for the indicator logic.
- *Input robustness* — every indicator validates its parameters and rejects
non-finite inputs at construction; behaviour on edge cases (flat markets,
warmup, reset) is pinned by unit tests, and the public update paths are
exercised by coverage-guided fuzzing (`cargo-fuzz` / libFuzzer) in CI.
- *Static and dynamic analysis* — every push and pull request runs Clippy
(`clippy::pedantic`, warnings-as-errors), CodeQL, fuzzing, and the full test
suite, with 100% line coverage on the core crate tracked by Codecov.
- *Artifact integrity* — releases are built in CI, commits and tags are signed,
the `main` branch requires signed commits, and release artifacts carry build
provenance attestations.
- *Supply chain* — dependencies are pinned and monitored with Dependabot and
audited with `cargo-deny` (license + advisory checks) on every change.
**Residual risk.** The optional `live-binance` feature opens a TLS WebSocket to
an exchange using the platform TLS library; transport security therefore
depends on that library, not on Wickra. Wickra is not a trading system and is
provided "as is" — see the disclaimers in `README.md` and the licenses.
## Secrets management
The project stores **no** secrets or credentials in the version control system.
Secrets required by automation (publishing tokens, the about-sync PAT) are kept
exclusively as **GitHub Actions encrypted secrets** and referenced via the
`secrets.*` context; they are never written to the repository, logs, or build
artifacts. GitHub **secret scanning with push protection** is enabled to block
accidental commits of credentials. Secrets follow least privilege (the narrowest
scope that works) and are rotated when a holder changes or on suspected
exposure.
## Verifying releases
Released artifacts can be verified for integrity and authenticity:
- **Build provenance.** Release assets carry GitHub build provenance
attestations. Verify a downloaded asset with the GitHub CLI:
`gh attestation verify <file> --repo wickra-lib/wickra`.
- **Signed tags.** Each release corresponds to a signed git tag (`vX.Y.Z`);
the tag signature identifies the maintainer who authorised the release.
- **Registry integrity.** Packages are distributed over HTTPS from crates.io,
PyPI and npm, which serve package checksums that package managers verify on
install.
The release is published only by the maintainer through the tag-triggered
release workflow, so a verified tag signature establishes the expected
publisher identity.
## Support timeline and end of support
Wickra is **pre-1.0**: only the **latest released `0.y.z`** version receives
security fixes. When a newer release is published, the previous version
**immediately reaches end of support** and will not receive further fixes;
users should upgrade to the latest release. The supported-versions table above
is authoritative. After the `1.0.0` release this policy will be revised to
support a defined window of releases.
## Remediation policy (dependencies and code scanning)
- **Severity threshold.** Vulnerabilities of **medium severity or higher** in
the project's own code or its dependencies are remediated promptly and before
the next release; lower-severity findings are addressed on a best-effort
basis.
- **Automated enforcement (SCA).** Every change is evaluated by `cargo-deny`
(RUSTSEC advisories + license policy) and Dependabot; a known-vulnerable
dependency fails CI and **blocks the change** until resolved or explicitly
waived with justification.
- **Automated enforcement (SAST).** Every change is evaluated by CodeQL and
Clippy (`-D warnings`); findings **block the change** in CI until fixed.
- **Pre-release gate.** A release is not cut while an unresolved medium-or-higher
SCA/SAST finding is outstanding.
## Vulnerability exploitability (VEX)
Advisories reported by `cargo-deny`/Dependabot for third-party dependencies that
do **not** affect Wickra (e.g. the vulnerable code path is not reachable, or the
affected feature is not enabled) are triaged and recorded — with the
not-affected justification — in the `cargo-deny` configuration (`deny.toml`) and
the relevant pull request, rather than forcing an unnecessary dependency bump.
This serves as the project's exploitability (VEX) record.
+37
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@@ -0,0 +1,37 @@
# Support
Thanks for using Wickra! Here is where to get help, depending on what you need.
## Documentation first
Most questions are answered in the documentation:
- **Docs site:** <https://docs.wickra.org> — quickstarts for Rust, Python,
Node.js and WebAssembly, a per-indicator reference, warmup periods, the data
layer, and an FAQ.
- **README:** <https://github.com/wickra-lib/wickra#readme> — installation and a
quick overview.
- **API docs (Rust):** <https://docs.rs/wickra>.
## Questions and help
- Ask a question with the
[question issue template](.github/ISSUE_TEMPLATE/question.md).
- Browse [existing issues](https://github.com/wickra-lib/wickra/issues) — your
question may already be answered.
## Bugs and feature requests
- **Bugs:** use the bug-report issue template.
- **Feature requests / new indicators:** use the feature-request template.
## Security issues
Please do **not** report security vulnerabilities through public issues. Follow
the process in [`SECURITY.md`](SECURITY.md) (private GitHub advisory or email).
## Support expectations
Wickra is maintained by a single maintainer on a best-effort basis. Issues are
triaged and acknowledged as time allows; there is no commercial support or SLA.
Clear, reproducible reports get help fastest.
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@@ -0,0 +1,54 @@
# Threat model
This document describes Wickra's attack surface and the threats considered,
together with their mitigations. It complements the security assurance case in
[`SECURITY.md`](SECURITY.md). Wickra is a computational technical-analysis
library (a Rust core with Python, Node.js and WebAssembly bindings), not a
network service or trading system; the attack surface is correspondingly small.
## Assets
- **Integrity of computed indicator values** — consumers may use them in
automated decisions, so silently wrong output is the primary concern.
- **Availability of the calling process** — a library must not crash or hang
its host on malformed input.
- **Integrity of published artifacts** — the crates, wheels and npm packages
users install.
- **The build and release pipeline** and its secrets (publishing tokens).
## Actors / trust boundaries
- **Library consumer** (trusted) — calls the API with numeric data. Data may
originate from untrusted sources (e.g. a market feed), so *input values* are
treated as untrusted even though the caller is trusted.
- **Optional live feed** — with the `live-binance` feature, data crosses a
network boundary from an exchange over TLS.
- **Contributors** (semi-trusted) — propose changes via pull requests.
- **Supply chain** — upstream dependencies and the CI/CD platform.
## Threats and mitigations
| Threat | Mitigation |
| --- | --- |
| Memory-safety exploit (buffer overflow, UAF) via crafted input | Pure safe Rust; `unsafe` is forbidden/minimised, so the compiler precludes these classes. |
| Denial of service via malformed/degenerate input (NaN, infinities, extreme magnitudes) | Indicators reject non-finite inputs and validate parameters at construction; update paths are exercised by coverage-guided fuzzing and unit tests for edge cases. |
| Silently incorrect results | 100% line coverage on the core crate; reference-value tests against known-good sources; streaming/batch parity tests. |
| Integer overflow / panics | `clippy::pedantic` with `-D warnings`; debug assertions and overflow checks enabled in test/fuzz builds. |
| Adversary-in-the-middle on the optional live feed | Connection uses TLS via the platform library; transport security is delegated to that reviewed implementation. |
| Compromised dependency (supply chain) | Dependencies pinned (`Cargo.lock`, hash-locked CI requirements), monitored by Dependabot, audited by `cargo-deny` (advisories + licenses) on every change. |
| Malicious or accidental change to `main` | Branch protection requires signed commits and blocks force-push and deletion; all changes flow through pull requests with required CI; static analysis (CodeQL, Clippy) and fuzzing run on every change. |
| Compromised CI / leaked secrets | Workflows use least-privilege `permissions:`; secrets live only as encrypted GitHub Actions secrets; secret scanning with push protection is enabled; workflows are linted by `zizmor`. |
| Tampered release artifact | Releases are built in CI, tags are signed, and assets carry build provenance attestations (verifiable with `gh attestation verify`). |
## Out of scope
- Wickra implements no authentication, authorization or cryptography of its own,
stores no user data, and exposes no network listener; those threat classes do
not apply.
- Vulnerabilities in third-party dependencies that do not affect Wickra are
tracked as exploitability (VEX) records (see [`SECURITY.md`](SECURITY.md)).
## Maintenance
This threat model is reviewed when the architecture changes materially (for
example, a new input family, a new network feature, or a new release channel).
+45 -280
View File
@@ -1,306 +1,71 @@
# Wickra
# Wickra — Node.js
[![CI](https://github.com/kingchenc/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/kingchenc/wickra/actions/workflows/ci.yml)
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[![npm](https://img.shields.io/npm/v/wickra.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](LICENSE)
[![License: MIT OR Apache-2.0](https://img.shields.io/badge/license-MIT_OR_Apache--2.0-blue)](https://github.com/wickra-lib/wickra#license)
**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators for Node.js. `npm install wickra`
prebuilt native binary, no system dependencies.**
Wickra is a multi-language technical-analysis library with a Rust core and
bindings for Python, Node.js, and WebAssembly. Every indicator is a state
machine that updates in O(1) per new data point, so live trading bots and
historical backtests share the exact same implementation.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading bots and historical backtests share
the exact same implementation. This package is the Node.js binding (napi-rs);
it exposes 200+ streaming-first indicators across sixteen families.
```python
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: same indicator, fed tick by tick
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # O(1) — no recomputation over history
if value is not None and value > 70:
print("overbought")
```
## Why Wickra exists
The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
talipp, tulipy — and every one of them shares the same blind spot:
| Library | Install pain | Streaming | Multi-language | Active |
|--------------------|-----------------|-----------|----------------|--------|
| TA-Lib (Python) | yes (C deps) | no | no | barely |
| pandas-ta | clean | no | no | slow |
| finta | clean | no | no | stale |
| ta-lib-python | yes (C deps) | no | no | barely |
| talipp | clean | yes | no | yes |
| Tulip Indicators | yes (C deps) | no | partial | stale |
| ooples (C#) | clean | no | C# only | yes |
| **Wickra** | **clean** | **yes** | **Python+Node+WASM+Rust** | **yes** |
Wickra is the only library that combines all of: clean install, streaming,
multi-language reach, and active maintenance.
## Benchmark: how much faster is "streaming-first"?
The numbers below were measured on a single developer workstation and are not
guaranteed to reproduce identically on different hardware — absolute µs values
depend on CPU, memory clock and OS scheduler. Read them as **relative
speedups** between libraries on identical input, not as a universal
performance contract.
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 7950X3D, 64 GB DDR5,
Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
Python 3.12, Node 20.
- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries`. The script auto-detects every
installed peer library and runs them on the same generated inputs as
Wickra. The CI job `cross-library-bench` runs the same script on every
push and uploads the raw report as a build artefact.
Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
### Batch — single full pass over a 20 000-bar series
Reading the table: each cell shows that library's runtime, plus how many times
slower it is than Wickra in parentheses. **★** marks the winner per row.
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### Streaming — per-tick latency after seeding with 5 000 historical bars
A batch-only library has to re-run its full indicator over the entire history on
every new tick; Wickra updates state in O(1).
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
> TA-Lib and pandas-ta are not included here because both fail to install
> cleanly on Windows without C build tooling — which is precisely the install
> pain Wickra was built to remove. The benchmark script auto-detects every
> peer library it can find and runs them on the same inputs as Wickra; install
> them in your environment to see those rows light up too.
Run the suite yourself:
## Install
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
npm install wickra
```
## Indicators
The native addon ships as a prebuilt binary per platform (Linux, macOS,
Windows — x64 and arm64), selected automatically through optional
dependencies. There is nothing to compile.
71 streaming-first indicators across eight families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
## Quick start
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator |
| Trend & Directional | MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility |
| Trailing Stops | Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle |
```js
const wickra = require('wickra');
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
// Batch: run an indicator over a whole array.
const prices = Array.from({ length: 1000 }, (_, i) => 100 + i * 0.1);
const values = new wickra.RSI(14).batch(prices); // null during warmup
## Languages
| Binding | Install | Example |
|-------------------|-----------------------------------------------|---------|
| Python (PyO3) | `pip install wickra` | `examples/python/backtest.py` |
| Node.js (napi-rs) | `npm install wickra` | `examples/node/backtest.js` |
| Browser / WASM | `npm install wickra-wasm` | `examples/wasm/index.html` |
| Rust | `cargo add wickra` | `examples/rust/src/bin/backtest.rs` |
Each binding ships several runnable examples (streaming, backtest, live feed);
[`examples/README.md`](examples/README.md) is the full cross-language index.
The wickra-core crate is `unsafe`-forbidden, so every binding inherits a
memory-safe implementation.
## Rust API
```rust
use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};
// Streaming or batch — same trait, same code.
let mut sma = Sma::new(14)?;
let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
let mut rsi = Rsi::new(14)?;
for price in live_feed {
if let Some(v) = rsi.update(price) {
println!("RSI = {v}");
}
}
// Compose indicators: RSI(7) on top of EMA(14).
let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
chain.update(price);
```
## Live data sources
`wickra-data` (separate crate, opt-in) ships:
- A streaming OHLCV **CSV reader**.
- A **tick-to-candle aggregator** with arbitrary timeframes.
- A **candle resampler** for multi-timeframe analysis (1m → 5m → 1h on the fly).
- A **Binance Spot WebSocket** kline adapter (feature `live-binance`).
```rust
use wickra::{Indicator, Rsi};
use wickra_data::live::binance::{BinanceKlineStream, Interval};
let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
let mut rsi = Rsi::new(14)?;
while let Some(event) = stream.next_event().await? {
if event.is_closed {
if let Some(v) = rsi.update(event.candle.close) {
println!("RSI = {v:.2}");
}
}
// Streaming: the same indicator, fed tick by tick in O(1).
const rsi = new wickra.RSI(14);
for (const price of liveFeed) {
const value = rsi.update(price); // no recomputation over history
if (value !== null && value > 70) {
console.log('overbought');
}
}
```
A Python live-trading example using the public `websockets` package lives at
`examples/python/live_trading.py`.
`batch(prices)` and feeding the same prices through `update()` produce
identical values — the equivalence is enforced by the test suite.
## Project layout
## Documentation
```
wickra/
├── crates/
│ ├── wickra-core/ core engine + all 71 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
│ └── wickra-data/ CSV reader, tick aggregator, live exchange feeds
├── bindings/
│ ├── python/ PyO3 + maturin (publishes on PyPI)
│ ├── node/ napi-rs (publishes on npm)
│ └── wasm/ wasm-bindgen (browsers, bundlers, Node)
├── examples/ examples/README.md indexes every language
│ ├── data/ real BTCUSDT OHLCV datasets, one per timeframe
│ ├── rust/ Rust workspace member (`wickra-examples`)
│ ├── python/ backtest, live trading, parallel assets, multi-tf
│ ├── node/ streaming, backtest, live trading (load `wickra`)
│ └── wasm/ browser demo for `wickra-wasm`
└── .github/workflows/ CI and release pipelines
```
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and documentation site:
Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
in the workspace member crate at `examples/rust/`. There is no top-level
`benches/` directory.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Docs** (quickstarts, cookbook, TA-Lib migration): <https://docs.wickra.org>
- **Runnable examples:** [`examples/node/`](https://github.com/wickra-lib/wickra/tree/main/examples/node)
## Building everything from source
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
```bash
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
## Disclaimer
# Python binding (requires Rust toolchain + maturin)
cd bindings/python
maturin develop --release
pytest
# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
wasm-pack build bindings/wasm --target web --release --features panic-hook
# Node binding (requires @napi-rs/cli)
cd bindings/node && npm install && npm run build && npm test
```
## Testing
Every layer is covered; run the suites with the commands in
[Building everything from source](#building-everything-from-source).
- `wickra-core`: unit tests per indicator — textbook reference values
(Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), `batch == streaming`
equivalence, `reset` semantics, NaN/Inf handling, and property tests.
- `wickra-data`: unit tests for CSV decoding, the tick aggregator, the
resampler, and the Binance payload parser.
- `bindings/python`: pytest covering smoke checks, streaming/batch
equivalence, reference values, lifecycle, input validation, and
dict/tuple candle inputs.
- `bindings/node`: `node --test` cases for batch, streaming, and reference
values across all indicators.
- `bindings/wasm`: `wasm-bindgen-test` cases for constructors, equivalence,
and reference values.
## Contributing
Contributions are very welcome — issues, bug reports, ideas, and pull requests
all land in the same place: <https://github.com/kingchenc/wickra>.
A short orientation for first-time contributors:
- **Adding an indicator.** Implement the `Indicator` trait in
`crates/wickra-core/src/indicators/<name>.rs`, wire it into
`indicators/mod.rs` and the crate root, and add reference-value tests,
a `batch == streaming` equivalence test, and (where it makes sense) a
proptest. The four bindings inherit your indicator automatically once
you expose it in the language wrappers.
- **Fixing a numeric bug.** Add a failing test that pins the textbook value
first, then fix the math. Property tests in `crates/wickra-core` catch
most regressions; please don't disable them.
- **Improving a binding.** Each binding lives under `bindings/<lang>` with
its own tests; please keep the `batch == streaming` invariant.
- **Style.** `cargo fmt --all` + `cargo clippy --workspace --all-targets -- -D warnings`
are CI gates; running them locally before pushing keeps reviews short.
For larger architectural changes, open an issue first so we can sketch the
shape together before you invest the time.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](LICENSE).
In plain English: use it, fork it, modify it, redistribute it, file issues, send
pull requests — all welcome. Personal projects, research, education, non-profits,
government, hobby trading bots: all fine. The one thing that's not allowed is
commercial sale of the software or of services built around it. If you want to
use Wickra commercially, get in touch about a license.
---
<p align="center">
<a href="https://github.com/kingchenc/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/kingchenc/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/kingchenc/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
Licensed under either of [Apache-2.0](https://github.com/wickra-lib/wickra/blob/main/LICENSE-APACHE)
or [MIT](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT) at your option.
@@ -0,0 +1,79 @@
// Completeness contract for the Wickra Node bindings: every exported indicator
// class must expose the full streaming + batch + lifecycle interface. This
// catches a new indicator being wired into the binding without the standard
// methods (or an export silently disappearing) without needing a hand-written
// test per indicator.
const test = require('node:test');
const assert = require('node:assert/strict');
const wickra = require('..');
// Bar builders (Renko / Kagi / Point & Figure) implement the `BarBuilder`
// contract, not `Indicator`: they emit a variable number of completed bars per
// candle and have no fixed warmup or ready state. They expose update/batch/reset
// but intentionally not isReady/warmupPeriod, so they are excluded from the
// Indicator completeness contract below (their interface is covered by the
// dedicated bar-builder tests).
const BAR_BUILDERS = new Set(['RenkoBars', 'KagiBars', 'PointAndFigureBars']);
// An "indicator class" is an exported constructor whose prototype carries the
// streaming `update` method. This excludes `version` (a plain function), the bar
// builders, and any non-indicator export.
function indicatorClasses() {
return Object.keys(wickra).filter((name) => {
const value = wickra[name];
return (
typeof value === 'function' &&
value.prototype &&
typeof value.prototype.update === 'function' &&
!BAR_BUILDERS.has(name)
);
});
}
test('the binding exports the full indicator catalogue', () => {
const names = indicatorClasses();
// The published catalogue is 214 indicators. Guard against a regression that
// silently drops exported classes (e.g. a stale or partial native build).
assert.ok(
names.length >= 200,
`expected at least 200 indicator classes, got ${names.length}`,
);
});
test('every exported indicator exposes update / batch / reset / isReady / warmupPeriod', () => {
const required = ['update', 'batch', 'reset', 'isReady', 'warmupPeriod'];
const missing = [];
for (const name of indicatorClasses()) {
const proto = wickra[name].prototype;
for (const method of required) {
if (typeof proto[method] !== 'function') {
missing.push(`${name}.${method}`);
}
}
}
assert.deepEqual(
missing,
[],
`indicator classes missing required methods: ${missing.join(', ')}`,
);
});
test('a freshly constructed indicator reports not-ready with a positive warmup', () => {
// Every indicator that takes no constructor arguments must still satisfy the
// pre-warmup contract. (Indicators with required parameters are exercised by
// the dedicated suites; here we cover the zero-arg ones generically.)
let checked = 0;
for (const name of indicatorClasses()) {
let instance;
try {
instance = new wickra[name]();
} catch {
continue; // needs constructor arguments — covered elsewhere
}
assert.equal(instance.isReady(), false, `${name} should start un-ready`);
assert.ok(instance.warmupPeriod() >= 1, `${name} warmup must be >= 1`);
checked += 1;
}
assert.ok(checked > 0, 'expected at least one zero-arg indicator to check');
});
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,84 @@
// Input-validation tests for the Wickra Node bindings: malformed constructor
// parameters and mismatched batch inputs must raise a JS Error (the napi
// wrapper turns the Rust `Err` into a thrown Error), not crash the process.
// Node counterpart of bindings/python/tests/test_input_validation.py.
const test = require('node:test');
const assert = require('node:assert/strict');
const wickra = require('..');
// --- Constructors reject invalid periods / parameters ---
test('ATR rejects a zero period at construction', () => {
// ATR validates its period (it drives the Wilder-smoothing length). The
// plain moving averages (SMA/EMA/RSI/StdDev) instead treat period 0 as a
// warmup-1 pass-through rather than an error, so they are not asserted here.
assert.throws(() => new wickra.ATR(0), /.*/);
});
test('MACD rejects zero and non-increasing fast/slow periods', () => {
assert.throws(() => new wickra.MACD(0, 0, 0), /.*/);
// fast must be strictly less than slow.
assert.throws(() => new wickra.MACD(26, 12, 9), /.*/);
});
test('BollingerBands rejects a negative standard-deviation multiplier', () => {
assert.throws(() => new wickra.BollingerBands(20, -1), /.*/);
});
test('PSAR rejects a step greater than its maximum', () => {
assert.throws(() => new wickra.PSAR(0.3, 0.02, 0.2), /.*/);
});
test('ValueArea rejects zero periods and out-of-range value-area percentages', () => {
assert.throws(() => new wickra.ValueArea(0, 50, 0.7), /.*/);
assert.throws(() => new wickra.ValueArea(20, 0, 0.7), /.*/);
assert.throws(() => new wickra.ValueArea(20, 50, 0.0), /.*/);
assert.throws(() => new wickra.ValueArea(20, 50, 1.5), /.*/);
});
test('InitialBalance and OpeningRange reject a zero period', () => {
assert.throws(() => new wickra.InitialBalance(0), /.*/);
assert.throws(() => new wickra.OpeningRange(0), /.*/);
});
test('Ichimoku rejects zero and non-increasing periods', () => {
assert.throws(() => new wickra.Ichimoku(0, 26, 52, 26), /.*/);
assert.throws(() => new wickra.Ichimoku(9, 26, 52, 0), /.*/);
// Periods must satisfy tenkan < kijun < senkouB.
assert.throws(() => new wickra.Ichimoku(26, 9, 52, 26), /.*/);
assert.throws(() => new wickra.Ichimoku(9, 52, 52, 26), /.*/);
});
test('Family 10 (Ehlers / cycle) indicators reject invalid parameters', () => {
// InverseFisherTransform needs a non-zero scaling factor.
assert.throws(() => new wickra.InverseFisherTransform(0.0), /.*/);
// DecyclerOscillator / RoofingFilter need the short cutoff below the long one.
assert.throws(() => new wickra.DecyclerOscillator(30, 10), /.*/);
assert.throws(() => new wickra.RoofingFilter(48, 10), /.*/);
// MAMA needs fast limit > slow limit.
assert.throws(() => new wickra.MAMA(0.05, 0.5), /.*/);
// EmpiricalModeDecomposition needs a positive fraction.
assert.throws(() => new wickra.EmpiricalModeDecomposition(20, 0.0), /.*/);
// NOTE: SuperSmoother(0) / FisherTransform(0) are NOT asserted: the Node
// binding treats their period 0 as a warmup-1 pass-through (same as the
// simple moving averages) rather than an error.
});
// --- Batch methods reject mismatched input lengths ---
test('candle batch methods reject unequal-length columns', () => {
const high = [10, 11, 12];
const low = [9, 10]; // one short
const close = [9.5, 10.5, 11.5];
assert.throws(() => new wickra.ATR(14).batch(high, low, close), /.*/);
assert.throws(() => new wickra.WilliamsR(14).batch(high, low, close), /.*/);
assert.throws(() => new wickra.Aroon(14).batch(high, low), /.*/);
});
test('ValueArea batch rejects unequal-length columns', () => {
const high = [1, 2, 3];
const low = [0.5, 1.5]; // short
const volume = [10, 10, 10];
assert.throws(() => new wickra.ValueArea(2, 10, 0.7).batch(high, low, volume), /.*/);
});
@@ -0,0 +1,96 @@
// Streaming-vs-batch equivalence and reference values for the Seasonality &
// Session family. These indicators consume the full candle (open, high, low,
// close, volume, timestamp), so they have a dedicated suite.
const test = require('node:test');
const assert = require('node:assert/strict');
const wickra = require('..');
const HOUR = 3_600_000;
const N = 240;
const close = Array.from({ length: N }, (_, i) => 100 + Math.sin(i * 0.3) * 5 + Math.cos(i * 0.1) * 3);
const open = close.map((c, i) => c + Math.sin(i * 0.5) * 0.5);
const high = close.map((c, i) => Math.max(open[i], c) + 1);
const low = close.map((c, i) => Math.min(open[i], c) - 1);
const volume = Array.from({ length: N }, (_, i) => 1000 + (i % 24) * 50);
const ts = Array.from({ length: N }, (_, i) => i * HOUR);
function eq(a, b) {
if (Number.isNaN(a)) return Number.isNaN(b);
return Math.abs(a - b) < 1e-9;
}
function streamScalar(ind, i) {
const v = ind.update(open[i], high[i], low[i], close[i], volume[i], ts[i]);
return v === null || v === undefined ? NaN : v;
}
function checkScalar(name, make) {
test(`${name} streaming equals batch`, () => {
const a = make();
const b = make();
const batch = b.batch(open, high, low, close, volume, ts);
for (let i = 0; i < N; i += 1) {
assert.ok(eq(streamScalar(a, i), batch[i]), `${name} row ${i}`);
}
});
}
function checkMatrix(name, make, k, pick) {
test(`${name} streaming equals batch`, () => {
const a = make();
const b = make();
const batch = b.batch(open, high, low, close, volume, ts);
for (let i = 0; i < N; i += 1) {
const out = a.update(open[i], high[i], low[i], close[i], volume[i], ts[i]);
for (let j = 0; j < k; j += 1) {
const s = out === null || out === undefined ? NaN : pick(out, j);
assert.ok(eq(s, batch[i * k + j]), `${name} row ${i} col ${j}`);
}
}
});
}
checkScalar('SessionVwap', () => new wickra.SessionVwap(0));
checkScalar('OvernightGap', () => new wickra.OvernightGap(0));
checkScalar('SeasonalZScore', () => new wickra.SeasonalZScore(0));
checkScalar('AverageDailyRange', () => new wickra.AverageDailyRange(3, 0));
checkScalar('TurnOfMonth', () => new wickra.TurnOfMonth(3, 1, 0));
checkMatrix('SessionHighLow', () => new wickra.SessionHighLow(0), 2, (o, j) => (j === 0 ? o.high : o.low));
checkMatrix('SessionRange', () => new wickra.SessionRange(0), 3, (o, j) => [o.asia, o.eu, o.us][j]);
checkMatrix(
'OvernightIntradayReturn',
() => new wickra.OvernightIntradayReturn(0),
2,
(o, j) => (j === 0 ? o.overnight : o.intraday),
);
checkMatrix('TimeOfDayReturnProfile', () => new wickra.TimeOfDayReturnProfile(24, 0), 24, (o, j) => o[j]);
checkMatrix('IntradayVolatilityProfile', () => new wickra.IntradayVolatilityProfile(12, 0), 12, (o, j) => o[j]);
checkMatrix('VolumeByTimeProfile', () => new wickra.VolumeByTimeProfile(24, 0), 24, (o, j) => o[j]);
checkMatrix('DayOfWeekProfile', () => new wickra.DayOfWeekProfile(0), 7, (o, j) => o[j]);
test('SessionVwap reference value', () => {
const vwap = new wickra.SessionVwap(0);
assert.ok(eq(vwap.update(100, 100, 100, 100, 10, 0), 100));
assert.ok(eq(vwap.update(110, 110, 110, 110, 30, HOUR), 107.5));
assert.ok(eq(vwap.update(200, 200, 200, 200, 5, 24 * HOUR), 200));
});
test('OvernightGap reference value', () => {
const gap = new wickra.OvernightGap(0);
assert.equal(gap.update(99, 101, 98, 100, 1, 0), null);
assert.ok(eq(gap.update(105, 106, 104, 105.5, 1, 24 * HOUR), 0.05));
});
test('SessionHighLow reference object', () => {
const shl = new wickra.SessionHighLow(0);
shl.update(100, 105, 99, 101, 1, 0);
const out = shl.update(101, 108, 100, 107, 1, HOUR);
assert.ok(eq(out.high, 108));
assert.ok(eq(out.low, 99));
});
test('AverageDailyRange rejects zero period', () => {
assert.throws(() => new wickra.AverageDailyRange(0, 0));
});
+6 -6
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@@ -73,10 +73,10 @@ test('ATR batch shape', () => {
}
});
test('zero period is clamped to a valid window', () => {
// Constructors cannot throw from JS (napi-rs 2.16 limitation), so they
// clamp pathological values like period=0 to the smallest valid window.
const sma = new wickra.SMA(0);
assert.equal(sma.warmupPeriod(), 1);
assert.equal(sma.update(42), 42);
test('zero period is rejected at construction', () => {
// The core rejects period 0 (Error::PeriodZero); the Node binding propagates
// it as a thrown JS error, consistent with the Python and WASM bindings.
assert.throws(() => new wickra.SMA(0), /period must be greater than zero/);
// A valid period still constructs and runs.
assert.equal(new wickra.SMA(1).update(42), 42);
});
+99
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@@ -0,0 +1,99 @@
// Throughput benchmark for the Wickra Node bindings.
//
// Measures how many indicator updates per second the native binding sustains,
// both per-tick (streaming `update`) and bulk (`batch`), over a synthetic
// OHLCV series. It is the Node counterpart of the Rust criterion benches and
// the Python `benchmarks/compare_libraries.py`; it benchmarks Wickra's own
// O(1) streaming engine (there is no install-free TA library on npm with a
// comparable surface to compare against), so the headline number is raw
// throughput, not a cross-library ratio.
//
// Run after building the binding:
//
// cd bindings/node && npm install && npx napi build --platform --release
// node benchmarks/throughput.js # 200k bars (default)
// node benchmarks/throughput.js --bars 1000000
const wickra = require('..');
function parseBars() {
const idx = process.argv.indexOf('--bars');
if (idx !== -1 && process.argv[idx + 1]) {
const n = Number(process.argv[idx + 1]);
if (Number.isFinite(n) && n >= 1000) return Math.floor(n);
console.error('--bars must be a number >= 1000');
process.exit(1);
}
return 200_000;
}
const BARS = parseBars();
// Deterministic synthetic OHLCV (no RNG, so runs are comparable).
const close = new Array(BARS);
const high = new Array(BARS);
const low = new Array(BARS);
const volume = new Array(BARS);
for (let i = 0; i < BARS; i++) {
const mid = 100 + Math.sin(i * 0.001) * 20 + i * 1e-4;
close[i] = mid + Math.sin(i * 0.05) * 2;
high[i] = Math.max(close[i], mid) + 1.5;
low[i] = Math.min(close[i], mid) - 1.5;
volume[i] = 1000 + (i % 97) * 13;
}
// Median elapsed-ns over a few repetitions, after one warmup pass.
function timeNs(fn, reps = 3) {
fn(); // warmup (JIT + cache)
const samples = [];
for (let r = 0; r < reps; r++) {
const t0 = process.hrtime.bigint();
fn();
samples.push(Number(process.hrtime.bigint() - t0));
}
samples.sort((a, b) => a - b);
return samples[Math.floor(samples.length / 2)];
}
function mupsFromNs(ns) {
return (BARS / (ns / 1e9)) / 1e6; // million updates per second
}
// Each indicator: a streaming step and a batch call over the full series.
const indicators = [
{ name: 'SMA(20)', make: () => new wickra.SMA(20), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'EMA(20)', make: () => new wickra.EMA(20), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'RSI(14)', make: () => new wickra.RSI(14), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'StdDev(20)', make: () => new wickra.StdDev(20), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'MACD(12,26,9)', make: () => new wickra.MACD(12, 26, 9), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'BollingerBands(20,2)', make: () => new wickra.BollingerBands(20, 2), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'KAMA(10,2,30)', make: () => new wickra.KAMA(10, 2, 30), step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
{ name: 'ATR(14)', make: () => new wickra.ATR(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
{ name: 'ADX(14)', make: () => new wickra.ADX(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
{ name: 'Stochastic(14,3)', make: () => new wickra.Stochastic(14, 3), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
{ name: 'SuperTrend(10,3)', make: () => new wickra.SuperTrend(10, 3), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
{ name: 'OBV', make: () => new wickra.OBV(), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
];
console.log(`Wickra Node throughput — ${BARS.toLocaleString('en-US')} bars (median of 3 runs)\n`);
console.log(`${'Indicator'.padEnd(22)}${'streaming (Mupd/s)'.padStart(20)}${'batch (Mupd/s)'.padStart(18)}`);
console.log('-'.repeat(60));
for (const ind of indicators) {
const streamNs = timeNs(() => {
const inst = ind.make();
for (let i = 0; i < BARS; i++) ind.step(inst, i);
});
const batchNs = timeNs(() => {
ind.batch(ind.make());
});
console.log(
`${ind.name.padEnd(22)}${mupsFromNs(streamNs).toFixed(1).padStart(20)}${mupsFromNs(batchNs).toFixed(1).padStart(18)}`,
);
}
console.log(
'\nMupd/s = million indicator updates per second. Streaming is the per-tick\n' +
'`update` path (one value at a time); batch is the bulk array path. Higher is\n' +
'better. Numbers are machine-dependent — use them for relative comparison.',
);
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+391 -1
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+4 -4
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@@ -1,12 +1,12 @@
{
"name": "wickra-darwin-arm64",
"version": "0.2.5",
"version": "0.6.5",
"description": "Native binding for wickra (macOS Apple Silicon). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.darwin-arm64.node",
"files": [
"wickra.darwin-arm64.node"
],
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
@@ -18,7 +18,7 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/kingchenc/wickra"
"homepage": "https://github.com/wickra-lib/wickra"
}
+4 -4
View File
@@ -1,12 +1,12 @@
{
"name": "wickra-darwin-x64",
"version": "0.2.5",
"version": "0.6.5",
"description": "Native binding for wickra (macOS Intel). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.darwin-x64.node",
"files": [
"wickra.darwin-x64.node"
],
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
@@ -18,7 +18,7 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/kingchenc/wickra"
"homepage": "https://github.com/wickra-lib/wickra"
}
@@ -1,12 +1,12 @@
{
"name": "wickra-linux-arm64-gnu",
"version": "0.2.5",
"version": "0.6.5",
"description": "Native binding for wickra (linux arm64 GNU). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.linux-arm64-gnu.node",
"files": [
"wickra.linux-arm64-gnu.node"
],
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
@@ -21,7 +21,7 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/kingchenc/wickra"
"homepage": "https://github.com/wickra-lib/wickra"
}
+4 -4
View File
@@ -1,12 +1,12 @@
{
"name": "wickra-linux-x64-gnu",
"version": "0.2.5",
"version": "0.6.5",
"description": "Native binding for wickra (linux x64 GNU). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.linux-x64-gnu.node",
"files": [
"wickra.linux-x64-gnu.node"
],
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
@@ -21,7 +21,7 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/kingchenc/wickra"
"homepage": "https://github.com/wickra-lib/wickra"
}
@@ -0,0 +1,24 @@
{
"name": "wickra-win32-arm64-msvc",
"version": "0.6.5",
"description": "Native binding for wickra (Windows arm64 MSVC). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.win32-arm64-msvc.node",
"files": [
"wickra.win32-arm64-msvc.node"
],
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
"os": [
"win32"
],
"cpu": [
"arm64"
],
"repository": {
"type": "git",
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/wickra-lib/wickra"
}
@@ -1,12 +1,12 @@
{
"name": "wickra-win32-x64-msvc",
"version": "0.2.5",
"version": "0.6.5",
"description": "Native binding for wickra (Windows x64 MSVC). Installed automatically as an optional dependency of wickra on matching platforms.",
"main": "wickra.win32-x64-msvc.node",
"files": [
"wickra.win32-x64-msvc.node"
],
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"engines": {
"node": ">= 18"
},
@@ -18,7 +18,7 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"homepage": "https://github.com/kingchenc/wickra"
"homepage": "https://github.com/wickra-lib/wickra"
}
+140
View File
@@ -0,0 +1,140 @@
{
"name": "wickra",
"version": "0.6.5",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "wickra",
"version": "0.6.5",
"license": "MIT OR Apache-2.0",
"devDependencies": {
"@napi-rs/cli": "^2.18.0"
},
"engines": {
"node": ">= 18"
},
"optionalDependencies": {
"wickra-darwin-arm64": "0.6.5",
"wickra-darwin-x64": "0.6.5",
"wickra-linux-arm64-gnu": "0.6.5",
"wickra-linux-x64-gnu": "0.6.5",
"wickra-win32-arm64-msvc": "0.6.5",
"wickra-win32-x64-msvc": "0.6.5"
}
},
"node_modules/@napi-rs/cli": {
"version": "2.18.4",
"resolved": "https://registry.npmjs.org/@napi-rs/cli/-/cli-2.18.4.tgz",
"integrity": "sha512-SgJeA4df9DE2iAEpr3M2H0OKl/yjtg1BnRI5/JyowS71tUWhrfSu2LT0V3vlHET+g1hBVlrO60PmEXwUEKp8Mg==",
"dev": true,
"license": "MIT",
"bin": {
"napi": "scripts/index.js"
},
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/wickra-darwin-arm64": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-darwin-arm64/-/wickra-darwin-arm64-0.6.5.tgz",
"integrity": "sha512-4eZiBR/yGUdr4nzhEUFy2i69XgNx64iI2ax/LPamsThgylC0KpHOZKK19QzJ2d9KbK4C8nMjME5FLuR+4GNEwQ==",
"cpu": [
"arm64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"darwin"
],
"engines": {
"node": ">= 18"
}
},
"node_modules/wickra-darwin-x64": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-darwin-x64/-/wickra-darwin-x64-0.6.5.tgz",
"integrity": "sha512-6hf8zI3QPjTFp4zCpmgUwDvNtu6jHqNUHKD5e55POo0CgA52HkpyxSPtVm8TGTIZDI7kPjlbOdBM8CJ76mmXwA==",
"cpu": [
"x64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"darwin"
],
"engines": {
"node": ">= 18"
}
},
"node_modules/wickra-linux-arm64-gnu": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-linux-arm64-gnu/-/wickra-linux-arm64-gnu-0.6.5.tgz",
"integrity": "sha512-kSe6y0xBMSiqdPLXNjwop5WZdHtvdBNKSEBCwZ4hFq33p4apW25/wrlzv9/oDuyD4kuPabJEhCCnFOplh58CUg==",
"cpu": [
"arm64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 18"
}
},
"node_modules/wickra-linux-x64-gnu": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-linux-x64-gnu/-/wickra-linux-x64-gnu-0.6.5.tgz",
"integrity": "sha512-tWBWS4qz7hxM4xnpFb59bhf6TaLwXq0Z3jEa/2l7r8PiHA94g8r8S53NRMiT+4yiL5hSWe/nUiC/YXdRrhEZ4g==",
"cpu": [
"x64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 18"
}
},
"node_modules/wickra-win32-arm64-msvc": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-win32-arm64-msvc/-/wickra-win32-arm64-msvc-0.6.5.tgz",
"integrity": "sha512-EXIckHxAtF75PUGDKRzXyqMe9ldP0JjSdu68WFN6iJfp+McYrGu6h40TEJlQ/oUEIoPqiZB/xhVyo/el5Lg7zw==",
"cpu": [
"arm64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 18"
}
},
"node_modules/wickra-win32-x64-msvc": {
"version": "0.6.5",
"resolved": "https://registry.npmjs.org/wickra-win32-x64-msvc/-/wickra-win32-x64-msvc-0.6.5.tgz",
"integrity": "sha512-Yfsqq1Xwp6hdxMyLze411vNdo7BDwI6+lPSe7A9XdqyPecNDbtKwYLpsal2r8EHbNzqM+R8XnuRtUaEQS5VlUQ==",
"cpu": [
"x64"
],
"license": "MIT OR Apache-2.0",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 18"
}
}
}
}
+16 -13
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@@ -1,11 +1,11 @@
{
"name": "wickra",
"version": "0.2.5",
"version": "0.6.5",
"description": "Streaming-first technical indicators: incremental, fast, install-free. Node bindings powered by Rust.",
"author": "kingchenc <kingchencp@gmail.com>",
"author": "kingchenc <support@wickra.org>",
"main": "index.js",
"types": "index.d.ts",
"license": "PolyForm-Noncommercial-1.0.0",
"license": "MIT OR Apache-2.0",
"keywords": [
"trading",
"indicators",
@@ -17,12 +17,12 @@
],
"repository": {
"type": "git",
"url": "https://github.com/kingchenc/wickra"
"url": "https://github.com/wickra-lib/wickra"
},
"bugs": {
"url": "https://github.com/kingchenc/wickra/issues"
"url": "https://github.com/wickra-lib/wickra/issues"
},
"homepage": "https://github.com/kingchenc/wickra",
"homepage": "https://github.com/wickra-lib/wickra",
"files": [
"index.js",
"index.d.ts",
@@ -38,7 +38,8 @@
"aarch64-unknown-linux-gnu",
"x86_64-apple-darwin",
"aarch64-apple-darwin",
"x86_64-pc-windows-msvc"
"x86_64-pc-windows-msvc",
"aarch64-pc-windows-msvc"
]
}
},
@@ -46,11 +47,12 @@
"node": ">= 18"
},
"optionalDependencies": {
"wickra-linux-x64-gnu": "0.2.5",
"wickra-linux-arm64-gnu": "0.2.5",
"wickra-darwin-x64": "0.2.5",
"wickra-darwin-arm64": "0.2.5",
"wickra-win32-x64-msvc": "0.2.5"
"wickra-linux-x64-gnu": "0.6.5",
"wickra-linux-arm64-gnu": "0.6.5",
"wickra-darwin-x64": "0.6.5",
"wickra-darwin-arm64": "0.6.5",
"wickra-win32-x64-msvc": "0.6.5",
"wickra-win32-arm64-msvc": "0.6.5"
},
"scripts": {
"build": "napi build --platform --release",
@@ -58,7 +60,8 @@
"artifacts": "napi artifacts",
"universal": "napi universal",
"version": "napi version",
"test": "node --test __tests__/"
"test": "node --test __tests__/",
"bench": "node benchmarks/throughput.js"
},
"devDependencies": {
"@napi-rs/cli": "^2.18.0"
+14838 -25
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+41 -277
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@@ -1,306 +1,70 @@
# Wickra
# Wickra — Python
[![CI](https://github.com/kingchenc/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/kingchenc/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/kingchenc/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/kingchenc/wickra)
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**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators for Python. `pip install wickra` — no
system dependencies, no C build tooling.**
Wickra is a multi-language technical-analysis library with a Rust core and
bindings for Python, Node.js, and WebAssembly. Every indicator is a state
machine that updates in O(1) per new data point, so live trading bots and
historical backtests share the exact same implementation.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading bots and historical backtests share
the exact same implementation. This package is the Python binding (PyO3); it
exposes 200+ streaming-first indicators across sixteen families.
## Install
```bash
pip install wickra
```
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to
compile and no C library to track down.
## Quick start
```python
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage
# Batch: classic TA-Lib-style usage over a whole array.
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: same indicator, fed tick by tick
# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # O(1) — no recomputation over history
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
```
## Why Wickra exists
`batch(prices)` and feeding the same prices through `update()` produce
identical values — the equivalence is enforced by the test suite.
The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
talipp, tulipy — and every one of them shares the same blind spot:
## Documentation
| Library | Install pain | Streaming | Multi-language | Active |
|--------------------|-----------------|-----------|----------------|--------|
| TA-Lib (Python) | yes (C deps) | no | no | barely |
| pandas-ta | clean | no | no | slow |
| finta | clean | no | no | stale |
| ta-lib-python | yes (C deps) | no | no | barely |
| talipp | clean | yes | no | yes |
| Tulip Indicators | yes (C deps) | no | partial | stale |
| ooples (C#) | clean | no | C# only | yes |
| **Wickra** | **clean** | **yes** | **Python+Node+WASM+Rust** | **yes** |
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and documentation site:
Wickra is the only library that combines all of: clean install, streaming,
multi-language reach, and active maintenance.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Docs** (quickstarts, cookbook, TA-Lib migration): <https://docs.wickra.org>
- **Runnable examples:** [`examples/python/`](https://github.com/wickra-lib/wickra/tree/main/examples/python)
## Benchmark: how much faster is "streaming-first"?
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
The numbers below were measured on a single developer workstation and are not
guaranteed to reproduce identically on different hardware — absolute µs values
depend on CPU, memory clock and OS scheduler. Read them as **relative
speedups** between libraries on identical input, not as a universal
performance contract.
## Disclaimer
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 7950X3D, 64 GB DDR5,
Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
Python 3.12, Node 20.
- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries`. The script auto-detects every
installed peer library and runs them on the same generated inputs as
Wickra. The CI job `cross-library-bench` runs the same script on every
push and uploads the raw report as a build artefact.
Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
### Batch — single full pass over a 20 000-bar series
Reading the table: each cell shows that library's runtime, plus how many times
slower it is than Wickra in parentheses. **★** marks the winner per row.
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### Streaming — per-tick latency after seeding with 5 000 historical bars
A batch-only library has to re-run its full indicator over the entire history on
every new tick; Wickra updates state in O(1).
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
> TA-Lib and pandas-ta are not included here because both fail to install
> cleanly on Windows without C build tooling — which is precisely the install
> pain Wickra was built to remove. The benchmark script auto-detects every
> peer library it can find and runs them on the same inputs as Wickra; install
> them in your environment to see those rows light up too.
Run the suite yourself:
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
```
## Indicators
71 streaming-first indicators across eight families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator |
| Trend & Directional | MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility |
| Trailing Stops | Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle |
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
## Languages
| Binding | Install | Example |
|-------------------|-----------------------------------------------|---------|
| Python (PyO3) | `pip install wickra` | `examples/python/backtest.py` |
| Node.js (napi-rs) | `npm install wickra` | `examples/node/backtest.js` |
| Browser / WASM | `npm install wickra-wasm` | `examples/wasm/index.html` |
| Rust | `cargo add wickra` | `examples/rust/src/bin/backtest.rs` |
Each binding ships several runnable examples (streaming, backtest, live feed);
[`examples/README.md`](examples/README.md) is the full cross-language index.
The wickra-core crate is `unsafe`-forbidden, so every binding inherits a
memory-safe implementation.
## Rust API
```rust
use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};
// Streaming or batch — same trait, same code.
let mut sma = Sma::new(14)?;
let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
let mut rsi = Rsi::new(14)?;
for price in live_feed {
if let Some(v) = rsi.update(price) {
println!("RSI = {v}");
}
}
// Compose indicators: RSI(7) on top of EMA(14).
let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
chain.update(price);
```
## Live data sources
`wickra-data` (separate crate, opt-in) ships:
- A streaming OHLCV **CSV reader**.
- A **tick-to-candle aggregator** with arbitrary timeframes.
- A **candle resampler** for multi-timeframe analysis (1m → 5m → 1h on the fly).
- A **Binance Spot WebSocket** kline adapter (feature `live-binance`).
```rust
use wickra::{Indicator, Rsi};
use wickra_data::live::binance::{BinanceKlineStream, Interval};
let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
let mut rsi = Rsi::new(14)?;
while let Some(event) = stream.next_event().await? {
if event.is_closed {
if let Some(v) = rsi.update(event.candle.close) {
println!("RSI = {v:.2}");
}
}
}
```
A Python live-trading example using the public `websockets` package lives at
`examples/python/live_trading.py`.
## Project layout
```
wickra/
├── crates/
│ ├── wickra-core/ core engine + all 71 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
│ └── wickra-data/ CSV reader, tick aggregator, live exchange feeds
├── bindings/
│ ├── python/ PyO3 + maturin (publishes on PyPI)
│ ├── node/ napi-rs (publishes on npm)
│ └── wasm/ wasm-bindgen (browsers, bundlers, Node)
├── examples/ examples/README.md indexes every language
│ ├── data/ real BTCUSDT OHLCV datasets, one per timeframe
│ ├── rust/ Rust workspace member (`wickra-examples`)
│ ├── python/ backtest, live trading, parallel assets, multi-tf
│ ├── node/ streaming, backtest, live trading (load `wickra`)
│ └── wasm/ browser demo for `wickra-wasm`
└── .github/workflows/ CI and release pipelines
```
Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
in the workspace member crate at `examples/rust/`. There is no top-level
`benches/` directory.
## Building everything from source
```bash
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
# Python binding (requires Rust toolchain + maturin)
cd bindings/python
maturin develop --release
pytest
# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
wasm-pack build bindings/wasm --target web --release --features panic-hook
# Node binding (requires @napi-rs/cli)
cd bindings/node && npm install && npm run build && npm test
```
## Testing
Every layer is covered; run the suites with the commands in
[Building everything from source](#building-everything-from-source).
- `wickra-core`: unit tests per indicator — textbook reference values
(Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), `batch == streaming`
equivalence, `reset` semantics, NaN/Inf handling, and property tests.
- `wickra-data`: unit tests for CSV decoding, the tick aggregator, the
resampler, and the Binance payload parser.
- `bindings/python`: pytest covering smoke checks, streaming/batch
equivalence, reference values, lifecycle, input validation, and
dict/tuple candle inputs.
- `bindings/node`: `node --test` cases for batch, streaming, and reference
values across all indicators.
- `bindings/wasm`: `wasm-bindgen-test` cases for constructors, equivalence,
and reference values.
## Contributing
Contributions are very welcome — issues, bug reports, ideas, and pull requests
all land in the same place: <https://github.com/kingchenc/wickra>.
A short orientation for first-time contributors:
- **Adding an indicator.** Implement the `Indicator` trait in
`crates/wickra-core/src/indicators/<name>.rs`, wire it into
`indicators/mod.rs` and the crate root, and add reference-value tests,
a `batch == streaming` equivalence test, and (where it makes sense) a
proptest. The four bindings inherit your indicator automatically once
you expose it in the language wrappers.
- **Fixing a numeric bug.** Add a failing test that pins the textbook value
first, then fix the math. Property tests in `crates/wickra-core` catch
most regressions; please don't disable them.
- **Improving a binding.** Each binding lives under `bindings/<lang>` with
its own tests; please keep the `batch == streaming` invariant.
- **Style.** `cargo fmt --all` + `cargo clippy --workspace --all-targets -- -D warnings`
are CI gates; running them locally before pushing keeps reviews short.
For larger architectural changes, open an issue first so we can sketch the
shape together before you invest the time.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](LICENSE).
In plain English: use it, fork it, modify it, redistribute it, file issues, send
pull requests — all welcome. Personal projects, research, education, non-profits,
government, hobby trading bots: all fine. The one thing that's not allowed is
commercial sale of the software or of services built around it. If you want to
use Wickra commercially, get in touch about a license.
---
<p align="center">
<a href="https://github.com/kingchenc/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/kingchenc/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/kingchenc/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
Licensed under either of [Apache-2.0](https://github.com/wickra-lib/wickra/blob/main/LICENSE-APACHE)
or [MIT](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT) at your option.
@@ -49,6 +49,7 @@ TALIB = _try_import("talib")
PANDAS_TA = _try_import("pandas_ta")
TALIPP = _try_import("talipp.indicators") or _try_import("talipp")
FINTA = _try_import("finta")
TULIPY = _try_import("tulipy")
PD = _try_import("pandas")
import wickra as WICKRA # noqa: E402 -- the library under test must be importable
@@ -275,6 +276,34 @@ def talipp_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return lambda: BB(period=20, std_dev_mult=2.0, input_values=list(prices))
# tulipy wraps the C "Tulip Indicators" library; it takes contiguous float64
# arrays and indicator options as positional arguments.
def tulipy_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.sma(prices, 20))
def tulipy_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.ema(prices, 20))
def tulipy_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.rsi(prices, 14))
def tulipy_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.macd(prices, 12, 26, 9))
def tulipy_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.bbands(prices, 20, 2.0))
def tulipy_atr_batch(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> Optional[Callable[[], None]]:
return None if TULIPY is None else (lambda: TULIPY.atr(high, low, close, 14))
# --------------------------------------------------------------------------- #
# Streaming scenario: per-tick latency
# --------------------------------------------------------------------------- #
@@ -329,6 +358,105 @@ def talipp_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callabl
return run
# Scalar streaming peers: Wickra and talipp both update incrementally in O(1),
# so this is the like-for-like per-tick comparison (batch-only libs are covered
# by the batch tables and the recompute contrast on RSI above).
def wickra_sma_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
def run() -> None:
sma = WICKRA.SMA(20)
sma.batch(seed)
for p in live:
sma.update(float(p))
return run
def talipp_sma_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
if TALIPP is None:
return None
from talipp.indicators import SMA # type: ignore
def run() -> None:
sma = SMA(period=20, input_values=list(seed))
for p in live:
sma.add(float(p))
return run
def wickra_ema_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
def run() -> None:
ema = WICKRA.EMA(20)
ema.batch(seed)
for p in live:
ema.update(float(p))
return run
def talipp_ema_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
if TALIPP is None:
return None
from talipp.indicators import EMA # type: ignore
def run() -> None:
ema = EMA(period=20, input_values=list(seed))
for p in live:
ema.add(float(p))
return run
def wickra_macd_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
def run() -> None:
macd = WICKRA.MACD()
macd.batch(seed)
for p in live:
macd.update(float(p))
return run
def talipp_macd_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
if TALIPP is None:
return None
from talipp.indicators import MACD # type: ignore
def run() -> None:
macd = MACD(
fast_period=12, slow_period=26, signal_period=9, input_values=list(seed)
)
for p in live:
macd.add(float(p))
return run
def wickra_bollinger_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
def run() -> None:
bb = WICKRA.BollingerBands(20, 2.0)
bb.batch(seed)
for p in live:
bb.update(float(p))
return run
def talipp_bollinger_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
if TALIPP is None:
return None
from talipp.indicators import BB # type: ignore
def run() -> None:
bb = BB(period=20, std_dev_mult=2.0, input_values=list(seed))
for p in live:
bb.add(float(p))
return run
# --------------------------------------------------------------------------- #
# Runner
# --------------------------------------------------------------------------- #
@@ -339,6 +467,7 @@ BATCH_INDICATORS = [
("Wickra", wickra_sma_batch),
("TA-Lib", talib_sma_batch),
("pandas-ta", pandas_ta_sma_batch),
("tulipy", tulipy_sma_batch),
("finta", finta_sma_batch),
("talipp", talipp_sma_batch),
]),
@@ -346,6 +475,7 @@ BATCH_INDICATORS = [
("Wickra", wickra_ema_batch),
("TA-Lib", talib_ema_batch),
("pandas-ta", pandas_ta_ema_batch),
("tulipy", tulipy_ema_batch),
("finta", finta_ema_batch),
("talipp", talipp_ema_batch),
]),
@@ -353,6 +483,7 @@ BATCH_INDICATORS = [
("Wickra", wickra_rsi_batch),
("TA-Lib", talib_rsi_batch),
("pandas-ta", pandas_ta_rsi_batch),
("tulipy", tulipy_rsi_batch),
("finta", finta_rsi_batch),
("talipp", talipp_rsi_batch),
]),
@@ -360,6 +491,7 @@ BATCH_INDICATORS = [
("Wickra", wickra_macd_batch),
("TA-Lib", talib_macd_batch),
("pandas-ta", pandas_ta_macd_batch),
("tulipy", tulipy_macd_batch),
("finta", finta_macd_batch),
("talipp", talipp_macd_batch),
]),
@@ -367,6 +499,7 @@ BATCH_INDICATORS = [
("Wickra", wickra_bollinger_batch),
("TA-Lib", talib_bollinger_batch),
("pandas-ta", pandas_ta_bollinger_batch),
("tulipy", tulipy_bollinger_batch),
("finta", finta_bollinger_batch),
("talipp", talipp_bollinger_batch),
]),
@@ -376,18 +509,35 @@ OHLC_INDICATORS = [
("ATR(14)", [
("Wickra", wickra_atr_batch),
("TA-Lib", talib_atr_batch),
("tulipy", tulipy_atr_batch),
("finta", finta_atr_batch),
("talipp", talipp_atr_batch),
]),
]
STREAMING_INDICATORS = [
("SMA(20)", [
("Wickra", wickra_sma_streaming),
("talipp", talipp_sma_streaming),
]),
("EMA(20)", [
("Wickra", wickra_ema_streaming),
("talipp", talipp_ema_streaming),
]),
("RSI(14)", [
("Wickra", wickra_rsi_streaming),
("TA-Lib", talib_rsi_streaming),
("pandas-ta", pandas_ta_rsi_streaming),
("talipp", talipp_rsi_streaming),
]),
("MACD(12, 26, 9)", [
("Wickra", wickra_macd_streaming),
("talipp", talipp_macd_streaming),
]),
("Bollinger(20, 2.0)", [
("Wickra", wickra_bollinger_streaming),
("talipp", talipp_bollinger_streaming),
]),
]
@@ -501,6 +651,7 @@ def main() -> None:
available = []
if TALIB is not None: available.append("TA-Lib")
if PANDAS_TA is not None: available.append("pandas-ta")
if TULIPY is not None: available.append("tulipy")
if FINTA is not None: available.append("finta")
if TALIPP is not None: available.append("talipp")
print(f"Wickra benchmark suite — wickra=v{WICKRA.__version__}")
+7 -7
View File
@@ -4,17 +4,16 @@ build-backend = "maturin"
[project]
name = "wickra"
version = "0.2.5"
version = "0.6.5"
description = "Streaming-first technical indicators: incremental, fast, install-free."
readme = "README.md"
license = { text = "PolyForm-Noncommercial-1.0.0" }
authors = [{ name = "kingchenc", email = "kingchencp@gmail.com" }]
license = "MIT OR Apache-2.0"
authors = [{ name = "kingchenc", email = "support@wickra.org" }]
requires-python = ">=3.9"
keywords = ["finance", "trading", "indicators", "technical-analysis", "ta-lib"]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Financial and Insurance Industry",
"License :: Free for non-commercial use",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.9",
@@ -40,6 +39,7 @@ bench = [
"pytest-benchmark>=4",
"TA-Lib; platform_system != 'Windows'",
"pandas-ta>=0.3.14b",
"tulipy>=0.4; platform_system != 'Windows'",
"talipp>=2",
"finta>=1.3",
"pandas>=2",
@@ -47,9 +47,9 @@ bench = [
]
[project.urls]
Homepage = "https://github.com/kingchenc/wickra"
Repository = "https://github.com/kingchenc/wickra"
Issues = "https://github.com/kingchenc/wickra/issues"
Homepage = "https://github.com/wickra-lib/wickra"
Repository = "https://github.com/wickra-lib/wickra"
Issues = "https://github.com/wickra-lib/wickra/issues"
[tool.maturin]
manifest-path = "Cargo.toml"
+822
View File
@@ -25,6 +25,80 @@ from __future__ import annotations
from ._wickra import (
__version__,
AUTOCORRPGRAM,
EVENBETTERSINE,
BANDPASS,
ADAPTIVECCI,
UNIVERSALOSC,
ADAPTIVERSI,
CTI,
TRENDFLEX,
REFLEX,
HIGHPASS,
SAMPLEENT,
SHANNONENT,
ROLLINGMINMAX,
JARQUEBERA,
TimeBasedStop,
ProjectionOscillator,
VolatilityCone,
VolatilityRatio,
BipowerVariation,
VolatilityOfVolatility,
Garch11,
EwmaVolatility,
PpoHistogram,
MacdHistogram,
TsfOscillator,
Qstick,
GatorOscillator,
KasePermissionStochastic,
WAVE_PM,
POLARIZED_FRACTAL_EFFICIENCY,
TREND_STRENGTH_INDEX,
TTM_TREND,
QQE,
IMI,
ElderRay,
DerivativeOscillator,
RMI,
StochasticCCI,
DynamicMomentumIndex,
RSX,
FisherRSI,
DisparityIndex,
HoltWinters,
GD,
AdaptiveLaguerre,
MedianMA,
EHMA,
GMA,
SWMA,
Expectancy,
WinRate,
RegimeLabel,
JumpIndicator,
TrendLabel,
HighLowRange,
WickRatio,
BodySizePct,
CloseVsOpen,
RollingQuantile,
RollingPercentileRank,
RollingIqr,
RealizedVolatility,
LogReturn,
TSF,
LINEARREG_INTERCEPT,
ROCR100,
ROCR,
ROCP,
AVGPRICE,
MIDPOINT,
MIDPRICE,
DX,
MINUS_DI,
PLUS_DI,
# Trend
SMA,
EMA,
@@ -38,14 +112,27 @@ from ._wickra import (
ZLEMA,
T3,
VWMA,
ALMA,
McGinleyDynamic,
FRAMA,
VIDYA,
JMA,
Alligator,
EVWMA,
# Momentum
RSI,
AnchoredRSI,
MACD,
MACDFIX,
MACDEXT,
Stochastic,
CCI,
ROC,
WilliamsR,
ADX,
ADXR,
PLUS_DM,
MINUS_DM,
MFI,
TRIX,
AwesomeOscillator,
@@ -54,13 +141,30 @@ from ._wickra import (
CMO,
TSI,
PMO,
TII,
KST,
StochRSI,
UltimateOscillator,
RVI,
PGO,
KST,
SMI,
LaguerreRSI,
ConnorsRSI,
Inertia,
APO,
AwesomeOscillatorHistogram,
CFO,
ZeroLagMACD,
ElderImpulse,
STC,
PPO,
DPO,
Coppock,
AroonOscillator,
Vortex,
RWI,
WaveTrend,
MassIndex,
AcceleratorOscillator,
BalanceOfPower,
@@ -72,19 +176,45 @@ from ._wickra import (
Keltner,
Donchian,
PSAR,
SAREXT,
NATR,
StdDev,
UlcerIndex,
HistoricalVolatility,
BollingerBandwidth,
PercentB,
# Trailing Stops
ModifiedMaStop,
Nrtr,
AtrRatchet,
ElderSafeZone,
SuperTrend,
ChandelierExit,
ChandeKrollStop,
AtrTrailingStop,
HiLoActivator,
VoltyStop,
YoyoExit,
DonchianStop,
PercentageTrailingStop,
StepTrailingStop,
RenkoTrailingStop,
KaseDevStop,
TrueRange,
ChaikinVolatility,
RVIVolatility,
ParkinsonVolatility,
GarmanKlassVolatility,
RogersSatchellVolatility,
YangZhangVolatility,
# Volume
VolumeWeightedMacd,
BetterVolume,
IntradayIntensity,
TradeVolumeIndex,
TwiggsMoneyFlow,
Wad,
VolumeRsi,
OBV,
VWAP,
RollingVWAP,
@@ -93,8 +223,29 @@ from ._wickra import (
ChaikinMoneyFlow,
ChaikinOscillator,
ForceIndex,
KVO,
VolumeOscillator,
NVI,
PVI,
WilliamsAD,
AnchoredVWAP,
DemandIndex,
TSV,
VZO,
MarketFacilitationIndex,
EaseOfMovement,
# Statistics
KendallTau,
SpreadBollingerBands,
KalmanHedgeRatio,
GrangerCausality,
VarianceRatio,
BetaNeutralSpread,
DistanceSsd,
SpreadHurst,
OuHalfLife,
RollingCovariance,
RollingCorrelation,
TypicalPrice,
MedianPrice,
WeightedClose,
@@ -102,9 +253,343 @@ from ._wickra import (
LinRegSlope,
ZScore,
LinRegAngle,
Variance,
CoefficientOfVariation,
Skewness,
Kurtosis,
StandardError,
DetrendedStdDev,
RSquared,
Autocorrelation,
MedianAbsoluteDeviation,
HurstExponent,
PearsonCorrelation,
Beta,
PairwiseBeta,
SpreadAr1Coefficient,
PairSpreadZScore,
LeadLagCrossCorrelation,
Cointegration,
RelativeStrengthAB,
SpearmanCorrelation,
# Ehlers / Cycle
SuperSmoother,
FisherTransform,
InverseFisherTransform,
Decycler,
DecyclerOscillator,
RoofingFilter,
CenterOfGravity,
CyberneticCycle,
InstantaneousTrendline,
EhlersStochastic,
EmpiricalModeDecomposition,
HilbertDominantCycle,
HT_DCPHASE,
HT_PHASOR,
HT_TRENDMODE,
AdaptiveCycle,
SineWave,
MAMA,
FAMA,
# Bands & Channels
ProjectionBands,
MedianChannel,
BomarBands,
QuartileBands,
MaEnvelope,
AccelerationBands,
StarcBands,
AtrBands,
HurstChannel,
LinRegChannel,
StandardErrorBands,
DoubleBollinger,
TtmSqueeze,
FractalChaosBands,
VwapStdDevBands,
# Pivots & S/R
ClassicPivots,
FibonacciPivots,
Camarilla,
WoodiePivots,
DemarkPivots,
WilliamsFractals,
ZigZag,
# DeMark
TDSetup,
TDSequential,
TDDeMarker,
TDREI,
TDPressure,
TDCombo,
TDCountdown,
TDLines,
TDRangeProjection,
TDDifferential,
TDOpen,
TDRiskLevel,
# Ichimoku & alternative charts
Ichimoku,
HeikinAshi,
# Market Profile
ValueArea,
VolumeProfile,
TpoProfile,
InitialBalance,
OpeningRange,
# Alt-Chart Bars
RenkoBars,
KagiBars,
PointAndFigureBars,
# Candlestick patterns
Doji,
Hammer,
InvertedHammer,
HangingMan,
ShootingStar,
Engulfing,
Harami,
MorningEveningStar,
ThreeSoldiersOrCrows,
PiercingDarkCloud,
Marubozu,
Tweezer,
SpinningTop,
ThreeInside,
ThreeOutside,
TwoCrows,
UpsideGapTwoCrows,
IdenticalThreeCrows,
ThreeLineStrike,
ThreeStarsInSouth,
AbandonedBaby,
AdvanceBlock,
BeltHold,
Breakaway,
Counterattack,
DojiStar,
DragonflyDoji,
GravestoneDoji,
LongLeggedDoji,
RickshawMan,
EveningDojiStar,
MorningDojiStar,
GapSideBySideWhite,
HighWave,
Hikkake,
HikkakeModified,
HomingPigeon,
OnNeck,
InNeck,
Thrusting,
SeparatingLines,
Kicking,
KickingByLength,
LadderBottom,
MatHold,
MatchingLow,
LongLine,
ShortLine,
RisingThreeMethods,
FallingThreeMethods,
UpsideGapThreeMethods,
DownsideGapThreeMethods,
StalledPattern,
StickSandwich,
Takuri,
ClosingMarubozu,
OpeningMarubozu,
TasukiGap,
UniqueThreeRiver,
ConcealingBabySwallow,
# Chart patterns
CupAndHandle,
RectangleRange,
FlagPennant,
Wedge,
Triangle,
HeadAndShoulders,
TripleTopBottom,
DoubleTopBottom,
# Harmonic patterns
ThreeDrives,
Cypher,
Shark,
Crab,
Bat,
Butterfly,
Gartley,
Abcd,
# Fibonacci
FibTimeZones,
FibChannel,
FibArcs,
FibFan,
FibConfluence,
GoldenPocket,
AutoFib,
FibProjection,
FibExtension,
FibRetracement,
# Microstructure: order book
OrderFlowImbalance,
OrderBookImbalanceTop1,
OrderBookImbalanceTopN,
OrderBookImbalanceFull,
Microprice,
QuotedSpread,
DepthSlope,
# Microstructure: trade flow
RollMeasure,
AmihudIlliquidity,
Vpin,
SignedVolume,
CumulativeVolumeDelta,
TradeImbalance,
# Microstructure: price impact
EffectiveSpread,
RealizedSpread,
KylesLambda,
# Microstructure: footprint
Footprint,
# Derivatives
FundingRate,
FundingRateMean,
FundingRateZScore,
FundingBasis,
OpenInterestDelta,
OIPriceDivergence,
OIWeighted,
LongShortRatio,
TakerBuySellRatio,
LiquidationFeatures,
TermStructureBasis,
CalendarSpread,
# Market Breadth
TickIndex,
AbsoluteBreadthIndex,
CumulativeVolumeIndex,
BullishPercentIndex,
UpDownVolumeRatio,
PercentAboveMa,
HighLowIndex,
NewHighsNewLows,
BreadthThrust,
Trin,
McClellanSummationIndex,
McClellanOscillator,
AdVolumeLine,
AdvanceDeclineRatio,
AdvanceDecline,
# Risk / Performance
SharpeRatio,
SortinoRatio,
CalmarRatio,
OmegaRatio,
MaxDrawdown,
AverageDrawdown,
DrawdownDuration,
PainIndex,
ValueAtRisk,
ConditionalValueAtRisk,
ProfitFactor,
GainLossRatio,
RecoveryFactor,
KellyCriterion,
TreynorRatio,
InformationRatio,
Alpha,
# Seasonality & Session
SessionVwap,
SessionHighLow,
SessionRange,
AverageDailyRange,
OvernightGap,
OvernightIntradayReturn,
TurnOfMonth,
SeasonalZScore,
TimeOfDayReturnProfile,
DayOfWeekProfile,
IntradayVolatilityProfile,
VolumeByTimeProfile,
)
__all__ = [
"AUTOCORRPGRAM",
"EVENBETTERSINE",
"BANDPASS",
"ADAPTIVECCI",
"UNIVERSALOSC",
"ADAPTIVERSI",
"CTI",
"TRENDFLEX",
"REFLEX",
"HIGHPASS",
"SAMPLEENT",
"SHANNONENT",
"ROLLINGMINMAX",
"JARQUEBERA",
"TimeBasedStop",
"ProjectionOscillator",
"VolatilityCone",
"VolatilityRatio",
"BipowerVariation",
"VolatilityOfVolatility",
"Garch11",
"EwmaVolatility",
"PpoHistogram",
"MacdHistogram",
"TsfOscillator",
"Qstick",
"GatorOscillator",
"KasePermissionStochastic",
"WAVE_PM",
"POLARIZED_FRACTAL_EFFICIENCY",
"TREND_STRENGTH_INDEX",
"TTM_TREND",
"QQE",
"IMI",
"ElderRay",
"DerivativeOscillator",
"RMI",
"StochasticCCI",
"DynamicMomentumIndex",
"RSX",
"FisherRSI",
"DisparityIndex",
"HoltWinters",
"GD",
"AdaptiveLaguerre",
"MedianMA",
"EHMA",
"GMA",
"SWMA",
"Expectancy",
"WinRate",
"RegimeLabel",
"JumpIndicator",
"TrendLabel",
"HighLowRange",
"WickRatio",
"BodySizePct",
"CloseVsOpen",
"RollingQuantile",
"RollingPercentileRank",
"RollingIqr",
"RealizedVolatility",
"LogReturn",
"TSF",
"LINEARREG_INTERCEPT",
"ROCR100",
"ROCR",
"ROCP",
"AVGPRICE",
"MIDPOINT",
"MIDPRICE",
"DX",
"MINUS_DI",
"PLUS_DI",
"__version__",
# Trend
"SMA",
@@ -119,14 +604,27 @@ __all__ = [
"ZLEMA",
"T3",
"VWMA",
"ALMA",
"McGinleyDynamic",
"FRAMA",
"VIDYA",
"JMA",
"Alligator",
"EVWMA",
# Momentum
"RSI",
"AnchoredRSI",
"MACD",
"MACDFIX",
"MACDEXT",
"Stochastic",
"CCI",
"ROC",
"WilliamsR",
"ADX",
"ADXR",
"PLUS_DM",
"MINUS_DM",
"MFI",
"TRIX",
"AwesomeOscillator",
@@ -135,13 +633,30 @@ __all__ = [
"CMO",
"TSI",
"PMO",
"TII",
"KST",
"StochRSI",
"UltimateOscillator",
"RVI",
"PGO",
"KST",
"SMI",
"LaguerreRSI",
"ConnorsRSI",
"Inertia",
"APO",
"AwesomeOscillatorHistogram",
"CFO",
"ZeroLagMACD",
"ElderImpulse",
"STC",
"PPO",
"DPO",
"Coppock",
"AroonOscillator",
"Vortex",
"RWI",
"WaveTrend",
"MassIndex",
"AcceleratorOscillator",
"BalanceOfPower",
@@ -153,19 +668,45 @@ __all__ = [
"Keltner",
"Donchian",
"PSAR",
"SAREXT",
"NATR",
"StdDev",
"UlcerIndex",
"HistoricalVolatility",
"BollingerBandwidth",
"PercentB",
# Trailing Stops
"ModifiedMaStop",
"Nrtr",
"AtrRatchet",
"ElderSafeZone",
"SuperTrend",
"ChandelierExit",
"ChandeKrollStop",
"AtrTrailingStop",
"HiLoActivator",
"VoltyStop",
"YoyoExit",
"DonchianStop",
"PercentageTrailingStop",
"StepTrailingStop",
"RenkoTrailingStop",
"KaseDevStop",
"TrueRange",
"ChaikinVolatility",
"RVIVolatility",
"ParkinsonVolatility",
"GarmanKlassVolatility",
"RogersSatchellVolatility",
"YangZhangVolatility",
# Volume
"VolumeWeightedMacd",
"BetterVolume",
"IntradayIntensity",
"TradeVolumeIndex",
"TwiggsMoneyFlow",
"Wad",
"VolumeRsi",
"OBV",
"VWAP",
"RollingVWAP",
@@ -174,8 +715,29 @@ __all__ = [
"ChaikinMoneyFlow",
"ChaikinOscillator",
"ForceIndex",
"KVO",
"VolumeOscillator",
"NVI",
"PVI",
"WilliamsAD",
"AnchoredVWAP",
"DemandIndex",
"TSV",
"VZO",
"MarketFacilitationIndex",
"EaseOfMovement",
# Statistics
"KendallTau",
"SpreadBollingerBands",
"KalmanHedgeRatio",
"GrangerCausality",
"VarianceRatio",
"BetaNeutralSpread",
"DistanceSsd",
"SpreadHurst",
"OuHalfLife",
"RollingCovariance",
"RollingCorrelation",
"TypicalPrice",
"MedianPrice",
"WeightedClose",
@@ -183,4 +745,264 @@ __all__ = [
"LinRegSlope",
"ZScore",
"LinRegAngle",
"Variance",
"CoefficientOfVariation",
"Skewness",
"Kurtosis",
"StandardError",
"DetrendedStdDev",
"RSquared",
"Autocorrelation",
"MedianAbsoluteDeviation",
"HurstExponent",
"PearsonCorrelation",
"Beta",
"PairwiseBeta",
"SpreadAr1Coefficient",
"PairSpreadZScore",
"LeadLagCrossCorrelation",
"Cointegration",
"RelativeStrengthAB",
"SpearmanCorrelation",
# Ehlers / Cycle
"SuperSmoother",
"FisherTransform",
"InverseFisherTransform",
"Decycler",
"DecyclerOscillator",
"RoofingFilter",
"CenterOfGravity",
"CyberneticCycle",
"InstantaneousTrendline",
"EhlersStochastic",
"EmpiricalModeDecomposition",
"HilbertDominantCycle",
"HT_DCPHASE",
"HT_PHASOR",
"HT_TRENDMODE",
"AdaptiveCycle",
"SineWave",
"MAMA",
"FAMA",
# Bands & Channels
"ProjectionBands",
"MedianChannel",
"BomarBands",
"QuartileBands",
"MaEnvelope",
"AccelerationBands",
"StarcBands",
"AtrBands",
"HurstChannel",
"LinRegChannel",
"StandardErrorBands",
"DoubleBollinger",
"TtmSqueeze",
"FractalChaosBands",
"VwapStdDevBands",
# Pivots & S/R
"ClassicPivots",
"FibonacciPivots",
"Camarilla",
"WoodiePivots",
"DemarkPivots",
"WilliamsFractals",
"ZigZag",
# DeMark
"TDSetup",
"TDSequential",
"TDDeMarker",
"TDREI",
"TDPressure",
"TDCombo",
"TDCountdown",
"TDLines",
"TDRangeProjection",
"TDDifferential",
"TDOpen",
"TDRiskLevel",
# Ichimoku & alternative charts
"Ichimoku",
"HeikinAshi",
# Market Profile
"ValueArea",
"VolumeProfile",
"TpoProfile",
"InitialBalance",
"OpeningRange",
# Alt-Chart Bars
"RenkoBars",
"KagiBars",
"PointAndFigureBars",
# Candlestick patterns
"Doji",
"Hammer",
"InvertedHammer",
"HangingMan",
"ShootingStar",
"Engulfing",
"Harami",
"MorningEveningStar",
"ThreeSoldiersOrCrows",
"PiercingDarkCloud",
"Marubozu",
"Tweezer",
"SpinningTop",
"ThreeInside",
"ThreeOutside",
"TwoCrows",
"UpsideGapTwoCrows",
"IdenticalThreeCrows",
"ThreeLineStrike",
"ThreeStarsInSouth",
"AbandonedBaby",
"AdvanceBlock",
"BeltHold",
"Breakaway",
"Counterattack",
"DojiStar",
"DragonflyDoji",
"GravestoneDoji",
"LongLeggedDoji",
"RickshawMan",
"EveningDojiStar",
"MorningDojiStar",
"GapSideBySideWhite",
"HighWave",
"Hikkake",
"HikkakeModified",
"HomingPigeon",
"OnNeck",
"InNeck",
"Thrusting",
"SeparatingLines",
"Kicking",
"KickingByLength",
"LadderBottom",
"MatHold",
"MatchingLow",
"LongLine",
"ShortLine",
"RisingThreeMethods",
"FallingThreeMethods",
"UpsideGapThreeMethods",
"DownsideGapThreeMethods",
"StalledPattern",
"StickSandwich",
"Takuri",
"ClosingMarubozu",
"OpeningMarubozu",
"TasukiGap",
"UniqueThreeRiver",
"ConcealingBabySwallow",
# Chart patterns
"CupAndHandle",
"RectangleRange",
"FlagPennant",
"Wedge",
"Triangle",
"HeadAndShoulders",
"TripleTopBottom",
"DoubleTopBottom",
# Harmonic patterns
"ThreeDrives",
"Cypher",
"Shark",
"Crab",
"Bat",
"Butterfly",
"Gartley",
"Abcd",
# Fibonacci
"FibTimeZones",
"FibChannel",
"FibArcs",
"FibFan",
"FibConfluence",
"GoldenPocket",
"AutoFib",
"FibProjection",
"FibExtension",
"FibRetracement",
# Microstructure: order book
"OrderFlowImbalance",
"OrderBookImbalanceTop1",
"OrderBookImbalanceTopN",
"OrderBookImbalanceFull",
"Microprice",
"QuotedSpread",
"DepthSlope",
# Microstructure: trade flow
"RollMeasure",
"AmihudIlliquidity",
"Vpin",
"SignedVolume",
"CumulativeVolumeDelta",
"TradeImbalance",
# Microstructure: price impact
"EffectiveSpread",
"RealizedSpread",
"KylesLambda",
# Microstructure: footprint
"Footprint",
# Derivatives
"FundingRate",
"FundingRateMean",
"FundingRateZScore",
"FundingBasis",
"OpenInterestDelta",
"OIPriceDivergence",
"OIWeighted",
"LongShortRatio",
"TakerBuySellRatio",
"LiquidationFeatures",
"TermStructureBasis",
"CalendarSpread",
# Market Breadth
"TickIndex",
"AbsoluteBreadthIndex",
"CumulativeVolumeIndex",
"BullishPercentIndex",
"UpDownVolumeRatio",
"PercentAboveMa",
"HighLowIndex",
"NewHighsNewLows",
"BreadthThrust",
"Trin",
"McClellanSummationIndex",
"McClellanOscillator",
"AdVolumeLine",
"AdvanceDeclineRatio",
"AdvanceDecline",
# Risk / Performance
"SharpeRatio",
"SortinoRatio",
"CalmarRatio",
"OmegaRatio",
"MaxDrawdown",
"AverageDrawdown",
"DrawdownDuration",
"PainIndex",
"ValueAtRisk",
"ConditionalValueAtRisk",
"ProfitFactor",
"GainLossRatio",
"RecoveryFactor",
"KellyCriterion",
"TreynorRatio",
"InformationRatio",
"Alpha",
# Seasonality & Session
"SessionVwap",
"SessionHighLow",
"SessionRange",
"AverageDailyRange",
"OvernightGap",
"OvernightIntradayReturn",
"TurnOfMonth",
"SeasonalZScore",
"TimeOfDayReturnProfile",
"DayOfWeekProfile",
"IntradayVolatilityProfile",
"VolumeByTimeProfile",
]
+19447 -2
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File diff suppressed because it is too large Load Diff
@@ -35,7 +35,246 @@ def test_unequal_length_candle_batch_raises(ohlc_series):
ta.Aroon(14).batch(high, short)
def test_pairwise_beta_rejects_bad_period():
with pytest.raises(ValueError):
ta.PairwiseBeta(0)
with pytest.raises(ValueError):
ta.PairwiseBeta(1)
def test_unequal_length_pair_batch_raises(sine_prices):
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
b = a[:-1]
with pytest.raises(ValueError):
ta.PairwiseBeta(20).batch(a, b)
with pytest.raises(ValueError):
ta.PairSpreadZScore(20, 20).batch(a, b)
def test_pair_spread_zscore_rejects_bad_periods():
with pytest.raises(ValueError):
ta.PairSpreadZScore(1, 20)
with pytest.raises(ValueError):
ta.PairSpreadZScore(20, 1)
def test_lead_lag_rejects_bad_params():
with pytest.raises(ValueError):
ta.LeadLagCrossCorrelation(1, 5)
with pytest.raises(ValueError):
ta.LeadLagCrossCorrelation(10, 0)
def test_lead_lag_unequal_length_batch_raises(sine_prices):
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
b = a[:-1]
with pytest.raises(ValueError):
ta.LeadLagCrossCorrelation(12, 5).batch(a, b)
def test_cointegration_rejects_too_small_period():
# period must be >= 2*adf_lags + 4.
with pytest.raises(ValueError):
ta.Cointegration(3, 0)
with pytest.raises(ValueError):
ta.Cointegration(5, 1)
def test_cointegration_unequal_length_batch_raises(sine_prices):
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
b = a[:-1]
with pytest.raises(ValueError):
ta.Cointegration(20, 1).batch(a, b)
def test_relative_strength_rejects_zero_periods():
with pytest.raises(ValueError):
ta.RelativeStrengthAB(0, 14)
with pytest.raises(ValueError):
ta.RelativeStrengthAB(20, 0)
def test_relative_strength_unequal_length_batch_raises(sine_prices):
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
b = a[:-1]
with pytest.raises(ValueError):
ta.RelativeStrengthAB(10, 14).batch(a, b)
def test_roc_and_trix_have_default_periods():
# ROC/TRIX gained constructor defaults matching the TA-Lib convention.
assert ta.ROC().period == 10
assert ta.TRIX() is not None
def test_value_area_rejects_zero_period():
with pytest.raises(ValueError):
ta.ValueArea(0, 50, 0.7)
with pytest.raises(ValueError):
ta.ValueArea(20, 0, 0.7)
def test_value_area_rejects_invalid_pct():
with pytest.raises(ValueError):
ta.ValueArea(20, 50, 0.0)
with pytest.raises(ValueError):
ta.ValueArea(20, 50, 1.5)
def test_initial_balance_rejects_zero_period():
with pytest.raises(ValueError):
ta.InitialBalance(0)
def test_opening_range_rejects_zero_period():
with pytest.raises(ValueError):
ta.OpeningRange(0)
def test_value_area_unequal_length_raises():
high = np.array([1.0, 2.0, 3.0])
low = np.array([0.5, 1.5])
volume = np.array([10.0, 10.0, 10.0])
with pytest.raises(ValueError):
ta.ValueArea(2, 10, 0.7).batch(high, low, volume)
def test_ichimoku_rejects_zero_and_non_increasing_periods():
with pytest.raises(ValueError):
ta.Ichimoku(0, 26, 52, 26)
with pytest.raises(ValueError):
ta.Ichimoku(9, 26, 52, 0)
# Periods must satisfy tenkan < kijun < senkou_b.
with pytest.raises(ValueError):
ta.Ichimoku(26, 9, 52, 26)
with pytest.raises(ValueError):
ta.Ichimoku(9, 52, 52, 26)
def test_family_10_ehlers_rejects_invalid_parameters():
with pytest.raises(ValueError):
ta.SuperSmoother(0)
with pytest.raises(ValueError):
ta.FisherTransform(0)
with pytest.raises(ValueError):
ta.InverseFisherTransform(0.0)
with pytest.raises(ValueError):
ta.DecyclerOscillator(30, 10)
with pytest.raises(ValueError):
ta.RoofingFilter(48, 10)
with pytest.raises(ValueError):
ta.MAMA(0.05, 0.5)
with pytest.raises(ValueError):
ta.EmpiricalModeDecomposition(20, 0.0)
def test_orderbook_topn_zero_levels_raises():
with pytest.raises(ValueError):
ta.OrderBookImbalanceTopN(0)
def test_orderbook_unequal_price_size_lengths_raise():
# bid_px has 2 entries but bid_sz has 1 -> mismatched -> ValueError.
with pytest.raises(ValueError):
ta.OrderBookImbalanceTop1().update([100.0, 99.0], [1.0], [101.0], [1.0])
with pytest.raises(ValueError):
ta.Microprice().update([100.0], [1.0], [101.0, 102.0], [1.0])
def test_orderbook_crossed_book_raises():
# best_bid (102) >= best_ask (101) is a crossed book -> rejected.
with pytest.raises(ValueError):
ta.QuotedSpread().update([102.0], [1.0], [101.0], [1.0])
def test_orderbook_misordered_levels_raise():
# Bids must be strictly descending in price.
with pytest.raises(ValueError):
ta.OrderBookImbalanceFull().update([99.0, 100.0], [1.0, 1.0], [101.0], [1.0])
def test_trade_imbalance_zero_window_raises():
with pytest.raises(ValueError):
ta.TradeImbalance(0)
def test_trade_negative_size_raises():
with pytest.raises(ValueError):
ta.SignedVolume().update(100.0, -1.0, True)
def test_trade_non_positive_price_raises():
with pytest.raises(ValueError):
ta.CumulativeVolumeDelta().update(0.0, 1.0, True)
def test_trade_batch_unequal_lengths_raise():
with pytest.raises(ValueError):
ta.SignedVolume().batch([100.0, 100.0], [1.0], [True, False])
def test_effective_spread_non_positive_mid_raises():
with pytest.raises(ValueError):
ta.EffectiveSpread().update(100.0, 1.0, True, 0.0)
def test_effective_spread_batch_unequal_lengths_raise():
with pytest.raises(ValueError):
ta.EffectiveSpread().batch([100.0, 100.0], [1.0, 1.0], [True, False], [100.0])
def test_realized_spread_zero_horizon_raises():
with pytest.raises(ValueError):
ta.RealizedSpread(0)
def test_kyles_lambda_window_below_two_raises():
with pytest.raises(ValueError):
ta.KylesLambda(1)
def test_footprint_non_positive_tick_raises():
with pytest.raises(ValueError):
ta.Footprint(0.0)
with pytest.raises(ValueError):
ta.Footprint(-1.0)
def test_funding_rate_mean_zero_window_raises():
with pytest.raises(ValueError):
ta.FundingRateMean(0)
def test_funding_rate_zscore_zero_window_raises():
with pytest.raises(ValueError):
ta.FundingRateZScore(0)
def test_funding_basis_non_positive_index_raises():
with pytest.raises(ValueError):
ta.FundingBasis().update(100.0, 0.0)
def test_funding_rate_non_finite_raises():
with pytest.raises(ValueError):
ta.FundingRate().update(float("nan"))
def test_oi_price_divergence_zero_window_raises():
with pytest.raises(ValueError):
ta.OIPriceDivergence(0)
def test_oi_weighted_non_positive_mark_raises():
with pytest.raises(ValueError):
ta.OIWeighted().update(0.0, 100.0)
def test_term_structure_basis_non_positive_index_raises():
with pytest.raises(ValueError):
ta.TermStructureBasis().update(100.0, 0.0)
def test_calendar_spread_non_positive_mark_raises():
with pytest.raises(ValueError):
ta.CalendarSpread().update(100.0, 0.0)
+922
View File
@@ -66,6 +66,232 @@ def test_rsi_wilder_textbook_first_value():
assert math.isclose(out[14], 70.464, abs_tol=0.05)
def test_anchored_rsi_cumulative_reference():
"""Cumulative anchored RSI: 10 -> 11 (+1) -> 9 (-2) -> 12 (+3)."""
out = ta.AnchoredRSI().batch(np.array([10.0, 11.0, 9.0, 12.0]))
assert math.isclose(out[1], 100.0, abs_tol=1e-9)
assert math.isclose(out[2], 100.0 - 100.0 / 1.5, abs_tol=1e-6)
assert math.isclose(out[3], 100.0 - 100.0 / 3.0, abs_tol=1e-6)
def test_inertia_constant_rvi_passes_through_linreg():
# Every bar identical (open, high, low, close) = (10, 11, 9, 10.5):
# RVI = (c-o) / (h-l) = 0.5 / 2 = 0.25 every bar. LinReg of a constant
# series equals that constant after warmup.
n = 60
out = ta.Inertia(3, 4).batch(
np.full(n, 10.0), np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.5)
)
# warmup_period = 3 + 4 - 1 = 6.
np.testing.assert_allclose(out[5:], 0.25, atol=1e-12)
def test_connors_rsi_output_is_bounded():
# CRSI is the average of three [0, 100] components, so the aggregate must
# also sit in [0, 100] after warmup.
prices = 100.0 + 20.0 * np.sin(np.linspace(0, 30, 250))
out = ta.ConnorsRSI(3, 2, 100).batch(prices.astype(np.float64))
ready = out[~np.isnan(out)]
assert ready.size > 0
assert ready.min() >= 0.0
assert ready.max() <= 100.0
def test_laguerre_rsi_constant_series_stays_at_mid_band():
# All four Laguerre stages seed to the first input, so subsequent flat
# inputs keep them equal and the up/down accumulator is 0 — Wickra maps
# that to the neutral 50.
out = ta.LaguerreRSI(0.5).batch(np.full(40, 42.0, dtype=np.float64))
np.testing.assert_allclose(out, 50.0, atol=1e-12)
def test_smi_close_at_centre_yields_zero():
# Close at the midpoint of a flat high/low range -> displacement is
# always zero -> SMI converges to 0.
n = 60
out = ta.SMI(5, 3, 3).batch(np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.0))
# warmup_period = 5 + 3 + 3 - 2 = 9.
np.testing.assert_allclose(out[8:], 0.0, atol=1e-12)
def test_kst_constant_series_yields_zero():
# ROC is zero on a flat input, so every RCMA is zero, so KST and its
# signal SMA are both zero after warmup.
kst = ta.KST(10, 15, 20, 30, 10, 10, 10, 15, 9)
out = kst.batch(np.full(80, 42.0, dtype=np.float64))
warmup = kst.warmup_period()
# Use NaN-safe comparison on the post-warmup tail.
tail = out[warmup - 1 :]
assert np.all(np.isfinite(tail))
np.testing.assert_allclose(tail, 0.0, atol=1e-12)
def test_pgo_flat_close_yields_zero():
# On a constant close the numerator (close SMA) is zero, so PGO emits 0
# regardless of the TR-EMA in the denominator.
n = 20
high = np.full(n, 11.0)
low = np.full(n, 9.0)
close = np.full(n, 10.0)
out = ta.PGO(5).batch(high, low, close)
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
def test_rvi_reference_value_period_2():
# Two bars: (open, high, low, close) = (10, 11, 9, 10.5), (10.5, 11.5, 10, 11).
# num = (0.5 + 0.5) = 1.0; den = (2.0 + 1.5) = 3.5; RVI = 1 / 3.5.
out = ta.RVI(2).batch(
np.array([10.0, 10.5]),
np.array([11.0, 11.5]),
np.array([9.0, 10.0]),
np.array([10.5, 11.0]),
)
assert math.isnan(out[0])
assert math.isclose(out[1], 1.0 / 3.5, abs_tol=1e-12)
def test_alma_constant_series_yields_the_constant():
# ALMA's Gaussian weights are normalised, so any constant series is
# reproduced exactly after warmup.
out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
assert np.all(np.isnan(out[:8]))
np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
def test_alma_reference_value_period_3():
# ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
# m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
assert math.isnan(out[0]) and math.isnan(out[1])
# Independently compute the expected Gaussian-weighted sum.
w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
expected = float(np.dot([10.0, 20.0, 30.0], w) / w.sum())
assert math.isclose(out[2], expected, abs_tol=1e-12)
# Sanity: heavy offset toward the newest sample lifts the average above
# the simple mean of 20.
assert out[2] > 20.0
def test_mcginley_dynamic_constant_series_yields_the_constant():
# ratio = 1, so the recurrence collapses to MD + 0 / divisor = MD.
out = ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
def test_mcginley_dynamic_reference_value():
# Period 3, seed = SMA([10, 20, 30]) = 20.0. Next price 40.0:
# ratio = 2; divisor = 0.6 * 3 * 16 = 28.8; next = 20 + 20/28.8.
out = ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0]))
assert math.isnan(out[0]) and math.isnan(out[1])
assert math.isclose(out[2], 20.0, abs_tol=1e-12)
expected = 20.0 + 20.0 / (0.6 * 3.0 * 16.0)
assert math.isclose(out[3], expected, abs_tol=1e-12)
def test_frama_constant_series_yields_the_constant():
# Flat input -> degenerate ranges -> alpha clamps to 0.01 and the EMA
# recurrence holds the seed value.
out = ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64))
assert np.all(np.isnan(out[:3]))
np.testing.assert_allclose(out[3:], 42.0, atol=1e-12)
def test_frama_pure_uptrend_hugs_latest():
# Monotonic uptrend -> alpha pushed toward 1.0, FRAMA tracks close.
out = ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64))
assert math.isclose(out[-1], 8.0, abs_tol=0.05)
def test_jma_constant_series_yields_the_constant():
# JMA seeds e0 and the output to the first input, so a constant series
# is reproduced exactly from the first sample.
out = ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64))
np.testing.assert_allclose(out, 42.0, atol=1e-12)
def test_evwma_reference_value_period_2():
# EVWMA(2). Bars: (close, volume) = (10, 1), (20, 3), (30, 1).
# Bar 2: sum_v = 4, seeded prev = 20, EVWMA = (1*20 + 3*20)/4 = 20.
# Bar 3: sum_v = 4 (drops 1, gains 1), EVWMA = (3*20 + 1*30)/4 = 22.5.
out = ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0]))
assert math.isnan(out[0])
assert math.isclose(out[1], 20.0, abs_tol=1e-12)
assert math.isclose(out[2], 22.5, abs_tol=1e-12)
def test_alligator_constant_series_holds_at_median_price():
# Median price = (11 + 9) / 2 = 10 on every candle, so all three SMMAs
# seed at 10 and stay there.
n = 30
high = np.full(n, 11.0)
low = np.full(n, 9.0)
out = ta.Alligator(13, 8, 5).batch(high, low)
assert out.shape == (n, 3)
for row in out[12:]:
assert math.isclose(row[0], 10.0, abs_tol=1e-12)
assert math.isclose(row[1], 10.0, abs_tol=1e-12)
assert math.isclose(row[2], 10.0, abs_tol=1e-12)
def test_vidya_constant_series_holds_seed():
# CMO = 0 on a flat series -> alpha = 0 -> VIDYA holds its seed value.
out = ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
def test_zero_lag_macd_constant_series_converges_to_zero():
# Each inner ZLEMA reproduces a constant, so macd, signal and histogram
# are all 0 once the slowest branch warms up.
out = ta.ZeroLagMACD(3, 5, 3).batch(np.full(60, 42.0, dtype=np.float64))
# Take the last row and verify all three columns are 0.
last = out[-1]
assert math.isclose(last[0], 0.0, abs_tol=1e-12)
assert math.isclose(last[1], 0.0, abs_tol=1e-12)
assert math.isclose(last[2], 0.0, abs_tol=1e-12)
def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
# Flat median price -> AO = 0 -> SMA(AO) = 0 -> AOHist = 0.
n = 50
high = np.full(n, 11.0)
low = np.full(n, 9.0)
out = ta.AwesomeOscillatorHistogram(3, 5, 3).batch(high, low)
# warmup = slow + sma - 1 = 5 + 3 - 1 = 7.
np.testing.assert_allclose(out[6:], 0.0, atol=1e-12)
def test_stc_constant_series_yields_zero():
# Flat input collapses both stochastic stages to zero -> STC stays at 0.
out = ta.STC(3, 5, 4, 0.5).batch(np.full(60, 42.0, dtype=np.float64))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_array_equal(ready[-5:], np.zeros(5))
def test_elder_impulse_constant_series_is_neutral():
# Flat input -> neither EMA nor MACD histogram moves -> Impulse stays at 0.
out = ta.ElderImpulse(13, 12, 26, 9).batch(np.full(120, 42.0, dtype=np.float64))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_array_equal(ready, np.zeros_like(ready))
def test_cfo_perfect_linear_series_yields_zero():
# LinReg of a perfectly linear series fits exactly, so CFO = 0 after warmup.
out = ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0)
np.testing.assert_allclose(out[4:], 0.0, atol=1e-9)
def test_apo_constant_series_converges_to_zero():
# Both EMAs reproduce a constant exactly, so APO = 0 after warmup.
out = ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
def test_macd_constant_series_converges_to_zero():
out = ta.MACD().batch(np.full(200, 100.0))
# Last row's MACD and signal must be ~0.
@@ -112,3 +338,699 @@ def test_obv_cumulative_known_sequence():
volume = np.array([100.0, 20.0, 30.0, 40.0, 10.0])
out = ta.OBV().batch(close, volume)
np.testing.assert_allclose(out, [0.0, 20.0, -10.0, -10.0, 0.0])
# --- Family 15: Risk / Performance ---------------------------------------
def test_sharpe_ratio_known_window():
# returns [0.01, 0.02, 0.03, 0.04], rf = 0; mean = 0.025;
# sample-var = 0.000166...; Sharpe = 0.025 / sqrt(var).
out = ta.SharpeRatio(4, 0.0).batch(np.array([0.01, 0.02, 0.03, 0.04]))
expected = 0.025 / math.sqrt(0.000_166_666_666_666_666_67)
assert math.isclose(out[3], expected, rel_tol=1e-9)
def test_sortino_ratio_known_window():
# returns [-0.02, 0.01, -0.01, 0.03], mar = 0; mean = 0.0025;
# downside_sq = 0.0005; dd = sqrt(0.0005/4); Sortino = 0.0025/dd.
out = ta.SortinoRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03]))
expected = 0.0025 / math.sqrt(0.000_125)
assert math.isclose(out[3], expected, rel_tol=1e-9)
def test_max_drawdown_known_window():
# window [100, 120, 90] -> peak 120, trough 90 -> 25% drawdown.
out = ta.MaxDrawdown(3).batch(np.array([100.0, 120.0, 90.0]))
assert math.isclose(out[2], 0.25, abs_tol=1e-12)
def test_pain_index_known_window():
# dd[0..2] = 0, 0, 0.25; mean = 0.25/3.
out = ta.PainIndex(3).batch(np.array([100.0, 120.0, 90.0]))
assert math.isclose(out[2], 0.25 / 3.0, abs_tol=1e-12)
def test_profit_factor_known_window():
# gains 0.05, losses 0.03 -> PF = 5/3.
out = ta.ProfitFactor(4).batch(np.array([0.02, -0.01, 0.03, -0.02]))
assert math.isclose(out[3], 5.0 / 3.0, rel_tol=1e-9)
def test_gain_loss_ratio_known_window():
# avg_win 0.03, avg_loss 0.02 -> GLR = 1.5.
out = ta.GainLossRatio(4).batch(np.array([0.02, -0.01, 0.04, -0.03]))
assert math.isclose(out[3], 1.5, rel_tol=1e-9)
def test_omega_ratio_known_window():
# gains 0.04, losses 0.03 -> Omega = 4/3.
out = ta.OmegaRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03]))
assert math.isclose(out[3], 4.0 / 3.0, rel_tol=1e-9)
def test_kelly_criterion_known_window():
# n_win=n_loss=2, payoff=2 -> Kelly = 0.5 - 0.5/2 = 0.25.
out = ta.KellyCriterion(4).batch(np.array([0.02, 0.04, -0.01, -0.02]))
assert math.isclose(out[3], 0.25, rel_tol=1e-9)
def test_drawdown_duration_under_water_counter():
out = ta.DrawdownDuration().batch(np.array([100.0, 95.0, 90.0, 85.0]))
np.testing.assert_allclose(out, [0.0, 1.0, 2.0, 3.0])
def test_recovery_factor_known_path():
# Start 100, peak 110, trough 88 -> max_dd = 0.20; end 130 ->
# net_return = 0.30 -> Recovery = 1.5.
prices = np.array([100.0, 110.0, 105.0, 95.0, 88.0, 100.0, 120.0, 130.0])
out = ta.RecoveryFactor().batch(prices)
assert math.isclose(out[-1], 1.5, rel_tol=1e-9)
def test_alpha_perfect_capm_fit_yields_zero():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = 2.0 * bench
out = ta.Alpha(20, 0.0).batch(asset, bench)
assert math.isclose(out[-1], 0.0, abs_tol=1e-12)
def test_alpha_additive_offset_recovered():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = bench + 0.005
out = ta.Alpha(20, 0.0).batch(asset, bench)
assert math.isclose(out[-1], 0.005, rel_tol=1e-9)
def test_treynor_ratio_known_window():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = 2.0 * bench
out = ta.TreynorRatio(20, 0.0).batch(asset, bench)
assert math.isclose(out[-1], bench.mean(), rel_tol=1e-9)
def test_information_ratio_known_window():
asset = np.array([0.02, 0.04, 0.06, 0.08])
bench = np.array([0.01, 0.02, 0.03, 0.04])
out = ta.InformationRatio(4).batch(asset, bench)
expected = 0.025 / math.sqrt(0.000_166_666_666_666_666_67)
assert math.isclose(out[-1], expected, rel_tol=1e-9)
def test_pairwise_beta_squared_price_is_two():
# a = b² ⇒ a's log-returns are exactly 2× b's ⇒ pairwise beta = 2.
# b must have *varying* returns (a constant-return path has zero variance
# and an undefined slope, which the indicator reports as 0).
b = np.array([100.0 + 10.0 * math.sin(i * 0.5) for i in range(20)])
a = b**2
out = ta.PairwiseBeta(5).batch(a, b)
assert math.isclose(out[-1], 2.0, rel_tol=1e-9)
def test_pairwise_beta_inverse_price_is_minus_one():
# a = 1/b ⇒ a's log-returns are 1× b's ⇒ pairwise beta = 1.
b = np.array([100.0 + 10.0 * math.sin(i * 0.5) for i in range(20)])
a = 1.0 / b
out = ta.PairwiseBeta(5).batch(a, b)
assert math.isclose(out[-1], -1.0, rel_tol=1e-9)
def test_pair_spread_zscore_flat_benchmark_sign():
# Flat b ⇒ hedge ratio 0 ⇒ spread = ln(a). With z_period = 2 the z-score
# collapses to the sign of the last move: rising a ⇒ +1, falling a ⇒ 1.
a = np.array([100.0, 100.0, 110.0, 105.0, 130.0])
b = np.full_like(a, 100.0)
out = ta.PairSpreadZScore(2, 2).batch(a, b)
assert math.isclose(out[-1], 1.0, abs_tol=1e-9)
assert math.isclose(out[-2], -1.0, abs_tol=1e-9)
def test_lead_lag_cross_correlation_negative_lead():
# a is a delayed copy of b ⇒ b leads a ⇒ lag = 2, correlation ≈ 1.
def sig(t):
return math.sin(t * 0.4) + 0.4 * math.sin(t * 1.1) + 0.2 * math.cos(t * 0.27)
n = 60
a = np.array([sig(t - 2) for t in range(n)])
b = np.array([sig(t) for t in range(n)])
out = ta.LeadLagCrossCorrelation(12, 5).batch(a, b)
assert int(out[-1, 0]) == -2
assert out[-1, 1] > 0.99
def test_cointegration_perfect_pair():
# a = 2*b + 5 exactly ⇒ hedge ratio 2, zero spread, degenerate ADF ⇒ 0.
b = np.array([100.0 + t for t in range(40)])
a = 2.0 * b + 5.0
out = ta.Cointegration(20, 1).batch(a, b)
assert math.isclose(out[-1, 0], 2.0, rel_tol=1e-9)
assert math.isclose(out[-1, 1], 0.0, abs_tol=1e-6)
assert math.isclose(out[-1, 2], 0.0, abs_tol=1e-12)
def test_relative_strength_rising_ratio_is_overbought():
# a rises while b is flat ⇒ ratio strictly increases ⇒ RSI saturates at 100.
n = 20
a = np.array([100.0 + 2.0 * t for t in range(n)])
b = np.full(n, 100.0)
out = ta.RelativeStrengthAB(5, 5).batch(a, b)
assert out[-1, 0] > 1.0
assert math.isclose(out[-1, 2], 100.0, abs_tol=1e-9)
def test_value_at_risk_known_window():
# returns -5..4 *0.01; q=0.05*9=0.45 -> -0.0455; VaR = 0.0455.
returns = np.array([i * 0.01 for i in range(-5, 5)])
out = ta.ValueAtRisk(10, 0.95).batch(returns)
assert math.isclose(out[-1], 0.0455, rel_tol=1e-9)
def test_conditional_value_at_risk_known_window():
# tail = {-0.10}; CVaR = 0.10.
returns = np.array([i * 0.01 for i in range(-10, 10)])
out = ta.ConditionalValueAtRisk(20, 0.95).batch(returns)
assert math.isclose(out[-1], 0.10, rel_tol=1e-9)
def test_calmar_ratio_known_path():
# returns [0.10, -0.20, 0.05]; equity 1.0->1.10->0.88->0.924;
# mdd = 0.20; mean = -0.01666...; Calmar = mean / 0.20.
out = ta.CalmarRatio(3).batch(np.array([0.10, -0.20, 0.05]))
expected = ((0.10 - 0.20 + 0.05) / 3.0) / 0.20
assert math.isclose(out[-1], expected, rel_tol=1e-9)
def test_average_drawdown_known_window():
# window [100, 120, 90, 110]: dd = 0, 0, 0.25, 10/120;
# mean = (0.25 + 10/120) / 4.
out = ta.AverageDrawdown(4).batch(np.array([100.0, 120.0, 90.0, 110.0]))
expected = (0.25 + 10.0 / 120.0) / 4.0
assert math.isclose(out[-1], expected, rel_tol=1e-12)
def test_value_area_concentrated_volume_locates_poc():
# Bars 0..3 sit at price 100 with low volume; bar 4 dumps massive volume
# at price 110. POC must fall inside the high-volume bar's [low, high]
# range; ties resolve to the lowest-index bin, so the POC may sit on the
# left edge of bar 4's range rather than at its midpoint.
high = np.array([100.5, 100.5, 100.5, 100.5, 110.5])
low = np.array([99.5, 99.5, 99.5, 99.5, 109.5])
volume = np.array([1.0, 1.0, 1.0, 1.0, 1000.0])
out = ta.ValueArea(5, 50, 0.70).batch(high, low, volume)
poc = out[-1, 0]
assert 109.5 <= poc <= 110.5
# VAH >= POC >= VAL.
assert out[-1, 1] >= poc >= out[-1, 2]
def test_initial_balance_locks_after_period():
# First two bars set IB = [99, 103]. Third bar (extreme) must be ignored.
high = np.array([102.0, 103.0, 200.0])
low = np.array([100.0, 99.0, 50.0])
out = ta.InitialBalance(2).batch(high, low)
# Bar 0: IB = [100, 102]; Bar 1: IB locked at [99, 103]; Bar 2: unchanged.
np.testing.assert_allclose(out[0], [102.0, 100.0])
np.testing.assert_allclose(out[1], [103.0, 99.0])
np.testing.assert_allclose(out[2], [103.0, 99.0])
def test_opening_range_breakout_distance_signed():
# OR locks after 2 bars at high 103 / low 100; mid 101.5. Third bar
# closes at 105 -> breakout +3.5; fourth bar closes at 95 -> -6.5.
high = np.array([102.0, 103.0, 110.0, 110.0])
low = np.array([100.0, 101.0, 102.0, 90.0])
close = np.array([101.0, 102.0, 105.0, 95.0])
out = ta.OpeningRange(2).batch(high, low, close)
assert math.isclose(out[2, 0], 103.0)
assert math.isclose(out[2, 1], 100.0)
assert math.isclose(out[2, 2], 105.0 - 101.5)
assert math.isclose(out[3, 2], 95.0 - 101.5)
# --- Family 10 — Ehlers / Cycle reference values ---
def test_inverse_fisher_saturates_for_large_input():
# tanh(10) ~ 0.99999996; very close to +1 without exceeding.
v = ta.InverseFisherTransform(1.0).batch(np.array([10.0]))[0]
assert v < 1.0
assert v > 0.999
def test_super_smoother_constant_input_is_constant():
out = ta.SuperSmoother(20).batch(np.full(200, 50.0))
# Steady-state gain is 1, so a flat input stays flat.
np.testing.assert_allclose(out[-50:], 50.0, atol=1e-9)
def test_decycler_oscillator_flat_series_is_zero():
out = ta.DecyclerOscillator(10, 30).batch(np.full(80, 42.0))
ready = out[~np.isnan(out)]
np.testing.assert_allclose(ready, 0.0, atol=1e-9)
def test_mama_constant_series_both_lines_converge_to_price():
out = ta.MAMA().batch(np.full(200, 100.0))
last = out[-1]
# MAMA and FAMA both track price closely on a flat series.
assert abs(last[0] - 100.0) < 1.0
assert abs(last[1] - 100.0) < 1.0
# --- DeMark family ---------------------------------------------------------
def test_td_setup_buy_setup_completes_at_minus_9_uptrend():
# Strictly rising closes -> every bar has close > close[-4] (sell setup);
# the streak hits -9 at index 12 and caps there.
h = np.arange(2.0, 22.0)
l = h - 1.0
c = h - 0.5
out = ta.TDSetup(4, 9).batch(h, l, c)
assert out[12] == pytest.approx(-9.0)
assert out[-1] == pytest.approx(-9.0)
def test_td_demarker_downtrend_pegs_at_zero():
n = 20
h = np.arange(30.0, 30.0 - n, -1.0)
l = h - 2.0
out = ta.TDDeMarker(5).batch(h, l)
assert out[-1] == pytest.approx(0.0)
def test_td_pressure_pure_bearish_yields_minus_100():
n = 20
open_ = np.full(n, 11.0)
high = np.full(n, 11.0)
low = np.full(n, 9.0)
close = np.full(n, 9.0)
volume = np.full(n, 100.0)
out = ta.TDPressure(5).batch(open_, high, low, close, volume)
assert out[-1] == pytest.approx(-100.0)
def test_td_combo_uptrend_completes_to_minus_13():
# Pure uptrend -> setup completes, then combo conditions (close>=high[-2],
# high>=prev.high, close>prev.close) all hold for every subsequent bar
# -> sell combo saturates at -13.
n = 40
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDCombo().batch(high, low, close)
assert out[-1] == pytest.approx(-13.0)
def test_td_countdown_uptrend_completes_to_minus_13():
n = 40
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDCountdown().batch(high, low, close)
assert out[-1] == pytest.approx(-13.0)
def test_td_range_projection_doji_reference():
# open=close=10, high=12, low=9 -> doji branch.
# pivot_sum = 12 + 9 + 2*10 = 41; half = 20.5.
# projHigh = 20.5 - 9 = 11.5; projLow = 20.5 - 12 = 8.5.
out = ta.TDRangeProjection().batch(
np.array([10.0]), np.array([12.0]), np.array([9.0]), np.array([10.0])
)
assert out[0, 0] == pytest.approx(11.5)
assert out[0, 1] == pytest.approx(8.5)
def test_td_open_sell_signal_reference():
# Prev high=12. Curr open=13 > 12, curr low=11 < 12 -> -1.
td = ta.TDOpen()
assert td.update((10.0, 12.0, 9.0, 11.0, 1.0, 0)) is None
assert td.update((13.0, 13.5, 11.0, 11.5, 1.0, 1)) == pytest.approx(-1.0)
def test_td_differential_sell_signal_reference():
# Prev high=10, low=8, close=9: buying=1, selling=1.
# Curr high=12, low=9.8, close=10.5: close>prev.close, selling=1.5>1,
# buying=0.7<1 -> sell signal -1.
td = ta.TDDifferential()
assert td.update((9.0, 10.0, 8.0, 9.0, 1.0, 0)) is None
assert td.update((10.5, 12.0, 9.8, 10.5, 1.0, 1)) == pytest.approx(-1.0)
def test_td_lines_uptrend_support_reference():
# Strictly rising series -> sell setup completes at idx 12, the
# lowest low across bars 4..=12 is the low at idx 4 = 4.5.
n = 20
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDLines().batch(high, low, close)
assert math.isnan(out[-1, 0])
assert out[-1, 1] == pytest.approx(4.5)
def test_td_risk_level_uptrend_sell_risk_reference():
# Strictly rising series -> sell setup completes at idx 12 with high
# 13.5 and true range 1.5 -> sell_risk = 13.5 + 1.5 = 15.0.
# Subsequent setups re-ratchet the level, so we check the first emission
# at idx 12 rather than the latest value.
n = 20
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDRiskLevel().batch(high, low, close)
assert math.isnan(out[12, 0])
assert out[12, 1] == pytest.approx(15.0)
def test_percentage_trailing_stop_seed_and_ratchet():
# 10% trail: first close 100 -> stop 90; next 110 -> stop max(90, 99) = 99.
s = ta.PercentageTrailingStop(10.0)
assert math.isclose(s.update(100.0), 90.0, abs_tol=1e-12)
assert math.isclose(s.update(110.0), 99.0, abs_tol=1e-12)
def test_step_trailing_stop_snaps_below_close():
# step 1: floor((100.4 - 1) / 1) = 99.
s = ta.StepTrailingStop(1.0)
assert math.isclose(s.update(100.4), 99.0, abs_tol=1e-12)
def test_renko_trailing_stop_holds_until_full_block():
# block 1: seed 100 -> stop 99; 100.5 still 99; 101 -> stop 100.
s = ta.RenkoTrailingStop(1.0)
assert math.isclose(s.update(100.0), 99.0, abs_tol=1e-12)
assert math.isclose(s.update(100.5), 99.0, abs_tol=1e-12)
assert math.isclose(s.update(101.0), 100.0, abs_tol=1e-12)
def test_donchian_stop_window_extremes():
# 5-bar window of highs 1..5 and lows 0..4.
high = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
low = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
out = ta.DonchianStop(5).batch(high, low)
# First 4 rows NaN, fifth row: stop_long = 0, stop_short = 5.
for i in range(4):
assert math.isnan(out[i, 0])
assert math.isnan(out[i, 1])
assert math.isclose(out[4, 0], 0.0, abs_tol=1e-12)
assert math.isclose(out[4, 1], 5.0, abs_tol=1e-12)
def test_hilo_activator_flat_market_holds_low_sma():
# Flat candles H=11, L=9, C=10 -> close (10) sits between bands, so the
# initial long seed is preserved: emitted stop = lo_sma = 9.
h = np.full(15, 11.0)
l = np.full(15, 9.0)
c = np.full(15, 10.0)
out = ta.HiLoActivator(3).batch(h, l, c)
# warmup_period == period + 1 == 4, so indices 0..2 are NaN; index 3 onwards is 9.
for i in range(3):
assert math.isnan(out[i])
for i in range(3, 15):
assert math.isclose(out[i], 9.0, abs_tol=1e-12)
def test_volty_stop_flat_market_constant_level():
# ATR=2, mult=2 -> band 4; anchor stays at close 10 -> stop = 10 - 4 = 6.
h = np.full(20, 11.0)
l = np.full(20, 9.0)
c = np.full(20, 10.0)
out = ta.VoltyStop(5, 2.0).batch(h, l, c)
for i in range(4):
assert math.isnan(out[i])
for i in range(4, 20):
assert math.isclose(out[i], 6.0, abs_tol=1e-12)
def test_yoyo_exit_flat_market_constant_level():
# ATR=2, mult=2 -> band 4; trail = close - band = 10 - 4 = 6 and holds.
h = np.full(20, 11.0)
l = np.full(20, 9.0)
c = np.full(20, 10.0)
out = ta.YoyoExit(5, 2.0).batch(h, l, c)
for i in range(4):
assert math.isnan(out[i])
for i in range(4, 20):
assert math.isclose(out[i], 6.0, abs_tol=1e-12)
def test_rvi_volatility_pure_uptrend_saturates_at_one_hundred():
# Strictly rising closes -> every stddev sample classified as "up" ->
# RVIVolatility saturates at 100. Renamed from the original ta.RVI in
# PR 42 to disambiguate from Family 02's Relative Vigor Index, which
# now owns the short ta.RVI name (candle input).
out = ta.RVIVolatility(5).batch(np.arange(1.0, 41.0, dtype=np.float64))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready[-10:], 100.0, atol=1e-9)
def test_parkinson_volatility_zero_range_yields_zero():
# H == L every bar -> ln(H/L) = 0 -> Parkinson sigma is zero.
h = np.full(30, 10.0)
l = np.full(30, 10.0)
out = ta.ParkinsonVolatility(14, 252).batch(h, l)
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
def test_garman_klass_zero_movement_yields_zero():
# O == H == L == C every bar -> both log terms are zero -> sigma is zero.
o = np.full(30, 10.0)
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.GarmanKlassVolatility(14, 252).batch(o, h, l, c)
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
def test_rogers_satchell_zero_movement_yields_zero():
o = np.full(30, 10.0)
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.RogersSatchellVolatility(14, 252).batch(o, h, l, c)
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
def test_yang_zhang_zero_movement_yields_zero():
# O == H == L == C and constant across bars -> every sub-component is
# zero -> Yang-Zhang sigma is zero.
o = np.full(30, 10.0)
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.YangZhangVolatility(14, 252).batch(o, h, l, c)
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
def test_doji_default_is_directionless_flag():
# Default Doji is a direction-less detection flag: +1 on a doji, 0 else.
d = ta.Doji()
assert d.is_signed() is False
# body 0, range 2 -> doji.
assert d.update((10.0, 11.0, 9.0, 10.0, 1.0, 0)) == pytest.approx(1.0)
# body 2 == range -> not a doji.
assert d.update((10.0, 12.0, 10.0, 12.0, 1.0, 1)) == pytest.approx(0.0)
def test_doji_signed_dragonfly_gravestone_neutral():
# Signed Doji classifies by body position within the range.
d = ta.Doji(signed=True)
assert d.is_signed() is True
# Dragonfly: body at the top, long lower shadow -> bullish +1.
assert d.update((10.0, 10.05, 6.0, 10.0, 1.0, 0)) == pytest.approx(1.0)
# Gravestone: body at the bottom, long upper shadow -> bearish -1.
assert d.update((10.0, 14.0, 9.95, 10.0, 1.0, 1)) == pytest.approx(-1.0)
# Long-legged: body centred, symmetric shadows -> neutral 0.
assert d.update((10.0, 12.0, 8.0, 10.0, 1.0, 2)) == pytest.approx(0.0)
# A large body is not a doji at all -> 0 regardless of position.
assert d.update((10.0, 12.0, 10.0, 12.0, 1.0, 3)) == pytest.approx(0.0)
def test_orderbook_imbalance_reference_values():
# Top-1: (3 - 1) / (3 + 1) = 0.5.
assert ta.OrderBookImbalanceTop1().update([100.0], [3.0], [101.0], [1.0]) == pytest.approx(0.5)
# Top-2: bidDepth 3, askDepth 2 -> (3 - 2) / 5 = 0.2.
topn = ta.OrderBookImbalanceTopN(2)
assert topn.update([100.0, 99.0], [2.0, 1.0], [101.0, 102.0], [1.0, 1.0]) == pytest.approx(0.2)
# Full: bidDepth 1, askDepth 3 -> (1 - 3) / 4 = -0.5.
full = ta.OrderBookImbalanceFull()
assert full.update([100.0], [1.0], [101.0, 102.0], [2.0, 1.0]) == pytest.approx(-0.5)
def test_microprice_reference_value():
# (100*3 + 101*1) / (1 + 3) = 401 / 4 = 100.25 — heavy ask pulls toward bid.
mp = ta.Microprice()
assert mp.update([100.0], [1.0], [101.0], [3.0]) == pytest.approx(100.25)
def test_quoted_spread_reference_value():
# spread 1.0, mid 100.5 -> 1 / 100.5 * 10_000 ≈ 99.5025 bps.
qs = ta.QuotedSpread()
assert qs.update([100.0], [1.0], [101.0], [1.0]) == pytest.approx(99.50248756, abs=1e-6)
def test_depth_slope_reference_value():
# Symmetric book, each side distances 1, 2 with cumulative sizes 1, 3.
# OLS slope of (1->1, 2->3) = 2; mean of two equal sides = 2.
ds = ta.DepthSlope()
out = ds.update([99.0, 98.0], [1.0, 2.0], [101.0, 102.0], [1.0, 2.0])
assert out == pytest.approx(2.0, abs=1e-9)
# A book with a single level per side has no slope -> 0.
assert ta.DepthSlope().update([100.0], [1.0], [101.0], [1.0]) == pytest.approx(0.0)
def test_footprint_buckets_buy_and_sell_volume():
fp = ta.Footprint(1.0)
fp.update(100.2, 2.0, True) # bucket 100 -> ask 2
fp.update(100.7, 3.0, False) # bucket 101 -> bid 3
out = fp.update(100.1, 1.0, True) # bucket 100 -> ask 3
# Columns are [price, bid_vol, ask_vol], rows sorted ascending by price.
assert out.shape == (2, 3)
assert list(out[0]) == [100.0, 0.0, 3.0]
assert list(out[1]) == [101.0, 3.0, 0.0]
def test_signed_volume_reference_values():
assert ta.SignedVolume().update(100.0, 2.0, True) == pytest.approx(2.0)
assert ta.SignedVolume().update(100.0, 3.0, False) == pytest.approx(-3.0)
def test_cumulative_volume_delta_reference_values():
cvd = ta.CumulativeVolumeDelta()
assert cvd.update(100.0, 5.0, True) == pytest.approx(5.0)
assert cvd.update(100.0, 2.0, False) == pytest.approx(3.0)
assert cvd.update(100.0, 4.0, False) == pytest.approx(-1.0)
def test_trade_imbalance_reference_value():
ti = ta.TradeImbalance(2)
assert ti.update(100.0, 3.0, True) is None # warming up
# Window full: buyVol 3, sellVol 1 -> (3 - 1) / 4 = 0.5.
assert ti.update(100.0, 1.0, False) == pytest.approx(0.5)
def test_effective_spread_reference_values():
# Buy at 100.05 vs mid 100.0: 2 * (100.05 - 100) / 100 * 10000 = 10 bps.
assert ta.EffectiveSpread().update(100.05, 1.0, True, 100.0) == pytest.approx(10.0)
# Sell at 99.95 vs mid 100.0: 2 * -1 * (99.95 - 100) / 100 * 10000 = 10 bps.
assert ta.EffectiveSpread().update(99.95, 1.0, False, 100.0) == pytest.approx(10.0)
# A buy filled below the mid is price improvement -> negative.
assert ta.EffectiveSpread().update(99.95, 1.0, True, 100.0) < 0.0
def test_realized_spread_reference_value():
rs = ta.RealizedSpread(1)
assert rs.update(100.10, 1.0, True, 100.0) is None # buffered
# Resolved against mid 100.20 one trade later:
# 2 * (+1) * (100.10 - 100.20) / 100.0 * 10000 = -20 bps (adverse selection).
assert rs.update(99.90, 1.0, False, 100.20) == pytest.approx(-20.0)
def test_kyles_lambda_recovers_constant_impact():
# Build a tape where each trade moves the mid by exactly 0.5 per unit of
# signed volume -> the rolling OLS slope is 0.5.
impact = 0.5
mid = 100.0
price, size, is_buy, mids = [], [], [], []
for i in range(20):
buy = i % 2 == 0
sz = 1.0 + (i % 3)
signed = sz if buy else -sz
mid += impact * signed
price.append(mid)
size.append(sz)
is_buy.append(buy)
mids.append(mid)
out = ta.KylesLambda(6).batch(price, size, is_buy, mids)
assert out[-1] == pytest.approx(0.5, abs=1e-9)
def test_funding_rate_reference_values():
assert ta.FundingRate().update(0.0001) == pytest.approx(0.0001)
assert ta.FundingRate().update(-0.0003) == pytest.approx(-0.0003)
def test_funding_rate_mean_reference_value():
frm = ta.FundingRateMean(2)
assert frm.update(0.001) is None # warming up
# Window [0.001, 0.003] -> mean 0.002.
assert frm.update(0.003) == pytest.approx(0.002)
def test_funding_rate_zscore_reference_value():
z = ta.FundingRateZScore(2)
assert z.update(0.001) is None # warming up
# Window [0.001, 0.003]: mean 0.002, population stddev 0.001 -> +1.
assert z.update(0.003) == pytest.approx(1.0, abs=1e-9)
def test_funding_basis_reference_value():
# mark 100.5 vs index 100.0 -> (100.5 - 100.0) / 100.0 = 0.005.
assert ta.FundingBasis().update(100.5, 100.0) == pytest.approx(0.005)
# A discount reads negative.
assert ta.FundingBasis().update(99.5, 100.0) == pytest.approx(-0.005)
def test_open_interest_delta_reference_value():
oid = ta.OpenInterestDelta()
assert oid.update(1000.0) is None # seeds the previous OI
assert oid.update(1250.0) == pytest.approx(250.0)
assert oid.update(1100.0) == pytest.approx(-150.0)
def test_oi_price_divergence_reference_value():
div = ta.OIPriceDivergence(1)
assert div.update(1000.0, 100.0) is None # warming up
# OI +10% while price flat -> divergence +0.1.
assert div.update(1100.0, 100.0) == pytest.approx(0.1)
def test_oi_weighted_reference_value():
oiw = ta.OIWeighted()
assert oiw.update(100.0, 10.0) == pytest.approx(100.0)
# (100·10 + 110·30) / 40 = 107.5.
assert oiw.update(110.0, 30.0) == pytest.approx(107.5)
def test_long_short_ratio_reference_value():
# 600 longs vs 400 shorts -> 1.5.
assert ta.LongShortRatio().update(600.0, 400.0) == pytest.approx(1.5)
# No short side -> 0.0.
assert ta.LongShortRatio().update(600.0, 0.0) == pytest.approx(0.0)
def test_taker_buy_sell_ratio_reference_value():
# 60 taker buys vs 40 taker sells -> 1.5.
assert ta.TakerBuySellRatio().update(60.0, 40.0) == pytest.approx(1.5)
# No taker sell volume -> 0.0.
assert ta.TakerBuySellRatio().update(60.0, 0.0) == pytest.approx(0.0)
def test_liquidation_features_reference_value():
# 30 long vs 10 short: (long, short, net, total, imbalance).
out = ta.LiquidationFeatures().update(30.0, 10.0)
assert out == pytest.approx((30.0, 10.0, 20.0, 40.0, 0.5))
def test_term_structure_basis_reference_value():
# futures 102 vs index 100 -> 0.02 (contango).
assert ta.TermStructureBasis().update(102.0, 100.0) == pytest.approx(0.02)
# Backwardation reads negative.
assert ta.TermStructureBasis().update(98.0, 100.0) == pytest.approx(-0.02)
def test_calendar_spread_reference_value():
# futures 101 vs perpetual mark 100 -> 0.01.
assert ta.CalendarSpread().update(101.0, 100.0) == pytest.approx(0.01)
assert ta.CalendarSpread().update(99.0, 100.0) == pytest.approx(-0.01)
+134
View File
@@ -12,6 +12,7 @@ SCALAR_INDICATORS = [
(ta.EMA, (14,)),
(ta.WMA, (14,)),
(ta.RSI, (14,)),
(ta.AnchoredRSI, ()),
(ta.MACD, ()),
(ta.BollingerBands, ()),
]
@@ -42,6 +43,7 @@ def test_reset_returns_to_initial_state(cls, args):
(ta.EMA, (14,), 14),
(ta.WMA, (14,), 14),
(ta.RSI, (14,), 15),
(ta.AnchoredRSI, (), 2),
(ta.BollingerBands, (20, 2.0), 20),
],
)
@@ -86,3 +88,135 @@ def test_candle_tuple_input_supported():
atr.update((10.0, 11.0, 9.0, 10.5, 1.0, 0))
v = atr.update((10.5, 12.0, 10.0, 11.0, 1.0, 1))
assert v is not None
def test_initial_balance_reset_unlocks():
ib = ta.InitialBalance(2)
assert not ib.is_ready()
ib.update((101.0, 102.0, 100.0, 101.0, 0.0, 0))
ib.update((102.0, 103.0, 101.0, 102.0, 0.0, 1))
assert ib.is_ready()
assert ib.is_locked()
ib.reset()
assert not ib.is_ready()
assert not ib.is_locked()
def test_opening_range_reset_unlocks():
or_ind = ta.OpeningRange(2)
or_ind.update((101.0, 102.0, 100.0, 101.0, 0.0, 0))
or_ind.update((102.0, 103.0, 101.0, 102.0, 0.0, 1))
assert or_ind.is_locked()
or_ind.reset()
assert not or_ind.is_locked()
def test_value_area_warmup_equals_period():
assert ta.ValueArea(20, 50, 0.70).warmup_period() == 20
assert ta.ValueArea(10, 30, 0.80).warmup_period() == 10
def test_ehlers_indicators_lifecycle():
# Spot-check a few Family-10 entries beyond what test_new_indicators covers.
series = np.linspace(1.0, 200.0, 200) + np.sin(np.arange(200) * 0.3) * 5.0
for ind in [
ta.SuperSmoother(10),
ta.FisherTransform(10),
ta.MAMA(),
ta.HilbertDominantCycle(),
ta.SineWave(),
]:
assert not ind.is_ready()
ind.batch(series)
assert ind.is_ready()
ind.reset()
assert not ind.is_ready()
def test_orderbook_lifecycle():
snapshot = ([100.0], [1.0], [101.0], [1.0])
for ind in [
ta.OrderBookImbalanceTop1(),
ta.OrderBookImbalanceTopN(3),
ta.OrderBookImbalanceFull(),
ta.Microprice(),
ta.QuotedSpread(),
ta.DepthSlope(),
]:
assert ind.warmup_period() == 1
assert not ind.is_ready()
ind.update(*snapshot)
assert ind.is_ready()
ind.reset()
assert not ind.is_ready()
def test_orderbook_topn_repr():
assert repr(ta.OrderBookImbalanceTopN(5)) == "OrderBookImbalanceTopN(levels=5)"
def test_tradeflow_lifecycle():
for ind in [ta.SignedVolume(), ta.CumulativeVolumeDelta()]:
assert ind.warmup_period() == 1
assert not ind.is_ready()
ind.update(100.0, 1.0, True)
assert ind.is_ready()
ind.reset()
assert not ind.is_ready()
def test_trade_imbalance_lifecycle_and_repr():
ti = ta.TradeImbalance(3)
assert ti.warmup_period() == 3
assert not ti.is_ready()
for _ in range(3):
ti.update(100.0, 1.0, True)
assert ti.is_ready()
ti.reset()
assert not ti.is_ready()
assert repr(ta.TradeImbalance(4)) == "TradeImbalance(window=4)"
def test_effective_spread_lifecycle():
es = ta.EffectiveSpread()
assert es.warmup_period() == 1
assert not es.is_ready()
es.update(100.05, 1.0, True, 100.0)
assert es.is_ready()
es.reset()
assert not es.is_ready()
def test_realized_spread_lifecycle_and_repr():
rs = ta.RealizedSpread(3)
assert rs.warmup_period() == 4
assert not rs.is_ready()
for _ in range(4):
rs.update(100.0, 1.0, True, 100.0)
assert rs.is_ready()
rs.reset()
assert not rs.is_ready()
assert repr(ta.RealizedSpread(5)) == "RealizedSpread(horizon=5)"
def test_kyles_lambda_lifecycle_and_repr():
kl = ta.KylesLambda(3)
assert kl.warmup_period() == 4
assert not kl.is_ready()
for i in range(4):
kl.update(100.0 + i, 1.0 + (i % 2), i % 2 == 0, 100.0 + i)
assert kl.is_ready()
kl.reset()
assert not kl.is_ready()
assert repr(ta.KylesLambda(7)) == "KylesLambda(window=7)"
def test_footprint_lifecycle_and_repr():
fp = ta.Footprint(0.5)
assert fp.warmup_period() == 1
assert not fp.is_ready()
fp.update(100.0, 1.0, True)
assert fp.is_ready()
fp.reset()
assert not fp.is_ready()
assert repr(ta.Footprint(0.25)) == "Footprint(tick_size=0.25)"
File diff suppressed because it is too large Load Diff
+132
View File
@@ -0,0 +1,132 @@
"""Streaming-vs-batch equivalence and reference values for the Seasonality &
Session family.
These indicators read the full candle (including ``timestamp``), so they have a
dedicated test rather than joining the timestamp-less parametrize harness in
``test_new_indicators.py``.
"""
import numpy as np
import pytest
import wickra as ta
HOUR_MS = 3_600_000
@pytest.fixture(scope="module")
def candle_columns():
"""240 hourly candles (10 days) with valid OHLCV and epoch-ms timestamps."""
n = 240
t = np.arange(n, dtype=np.float64)
close = 100.0 + np.sin(t * 0.3) * 5.0 + np.cos(t * 0.1) * 3.0
open_ = close + np.sin(t * 0.5) * 0.5
high = np.maximum(open_, close) + 1.0
low = np.minimum(open_, close) - 1.0
volume = 1000.0 + (t % 24) * 50.0
timestamp = (np.arange(n, dtype=np.int64)) * HOUR_MS
return open_, high, low, close, volume, timestamp
def _candles(cols):
open_, high, low, close, volume, timestamp = cols
return [
(open_[i], high[i], low[i], close[i], volume[i], int(timestamp[i]))
for i in range(len(close))
]
def _check_scalar(make, cols):
candles = _candles(cols)
a, b = make(), make()
stream = np.array(
[np.nan if (v := a.update(c)) is None else v for c in candles],
dtype=np.float64,
)
batch = np.asarray(b.batch(*cols))
np.testing.assert_allclose(stream, batch, equal_nan=True, rtol=1e-9, atol=1e-9)
def _check_matrix(make, k, cols):
candles = _candles(cols)
a, b = make(), make()
rows = []
for c in candles:
out = a.update(c)
rows.append(np.full(k, np.nan) if out is None else np.asarray(out, dtype=float))
stream = np.vstack(rows)
batch = np.asarray(b.batch(*cols))
assert batch.shape == (len(candles), k)
np.testing.assert_allclose(stream, batch, equal_nan=True, rtol=1e-9, atol=1e-9)
SCALAR = [
lambda: ta.SessionVwap(0),
lambda: ta.OvernightGap(0),
lambda: ta.SeasonalZScore(0),
lambda: ta.AverageDailyRange(3, 0),
lambda: ta.TurnOfMonth(3, 1, 0),
]
MATRIX = [
(lambda: ta.SessionHighLow(0), 2),
(lambda: ta.SessionRange(0), 3),
(lambda: ta.OvernightIntradayReturn(0), 2),
(lambda: ta.TimeOfDayReturnProfile(24, 0), 24),
(lambda: ta.IntradayVolatilityProfile(12, 0), 12),
(lambda: ta.VolumeByTimeProfile(24, 0), 24),
(lambda: ta.DayOfWeekProfile(0), 7),
]
@pytest.mark.parametrize("make", SCALAR)
def test_scalar_streaming_equals_batch(make, candle_columns):
_check_scalar(make, candle_columns)
@pytest.mark.parametrize("make,k", MATRIX)
def test_matrix_streaming_equals_batch(make, k, candle_columns):
_check_matrix(make, k, candle_columns)
def test_session_vwap_reference():
vwap = ta.SessionVwap(0)
# typical = close for a flat candle; volume-weighted within the day.
v1 = vwap.update((100.0, 100.0, 100.0, 100.0, 10.0, 0))
assert v1 == pytest.approx(100.0)
v2 = vwap.update((110.0, 110.0, 110.0, 110.0, 30.0, HOUR_MS))
assert v2 == pytest.approx(107.5)
# New day re-anchors.
v3 = vwap.update((200.0, 200.0, 200.0, 200.0, 5.0, 24 * HOUR_MS))
assert v3 == pytest.approx(200.0)
def test_overnight_gap_reference():
gap = ta.OvernightGap(0)
assert gap.update((99.0, 101.0, 98.0, 100.0, 1.0, 0)) is None
g = gap.update((105.0, 106.0, 104.0, 105.5, 1.0, 24 * HOUR_MS))
assert g == pytest.approx(0.05)
def test_session_high_low_reference():
shl = ta.SessionHighLow(0)
shl.update((100.0, 105.0, 99.0, 101.0, 1.0, 0))
out = shl.update((101.0, 108.0, 100.0, 107.0, 1.0, HOUR_MS))
assert out == (108.0, 99.0)
def test_volume_by_time_profile_reference():
prof = ta.VolumeByTimeProfile(24, 0)
out = prof.update((100.0, 100.0, 100.0, 100.0, 500.0, HOUR_MS)) # 01:00 -> bucket 1
assert out[1] == pytest.approx(500.0)
assert out[0] == pytest.approx(0.0)
def test_rejects_zero_buckets():
with pytest.raises(ValueError):
ta.TimeOfDayReturnProfile(0, 0)
def test_average_daily_range_rejects_zero_period():
with pytest.raises(ValueError):
ta.AverageDailyRange(0, 0)
+112
View File
@@ -55,3 +55,115 @@ def test_obv_batch_shape(ohlc_series):
volume = np.ones_like(close)
out = ta.OBV().batch(close, volume)
assert out.shape == close.shape
def test_value_area_batch_shape(ohlc_series):
high, low, close = ohlc_series
volume = np.ones_like(close)
out = ta.ValueArea(20, 50, 0.70).batch(high, low, volume)
assert out.shape == (close.size, 3)
def test_initial_balance_batch_shape(ohlc_series):
high, low, _close = ohlc_series
out = ta.InitialBalance(12).batch(high, low)
assert out.shape == (high.size, 2)
def test_opening_range_batch_shape(ohlc_series):
high, low, close = ohlc_series
out = ta.OpeningRange(6).batch(high, low, close)
assert out.shape == (close.size, 3)
def test_ichimoku_batch_returns_n_by_5(ohlc_series):
high, low, close = ohlc_series
out = ta.Ichimoku().batch(high, low, close)
assert out.shape == (close.size, 5)
def test_heikin_ashi_batch_returns_n_by_4(ohlc_series):
high, low, close = ohlc_series
open_ = (high + low) / 2.0
out = ta.HeikinAshi().batch(open_, high, low, close)
assert out.shape == (close.size, 4)
def test_ehlers_super_smoother_batch_shape(sine_prices):
out = ta.SuperSmoother(10).batch(sine_prices)
assert out.shape == sine_prices.shape
def test_mama_batch_shape(sine_prices):
out = ta.MAMA().batch(sine_prices)
assert out.shape == (sine_prices.size, 2)
def test_orderbook_indicators_construct_and_emit():
# All five order-book indicators accept a four-array snapshot and emit a float.
snapshot = ([100.0, 99.0], [2.0, 1.0], [101.0, 102.0], [1.0, 1.0])
indicators = [
ta.OrderBookImbalanceTop1(),
ta.OrderBookImbalanceTopN(2),
ta.OrderBookImbalanceFull(),
ta.Microprice(),
ta.QuotedSpread(),
ta.DepthSlope(),
]
for ind in indicators:
out = ind.update(*snapshot)
assert isinstance(out, float)
def test_orderbook_batch_returns_one_value_per_snapshot():
snapshots = [([100.0], [3.0], [101.0], [1.0])] * 5
out = ta.OrderBookImbalanceTop1().batch(snapshots)
assert out.shape == (5,)
assert out.dtype == np.float64
def test_tradeflow_indicators_construct_and_emit():
# SignedVolume and CVD emit from the first trade; TradeImbalance(1) too.
assert isinstance(ta.SignedVolume().update(100.0, 2.0, True), float)
assert isinstance(ta.CumulativeVolumeDelta().update(100.0, 2.0, True), float)
assert isinstance(ta.TradeImbalance(1).update(100.0, 2.0, True), float)
def test_tradeflow_batch_returns_one_value_per_trade():
price = np.full(6, 100.0)
size = np.array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0])
is_buy = [True, False, True, False, True, False]
out = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
assert out.shape == (6,)
assert out.dtype == np.float64
def test_price_impact_indicators_construct_and_emit():
# Price-impact indicators take a trade paired with the prevailing mid.
assert isinstance(ta.EffectiveSpread().update(100.05, 1.0, True, 100.0), float)
# RealizedSpread buffers until its horizon elapses.
assert ta.RealizedSpread(1).update(100.05, 1.0, True, 100.0) is None
def test_price_impact_batch_returns_one_value_per_trade():
price = np.array([100.05, 99.95, 100.10, 99.90])
size = np.array([1.0, 2.0, 1.0, 2.0])
is_buy = [True, False, True, False]
mid = np.full(4, 100.0)
for ind in (ta.EffectiveSpread(), ta.RealizedSpread(2), ta.KylesLambda(2)):
out = ind.batch(price, size, is_buy, mid)
assert out.shape == (4,)
assert out.dtype == np.float64
def test_footprint_constructs_and_emits():
out = ta.Footprint(1.0).update(100.2, 2.0, True)
assert out.shape == (1, 3)
assert out.dtype == np.float64
def test_footprint_batch_returns_list_of_arrays():
res = ta.Footprint(1.0).batch([100.2, 100.7], [2.0, 3.0], [True, False])
assert isinstance(res, list)
assert len(res) == 2
assert res[-1].shape[1] == 3
@@ -117,6 +117,30 @@ def test_obv_streaming_matches_batch(ohlc_series):
assert _equal_with_nan(batch, streamed)
def test_mama_streaming_matches_batch(sine_prices):
batch = ta.MAMA().batch(sine_prices)
streamer = ta.MAMA()
rows = []
for p in sine_prices:
v = streamer.update(float(p))
if v is None:
rows.append([math.nan, math.nan])
else:
rows.append(list(v))
streamed = np.array(rows, dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_super_smoother_streaming_matches_batch(sine_prices):
batch = ta.SuperSmoother(10).batch(sine_prices)
streamer = ta.SuperSmoother(10)
streamed = np.array(
[math.nan if (v := streamer.update(float(p))) is None else float(v) for p in sine_prices],
dtype=np.float64,
)
assert _equal_with_nan(batch, streamed)
def test_rolling_vwap_streaming_matches_batch(ohlc_series):
# RollingVWAP(20) on the shared OHLC series. Provides finite-memory VWAP
# parity coverage now that the indicator is exposed across all bindings.
@@ -135,3 +159,92 @@ def test_rolling_vwap_streaming_matches_batch(ohlc_series):
assert streamer.is_ready()
streamer.reset()
assert not streamer.is_ready()
def test_value_area_streaming_matches_batch(ohlc_series):
high, low, close = ohlc_series
volume = np.linspace(100.0, 200.0, num=close.size, dtype=np.float64)
batch = ta.ValueArea(20, 50, 0.70).batch(high, low, volume)
streamer = ta.ValueArea(20, 50, 0.70)
rows = []
for h, l, v in zip(high, low, volume):
mid = float((h + l) / 2.0)
out = streamer.update((mid, float(h), float(l), mid, float(v), 0))
rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
streamed = np.array(rows, dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_initial_balance_streaming_matches_batch(ohlc_series):
high, low, _close = ohlc_series
batch = ta.InitialBalance(12).batch(high, low)
streamer = ta.InitialBalance(12)
rows = []
for h, l in zip(high, low):
mid = float((h + l) / 2.0)
out = streamer.update((mid, float(h), float(l), mid, 0.0, 0))
rows.append([math.nan, math.nan] if out is None else list(out))
streamed = np.array(rows, dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_opening_range_streaming_matches_batch(ohlc_series):
high, low, close = ohlc_series
batch = ta.OpeningRange(6).batch(high, low, close)
streamer = ta.OpeningRange(6)
rows = []
for h, l, c in zip(high, low, close):
out = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
streamed = np.array(rows, dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_orderbook_streaming_matches_batch():
snaps = [
(
[100.0, 99.0],
[1.0 + (i % 5), 1.0],
[101.0, 102.0],
[1.0 + ((i + 1) % 3), 1.0],
)
for i in range(30)
]
batch = ta.Microprice().batch(snaps)
streamer = ta.Microprice()
streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_tradeflow_streaming_matches_batch():
n = 30
price = np.full(n, 100.0)
size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
is_buy = [i % 3 != 0 for i in range(n)]
batch = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
streamer = ta.CumulativeVolumeDelta()
streamed = np.array(
[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
dtype=np.float64,
)
assert _equal_with_nan(batch, streamed)
def test_price_impact_streaming_matches_batch():
n = 30
mid = np.array([100.0 + 0.25 * math.sin(i * 0.5) for i in range(n)], dtype=np.float64)
is_buy = [i % 3 != 0 for i in range(n)]
price = np.array(
[mid[i] + (0.03 if is_buy[i] else -0.03) for i in range(n)], dtype=np.float64
)
size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
batch = ta.EffectiveSpread().batch(price, size, is_buy, mid)
streamer = ta.EffectiveSpread()
streamed = np.array(
[streamer.update(price[i], size[i], is_buy[i], mid[i]) for i in range(n)],
dtype=np.float64,
)
assert _equal_with_nan(batch, streamed)
+45 -281
View File
@@ -1,306 +1,70 @@
# Wickra
# Wickra — WebAssembly
[![CI](https://github.com/kingchenc/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/kingchenc/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/kingchenc/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/kingchenc/wickra)
[![crates.io](https://img.shields.io/crates/v/wickra.svg?logo=rust&color=orange)](https://crates.io/crates/wickra)
[![PyPI](https://img.shields.io/pypi/v/wickra.svg?logo=pypi&color=blue)](https://pypi.org/project/wickra/)
[![npm](https://img.shields.io/npm/v/wickra.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](LICENSE)
[![CI](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/wickra-lib/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/wickra-lib/wickra)
[![npm](https://img.shields.io/npm/v/wickra-wasm.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra-wasm)
[![License: MIT OR Apache-2.0](https://img.shields.io/badge/license-MIT_OR_Apache--2.0-blue)](https://github.com/wickra-lib/wickra#license)
**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators in the browser. `npm install
wickra-wasm` — pure WebAssembly, runs anywhere a modern JS engine does.**
Wickra is a multi-language technical-analysis library with a Rust core and
bindings for Python, Node.js, and WebAssembly. Every indicator is a state
machine that updates in O(1) per new data point, so live trading bots and
historical backtests share the exact same implementation.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading dashboards and historical backtests
share the exact same implementation. This package is the WebAssembly binding
(wasm-bindgen, built for the `web` target); it exposes 200+ streaming-first
indicators across sixteen families.
```python
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: same indicator, fed tick by tick
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # O(1) — no recomputation over history
if value is not None and value > 70:
print("overbought")
```
## Why Wickra exists
The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
talipp, tulipy — and every one of them shares the same blind spot:
| Library | Install pain | Streaming | Multi-language | Active |
|--------------------|-----------------|-----------|----------------|--------|
| TA-Lib (Python) | yes (C deps) | no | no | barely |
| pandas-ta | clean | no | no | slow |
| finta | clean | no | no | stale |
| ta-lib-python | yes (C deps) | no | no | barely |
| talipp | clean | yes | no | yes |
| Tulip Indicators | yes (C deps) | no | partial | stale |
| ooples (C#) | clean | no | C# only | yes |
| **Wickra** | **clean** | **yes** | **Python+Node+WASM+Rust** | **yes** |
Wickra is the only library that combines all of: clean install, streaming,
multi-language reach, and active maintenance.
## Benchmark: how much faster is "streaming-first"?
The numbers below were measured on a single developer workstation and are not
guaranteed to reproduce identically on different hardware — absolute µs values
depend on CPU, memory clock and OS scheduler. Read them as **relative
speedups** between libraries on identical input, not as a universal
performance contract.
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 7950X3D, 64 GB DDR5,
Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
Python 3.12, Node 20.
- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries`. The script auto-detects every
installed peer library and runs them on the same generated inputs as
Wickra. The CI job `cross-library-bench` runs the same script on every
push and uploads the raw report as a build artefact.
Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
### Batch — single full pass over a 20 000-bar series
Reading the table: each cell shows that library's runtime, plus how many times
slower it is than Wickra in parentheses. **★** marks the winner per row.
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### Streaming — per-tick latency after seeding with 5 000 historical bars
A batch-only library has to re-run its full indicator over the entire history on
every new tick; Wickra updates state in O(1).
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
> TA-Lib and pandas-ta are not included here because both fail to install
> cleanly on Windows without C build tooling — which is precisely the install
> pain Wickra was built to remove. The benchmark script auto-detects every
> peer library it can find and runs them on the same inputs as Wickra; install
> them in your environment to see those rows light up too.
Run the suite yourself:
## Install
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
npm install wickra-wasm
```
## Indicators
## Quick start
71 streaming-first indicators across eight families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
The module ships a default `init` export that loads the `.wasm` payload; await
it once before constructing indicators.
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator |
| Trend & Directional | MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility |
| Trailing Stops | Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle |
```js
import init, { RSI } from 'wickra-wasm';
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
await init(); // load the WebAssembly module once
## Languages
| Binding | Install | Example |
|-------------------|-----------------------------------------------|---------|
| Python (PyO3) | `pip install wickra` | `examples/python/backtest.py` |
| Node.js (napi-rs) | `npm install wickra` | `examples/node/backtest.js` |
| Browser / WASM | `npm install wickra-wasm` | `examples/wasm/index.html` |
| Rust | `cargo add wickra` | `examples/rust/src/bin/backtest.rs` |
Each binding ships several runnable examples (streaming, backtest, live feed);
[`examples/README.md`](examples/README.md) is the full cross-language index.
The wickra-core crate is `unsafe`-forbidden, so every binding inherits a
memory-safe implementation.
## Rust API
```rust
use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};
// Streaming or batch — same trait, same code.
let mut sma = Sma::new(14)?;
let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
let mut rsi = Rsi::new(14)?;
for price in live_feed {
if let Some(v) = rsi.update(price) {
println!("RSI = {v}");
}
}
// Compose indicators: RSI(7) on top of EMA(14).
let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
chain.update(price);
```
## Live data sources
`wickra-data` (separate crate, opt-in) ships:
- A streaming OHLCV **CSV reader**.
- A **tick-to-candle aggregator** with arbitrary timeframes.
- A **candle resampler** for multi-timeframe analysis (1m → 5m → 1h on the fly).
- A **Binance Spot WebSocket** kline adapter (feature `live-binance`).
```rust
use wickra::{Indicator, Rsi};
use wickra_data::live::binance::{BinanceKlineStream, Interval};
let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
let mut rsi = Rsi::new(14)?;
while let Some(event) = stream.next_event().await? {
if event.is_closed {
if let Some(v) = rsi.update(event.candle.close) {
println!("RSI = {v:.2}");
}
}
// Streaming: feed prices tick by tick in O(1).
const rsi = new RSI(14);
for (const price of liveFeed) {
const value = rsi.update(price); // null during warmup
if (value !== null && value > 70) {
console.log('overbought');
}
}
```
A Python live-trading example using the public `websockets` package lives at
`examples/python/live_trading.py`.
Constructors mirror the other bindings (`new SMA(20)`, `new MACD(12, 26, 9)`,
`new BollingerBands(20, 2.0)`, …); `update()` returns the latest value or
`null` while the indicator is still warming up.
## Project layout
## Documentation
```
wickra/
├── crates/
│ ├── wickra-core/ core engine + all 71 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
│ └── wickra-data/ CSV reader, tick aggregator, live exchange feeds
├── bindings/
│ ├── python/ PyO3 + maturin (publishes on PyPI)
│ ├── node/ napi-rs (publishes on npm)
│ └── wasm/ wasm-bindgen (browsers, bundlers, Node)
├── examples/ examples/README.md indexes every language
│ ├── data/ real BTCUSDT OHLCV datasets, one per timeframe
│ ├── rust/ Rust workspace member (`wickra-examples`)
│ ├── python/ backtest, live trading, parallel assets, multi-tf
│ ├── node/ streaming, backtest, live trading (load `wickra`)
│ └── wasm/ browser demo for `wickra-wasm`
└── .github/workflows/ CI and release pipelines
```
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and documentation site:
Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
in the workspace member crate at `examples/rust/`. There is no top-level
`benches/` directory.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Docs** (quickstarts, cookbook, TA-Lib migration): <https://docs.wickra.org>
- **Runnable browser examples:** [`examples/wasm/`](https://github.com/wickra-lib/wickra/tree/main/examples/wasm)
## Building everything from source
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
```bash
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
## Disclaimer
# Python binding (requires Rust toolchain + maturin)
cd bindings/python
maturin develop --release
pytest
# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
wasm-pack build bindings/wasm --target web --release --features panic-hook
# Node binding (requires @napi-rs/cli)
cd bindings/node && npm install && npm run build && npm test
```
## Testing
Every layer is covered; run the suites with the commands in
[Building everything from source](#building-everything-from-source).
- `wickra-core`: unit tests per indicator — textbook reference values
(Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), `batch == streaming`
equivalence, `reset` semantics, NaN/Inf handling, and property tests.
- `wickra-data`: unit tests for CSV decoding, the tick aggregator, the
resampler, and the Binance payload parser.
- `bindings/python`: pytest covering smoke checks, streaming/batch
equivalence, reference values, lifecycle, input validation, and
dict/tuple candle inputs.
- `bindings/node`: `node --test` cases for batch, streaming, and reference
values across all indicators.
- `bindings/wasm`: `wasm-bindgen-test` cases for constructors, equivalence,
and reference values.
## Contributing
Contributions are very welcome — issues, bug reports, ideas, and pull requests
all land in the same place: <https://github.com/kingchenc/wickra>.
A short orientation for first-time contributors:
- **Adding an indicator.** Implement the `Indicator` trait in
`crates/wickra-core/src/indicators/<name>.rs`, wire it into
`indicators/mod.rs` and the crate root, and add reference-value tests,
a `batch == streaming` equivalence test, and (where it makes sense) a
proptest. The four bindings inherit your indicator automatically once
you expose it in the language wrappers.
- **Fixing a numeric bug.** Add a failing test that pins the textbook value
first, then fix the math. Property tests in `crates/wickra-core` catch
most regressions; please don't disable them.
- **Improving a binding.** Each binding lives under `bindings/<lang>` with
its own tests; please keep the `batch == streaming` invariant.
- **Style.** `cargo fmt --all` + `cargo clippy --workspace --all-targets -- -D warnings`
are CI gates; running them locally before pushing keeps reviews short.
For larger architectural changes, open an issue first so we can sketch the
shape together before you invest the time.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](LICENSE).
In plain English: use it, fork it, modify it, redistribute it, file issues, send
pull requests — all welcome. Personal projects, research, education, non-profits,
government, hobby trading bots: all fine. The one thing that's not allowed is
commercial sale of the software or of services built around it. If you want to
use Wickra commercially, get in touch about a license.
---
<p align="center">
<a href="https://github.com/kingchenc/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/kingchenc/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/kingchenc/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
Licensed under either of [Apache-2.0](https://github.com/wickra-lib/wickra/blob/main/LICENSE-APACHE)
or [MIT](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT) at your option.
+10750 -2
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@@ -1,4 +1,4 @@
# Proper nouns that appear in indicator documentation. They are real names,
# not code identifiers, so `clippy::doc_markdown` must not demand backticks.
# `..` keeps clippy's built-in default identifier list in addition to these.
doc-valid-idents = ["LeBeau", ".."]
doc-valid-idents = ["LeBeau", "McClellan", ".."]
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@@ -0,0 +1,22 @@
[package]
name = "wickra-bench"
version.workspace = true
edition.workspace = true
license.workspace = true
publish = false
description = "Internal cross-library benchmark harness (not published)."
[lints]
workspace = true
[dev-dependencies]
wickra = { path = "../wickra" }
wickra-data = { path = "../wickra-data" }
criterion = { workspace = true }
kand = "0.2.2"
ta = "0.5.0"
yata = "0.7.0"
[[bench]]
name = "cross_lib"
harness = false
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@@ -0,0 +1,695 @@
//! Cross-library Criterion benchmark: Wickra vs `kand` vs `ta` (ta-rs) vs `yata`.
//!
//! All four are pure-Rust technical-analysis crates, so this is a like-for-like
//! Rust-vs-Rust comparison with no language-binding overhead. It feeds the exact
//! same BTCUSDT 1-minute candle series used by `crates/wickra/benches/indicators.rs`.
//!
//! Two arenas, kept honest:
//!
//! * **Streaming** (`*/stream`): one value fed at a time. Wickra (`Indicator::update`),
//! ta-rs (`Next::next`) and yata (`Method::next`) carry their own state; `kand`
//! exposes stateless `*_inc` helpers, so the per-tick state is threaded manually
//! here, seeded from `kand`'s own batch output (the seed is computed outside the
//! timed closure). yata only appears for SMA/EMA — its RSI/MACD/Bollinger/ATR are
//! exposed through a heavier signal-oriented indicator API, not a raw-value method,
//! so they are intentionally left out rather than compared unfairly.
//! * **Batch** (`*/batch`): the whole series at once. Only Wickra (`BatchExt::batch`)
//! and `kand` (TA-Lib-style fill-the-output-slice functions) have a real batch API;
//! ta-rs and yata are streaming-only and are deliberately absent from this arena.
//!
//! Run: `cargo bench -p wickra-bench`
// Each indicator's benchmark group spells out every library arm explicitly, which
// runs a few groups over the 100-line lint threshold; that verbosity is the point.
#![allow(clippy::too_many_lines)]
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use std::hint::black_box;
use wickra::{Atr, BatchExt, BollingerBands, Candle, Ema, Indicator, MacdIndicator, Rsi, Sma};
use wickra_data::csv::CandleReader;
use yata::prelude::Method;
const SIZES: &[usize] = &[1_000, 10_000, 50_000];
const SMA_PERIOD: usize = 20;
const EMA_PERIOD: usize = 20;
const RSI_PERIOD: usize = 14;
const ATR_PERIOD: usize = 14;
const BB_PERIOD: usize = 20;
const BB_DEV: f64 = 2.0;
const MACD_FAST: usize = 12;
const MACD_SLOW: usize = 26;
const MACD_SIGNAL: usize = 9;
fn load_candles() -> Vec<Candle> {
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/../../examples/data/btcusdt-1m.csv"
);
CandleReader::open(path)
.expect("dataset present")
.read_all()
.expect("valid OHLCV rows")
}
/// Mean of the first `period` samples — the warmup seed for `kand`'s SMA/EMA `*_inc`.
fn window_mean(series: &[f64], period: usize) -> f64 {
series[..period].iter().sum::<f64>() / period as f64
}
fn sma_group(crit: &mut Criterion, closes: &[f64]) {
let mut group = crit.benchmark_group("sma_20");
for &len in SIZES {
let len = len.min(closes.len());
let series: &[f64] = &closes[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Sma::new(SMA_PERIOD).unwrap();
for &price in series {
black_box(ind.update(price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Sma::new(SMA_PERIOD).unwrap();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
let seed = window_mean(series, SMA_PERIOD);
bencher.iter(|| {
let mut prev = seed;
for idx in SMA_PERIOD..series.len() {
prev = kand::ohlcv::sma::sma_inc(
prev,
series[idx],
series[idx - SMA_PERIOD],
SMA_PERIOD,
)
.unwrap();
black_box(prev);
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut out = vec![0.0; series.len()];
kand::ohlcv::sma::sma(series, SMA_PERIOD, &mut out).unwrap();
black_box(&out);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = ta::indicators::SimpleMovingAverage::new(SMA_PERIOD).unwrap();
for &price in series {
black_box(ta::Next::next(&mut ind, price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("yata/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = yata::methods::SMA::new(SMA_PERIOD as u8, &series[0]).unwrap();
for price in series {
black_box(ind.next(price));
}
});
},
);
}
group.finish();
}
fn ema_group(crit: &mut Criterion, closes: &[f64]) {
let mut group = crit.benchmark_group("ema_20");
for &len in SIZES {
let len = len.min(closes.len());
let series: &[f64] = &closes[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Ema::new(EMA_PERIOD).unwrap();
for &price in series {
black_box(ind.update(price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Ema::new(EMA_PERIOD).unwrap();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
let seed = window_mean(series, EMA_PERIOD);
bencher.iter(|| {
let mut prev = seed;
for &price in &series[EMA_PERIOD..] {
prev = kand::ohlcv::ema::ema_inc(price, prev, EMA_PERIOD, None).unwrap();
black_box(prev);
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut out = vec![0.0; series.len()];
kand::ohlcv::ema::ema(series, EMA_PERIOD, None, &mut out).unwrap();
black_box(&out);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind =
ta::indicators::ExponentialMovingAverage::new(EMA_PERIOD).unwrap();
for &price in series {
black_box(ta::Next::next(&mut ind, price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("yata/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = yata::methods::EMA::new(EMA_PERIOD as u8, &series[0]).unwrap();
for price in series {
black_box(ind.next(price));
}
});
},
);
}
group.finish();
}
fn rsi_group(crit: &mut Criterion, closes: &[f64]) {
let mut group = crit.benchmark_group("rsi_14");
for &len in SIZES {
let len = len.min(closes.len());
let series: &[f64] = &closes[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Rsi::new(RSI_PERIOD).unwrap();
for &price in series {
black_box(ind.update(price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Rsi::new(RSI_PERIOD).unwrap();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
// Wilder seed: simple average of the first `period` gains and losses.
let mut gain = 0.0;
let mut loss = 0.0;
for idx in 1..=RSI_PERIOD {
let delta = series[idx] - series[idx - 1];
if delta > 0.0 {
gain += delta;
} else {
loss -= delta;
}
}
let seed_gain = gain / RSI_PERIOD as f64;
let seed_loss = loss / RSI_PERIOD as f64;
bencher.iter(|| {
let mut avg_gain = seed_gain;
let mut avg_loss = seed_loss;
let mut prev_price = series[RSI_PERIOD];
for &price in &series[RSI_PERIOD + 1..] {
let (rsi, next_gain, next_loss) = kand::ohlcv::rsi::rsi_inc(
price, prev_price, avg_gain, avg_loss, RSI_PERIOD,
)
.unwrap();
avg_gain = next_gain;
avg_loss = next_loss;
prev_price = price;
black_box(rsi);
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut rsi = vec![0.0; series.len()];
let mut avg_gain = vec![0.0; series.len()];
let mut avg_loss = vec![0.0; series.len()];
kand::ohlcv::rsi::rsi(
series,
RSI_PERIOD,
&mut rsi,
&mut avg_gain,
&mut avg_loss,
)
.unwrap();
black_box(&rsi);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = ta::indicators::RelativeStrengthIndex::new(RSI_PERIOD).unwrap();
for &price in series {
black_box(ta::Next::next(&mut ind, price));
}
});
},
);
}
group.finish();
}
fn macd_group(crit: &mut Criterion, closes: &[f64]) {
let mut group = crit.benchmark_group("macd_12_26_9");
for &len in SIZES {
let len = len.min(closes.len());
let series: &[f64] = &closes[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = MacdIndicator::classic();
for &price in series {
black_box(ind.update(price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = MacdIndicator::classic();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
// Seed the fast/slow/signal EMAs from kand's own warmed-up batch state.
let lookback =
kand::ohlcv::macd::lookback(MACD_FAST, MACD_SLOW, MACD_SIGNAL).unwrap();
let mut macd_line = vec![0.0; series.len()];
let mut signal_line = vec![0.0; series.len()];
let mut histogram = vec![0.0; series.len()];
let mut fast_ema = vec![0.0; series.len()];
let mut slow_ema = vec![0.0; series.len()];
kand::ohlcv::macd::macd(
series,
MACD_FAST,
MACD_SLOW,
MACD_SIGNAL,
&mut macd_line,
&mut signal_line,
&mut histogram,
&mut fast_ema,
&mut slow_ema,
)
.unwrap();
let seed_fast = fast_ema[lookback];
let seed_slow = slow_ema[lookback];
let seed_signal = signal_line[lookback];
bencher.iter(|| {
// macd_inc returns (macd, signal, hist) but not the new EMAs, so the
// fast/slow/signal state is threaded with kand's own ema_inc primitive.
let mut prev_fast = seed_fast;
let mut prev_slow = seed_slow;
let mut prev_signal = seed_signal;
for &price in &series[lookback + 1..] {
let fast =
kand::ohlcv::ema::ema_inc(price, prev_fast, MACD_FAST, None).unwrap();
let slow =
kand::ohlcv::ema::ema_inc(price, prev_slow, MACD_SLOW, None).unwrap();
let macd = fast - slow;
let signal =
kand::ohlcv::ema::ema_inc(macd, prev_signal, MACD_SIGNAL, None)
.unwrap();
prev_fast = fast;
prev_slow = slow;
prev_signal = signal;
black_box((macd, signal, macd - signal));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut macd_line = vec![0.0; series.len()];
let mut signal_line = vec![0.0; series.len()];
let mut histogram = vec![0.0; series.len()];
let mut fast_ema = vec![0.0; series.len()];
let mut slow_ema = vec![0.0; series.len()];
kand::ohlcv::macd::macd(
series,
MACD_FAST,
MACD_SLOW,
MACD_SIGNAL,
&mut macd_line,
&mut signal_line,
&mut histogram,
&mut fast_ema,
&mut slow_ema,
)
.unwrap();
black_box(&macd_line);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = ta::indicators::MovingAverageConvergenceDivergence::new(
MACD_FAST,
MACD_SLOW,
MACD_SIGNAL,
)
.unwrap();
for &price in series {
black_box(ta::Next::next(&mut ind, price));
}
});
},
);
}
group.finish();
}
fn bbands_group(crit: &mut Criterion, closes: &[f64]) {
let mut group = crit.benchmark_group("bollinger_20_2");
for &len in SIZES {
let len = len.min(closes.len());
let series: &[f64] = &closes[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
for &price in series {
black_box(ind.update(price));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
// Seed running sma/sum/sum_sq from kand's batch state at the warmup edge.
let mut upper = vec![0.0; series.len()];
let mut middle = vec![0.0; series.len()];
let mut lower = vec![0.0; series.len()];
let mut sma = vec![0.0; series.len()];
let mut variance = vec![0.0; series.len()];
let mut sum = vec![0.0; series.len()];
let mut sum_sq = vec![0.0; series.len()];
kand::ohlcv::bbands::bbands(
series,
BB_PERIOD,
BB_DEV,
BB_DEV,
&mut upper,
&mut middle,
&mut lower,
&mut sma,
&mut variance,
&mut sum,
&mut sum_sq,
)
.unwrap();
let seed_sma = sma[BB_PERIOD - 1];
let seed_sum = sum[BB_PERIOD - 1];
let seed_sum_sq = sum_sq[BB_PERIOD - 1];
bencher.iter(|| {
let mut prev_sma = seed_sma;
let mut prev_sum = seed_sum;
let mut prev_sum_sq = seed_sum_sq;
for idx in BB_PERIOD..series.len() {
let result = kand::ohlcv::bbands::bbands_inc(
series[idx],
prev_sma,
prev_sum,
prev_sum_sq,
series[idx - BB_PERIOD],
BB_PERIOD,
BB_DEV,
BB_DEV,
)
.unwrap();
prev_sma = result.1;
prev_sum = result.4;
prev_sum_sq = result.5;
black_box((result.0, result.1, result.2));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut upper = vec![0.0; series.len()];
let mut middle = vec![0.0; series.len()];
let mut lower = vec![0.0; series.len()];
let mut sma = vec![0.0; series.len()];
let mut variance = vec![0.0; series.len()];
let mut sum = vec![0.0; series.len()];
let mut sum_sq = vec![0.0; series.len()];
kand::ohlcv::bbands::bbands(
series,
BB_PERIOD,
BB_DEV,
BB_DEV,
&mut upper,
&mut middle,
&mut lower,
&mut sma,
&mut variance,
&mut sum,
&mut sum_sq,
)
.unwrap();
black_box(&upper);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = ta::indicators::BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
for &price in series {
black_box(ta::Next::next(&mut ind, price));
}
});
},
);
}
group.finish();
}
fn atr_group(crit: &mut Criterion, candles: &[Candle]) {
let mut group = crit.benchmark_group("atr_14");
for &len in SIZES {
let len = len.min(candles.len());
let series: &[Candle] = &candles[..len];
group.throughput(Throughput::Elements(len as u64));
group.bench_with_input(
BenchmarkId::new("wickra/stream", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Atr::new(ATR_PERIOD).unwrap();
for &candle in series {
black_box(ind.update(candle));
}
});
},
);
group.bench_with_input(
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
bencher.iter(|| {
let mut ind = Atr::new(ATR_PERIOD).unwrap();
black_box(ind.batch(series));
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/stream", len),
&series,
|bencher, &series| {
let high: Vec<f64> = series.iter().map(|candle| candle.high).collect();
let low: Vec<f64> = series.iter().map(|candle| candle.low).collect();
let close: Vec<f64> = series.iter().map(|candle| candle.close).collect();
// Seed prev_atr from kand's batch ATR at the first valid index (= period).
let mut atr_out = vec![0.0; series.len()];
kand::ohlcv::atr::atr(&high, &low, &close, ATR_PERIOD, &mut atr_out).unwrap();
let seed_atr = atr_out[ATR_PERIOD];
bencher.iter(|| {
let mut prev_atr = seed_atr;
for idx in ATR_PERIOD + 1..series.len() {
prev_atr = kand::ohlcv::atr::atr_inc(
high[idx],
low[idx],
close[idx - 1],
prev_atr,
ATR_PERIOD,
)
.unwrap();
black_box(prev_atr);
}
});
},
);
group.bench_with_input(
BenchmarkId::new("kand/batch", len),
&series,
|bencher, &series| {
let high: Vec<f64> = series.iter().map(|candle| candle.high).collect();
let low: Vec<f64> = series.iter().map(|candle| candle.low).collect();
let close: Vec<f64> = series.iter().map(|candle| candle.close).collect();
bencher.iter(|| {
let mut atr_out = vec![0.0; series.len()];
kand::ohlcv::atr::atr(&high, &low, &close, ATR_PERIOD, &mut atr_out).unwrap();
black_box(&atr_out);
});
},
);
group.bench_with_input(
BenchmarkId::new("ta-rs/stream", len),
&series,
|bencher, &series| {
let items: Vec<ta::DataItem> = series
.iter()
.map(|candle| {
ta::DataItem::builder()
.open(candle.open)
.high(candle.high)
.low(candle.low)
.close(candle.close)
.volume(candle.volume)
.build()
.unwrap()
})
.collect();
bencher.iter(|| {
let mut ind = ta::indicators::AverageTrueRange::new(ATR_PERIOD).unwrap();
for item in &items {
black_box(ta::Next::next(&mut ind, item));
}
});
},
);
}
group.finish();
}
fn benches(crit: &mut Criterion) {
let candles = load_candles();
let closes: Vec<f64> = candles.iter().map(|candle| candle.close).collect();
sma_group(crit, &closes);
ema_group(crit, &closes);
rsi_group(crit, &closes);
macd_group(crit, &closes);
bbands_group(crit, &closes);
atr_group(crit, &candles);
}
criterion_group!(name = cross_lib; config = Criterion::default(); targets = benches);
criterion_main!(cross_lib);
+6
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@@ -0,0 +1,6 @@
//! Internal cross-library benchmark harness for Wickra.
//!
//! This crate is `publish = false`. It exists only to host the Criterion
//! benchmark in `benches/cross_lib.rs`, which compares Wickra against the
//! Rust technical-analysis crates `kand`, `ta` (ta-rs) and `yata` on an
//! identical candle series. It deliberately carries no library code.
+203
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@@ -0,0 +1,203 @@
//! Pure calendar arithmetic for the timestamp-driven seasonality indicators.
//!
//! Every indicator in the *Seasonality & Session* family keys off the wall-clock
//! fields of [`Candle::timestamp`](crate::Candle) (epoch milliseconds), shifted
//! by a caller-supplied `utc_offset_minutes` so the buckets line up with the
//! relevant exchange session rather than UTC. This module turns an epoch
//! millisecond instant into its civil fields using Howard Hinnant's
//! branch-light `civil_from_days` algorithm (the same one libc++ ships).
//!
//! All arithmetic is floor-based (`div_euclid`/`rem_euclid`) so instants before
//! the Unix epoch decompose correctly without a dedicated negative-input branch.
/// Civil (wall-clock) decomposition of an epoch-millisecond instant.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub(crate) struct CivilTime {
/// Proleptic Gregorian year (can be negative for instants before year 1).
pub(crate) year: i64,
/// Month of year, `1..=12`.
pub(crate) month: u32,
/// Day of month, `1..=31`.
pub(crate) day: u32,
/// Hour of day, `0..=23`.
pub(crate) hour: u32,
/// Minute of hour, `0..=59`.
pub(crate) minute: u32,
/// Day of week with Monday as `0` through Sunday as `6`.
pub(crate) weekday: u32,
}
impl CivilTime {
/// Minute of day, `0..=1439`.
pub(crate) const fn minute_of_day(&self) -> u32 {
self.hour * 60 + self.minute
}
}
/// Decompose an epoch-millisecond instant into local civil fields.
///
/// `utc_offset_minutes` shifts the instant before decomposition: `0` yields
/// UTC, `-300` U.S. Eastern standard time, `60` Central European time, etc.
pub(crate) fn civil_from_timestamp(millis: i64, utc_offset_minutes: i32) -> CivilTime {
let local_secs = millis.div_euclid(1000) + i64::from(utc_offset_minutes) * 60;
let days = local_secs.div_euclid(86_400);
let secs_of_day = local_secs.rem_euclid(86_400);
let hour = (secs_of_day / 3600) as u32;
let minute = ((secs_of_day % 3600) / 60) as u32;
let (year, month, day) = civil_from_days(days);
// 1970-01-01 was a Thursday; Monday-based weekday is `(z + 3) mod 7`.
let weekday = (days + 3).rem_euclid(7) as u32;
CivilTime {
year,
month,
day,
hour,
minute,
weekday,
}
}
/// Gregorian `(year, month, day)` for a day count `z` relative to 1970-01-01.
///
/// Howard Hinnant, "chrono-Compatible Low-Level Date Algorithms".
fn civil_from_days(z: i64) -> (i64, u32, u32) {
let z = z + 719_468;
let era = if z >= 0 { z } else { z - 146_096 } / 146_097;
let doe = z - era * 146_097; // [0, 146096]
let yoe = (doe - doe / 1460 + doe / 36_524 - doe / 146_096) / 365; // [0, 399]
let year = yoe + era * 400;
let doy = doe - (365 * yoe + yoe / 4 - yoe / 100); // [0, 365]
let mp = (5 * doy + 2) / 153; // [0, 11]
let day = (doy - (153 * mp + 2) / 5 + 1) as u32; // [1, 31]
let month = if mp < 10 { mp + 3 } else { mp - 9 } as u32; // [1, 12]
(if month <= 2 { year + 1 } else { year }, month, day)
}
/// Whether `year` is a Gregorian leap year.
pub(crate) const fn is_leap(year: i64) -> bool {
(year % 4 == 0 && year % 100 != 0) || year % 400 == 0
}
/// Number of days in `month` (`1..=12`) of `year`.
pub(crate) const fn days_in_month(year: i64, month: u32) -> u32 {
match month {
1 | 3 | 5 | 7 | 8 | 10 | 12 => 31,
4 | 6 | 9 | 11 => 30,
_ => {
if is_leap(year) {
29
} else {
28
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn epoch_zero_is_thursday_midnight() {
let t = civil_from_timestamp(0, 0);
assert_eq!(
t,
CivilTime {
year: 1970,
month: 1,
day: 1,
hour: 0,
minute: 0,
weekday: 3, // Thursday
}
);
assert_eq!(t.minute_of_day(), 0);
}
#[test]
fn known_utc_instant_mid_year() {
// 2021-06-15 13:45:00 UTC = 1623764700 s.
let t = civil_from_timestamp(1_623_764_700_000, 0);
assert_eq!(t.year, 2021);
assert_eq!(t.month, 6);
assert_eq!(t.day, 15);
assert_eq!(t.hour, 13);
assert_eq!(t.minute, 45);
assert_eq!(t.weekday, 1); // Tuesday
assert_eq!(t.minute_of_day(), 13 * 60 + 45);
}
#[test]
fn new_year_2021_is_friday() {
// 2021-01-01 00:00:00 UTC = 1609459200 s — exercises the m<=2 year bump.
let t = civil_from_timestamp(1_609_459_200_000, 0);
assert_eq!((t.year, t.month, t.day), (2021, 1, 1));
assert_eq!(t.weekday, 4); // Friday
}
#[test]
fn positive_offset_rolls_to_next_day() {
// 2021-01-01 23:30 UTC shifted +60 min -> 2021-01-02 00:30 local.
let base = 1_609_459_200_000 + (23 * 3600 + 30 * 60) * 1000;
let t = civil_from_timestamp(base, 60);
assert_eq!((t.year, t.month, t.day), (2021, 1, 2));
assert_eq!((t.hour, t.minute), (0, 30));
assert_eq!(t.weekday, 5); // Saturday
}
#[test]
fn negative_offset_rolls_to_previous_day() {
// 2021-01-01 00:30 UTC shifted -60 min -> 2020-12-31 23:30 local.
let base = 1_609_459_200_000 + 30 * 60 * 1000;
let t = civil_from_timestamp(base, -60);
assert_eq!((t.year, t.month, t.day), (2020, 12, 31));
assert_eq!((t.hour, t.minute), (23, 30));
assert_eq!(t.weekday, 3); // Thursday
}
#[test]
fn sub_epoch_millis_floor_correctly() {
// -1 ms -> 1969-12-31 23:59:59.999, a Wednesday.
let t = civil_from_timestamp(-1, 0);
assert_eq!((t.year, t.month, t.day), (1969, 12, 31));
assert_eq!((t.hour, t.minute), (23, 59));
assert_eq!(t.weekday, 2); // Wednesday
}
#[test]
fn far_negative_day_count_hits_pre_era_branch() {
// A day count below -719468 drives `z + 719468` negative, exercising the
// `z - 146096` era branch in civil_from_days (year < 1).
let (year, month, day) = civil_from_days(-1_000_000);
// -1_000_000 days before 1970-01-01 is 0768-02-04 BCE (proleptic
// Gregorian, astronomical year numbering where year 0 exists).
assert_eq!((year, month, day), (-768, 2, 4));
}
#[test]
fn leap_year_rules() {
assert!(is_leap(2000));
assert!(!is_leap(1900));
assert!(is_leap(2024));
assert!(!is_leap(2023));
}
#[test]
fn days_in_month_all_cases() {
assert_eq!(days_in_month(2023, 1), 31);
assert_eq!(days_in_month(2023, 4), 30);
assert_eq!(days_in_month(2023, 2), 28);
assert_eq!(days_in_month(2024, 2), 29);
assert_eq!(days_in_month(2023, 12), 31);
assert_eq!(days_in_month(2023, 11), 30);
}
#[test]
fn leap_day_decodes() {
// 2024-02-29 12:00 UTC.
let secs = 1_709_208_000; // 2024-02-29T12:00:00Z
let t = civil_from_timestamp(secs * 1000, 0);
assert_eq!((t.year, t.month, t.day), (2024, 2, 29));
assert_eq!(t.hour, 12);
}
}
+387
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@@ -0,0 +1,387 @@
//! Cross-section value type: a market-breadth snapshot across a whole universe.
//!
//! A [`CrossSection`] is a single tick that carries the per-symbol state of
//! *every* symbol in a universe at one point in time. It is the non-OHLCV input
//! consumed by the market-breadth indicator family (advance/decline, `McClellan`,
//! the TRIN / Arms index, the high-low index, ...), each of which aggregates the
//! whole cross-section into a single breadth reading. This is the same
//! one-rich-type-per-family pattern as [`DerivativesTick`] and [`OrderBook`].
//!
//! Each [`Member`] precomputes the per-symbol signals the breadth indicators
//! need — a signed price `change` (whose sign classifies the symbol as
//! advancing, declining or unchanged), the period `volume`, the
//! `new_high` / `new_low` extreme flags, and the `above_ma` / `on_buy_signal`
//! state flags — so the indicators stay stateless per tick and never have to
//! track per-symbol history.
//!
//! [`DerivativesTick`]: crate::DerivativesTick
//! [`OrderBook`]: crate::OrderBook
use crate::error::{Error, Result};
/// One symbol's contribution to a [`CrossSection`] tick.
///
/// Field invariants enforced by [`CrossSection::new`] when the member is placed
/// into a tick:
///
/// - `change` is finite (its sign classifies the symbol — positive is
/// advancing, negative is declining, zero is unchanged).
/// - `volume` is finite and non-negative.
///
/// `new_high` / `new_low` are caller-supplied flags marking whether the symbol
/// printed a new period extreme; `above_ma` / `on_buy_signal` are caller-supplied
/// per-symbol state signals (whether the symbol trades above its reference moving
/// average, and whether it is on a point-and-figure buy signal). None of the four
/// flags carries a numeric invariant.
#[non_exhaustive]
#[derive(Debug, Clone, Copy, PartialEq)]
#[allow(
clippy::struct_excessive_bools,
reason = "the four flags are independent per-symbol breadth signals, not a state machine"
)]
pub struct Member {
/// Price change versus the previous close. Sign classifies the symbol:
/// positive is advancing, negative is declining, zero is unchanged.
pub change: f64,
/// Period volume for the symbol (finite, non-negative).
pub volume: f64,
/// Whether the symbol printed a new period high.
pub new_high: bool,
/// Whether the symbol printed a new period low.
pub new_low: bool,
/// Whether the symbol is trading above its reference moving average
/// (consumed by the `% Above Moving Average` breadth indicator).
pub above_ma: bool,
/// Whether the symbol is on a point-and-figure buy signal
/// (consumed by the `Bullish Percent Index` breadth indicator).
pub on_buy_signal: bool,
}
impl Member {
/// Assemble a cross-section member from its core signals, leaving the
/// extended per-symbol state flags (`above_ma`, `on_buy_signal`) cleared.
///
/// The field invariants documented on [`Member`] are validated centrally by
/// [`CrossSection::new`] when the member is placed into a tick; this
/// constructor only assembles the value so the `#[non_exhaustive]` struct can
/// be built from outside the crate.
#[must_use]
pub const fn new(change: f64, volume: f64, new_high: bool, new_low: bool) -> Self {
Self {
change,
volume,
new_high,
new_low,
above_ma: false,
on_buy_signal: false,
}
}
/// Assemble a cross-section member including the extended per-symbol state
/// signals `above_ma` and `on_buy_signal`.
///
/// Use this constructor for the breadth indicators that read per-symbol
/// state (`% Above Moving Average`, `Bullish Percent Index`); [`new`](Member::new)
/// is the shorthand that leaves both flags `false`.
#[must_use]
#[allow(
clippy::fn_params_excessive_bools,
reason = "mirrors the four independent per-symbol flag fields of Member"
)]
pub const fn with_signals(
change: f64,
volume: f64,
new_high: bool,
new_low: bool,
above_ma: bool,
on_buy_signal: bool,
) -> Self {
Self {
change,
volume,
new_high,
new_low,
above_ma,
on_buy_signal,
}
}
}
/// A market-breadth cross-section: the per-symbol state of an entire universe at
/// a single point in time.
///
/// Invariants enforced by [`new`](CrossSection::new):
///
/// - `members` is non-empty (a breadth reading needs at least one symbol).
/// - every member's `change` is finite, and `volume` is finite and non-negative.
///
/// `timestamp` is a caller-defined epoch / resolution and is not validated.
#[non_exhaustive]
#[derive(Debug, Clone, PartialEq)]
pub struct CrossSection {
/// Per-symbol members of the universe for this tick.
pub members: Vec<Member>,
/// Tick timestamp (caller-defined epoch / resolution).
pub timestamp: i64,
}
impl CrossSection {
/// Construct a cross-section, validating every member invariant.
///
/// # Errors
///
/// Returns [`Error::InvalidCrossSection`] if `members` is empty, if any
/// member has a non-finite `change`, or if any member has a `volume` that is
/// not a finite non-negative number.
pub fn new(members: Vec<Member>, timestamp: i64) -> Result<Self> {
if members.is_empty() {
return Err(Error::InvalidCrossSection {
message: "cross-section must contain at least one member",
});
}
for member in &members {
if !member.change.is_finite() {
return Err(Error::InvalidCrossSection {
message: "member change must be finite",
});
}
if !member.volume.is_finite() || member.volume < 0.0 {
return Err(Error::InvalidCrossSection {
message: "member volume must be finite and non-negative",
});
}
}
Ok(Self { members, timestamp })
}
/// Construct a cross-section without validation. The caller asserts that
/// every invariant documented on [`CrossSection`] holds.
#[must_use]
pub const fn new_unchecked(members: Vec<Member>, timestamp: i64) -> Self {
Self { members, timestamp }
}
/// Number of advancing symbols (those with a strictly positive `change`).
#[must_use]
pub fn advancers(&self) -> usize {
self.members.iter().filter(|m| m.change > 0.0).count()
}
/// Number of declining symbols (those with a strictly negative `change`).
#[must_use]
pub fn decliners(&self) -> usize {
self.members.iter().filter(|m| m.change < 0.0).count()
}
/// Total volume traded by advancing symbols (those with positive `change`).
#[must_use]
pub fn advancing_volume(&self) -> f64 {
self.members
.iter()
.filter(|m| m.change > 0.0)
.map(|m| m.volume)
.sum()
}
/// Total volume traded by declining symbols (those with negative `change`).
#[must_use]
pub fn declining_volume(&self) -> f64 {
self.members
.iter()
.filter(|m| m.change < 0.0)
.map(|m| m.volume)
.sum()
}
/// Total volume traded across the whole universe.
#[must_use]
pub fn total_volume(&self) -> f64 {
self.members.iter().map(|m| m.volume).sum()
}
/// Number of symbols that printed a new period high.
#[must_use]
pub fn new_highs(&self) -> usize {
self.members.iter().filter(|m| m.new_high).count()
}
/// Number of symbols that printed a new period low.
#[must_use]
pub fn new_lows(&self) -> usize {
self.members.iter().filter(|m| m.new_low).count()
}
/// Number of symbols trading above their reference moving average.
#[must_use]
pub fn above_ma_count(&self) -> usize {
self.members.iter().filter(|m| m.above_ma).count()
}
/// Number of symbols on a point-and-figure buy signal.
#[must_use]
pub fn on_buy_signal_count(&self) -> usize {
self.members.iter().filter(|m| m.on_buy_signal).count()
}
}
#[cfg(test)]
mod tests {
use super::*;
fn members() -> Vec<Member> {
vec![
Member::new(1.5, 100.0, true, false),
Member::new(-0.5, 50.0, false, true),
Member::new(0.0, 0.0, false, false),
]
}
#[test]
fn new_accepts_valid() {
let cs = CrossSection::new(members(), 42).unwrap();
assert_eq!(cs.members.len(), 3);
assert_eq!(cs.timestamp, 42);
assert_eq!(cs.members[0].change, 1.5);
assert_eq!(cs.members[0].volume, 100.0);
assert!(cs.members[0].new_high);
assert!(cs.members[1].new_low);
}
#[test]
fn member_new_assembles_fields() {
let m = Member::new(2.0, 10.0, true, false);
assert_eq!(m.change, 2.0);
assert_eq!(m.volume, 10.0);
assert!(m.new_high);
assert!(!m.new_low);
}
#[test]
fn new_rejects_empty() {
assert!(matches!(
CrossSection::new(Vec::new(), 0),
Err(Error::InvalidCrossSection { .. })
));
}
#[test]
fn new_rejects_non_finite_change() {
assert!(matches!(
CrossSection::new(vec![Member::new(f64::NAN, 10.0, false, false)], 0),
Err(Error::InvalidCrossSection { .. })
));
assert!(matches!(
CrossSection::new(vec![Member::new(f64::INFINITY, 10.0, false, false)], 0),
Err(Error::InvalidCrossSection { .. })
));
}
#[test]
fn new_rejects_negative_volume() {
assert!(matches!(
CrossSection::new(vec![Member::new(1.0, -1.0, false, false)], 0),
Err(Error::InvalidCrossSection { .. })
));
}
#[test]
fn new_rejects_non_finite_volume() {
assert!(matches!(
CrossSection::new(vec![Member::new(1.0, f64::NAN, false, false)], 0),
Err(Error::InvalidCrossSection { .. })
));
}
#[test]
fn new_unchecked_skips_validation() {
let cs = CrossSection::new_unchecked(vec![Member::new(f64::NAN, -1.0, false, false)], 7);
assert_eq!(cs.members.len(), 1);
assert_eq!(cs.timestamp, 7);
}
#[test]
fn advancers_and_decliners_count_by_sign() {
let cs = CrossSection::new(members(), 0).unwrap();
assert_eq!(cs.advancers(), 1);
assert_eq!(cs.decliners(), 1);
}
#[test]
fn unchanged_members_count_as_neither() {
let cs = CrossSection::new(
vec![
Member::new(0.0, 1.0, false, false),
Member::new(0.0, 1.0, false, false),
],
0,
)
.unwrap();
assert_eq!(cs.advancers(), 0);
assert_eq!(cs.decliners(), 0);
}
#[test]
fn new_leaves_extended_flags_cleared() {
let m = Member::new(1.0, 10.0, true, false);
assert!(!m.above_ma);
assert!(!m.on_buy_signal);
}
#[test]
fn with_signals_assembles_all_fields() {
let m = Member::with_signals(2.0, 10.0, true, false, true, true);
assert_eq!(m.change, 2.0);
assert_eq!(m.volume, 10.0);
assert!(m.new_high);
assert!(!m.new_low);
assert!(m.above_ma);
assert!(m.on_buy_signal);
}
#[test]
fn volume_helpers_bucket_by_change_sign() {
let cs = CrossSection::new(
vec![
Member::new(1.5, 100.0, false, false), // advancing
Member::new(2.0, 40.0, false, false), // advancing
Member::new(-0.5, 50.0, false, false), // declining
Member::new(0.0, 7.0, false, false), // unchanged
],
0,
)
.unwrap();
assert_eq!(cs.advancing_volume(), 140.0);
assert_eq!(cs.declining_volume(), 50.0);
assert_eq!(cs.total_volume(), 197.0);
}
#[test]
fn high_low_helpers_count_flags() {
let cs = CrossSection::new(
vec![
Member::new(1.0, 1.0, true, false),
Member::new(1.0, 1.0, true, false),
Member::new(-1.0, 1.0, false, true),
],
0,
)
.unwrap();
assert_eq!(cs.new_highs(), 2);
assert_eq!(cs.new_lows(), 1);
}
#[test]
fn state_helpers_count_extended_flags() {
let cs = CrossSection::new(
vec![
Member::with_signals(1.0, 1.0, false, false, true, true),
Member::with_signals(1.0, 1.0, false, false, true, false),
Member::with_signals(-1.0, 1.0, false, false, false, true),
],
0,
)
.unwrap();
assert_eq!(cs.above_ma_count(), 2);
assert_eq!(cs.on_buy_signal_count(), 2);
}
}
+321
View File
@@ -0,0 +1,321 @@
//! Derivatives value type: the perpetual / futures tick.
//!
//! [`DerivativesTick`] is the non-OHLCV input consumed by the derivatives /
//! perpetual-futures indicator family. A single tick bundles the funding,
//! price, open-interest, positioning, taker-flow and liquidation fields a
//! perp/futures venue publishes per update; each indicator reads only the
//! subset it needs (the same one-rich-type-per-family pattern as [`Trade`] /
//! [`OrderBook`] in [`crate::microstructure`]).
//!
//! [`Trade`]: crate::microstructure::Trade
//! [`OrderBook`]: crate::microstructure::OrderBook
use crate::error::{Error, Result};
/// A single derivatives / perpetual-futures market tick.
///
/// Field invariants enforced by [`new`](DerivativesTick::new):
///
/// - `funding_rate` is finite and **may be negative** (a negative funding rate
/// means shorts pay longs).
/// - `mark_price`, `index_price` and `futures_price` are finite and strictly
/// positive.
/// - `open_interest`, `long_size`, `short_size`, `taker_buy_volume`,
/// `taker_sell_volume`, `long_liquidation` and `short_liquidation` are finite
/// and non-negative.
///
/// `timestamp` is a caller-defined epoch / resolution and is not validated.
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct DerivativesTick {
/// Current funding rate for the interval (finite; may be negative).
pub funding_rate: f64,
/// Perpetual mark price (finite, strictly positive).
pub mark_price: f64,
/// Spot / index price the perpetual tracks (finite, strictly positive).
pub index_price: f64,
/// Dated (e.g. quarterly) futures mark price (finite, strictly positive).
pub futures_price: f64,
/// Open interest — outstanding contracts / notional (finite, non-negative).
pub open_interest: f64,
/// Aggregate long size / long account count (finite, non-negative).
pub long_size: f64,
/// Aggregate short size / short account count (finite, non-negative).
pub short_size: f64,
/// Taker buy (ask-lifting) volume (finite, non-negative).
pub taker_buy_volume: f64,
/// Taker sell (bid-hitting) volume (finite, non-negative).
pub taker_sell_volume: f64,
/// Long-side liquidation notional (finite, non-negative).
pub long_liquidation: f64,
/// Short-side liquidation notional (finite, non-negative).
pub short_liquidation: f64,
/// Tick timestamp (caller-defined epoch / resolution).
pub timestamp: i64,
}
impl DerivativesTick {
/// Construct a derivatives tick, validating every field invariant.
///
/// # Errors
///
/// Returns [`Error::InvalidDerivatives`] if `funding_rate` is not finite;
/// any of `mark_price`, `index_price`, `futures_price` is not a finite
/// positive number; or any of the six size / volume / liquidation fields is
/// not a finite non-negative number.
#[allow(clippy::too_many_arguments)]
pub fn new(
funding_rate: f64,
mark_price: f64,
index_price: f64,
futures_price: f64,
open_interest: f64,
long_size: f64,
short_size: f64,
taker_buy_volume: f64,
taker_sell_volume: f64,
long_liquidation: f64,
short_liquidation: f64,
timestamp: i64,
) -> Result<Self> {
if !funding_rate.is_finite() {
return Err(Error::InvalidDerivatives {
message: "funding_rate must be finite",
});
}
for price in [mark_price, index_price, futures_price] {
if !price.is_finite() || price <= 0.0 {
return Err(Error::InvalidDerivatives {
message:
"mark_price, index_price and futures_price must be finite and positive",
});
}
}
for amount in [
open_interest,
long_size,
short_size,
taker_buy_volume,
taker_sell_volume,
long_liquidation,
short_liquidation,
] {
if !amount.is_finite() || amount < 0.0 {
return Err(Error::InvalidDerivatives {
message: "open interest, sizes, volumes and liquidations must be finite and non-negative",
});
}
}
Ok(Self {
funding_rate,
mark_price,
index_price,
futures_price,
open_interest,
long_size,
short_size,
taker_buy_volume,
taker_sell_volume,
long_liquidation,
short_liquidation,
timestamp,
})
}
/// Construct a derivatives tick without validation. The caller asserts that
/// every field invariant documented on [`DerivativesTick`] holds.
#[allow(clippy::too_many_arguments)]
#[must_use]
pub const fn new_unchecked(
funding_rate: f64,
mark_price: f64,
index_price: f64,
futures_price: f64,
open_interest: f64,
long_size: f64,
short_size: f64,
taker_buy_volume: f64,
taker_sell_volume: f64,
long_liquidation: f64,
short_liquidation: f64,
timestamp: i64,
) -> Self {
Self {
funding_rate,
mark_price,
index_price,
futures_price,
open_interest,
long_size,
short_size,
taker_buy_volume,
taker_sell_volume,
long_liquidation,
short_liquidation,
timestamp,
}
}
}
#[cfg(test)]
mod tests {
use super::*;
/// A fully valid tick used as a baseline; individual tests override one
/// field to exercise a single reject branch.
fn valid() -> DerivativesTick {
DerivativesTick::new(
0.0001, 100.0, 99.5, 100.5, 1_000.0, 600.0, 400.0, 50.0, 40.0, 5.0, 3.0, 42,
)
.unwrap()
}
#[test]
fn new_accepts_valid() {
let tick = valid();
assert_eq!(tick.funding_rate, 0.0001);
assert_eq!(tick.mark_price, 100.0);
assert_eq!(tick.index_price, 99.5);
assert_eq!(tick.futures_price, 100.5);
assert_eq!(tick.open_interest, 1_000.0);
assert_eq!(tick.long_size, 600.0);
assert_eq!(tick.short_size, 400.0);
assert_eq!(tick.taker_buy_volume, 50.0);
assert_eq!(tick.taker_sell_volume, 40.0);
assert_eq!(tick.long_liquidation, 5.0);
assert_eq!(tick.short_liquidation, 3.0);
assert_eq!(tick.timestamp, 42);
}
#[test]
fn new_accepts_negative_funding_and_zero_amounts() {
let tick = DerivativesTick::new(
-0.0005, 100.0, 100.0, 100.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,
)
.unwrap();
assert_eq!(tick.funding_rate, -0.0005);
assert_eq!(tick.open_interest, 0.0);
}
#[test]
fn new_rejects_non_finite_funding() {
assert!(matches!(
DerivativesTick::new(
f64::NAN,
100.0,
100.0,
100.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0
),
Err(Error::InvalidDerivatives { .. })
));
assert!(matches!(
DerivativesTick::new(
f64::INFINITY,
100.0,
100.0,
100.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0
),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_non_positive_mark() {
assert!(matches!(
DerivativesTick::new(0.0, 0.0, 100.0, 100.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_non_positive_index() {
assert!(matches!(
DerivativesTick::new(0.0, 100.0, -1.0, 100.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_non_finite_futures() {
assert!(matches!(
DerivativesTick::new(
0.0,
100.0,
100.0,
f64::NAN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0
),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_negative_open_interest() {
assert!(matches!(
DerivativesTick::new(0.0, 100.0, 100.0, 100.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_non_finite_size() {
assert!(matches!(
DerivativesTick::new(
0.0,
100.0,
100.0,
100.0,
0.0,
f64::INFINITY,
0.0,
0.0,
0.0,
0.0,
0.0,
0
),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_rejects_negative_liquidation() {
assert!(matches!(
DerivativesTick::new(0.0, 100.0, 100.0, 100.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, 0),
Err(Error::InvalidDerivatives { .. })
));
}
#[test]
fn new_unchecked_preserves_fields() {
let tick = DerivativesTick::new_unchecked(
-1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0, -9.0, -10.0, -11.0, 7,
);
assert_eq!(tick.funding_rate, -1.0);
assert_eq!(tick.mark_price, -2.0);
assert_eq!(tick.short_liquidation, -11.0);
assert_eq!(tick.timestamp, 7);
}
}
+36
View File
@@ -31,6 +31,42 @@ pub enum Error {
/// A multiplier or factor must be strictly positive.
#[error("multiplier must be greater than zero")]
NonPositiveMultiplier,
/// An order-book snapshot whose levels do not satisfy the book invariants
/// (e.g. a crossed book, non-finite price, negative size, or mis-sorted
/// levels) was provided. Order books are a microstructure input distinct
/// from candles and ticks, so they surface as their own variant.
#[error("invalid order book: {message}")]
InvalidOrderBook { message: &'static str },
/// A trade whose components do not satisfy the trade invariants (e.g.
/// non-finite price or negative size) was provided.
#[error("invalid trade: {message}")]
InvalidTrade { message: &'static str },
/// A derivatives tick whose components do not satisfy the tick invariants
/// (e.g. a non-positive price, a non-finite funding rate, or a negative
/// size/volume/liquidation) was provided. Derivatives ticks (funding /
/// open-interest / liquidation feeds) are a perpetual-futures input
/// distinct from candles, order books and trades, so they surface as their
/// own variant.
#[error("invalid derivatives tick: {message}")]
InvalidDerivatives { message: &'static str },
/// A market-breadth cross-section whose members do not satisfy the
/// cross-section invariants (an empty universe, a non-finite change, or a
/// negative / non-finite volume) was provided. A cross-section is a
/// breadth input distinct from candles, ticks, order books and trades, so
/// it surfaces as its own variant.
#[error("invalid cross-section: {message}")]
InvalidCrossSection { message: &'static str },
/// A real-valued configuration parameter was outside its admissible range
/// (e.g. a non-positive standard-deviation multiplier, or a Kalman filter
/// covariance that is not strictly positive). This is the floating-point
/// analogue of [`Error::InvalidPeriod`], which only covers integer windows.
#[error("invalid parameter: {message}")]
InvalidParameter { message: &'static str },
}
/// Convenience alias for `Result<T, wickra_core::Error>`.
@@ -0,0 +1,247 @@
//! Abandoned Baby candlestick pattern.
use crate::error::{Error, Result};
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// Abandoned Baby — a strong 3-bar reversal where a doji is "abandoned" by price
/// gaps on both sides, isolating it from the candles before and after.
///
/// ```text
/// tol = tolerance * max(|bar2.open|, |bar2.close|)
/// bar2 doji (|bar2.close bar2.open| <= tol)
///
/// bullish (+1.0): bar1 red, bar2 gaps fully below bar1 (bar2.high < bar1.low),
/// bar3 green and gaps fully above bar2 (bar3.low > bar2.high)
/// bearish (1.0): bar1 green, bar2 gaps fully above bar1 (bar2.low > bar1.high),
/// bar3 red and gaps fully below bar2 (bar3.high < bar2.low)
/// ```
///
/// Output is `0.0` otherwise. The first two bars always return `0.0` because the
/// three-bar window is not yet filled. `tolerance` defaults to `0.001` (10 bps
/// relative) and bounds how flat the middle candle must be to count as a doji; it
/// must lie in `[0, 1)`. Pattern-shape check only — no trend filter is applied;
/// combine with a trend indicator for actionable signals.
///
/// # Signed ±1 encoding
///
/// This detector emits the uniform candlestick sign convention shared across the
/// pattern family — `+1.0` bullish, `1.0` bearish, `0.0` no pattern — so it
/// drops straight into a machine-learning feature matrix where the bullish and
/// bearish variants occupy a single dimension.
///
/// # Example
///
/// ```
/// use wickra_core::{AbandonedBaby, Candle, Indicator};
///
/// let mut indicator = AbandonedBaby::new();
/// indicator.update(Candle::new(20.0, 20.1, 14.9, 15.0, 1.0, 0).unwrap());
/// indicator.update(Candle::new(13.0, 13.1, 12.9, 13.0, 1.0, 1).unwrap());
/// let out = indicator
/// .update(Candle::new(16.0, 18.1, 15.9, 18.0, 1.0, 2).unwrap());
/// assert_eq!(out, Some(1.0));
/// ```
#[derive(Debug, Clone)]
pub struct AbandonedBaby {
tolerance: f64,
prev: Option<Candle>,
prev_prev: Option<Candle>,
has_emitted: bool,
}
impl Default for AbandonedBaby {
fn default() -> Self {
Self::new()
}
}
impl AbandonedBaby {
/// Construct a detector with the default relative doji tolerance (1e-3).
pub const fn new() -> Self {
Self {
tolerance: 0.001,
prev: None,
prev_prev: None,
has_emitted: false,
}
}
/// Construct a detector with a custom relative doji tolerance.
///
/// `tolerance` must lie in `[0, 1)`.
pub fn with_tolerance(tolerance: f64) -> Result<Self> {
if !(0.0..1.0).contains(&tolerance) {
return Err(Error::InvalidPeriod {
message: "abandoned baby tolerance must lie in [0, 1)",
});
}
Ok(Self {
tolerance,
prev: None,
prev_prev: None,
has_emitted: false,
})
}
/// Configured relative doji tolerance.
pub fn tolerance(&self) -> f64 {
self.tolerance
}
}
impl Indicator for AbandonedBaby {
type Input = Candle;
type Output = f64;
fn update(&mut self, candle: Candle) -> Option<f64> {
self.has_emitted = true;
let pp = self.prev_prev;
let p = self.prev;
self.prev_prev = self.prev;
self.prev = Some(candle);
let (Some(bar1), Some(bar2)) = (pp, p) else {
return Some(0.0);
};
let tol = self.tolerance * bar2.open.abs().max(bar2.close.abs());
let bar2_is_doji = (bar2.close - bar2.open).abs() <= tol;
if !bar2_is_doji {
return Some(0.0);
}
// Bullish: red bar1, doji gaps below, green bar3 gaps above.
if bar1.close < bar1.open
&& bar2.high < bar1.low
&& candle.close > candle.open
&& candle.low > bar2.high
{
return Some(1.0);
}
// Bearish: green bar1, doji gaps above, red bar3 gaps below.
if bar1.close > bar1.open
&& bar2.low > bar1.high
&& candle.close < candle.open
&& candle.high < bar2.low
{
return Some(-1.0);
}
Some(0.0)
}
fn reset(&mut self) {
self.prev = None;
self.prev_prev = None;
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
3
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AbandonedBaby"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
fn c(open: f64, high: f64, low: f64, close: f64, ts: i64) -> Candle {
Candle::new(open, high, low, close, 1.0, ts).unwrap()
}
#[test]
fn rejects_invalid_tolerance() {
assert!(AbandonedBaby::with_tolerance(-0.01).is_err());
assert!(AbandonedBaby::with_tolerance(1.0).is_err());
}
#[test]
fn accepts_valid_tolerance() {
let t = AbandonedBaby::with_tolerance(0.0).unwrap();
assert!((t.tolerance() - 0.0).abs() < 1e-12);
}
#[test]
fn accessors_and_metadata() {
let t = AbandonedBaby::default();
assert_eq!(t.name(), "AbandonedBaby");
assert_eq!(t.warmup_period(), 3);
assert!(!t.is_ready());
assert!((t.tolerance() - 0.001).abs() < 1e-12);
}
#[test]
fn bullish_abandoned_baby_is_plus_one() {
let mut t = AbandonedBaby::new();
assert_eq!(t.update(c(20.0, 20.1, 14.9, 15.0, 0)), Some(0.0));
assert_eq!(t.update(c(13.0, 13.1, 12.9, 13.0, 1)), Some(0.0));
assert_eq!(t.update(c(16.0, 18.1, 15.9, 18.0, 2)), Some(1.0));
}
#[test]
fn bearish_abandoned_baby_is_minus_one() {
let mut t = AbandonedBaby::new();
assert_eq!(t.update(c(15.0, 20.1, 14.9, 20.0, 0)), Some(0.0));
assert_eq!(t.update(c(22.0, 22.1, 21.9, 22.0, 1)), Some(0.0));
assert_eq!(t.update(c(19.0, 19.1, 16.9, 17.0, 2)), Some(-1.0));
}
#[test]
fn middle_not_doji_yields_zero() {
let mut t = AbandonedBaby::new();
t.update(c(20.0, 20.1, 14.9, 15.0, 0));
// Middle bar has a wide body -> not a doji.
assert_eq!(t.update(c(13.0, 14.0, 11.0, 11.5, 1)), Some(0.0));
assert_eq!(t.update(c(16.0, 18.1, 15.9, 18.0, 2)), Some(0.0));
}
#[test]
fn no_gap_yields_zero() {
let mut t = AbandonedBaby::new();
t.update(c(20.0, 20.1, 14.9, 15.0, 0));
// Doji overlaps bar1's range -> no gap.
assert_eq!(t.update(c(15.0, 15.1, 14.9, 15.0, 1)), Some(0.0));
assert_eq!(t.update(c(16.0, 18.1, 15.9, 18.0, 2)), Some(0.0));
}
#[test]
fn first_two_bars_return_zero() {
let mut t = AbandonedBaby::new();
assert_eq!(t.update(c(20.0, 20.1, 14.9, 15.0, 0)), Some(0.0));
assert_eq!(t.update(c(13.0, 13.1, 12.9, 13.0, 1)), Some(0.0));
}
#[test]
fn batch_equals_streaming() {
let candles: Vec<Candle> = (0..40)
.map(|i| {
let base = 100.0 + (i as f64 * 0.3).sin() * 5.0;
c(base, base + 1.0, base - 1.0, base + 0.5, i)
})
.collect();
let mut a = AbandonedBaby::new();
let mut b = AbandonedBaby::new();
assert_eq!(
a.batch(&candles),
candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
);
}
#[test]
fn reset_clears_state() {
let mut t = AbandonedBaby::new();
t.update(c(20.0, 20.1, 14.9, 15.0, 0));
t.update(c(13.0, 13.1, 12.9, 13.0, 1));
t.update(c(16.0, 18.1, 15.9, 18.0, 2));
assert!(t.is_ready());
t.reset();
assert!(!t.is_ready());
assert_eq!(t.update(c(20.0, 20.1, 14.9, 15.0, 0)), Some(0.0));
}
}
+154
View File
@@ -0,0 +1,154 @@
//! AB=CD harmonic pattern.
use crate::indicators::pattern_swing::{approx_equal, ratios_in, SwingTracker, SWING_THRESHOLD};
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// AB=CD — the simplest four-point harmonic pattern: an A→B leg, a B→C
/// retracement, and a C→D leg that mirrors A→B in length:
///
/// ```text
/// BC / AB ∈ [0.382, 0.886] (C retraces AB)
/// CD / BC ∈ [1.13, 2.618] (D extends BC)
/// AB ≈ CD (within 10%) (the two legs are equal — the defining symmetry)
/// ```
///
/// Read from the last four confirmed pivots `A-B-C-D`. Output is `+1.0`
/// (bullish, D a swing low), `-1.0` (bearish, D a swing high), or `0.0`; never
/// `None`. See `crates/wickra-core/src/indicators/abcd.rs`.
#[derive(Debug, Clone)]
pub struct Abcd {
swing: SwingTracker,
has_emitted: bool,
}
impl Abcd {
/// Construct a new AB=CD detector.
pub const fn new() -> Self {
Self {
swing: SwingTracker::new(SWING_THRESHOLD, 4),
has_emitted: false,
}
}
}
impl Default for Abcd {
fn default() -> Self {
Self::new()
}
}
impl Indicator for Abcd {
type Input = Candle;
type Output = f64;
fn update(&mut self, candle: Candle) -> Option<f64> {
self.has_emitted = true;
if !self.swing.update(candle) {
return Some(0.0);
}
let pivots = self.swing.pivots();
if pivots.len() < 4 {
return Some(0.0);
}
let len = pivots.len();
let pa = pivots[len - 4];
let pb = pivots[len - 3];
let pc = pivots[len - 2];
let pd = pivots[len - 1];
let ab = (pb.price - pa.price).abs();
let bc = (pc.price - pb.price).abs();
let cd = (pd.price - pc.price).abs();
let ratios_ok = ratios_in(&[(bc / ab, 0.382, 0.886), (cd / bc, 1.13, 2.618)]);
let legs_equal = approx_equal(ab, cd, 0.10);
if ratios_ok && legs_equal {
return Some(if pd.direction < 0.0 { 1.0 } else { -1.0 });
}
Some(0.0)
}
fn reset(&mut self) {
self.swing.reset();
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
5
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"Abcd"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::indicators::pattern_swing::candles_for_pivots;
use crate::traits::BatchExt;
fn run(pivots: &[f64]) -> Vec<f64> {
let mut indicator = Abcd::new();
candles_for_pivots(pivots)
.into_iter()
.map(|c| indicator.update(c).unwrap())
.collect()
}
#[test]
fn accessors_and_metadata() {
let indicator = Abcd::new();
assert_eq!(indicator.name(), "Abcd");
assert_eq!(indicator.warmup_period(), 5);
assert!(!indicator.is_ready());
assert!(!Abcd::default().is_ready());
}
#[test]
fn bullish_abcd_is_plus_one() {
// AB = 40 down, BC = 24.7 up (0.618), CD = 40 down → AB = CD.
let out = run(&[140.0, 100.0, 124.7, 84.7]);
assert_eq!(*out.last().unwrap(), 1.0);
assert!(out[..out.len() - 1].iter().all(|&x| x == 0.0));
}
#[test]
fn bearish_abcd_is_minus_one() {
let out = run(&[150.0, 100.0, 140.0, 115.3, 155.3]);
assert_eq!(*out.last().unwrap(), -1.0);
}
#[test]
fn unequal_legs_do_not_trigger() {
// CD (82) far longer than AB (40) → not an AB=CD.
let out = run(&[150.0, 100.0, 140.0, 118.0, 200.0]);
assert_eq!(*out.last().unwrap(), 0.0);
}
#[test]
fn reset_clears_state() {
let mut indicator = Abcd::new();
for c in candles_for_pivots(&[140.0, 100.0, 124.7]) {
let _ = indicator.update(c);
}
indicator.reset();
assert!(!indicator.is_ready());
let c = Candle::new(99.5, 100.0, 99.5, 99.5, 1.0, 0).unwrap();
assert_eq!(indicator.update(c), Some(0.0));
}
#[test]
fn batch_equals_streaming() {
let candles = candles_for_pivots(&[140.0, 100.0, 124.7, 84.7]);
let mut a = Abcd::new();
let mut b = Abcd::new();
assert_eq!(
a.batch(&candles),
candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
);
}
}
@@ -0,0 +1,144 @@
//! Absolute Breadth Index — the magnitude of net advancing-minus-declining issues.
use crate::cross_section::CrossSection;
use crate::traits::Indicator;
/// Absolute Breadth Index (ABI) — the absolute value of net advancing issues,
/// `|advancers - decliners|`.
///
/// The ABI ignores the *direction* of breadth and measures only its *magnitude*:
/// a high reading means the universe moved decisively one way or the other (high
/// internal activity / volatility), while a low reading means advances and
/// declines were nearly balanced (a quiet, directionless market). It is sometimes
/// called a "market thermometer" because elevated readings often cluster around
/// turning points.
///
/// `Input = CrossSection`, `Output = f64`, `warmup_period == 1`.
///
/// # Example
///
/// ```
/// use wickra_core::{AbsoluteBreadthIndex, CrossSection, Indicator, Member};
///
/// let mut abi = AbsoluteBreadthIndex::new();
/// // 2 advancers, 5 decliners -> |2 - 5| = 3.
/// let tick = CrossSection::new(
/// vec![
/// Member::new(1.0, 10.0, false, false),
/// Member::new(1.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// ],
/// 0,
/// )
/// .unwrap();
/// assert_eq!(abi.update(tick), Some(3.0));
/// ```
#[derive(Debug, Clone, Default)]
pub struct AbsoluteBreadthIndex {
has_emitted: bool,
}
impl AbsoluteBreadthIndex {
/// Construct a new Absolute Breadth Index indicator.
#[must_use]
pub const fn new() -> Self {
Self { has_emitted: false }
}
}
impl Indicator for AbsoluteBreadthIndex {
type Input = CrossSection;
type Output = f64;
fn update(&mut self, section: CrossSection) -> Option<f64> {
let net = section.advancers() as f64 - section.decliners() as f64;
self.has_emitted = true;
Some(net.abs())
}
fn reset(&mut self) {
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
1
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AbsoluteBreadthIndex"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::cross_section::Member;
use crate::traits::BatchExt;
fn section(up: usize, down: usize) -> CrossSection {
let mut members = Vec::new();
for _ in 0..up {
members.push(Member::new(1.0, 10.0, false, false));
}
for _ in 0..down {
members.push(Member::new(-1.0, 10.0, false, false));
}
members.push(Member::new(0.0, 10.0, false, false));
CrossSection::new(members, 0).unwrap()
}
#[test]
fn accessors_and_metadata() {
let abi = AbsoluteBreadthIndex::new();
assert_eq!(abi.name(), "AbsoluteBreadthIndex");
assert_eq!(abi.warmup_period(), 1);
assert!(!abi.is_ready());
}
#[test]
fn magnitude_ignores_direction() {
let mut abi = AbsoluteBreadthIndex::new();
assert_eq!(abi.update(section(2, 5)), Some(3.0));
// Same magnitude with the direction reversed.
let mut abi2 = AbsoluteBreadthIndex::new();
assert_eq!(abi2.update(section(5, 2)), Some(3.0));
}
#[test]
fn balanced_universe_yields_zero() {
let mut abi = AbsoluteBreadthIndex::new();
assert_eq!(abi.update(section(3, 3)), Some(0.0));
assert!(abi.is_ready());
}
#[test]
fn reset_clears_state() {
let mut abi = AbsoluteBreadthIndex::new();
abi.update(section(2, 5));
assert!(abi.is_ready());
abi.reset();
assert!(!abi.is_ready());
}
#[test]
fn batch_equals_streaming() {
let sections = vec![section(2, 5), section(5, 2), section(3, 3)];
let mut a = AbsoluteBreadthIndex::new();
let mut b = AbsoluteBreadthIndex::new();
assert_eq!(
a.batch(&sections),
sections
.iter()
.map(|s| b.update(s.clone()))
.collect::<Vec<_>>()
);
}
}
@@ -0,0 +1,277 @@
//! Acceleration Bands (Price Headley).
use crate::error::{Error, Result};
use crate::indicators::sma::Sma;
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// Acceleration Bands output: SMA of close with momentum-biased envelopes
/// driven by the bar's high/low geometry.
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct AccelerationBandsOutput {
/// Upper band: SMA of `high · (1 + factor · (high low) / (high + low))`.
pub upper: f64,
/// Middle band: SMA of close.
pub middle: f64,
/// Lower band: SMA of `low · (1 factor · (high low) / (high + low))`.
pub lower: f64,
}
/// Acceleration Bands (Price Headley): SMA-smoothed bands that widen with each
/// bar's relative range `(high low) / (high + low)`.
///
/// ```text
/// ratio = (high low) / (high + low)
/// raw_up = high · (1 + factor · ratio)
/// raw_lo = low · (1 factor · ratio)
/// upper = SMA(raw_up, period)
/// middle = SMA(close, period)
/// lower = SMA(raw_lo, period)
/// ```
///
/// Headley's reference parameters are `period = 20`, `factor = 0.001` for
/// intraday equity markets — the geometric `ratio` term tends to scale on
/// fractional moves, so the literal `factor` is small. The bands compress in
/// quiet markets and flare on impulsive bars, making them a momentum-biased
/// alternative to the volatility-driven Bollinger or Keltner envelopes.
///
/// # Example
///
/// ```
/// use wickra_core::{AccelerationBands, Candle, Indicator};
///
/// let mut indicator = AccelerationBands::new(20, 0.001).unwrap();
/// let mut last = None;
/// for i in 0..40 {
/// let base = 100.0 + f64::from(i);
/// let candle =
/// Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 10.0, i64::from(i)).unwrap();
/// last = indicator.update(candle);
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone)]
pub struct AccelerationBands {
upper_sma: Sma,
middle_sma: Sma,
lower_sma: Sma,
factor: f64,
period: usize,
}
impl AccelerationBands {
/// Construct a new Acceleration Bands indicator.
///
/// # Errors
/// Returns [`Error::PeriodZero`] if `period == 0` and
/// [`Error::NonPositiveMultiplier`] if `factor` is not strictly positive
/// and finite.
pub fn new(period: usize, factor: f64) -> Result<Self> {
if !factor.is_finite() || factor <= 0.0 {
return Err(Error::NonPositiveMultiplier);
}
Ok(Self {
upper_sma: Sma::new(period)?,
middle_sma: Sma::new(period)?,
lower_sma: Sma::new(period)?,
factor,
period,
})
}
/// Headley's classic configuration: `period = 20`, `factor = 0.001`.
pub fn classic() -> Self {
Self::new(20, 0.001).expect("classic Acceleration Bands parameters are valid")
}
/// Configured `(period, factor)`.
pub const fn parameters(&self) -> (usize, f64) {
(self.period, self.factor)
}
}
impl Indicator for AccelerationBands {
type Input = Candle;
type Output = AccelerationBandsOutput;
fn update(&mut self, candle: Candle) -> Option<AccelerationBandsOutput> {
// (high + low) == 0 is geometrically impossible for valid OHLC
// (high >= low and a zero-sum requires both equal to 0, which would
// make the bar degenerate). Guard anyway so a hypothetical zero-price
// bar collapses the ratio to zero rather than emitting NaN.
let sum_hl = candle.high + candle.low;
let ratio = if sum_hl == 0.0 {
0.0
} else {
(candle.high - candle.low) / sum_hl
};
let raw_up = candle.high * self.factor.mul_add(ratio, 1.0);
let raw_lo = candle.low * (-self.factor).mul_add(ratio, 1.0);
// Feed all three SMAs unconditionally so they warm up in lock-step.
let upper = self.upper_sma.update(raw_up);
let middle = self.middle_sma.update(candle.close);
let lower = self.lower_sma.update(raw_lo);
let (upper, middle, lower) = (upper?, middle?, lower?);
Some(AccelerationBandsOutput {
upper,
middle,
lower,
})
}
fn reset(&mut self) {
self.upper_sma.reset();
self.middle_sma.reset();
self.lower_sma.reset();
}
fn warmup_period(&self) -> usize {
self.period
}
fn is_ready(&self) -> bool {
self.middle_sma.is_ready()
}
fn name(&self) -> &'static str {
"AccelerationBands"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
fn c(h: f64, l: f64, cl: f64) -> Candle {
Candle::new(cl, h, l, cl, 1.0, 0).unwrap()
}
#[test]
fn rejects_zero_period() {
assert!(matches!(
AccelerationBands::new(0, 0.001),
Err(Error::PeriodZero)
));
}
#[test]
fn rejects_non_positive_factor() {
assert!(matches!(
AccelerationBands::new(20, 0.0),
Err(Error::NonPositiveMultiplier)
));
assert!(matches!(
AccelerationBands::new(20, -1.0),
Err(Error::NonPositiveMultiplier)
));
assert!(matches!(
AccelerationBands::new(20, f64::NAN),
Err(Error::NonPositiveMultiplier)
));
}
#[test]
fn accessors_and_metadata() {
let ab = AccelerationBands::classic();
let (p, f) = ab.parameters();
assert_eq!(p, 20);
assert_relative_eq!(f, 0.001, epsilon = 1e-12);
assert_eq!(ab.warmup_period(), 20);
assert_eq!(ab.name(), "AccelerationBands");
}
#[test]
fn flat_market_collapses_to_constant() {
// high == low so the ratio term is zero; all three SMAs converge to
// the same constant.
let candles: Vec<Candle> = (0..30).map(|_| c(10.0, 10.0, 10.0)).collect();
let mut ab = AccelerationBands::new(5, 0.5).unwrap();
let last = ab.batch(&candles).into_iter().flatten().last().unwrap();
assert_relative_eq!(last.middle, 10.0, epsilon = 1e-9);
assert_relative_eq!(last.upper, 10.0, epsilon = 1e-9);
assert_relative_eq!(last.lower, 10.0, epsilon = 1e-9);
}
#[test]
fn warmup_returns_none() {
let mut ab = AccelerationBands::new(5, 0.001).unwrap();
for i in 0..4 {
let base = 100.0 + f64::from(i);
assert!(ab.update(c(base + 1.0, base - 1.0, base)).is_none());
}
assert!(ab.update(c(105.0, 103.0, 104.0)).is_some());
}
#[test]
fn upper_above_middle_above_lower() {
let candles: Vec<Candle> = (0..50)
.map(|i| {
let m = 100.0 + (f64::from(i) * 0.2).sin() * 5.0;
c(m + 1.0, m - 1.0, m)
})
.collect();
let mut ab = AccelerationBands::new(20, 0.5).unwrap();
for o in ab.batch(&candles).into_iter().flatten() {
assert!(o.upper >= o.middle, "{} < {}", o.upper, o.middle);
assert!(o.middle >= o.lower, "{} < {}", o.middle, o.lower);
}
}
#[test]
fn batch_equals_streaming() {
let candles: Vec<Candle> = (0..40)
.map(|i| c(f64::from(i) + 2.0, f64::from(i), f64::from(i) + 1.0))
.collect();
let mut a = AccelerationBands::new(10, 0.5).unwrap();
let mut b = AccelerationBands::new(10, 0.5).unwrap();
assert_eq!(
a.batch(&candles),
candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
);
}
#[test]
fn reset_clears_state() {
let candles: Vec<Candle> = (0..10)
.map(|i| c(f64::from(i) + 2.0, f64::from(i), f64::from(i) + 1.0))
.collect();
let mut ab = AccelerationBands::new(5, 0.5).unwrap();
ab.batch(&candles);
assert!(ab.is_ready());
ab.reset();
assert!(!ab.is_ready());
assert_eq!(ab.update(candles[0]), None);
}
#[test]
fn zero_price_candle_collapses_ratio_to_zero() {
// `high + low == 0` is geometrically only reachable with a fully-zero
// bar (high >= low and both non-negative for a real market, but
// `Candle::new` accepts the degenerate `(0, 0, 0, 0)` case). The
// ratio guard must fire and the bands all collapse to zero.
let zero = Candle::new(0.0, 0.0, 0.0, 0.0, 1.0, 0).unwrap();
let mut ab = AccelerationBands::new(1, 0.5).unwrap();
let v = ab.update(zero).unwrap();
assert_relative_eq!(v.upper, 0.0, epsilon = 1e-12);
assert_relative_eq!(v.middle, 0.0, epsilon = 1e-12);
assert_relative_eq!(v.lower, 0.0, epsilon = 1e-12);
}
/// Hand-computed reference. Single bar with `high = 12`, `low = 8`,
/// `close = 10`, `factor = 0.5`, `period = 1`.
/// `ratio = (12 8) / (12 + 8) = 0.2`
/// `raw_up = 12 · (1 + 0.5 · 0.2) = 12 · 1.1 = 13.2`
/// `raw_lo = 8 · (1 0.5 · 0.2) = 8 · 0.9 = 7.2`
/// `middle = SMA(close, 1) = 10`
#[test]
fn reference_value_single_bar() {
let mut ab = AccelerationBands::new(1, 0.5).unwrap();
let v = ab.update(c(12.0, 8.0, 10.0)).unwrap();
assert_relative_eq!(v.upper, 13.2, epsilon = 1e-12);
assert_relative_eq!(v.middle, 10.0, epsilon = 1e-12);
assert_relative_eq!(v.lower, 7.2, epsilon = 1e-12);
}
}
@@ -0,0 +1,220 @@
//! Williams Accumulation/Distribution.
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// Larry Williams' Accumulation/Distribution — a cumulative volume-less price
/// flow that classifies each bar as accumulation or distribution based on its
/// close relative to the previous close, then sums the directional component.
///
/// Williams' definition (1972) uses a *true* high/low that includes the prior
/// close as an anchor — the same idea that motivates true range:
///
/// ```text
/// TR_h_t = max(close_{t1}, high_t)
/// TR_l_t = min(close_{t1}, low_t)
/// AD_t = AD_{t1} + (close_t TR_l_t) if close_t > close_{t1} (accumulation)
/// AD_t = AD_{t1} + (close_t TR_h_t) if close_t < close_{t1} (distribution)
/// AD_t = AD_{t1} if close_t == close_{t1} (no change)
/// ```
///
/// Unlike Chaikin's Accumulation/Distribution Line, the Williams A/D ignores
/// volume entirely — Williams argued that the relative position of the close
/// already encodes the day's "true" buying or selling pressure. The series is
/// unbounded and used primarily for divergence analysis. The first candle only
/// seeds the previous close; the first emission lands at bar 2.
///
/// # Example
///
/// ```
/// use wickra_core::{Candle, Indicator, AdOscillator};
///
/// let mut indicator = AdOscillator::new();
/// let mut last = None;
/// for i in 0..80 {
/// let base = 100.0 + f64::from(i);
/// let candle =
/// Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 10.0, i64::from(i)).unwrap();
/// last = indicator.update(candle);
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdOscillator {
prev_close: Option<f64>,
total: f64,
has_emitted: bool,
}
impl AdOscillator {
/// Construct a new Williams A/D starting at zero.
pub const fn new() -> Self {
Self {
prev_close: None,
total: 0.0,
has_emitted: false,
}
}
/// Current cumulative value if at least one emission has happened.
pub const fn value(&self) -> Option<f64> {
if self.has_emitted {
Some(self.total)
} else {
None
}
}
}
impl Indicator for AdOscillator {
type Input = Candle;
type Output = f64;
fn update(&mut self, candle: Candle) -> Option<f64> {
let Some(prev) = self.prev_close else {
// The first bar only establishes the previous close anchor.
self.prev_close = Some(candle.close);
return None;
};
let delta = if candle.close > prev {
// Accumulation: distance from the true low.
let tr_l = prev.min(candle.low);
candle.close - tr_l
} else if candle.close < prev {
// Distribution: distance from the true high (negative).
let tr_h = prev.max(candle.high);
candle.close - tr_h
} else {
// Unchanged close contributes nothing.
0.0
};
self.total += delta;
self.prev_close = Some(candle.close);
self.has_emitted = true;
Some(self.total)
}
fn reset(&mut self) {
self.prev_close = None;
self.total = 0.0;
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
// One seed bar; the second bar is the first emission.
2
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"WilliamsAD"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
fn c(open: f64, high: f64, low: f64, close: f64, ts: i64) -> Candle {
Candle::new(open, high, low, close, 100.0, ts).unwrap()
}
#[test]
fn accessors_and_metadata() {
let ad = AdOscillator::new();
assert_eq!(ad.name(), "WilliamsAD");
assert_eq!(ad.warmup_period(), 2);
assert_eq!(ad.value(), None);
}
#[test]
fn value_returns_total_after_first_emission() {
let mut ad = AdOscillator::new();
ad.update(c(10.0, 11.0, 9.0, 10.0, 0));
let v = ad.update(c(11.0, 13.0, 8.0, 12.0, 1)).unwrap();
assert_relative_eq!(ad.value().unwrap(), v, epsilon = 1e-12);
}
#[test]
fn first_bar_only_seeds() {
let mut ad = AdOscillator::new();
assert_eq!(ad.update(c(10.0, 11.0, 9.0, 10.0, 0)), None);
assert!(!ad.is_ready());
}
#[test]
fn accumulation_adds_distance_from_true_low() {
// prev close = 10, today low = 8, today close = 12 (up day).
// TR_l = min(10, 8) = 8, delta = 12 - 8 = 4. AD = 0 + 4 = 4.
let mut ad = AdOscillator::new();
ad.update(c(10.0, 11.0, 9.0, 10.0, 0));
let v = ad.update(c(11.0, 13.0, 8.0, 12.0, 1)).unwrap();
assert_relative_eq!(v, 4.0, epsilon = 1e-12);
}
#[test]
fn distribution_adds_distance_from_true_high() {
// prev close = 10, today high = 11, today close = 7 (down day).
// TR_h = max(10, 11) = 11, delta = 7 - 11 = -4. AD = -4.
let mut ad = AdOscillator::new();
ad.update(c(10.0, 11.0, 9.0, 10.0, 0));
let v = ad.update(c(10.0, 11.0, 7.0, 7.0, 1)).unwrap();
assert_relative_eq!(v, -4.0, epsilon = 1e-12);
}
#[test]
fn unchanged_close_keeps_total() {
// close equals prev close -> no contribution.
let mut ad = AdOscillator::new();
ad.update(c(10.0, 11.0, 9.0, 10.0, 0));
let v = ad.update(c(10.0, 12.0, 8.0, 10.0, 1)).unwrap();
assert_relative_eq!(v, 0.0, epsilon = 1e-12);
}
#[test]
fn constant_series_yields_zero() {
// Every close equals the previous -> AD stays at zero forever.
let candles: Vec<Candle> = (0..40).map(|i| c(10.0, 11.0, 9.0, 10.0, i)).collect();
let mut ad = AdOscillator::new();
for v in ad.batch(&candles).into_iter().flatten() {
assert_relative_eq!(v, 0.0, epsilon = 1e-12);
}
}
#[test]
fn batch_equals_streaming() {
let candles: Vec<Candle> = (0..80i64)
.map(|i| {
let f = i as f64;
let mid = 100.0 + (f * 0.3).sin() * 5.0;
c(mid, mid + 2.0, mid - 2.0, mid + 0.5, i)
})
.collect();
let mut a = AdOscillator::new();
let mut b = AdOscillator::new();
assert_eq!(
a.batch(&candles),
candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
);
}
#[test]
fn reset_clears_state() {
let mut ad = AdOscillator::new();
ad.batch(&[
c(10.0, 11.0, 9.0, 10.0, 0),
c(10.0, 12.0, 9.0, 11.0, 1),
c(11.0, 13.0, 10.0, 12.0, 2),
]);
assert!(ad.is_ready());
ad.reset();
assert!(!ad.is_ready());
assert_eq!(ad.value(), None);
assert_eq!(ad.update(c(10.0, 11.0, 9.0, 10.0, 3)), None);
}
}
@@ -0,0 +1,157 @@
//! Advance/Decline Volume Line — cumulative net advancing-minus-declining volume.
use crate::cross_section::CrossSection;
use crate::traits::Indicator;
/// Advance/Decline Volume Line (AD Volume Line) — the running cumulative sum of
/// net advancing volume across a universe.
///
/// On each [`CrossSection`] tick the net is `advancing volume - declining volume`,
/// where advancing volume is the total volume of symbols with a positive change
/// and declining volume the total volume of symbols with a negative change. The
/// line accumulates this net over time, so a rising line means volume is flowing
/// into advancing issues (healthy participation) while a falling line warns that
/// declining issues are carrying the volume — the volume-weighted analogue of the
/// plain Advance/Decline Line.
///
/// `Input = CrossSection`, `Output = f64`, `warmup_period == 1` (defined from the
/// first tick).
///
/// # Example
///
/// ```
/// use wickra_core::{AdVolumeLine, CrossSection, Indicator, Member};
///
/// let mut adv = AdVolumeLine::new();
/// // advancing volume 150, declining volume 50 -> net +100.
/// let tick = CrossSection::new(
/// vec![
/// Member::new(1.0, 150.0, false, false),
/// Member::new(-1.0, 50.0, false, false),
/// ],
/// 0,
/// )
/// .unwrap();
/// assert_eq!(adv.update(tick), Some(100.0));
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdVolumeLine {
line: f64,
has_emitted: bool,
}
impl AdVolumeLine {
/// Construct a new Advance/Decline Volume Line indicator.
#[must_use]
pub const fn new() -> Self {
Self {
line: 0.0,
has_emitted: false,
}
}
}
impl Indicator for AdVolumeLine {
type Input = CrossSection;
type Output = f64;
fn update(&mut self, section: CrossSection) -> Option<f64> {
let net = section.advancing_volume() - section.declining_volume();
self.line += net;
self.has_emitted = true;
Some(self.line)
}
fn reset(&mut self) {
self.line = 0.0;
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
1
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AdVolumeLine"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::cross_section::Member;
use crate::traits::BatchExt;
fn tick(items: &[(f64, f64)]) -> CrossSection {
CrossSection::new(
items
.iter()
.map(|&(change, volume)| Member::new(change, volume, false, false))
.collect(),
0,
)
.unwrap()
}
#[test]
fn accessors_and_metadata() {
let adv = AdVolumeLine::new();
assert_eq!(adv.name(), "AdVolumeLine");
assert_eq!(adv.warmup_period(), 1);
assert!(!adv.is_ready());
}
#[test]
fn first_tick_emits_net_volume() {
let mut adv = AdVolumeLine::new();
assert_eq!(adv.update(tick(&[(1.0, 150.0), (-1.0, 50.0)])), Some(100.0));
assert!(adv.is_ready());
}
#[test]
fn line_accumulates_across_ticks() {
let mut adv = AdVolumeLine::new();
assert_eq!(adv.update(tick(&[(1.0, 150.0), (-1.0, 50.0)])), Some(100.0));
assert_eq!(adv.update(tick(&[(1.0, 60.0), (-1.0, 60.0)])), Some(100.0));
assert_eq!(adv.update(tick(&[(1.0, 30.0)])), Some(130.0));
}
#[test]
fn unchanged_volume_is_ignored() {
let mut adv = AdVolumeLine::new();
// Unchanged symbols (zero change) contribute to neither bucket.
assert_eq!(adv.update(tick(&[(0.0, 1000.0), (1.0, 10.0)])), Some(10.0));
}
#[test]
fn reset_clears_state() {
let mut adv = AdVolumeLine::new();
adv.update(tick(&[(1.0, 100.0)]));
assert!(adv.is_ready());
adv.reset();
assert!(!adv.is_ready());
assert_eq!(adv.update(tick(&[(1.0, 20.0)])), Some(20.0));
}
#[test]
fn batch_equals_streaming() {
let sections = vec![
tick(&[(1.0, 150.0), (-1.0, 50.0)]),
tick(&[(1.0, 60.0), (-1.0, 60.0)]),
tick(&[(1.0, 30.0)]),
];
let mut a = AdVolumeLine::new();
let mut b = AdVolumeLine::new();
assert_eq!(
a.batch(&sections),
sections
.iter()
.map(|s| b.update(s.clone()))
.collect::<Vec<_>>()
);
}
}
@@ -0,0 +1,245 @@
//! Adaptive CCI — a CCI whose centre line adapts to the efficiency ratio.
use std::collections::VecDeque;
use crate::error::{Error, Result};
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// Adaptive CCI — Lambert's Commodity Channel Index whose centre line is an
/// **efficiency-ratio-adaptive** moving average of typical price instead of a
/// plain SMA, so it leads in trends and stays calm in chop.
///
/// ```text
/// TP = (high + low + close) / 3
/// ER = |TP_t TP_oldest| / Σ |ΔTP| over the window (0..1)
/// sc = ( ER·(2/3 2/31) + 2/31 )²
/// mean += sc·(TP_t mean) (adaptive centre, seeded with SMA)
/// MD = mean(|TP_i mean|) over the window (mean deviation)
/// CCI = (TP_t mean) / (0.015 · MD)
/// ```
///
/// The classic [`Cci`](crate::Cci) centres typical price on its simple moving
/// average; the lag of that SMA delays the oscillator in fast moves. Replacing it
/// with a KAMA-style adaptive average — driven by Kaufman's efficiency ratio —
/// lets the centre line accelerate toward price in a clean trend (so the CCI
/// reaches its `±100` bands sooner) and slow down in noise (fewer false pokes).
/// The `0.015` scaling keeps Lambert's convention that roughly 7080% of readings
/// fall in `[100, +100]`.
///
/// The output is unbounded around `0`; a flat window (zero mean deviation) returns
/// `0`. The first value lands after `period` inputs; each `update` is O(`period`).
///
/// # Example
///
/// ```
/// use wickra_core::{Candle, Indicator, AdaptiveCci};
///
/// let mut indicator = AdaptiveCci::new(20).unwrap();
/// let mut last = None;
/// for i in 0..60 {
/// let base = 100.0 + (f64::from(i) * 0.3).sin() * 5.0;
/// let c = Candle::new(base, base + 1.0, base - 1.0, base, 1_000.0, 0).unwrap();
/// last = indicator.update(c);
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone)]
pub struct AdaptiveCci {
period: usize,
window: VecDeque<f64>,
mean: Option<f64>,
last: Option<f64>,
}
impl AdaptiveCci {
/// Construct an adaptive CCI with the given `period`.
///
/// # Errors
///
/// Returns [`Error::PeriodZero`] if `period == 0` and
/// [`Error::InvalidPeriod`] if `period < 2` (the efficiency ratio needs a
/// path of at least one step).
pub fn new(period: usize) -> Result<Self> {
if period == 0 {
return Err(Error::PeriodZero);
}
if period < 2 {
return Err(Error::InvalidPeriod {
message: "adaptive CCI needs period >= 2",
});
}
Ok(Self {
period,
window: VecDeque::with_capacity(period),
mean: None,
last: None,
})
}
/// Configured period.
pub const fn period(&self) -> usize {
self.period
}
/// Current value if available.
pub const fn value(&self) -> Option<f64> {
self.last
}
}
impl Indicator for AdaptiveCci {
type Input = Candle;
type Output = f64;
fn update(&mut self, candle: Candle) -> Option<f64> {
let tp = candle.typical_price();
if self.window.len() == self.period {
self.window.pop_front();
}
self.window.push_back(tp);
if self.window.len() < self.period {
return None;
}
let n = self.period as f64;
// Efficiency ratio over the window.
let oldest = self.window[0];
let direction = (tp - oldest).abs();
let mut path = 0.0;
for pair in self.window.iter().collect::<Vec<_>>().windows(2) {
path += (pair[1] - pair[0]).abs();
}
let er = if path > 0.0 {
(direction / path).clamp(0.0, 1.0)
} else {
0.0
};
let fast = 2.0 / 3.0;
let slow = 2.0 / 31.0;
let sc = (er * (fast - slow) + slow).powi(2);
let mean = match self.mean {
None => self.window.iter().sum::<f64>() / n,
Some(prev) => prev + sc * (tp - prev),
};
self.mean = Some(mean);
let md = self.window.iter().map(|&v| (v - mean).abs()).sum::<f64>() / n;
let cci = if md > 0.0 {
(tp - mean) / (0.015 * md)
} else {
0.0
};
self.last = Some(cci);
Some(cci)
}
fn reset(&mut self) {
self.window.clear();
self.mean = None;
self.last = None;
}
fn warmup_period(&self) -> usize {
self.period
}
fn is_ready(&self) -> bool {
self.last.is_some()
}
fn name(&self) -> &'static str {
"AdaptiveCci"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
fn candle(tp: f64) -> Candle {
// open=high=low=close=tp -> typical price == tp.
Candle::new_unchecked(tp, tp, tp, tp, 1_000.0, 0)
}
#[test]
fn rejects_invalid_period() {
assert!(matches!(AdaptiveCci::new(0), Err(Error::PeriodZero)));
assert!(matches!(
AdaptiveCci::new(1),
Err(Error::InvalidPeriod { .. })
));
}
#[test]
fn accessors_and_metadata() {
let c = AdaptiveCci::new(20).unwrap();
assert_eq!(c.period(), 20);
assert_eq!(c.warmup_period(), 20);
assert_eq!(c.name(), "AdaptiveCci");
assert!(!c.is_ready());
assert_eq!(c.value(), None);
}
#[test]
fn first_emission_at_warmup_period() {
let mut c = AdaptiveCci::new(4).unwrap();
let candles: Vec<Candle> = (0..6).map(|i| candle(100.0 + f64::from(i))).collect();
let out = c.batch(&candles);
for v in out.iter().take(3) {
assert!(v.is_none());
}
assert!(out[3].is_some());
}
#[test]
fn uptrend_is_positive() {
let mut c = AdaptiveCci::new(10).unwrap();
let candles: Vec<Candle> = (0..40).map(|i| candle(100.0 + f64::from(i))).collect();
let last = c.batch(&candles).into_iter().flatten().last().unwrap();
assert!(last > 0.0, "uptrend should give positive CCI, got {last}");
}
#[test]
fn downtrend_is_negative() {
let mut c = AdaptiveCci::new(10).unwrap();
let candles: Vec<Candle> = (0..40).map(|i| candle(200.0 - f64::from(i))).collect();
let last = c.batch(&candles).into_iter().flatten().last().unwrap();
assert!(last < 0.0, "downtrend should give negative CCI, got {last}");
}
#[test]
fn flat_window_is_zero() {
let mut c = AdaptiveCci::new(5).unwrap();
let candles: Vec<Candle> = (0..10).map(|_| candle(100.0)).collect();
for v in c.batch(&candles).into_iter().flatten() {
assert_relative_eq!(v, 0.0, epsilon = 1e-9);
}
}
#[test]
fn reset_clears_state() {
let mut c = AdaptiveCci::new(5).unwrap();
let candles: Vec<Candle> = (0..20).map(|i| candle(100.0 + f64::from(i))).collect();
c.batch(&candles);
assert!(c.is_ready());
c.reset();
assert!(!c.is_ready());
assert_eq!(c.value(), None);
assert_eq!(c.update(candle(100.0)), None);
}
#[test]
fn batch_equals_streaming() {
let candles: Vec<Candle> = (0..120)
.map(|i| candle(100.0 + (f64::from(i) * 0.25).sin() * 9.0))
.collect();
let batch = AdaptiveCci::new(20).unwrap().batch(&candles);
let mut b = AdaptiveCci::new(20).unwrap();
let streamed: Vec<_> = candles.iter().map(|x| b.update(*x)).collect();
assert_eq!(batch, streamed);
}
}
@@ -0,0 +1,143 @@
//! Ehlers Adaptive Cycle period estimator (for adaptive oscillators).
use crate::indicators::hilbert_dominant_cycle::HilbertDominantCycle;
use crate::traits::Indicator;
/// Ehlers' Adaptive Cycle Indicator.
///
/// Returns half the current dominant cycle period — the "best" lookback for
/// downstream oscillators like an adaptive RSI or adaptive Stochastic, per
/// Ehlers' *Cycle Analytics for Traders* (2013, ch. 11). Halving accounts for
/// the fact that an oscillator over a half-cycle captures the full peak-to-
/// trough swing without aliasing.
///
/// The output is rounded to an integer-valued `f64` and clamped to `[3, 25]`,
/// matching the typical operating range of period-adaptive oscillators.
///
/// # Example
///
/// ```
/// use wickra_core::{Indicator, AdaptiveCycle};
///
/// let mut ac = AdaptiveCycle::new();
/// let mut last = None;
/// for i in 0..200 {
/// last = ac.update(100.0 + (f64::from(i) * 0.4).sin() * 5.0);
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdaptiveCycle {
cycle: HilbertDominantCycle,
last_value: Option<f64>,
}
impl AdaptiveCycle {
/// Construct a new adaptive cycle estimator.
pub fn new() -> Self {
Self::default()
}
/// Current adaptive period if available.
pub const fn value(&self) -> Option<f64> {
self.last_value
}
}
impl Indicator for AdaptiveCycle {
type Input = f64;
type Output = f64;
fn update(&mut self, input: f64) -> Option<f64> {
let period = self.cycle.update(input)?;
let half = (period * 0.5).round().clamp(3.0, 25.0);
self.last_value = Some(half);
Some(half)
}
fn reset(&mut self) {
self.cycle.reset();
self.last_value = None;
}
fn warmup_period(&self) -> usize {
self.cycle.warmup_period()
}
fn is_ready(&self) -> bool {
self.last_value.is_some()
}
fn name(&self) -> &'static str {
"AdaptiveCycle"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
#[test]
fn accessors_and_metadata() {
let mut ac = AdaptiveCycle::new();
assert_eq!(ac.warmup_period(), 50);
assert_eq!(ac.name(), "AdaptiveCycle");
assert!(!ac.is_ready());
assert!(ac.value().is_none());
let prices: Vec<f64> = (0..120)
.map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 5.0)
.collect();
ac.batch(&prices);
assert!(ac.is_ready());
assert!(ac.value().is_some());
}
#[test]
fn output_within_clamp_band() {
let prices: Vec<f64> = (0..200)
.map(|i| 100.0 + (f64::from(i) * 0.5).sin() * 5.0)
.collect();
let mut ac = AdaptiveCycle::new();
for v in ac.batch(&prices).into_iter().flatten() {
assert!((3.0..=25.0).contains(&v), "period {v} out of band");
assert_eq!(v, v.round(), "expected integer-valued output");
}
}
#[test]
fn batch_equals_streaming() {
let prices: Vec<f64> = (0..200)
.map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 5.0)
.collect();
let mut a = AdaptiveCycle::new();
let mut b = AdaptiveCycle::new();
let batch = a.batch(&prices);
let streamed: Vec<_> = prices.iter().map(|p| b.update(*p)).collect();
assert_eq!(batch, streamed);
}
#[test]
fn ignores_non_finite_input() {
let mut ac = AdaptiveCycle::new();
let prices: Vec<f64> = (0..120)
.map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 5.0)
.collect();
ac.batch(&prices);
let before = ac.value();
assert!(before.is_some());
assert_eq!(ac.update(f64::NAN), before);
}
#[test]
fn reset_clears_state() {
let mut ac = AdaptiveCycle::new();
let prices: Vec<f64> = (0..120)
.map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 5.0)
.collect();
ac.batch(&prices);
assert!(ac.is_ready());
ac.reset();
assert!(!ac.is_ready());
}
}
@@ -0,0 +1,344 @@
//! Ehlers' Adaptive Laguerre Filter.
use std::collections::VecDeque;
use crate::error::{Error, Result};
use crate::traits::Indicator;
/// John Ehlers' Adaptive Laguerre Filter — a four-stage Laguerre polynomial
/// smoother whose damping factor `gamma` is recomputed every bar from how well
/// the filter is currently tracking price.
///
/// The Laguerre cascade is the same one used by [`LaguerreRsi`](crate::LaguerreRsi),
/// but instead of a fixed `gamma` the filter adapts: it measures the recent
/// absolute error `|price filter|`, normalises those errors across a window of
/// `period` bars to `[0, 1]`, and takes their **median** as `gamma`. When price
/// is tracking smoothly the errors are small and uniform (low `gamma`, fast
/// response); when price jumps, the spread of errors widens and `gamma` rises,
/// slowing the filter to reject the noise.
///
/// ```text
/// diff_t = |price_t filter_{t-1}|
/// over the last `period` diffs:
/// HH = max(diff), LL = min(diff)
/// norm_i = (diff_i LL) / (HH LL) (0 if HH == LL)
/// gamma = median(norm)
/// alpha = 1 gamma
/// L0_t = alpha·price_t + gamma·L0_{t-1}
/// L1_t = gamma·L0_t + L0_{t-1} + gamma·L1_{t-1}
/// L2_t = gamma·L1_t + L1_{t-1} + gamma·L2_{t-1}
/// L3_t = gamma·L2_t + L2_{t-1} + gamma·L3_{t-1}
/// filter_t = (L0_t + 2·L1_t + 2·L2_t + L3_t) / 6
/// ```
///
/// The output is a smoothed price on the same scale as the input. The first
/// emission lands once the error window holds `period` values.
///
/// Reference: John F. Ehlers, *"Adaptive Laguerre Filter"*, Technical Analysis
/// of Stocks & Commodities, 2007.
///
/// # Example
///
/// ```
/// use wickra_core::{Indicator, AdaptiveLaguerreFilter};
///
/// let mut indicator = AdaptiveLaguerreFilter::new(13).unwrap();
/// let mut last = None;
/// for i in 0..80 {
/// last = indicator.update(100.0 + f64::from(i));
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone)]
pub struct AdaptiveLaguerreFilter {
period: usize,
l0: f64,
l1: f64,
l2: f64,
l3: f64,
/// Previous filter output, or `None` before the first bar.
filter: Option<f64>,
/// The last `period` absolute errors `|price filter|`.
diffs: VecDeque<f64>,
}
impl AdaptiveLaguerreFilter {
/// Construct a new adaptive Laguerre filter with the given error-window
/// length.
///
/// # Errors
///
/// Returns [`Error::PeriodZero`] if `period == 0`.
pub fn new(period: usize) -> Result<Self> {
if period == 0 {
return Err(Error::PeriodZero);
}
Ok(Self {
period,
l0: 0.0,
l1: 0.0,
l2: 0.0,
l3: 0.0,
filter: None,
diffs: VecDeque::with_capacity(period),
})
}
/// Configured error-window length.
pub const fn period(&self) -> usize {
self.period
}
/// Current value if the error window is full.
pub fn value(&self) -> Option<f64> {
if self.diffs.len() == self.period {
self.filter
} else {
None
}
}
/// Median of the normalised errors currently in the window. Returns `0.0`
/// when every error is equal (e.g. during a constant warmup), which makes
/// the filter maximally fast.
fn adaptive_gamma(&self) -> f64 {
let mut hh = f64::MIN;
let mut ll = f64::MAX;
for &d in &self.diffs {
if d > hh {
hh = d;
}
if d < ll {
ll = d;
}
}
let range = hh - ll;
if range <= 0.0 {
return 0.0;
}
let mut norm: Vec<f64> = self.diffs.iter().map(|&d| (d - ll) / range).collect();
// `total_cmp` never panics — under pathological (e.g. overflowing) fuzz
// inputs a normalised error can be non-finite; a total order keeps the
// sort sound where `partial_cmp` would return `None`.
norm.sort_by(f64::total_cmp);
let mid = norm.len() / 2;
if norm.len() % 2 == 1 {
norm[mid]
} else {
f64::midpoint(norm[mid - 1], norm[mid])
}
}
}
impl Indicator for AdaptiveLaguerreFilter {
type Input = f64;
type Output = f64;
fn update(&mut self, price: f64) -> Option<f64> {
if !price.is_finite() {
return self.value();
}
// Absolute tracking error against the previous filter (0 on the first
// bar, where there is no prior filter value).
let diff = self.filter.map_or(0.0, |f| (price - f).abs());
if self.diffs.len() == self.period {
self.diffs.pop_front();
}
self.diffs.push_back(diff);
let gamma = self.adaptive_gamma();
let alpha = 1.0 - gamma;
let l0 = alpha * price + gamma * self.l0;
let l1 = -gamma * l0 + self.l0 + gamma * self.l1;
let l2 = -gamma * l1 + self.l1 + gamma * self.l2;
let l3 = -gamma * l2 + self.l2 + gamma * self.l3;
self.l0 = l0;
self.l1 = l1;
self.l2 = l2;
self.l3 = l3;
let filter = (l0 + 2.0 * l1 + 2.0 * l2 + l3) / 6.0;
self.filter = Some(filter);
self.value()
}
fn reset(&mut self) {
self.l0 = 0.0;
self.l1 = 0.0;
self.l2 = 0.0;
self.l3 = 0.0;
self.filter = None;
self.diffs.clear();
}
fn warmup_period(&self) -> usize {
self.period
}
fn is_ready(&self) -> bool {
self.diffs.len() == self.period
}
fn name(&self) -> &'static str {
"AdaptiveLaguerre"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
/// Independent reference: replays the exact recurrence from scratch.
fn naive(prices: &[f64], period: usize) -> Vec<Option<f64>> {
let (mut l0, mut l1, mut l2, mut l3) = (0.0_f64, 0.0_f64, 0.0_f64, 0.0_f64);
let mut filter: Option<f64> = None;
let mut diffs: Vec<f64> = Vec::new();
let mut out = Vec::with_capacity(prices.len());
for &price in prices {
let diff = filter.map_or(0.0, |f: f64| (price - f).abs());
diffs.push(diff);
if diffs.len() > period {
diffs.remove(0);
}
let hh = diffs.iter().copied().fold(f64::MIN, f64::max);
let ll = diffs.iter().copied().fold(f64::MAX, f64::min);
let range = hh - ll;
let gamma = if range <= 0.0 {
0.0
} else {
let mut norm: Vec<f64> = diffs.iter().map(|&d| (d - ll) / range).collect();
norm.sort_by(|a, b| a.partial_cmp(b).unwrap());
let mid = norm.len() / 2;
if norm.len() % 2 == 1 {
norm[mid]
} else {
f64::midpoint(norm[mid - 1], norm[mid])
}
};
let alpha = 1.0 - gamma;
let n0 = alpha * price + gamma * l0;
let n1 = -gamma * n0 + l0 + gamma * l1;
let n2 = -gamma * n1 + l1 + gamma * l2;
let n3 = -gamma * n2 + l2 + gamma * l3;
l0 = n0;
l1 = n1;
l2 = n2;
l3 = n3;
let f = (n0 + 2.0 * n1 + 2.0 * n2 + n3) / 6.0;
filter = Some(f);
out.push(if diffs.len() == period { Some(f) } else { None });
}
out
}
#[test]
fn new_rejects_zero_period() {
assert!(matches!(
AdaptiveLaguerreFilter::new(0),
Err(Error::PeriodZero)
));
}
/// Cover the const accessor `period` and the Indicator-impl `warmup_period`
/// + `name`.
#[test]
fn accessors_and_metadata() {
let alf = AdaptiveLaguerreFilter::new(13).unwrap();
assert_eq!(alf.period(), 13);
assert_eq!(alf.warmup_period(), 13);
assert_eq!(alf.name(), "AdaptiveLaguerre");
}
#[test]
fn warmup_returns_none_until_window_full() {
let mut alf = AdaptiveLaguerreFilter::new(3).unwrap();
assert_eq!(alf.update(10.0), None);
assert_eq!(alf.update(11.0), None);
assert!(alf.update(12.0).is_some());
}
#[test]
fn constant_series_converges_to_constant() {
// Errors are all zero -> gamma 0 -> the 4-stage delay line fills with
// the constant and the filter settles on it.
let mut alf = AdaptiveLaguerreFilter::new(5).unwrap();
let out = alf.batch(&[42.0_f64; 40]);
let last = out.iter().rev().flatten().next().unwrap();
assert_relative_eq!(*last, 42.0, epsilon = 1e-9);
}
#[test]
fn converged_output_stays_within_price_range() {
// Once the Laguerre cascade has filled (it cold-starts from zero, so the
// first few post-warmup values ramp up toward price), the filter is a
// convex blend of recent prices and must stay inside the data range.
let prices: Vec<f64> = (0..120)
.map(|i| 50.0 + (f64::from(i) * 0.4).sin() * 10.0)
.collect();
let lo = prices.iter().copied().fold(f64::MAX, f64::min);
let hi = prices.iter().copied().fold(f64::MIN, f64::max);
let period = 8;
let mut alf = AdaptiveLaguerreFilter::new(period).unwrap();
for (i, v) in alf.batch(&prices).into_iter().enumerate() {
// Skip the cold-start transient (a few multiples of the window).
if i < 4 * period {
continue;
}
let v = v.expect("filter is ready well past warmup");
assert!(
v >= lo - 1e-6 && v <= hi + 1e-6,
"filter out of range at {i}"
);
}
}
#[test]
fn matches_naive_recurrence() {
let prices: Vec<f64> = (0..80)
.map(|i| 100.0 + (f64::from(i) * 0.5).sin() * 8.0 + f64::from(i) * 0.1)
.collect();
let mut alf = AdaptiveLaguerreFilter::new(10).unwrap();
let got = alf.batch(&prices);
let want = naive(&prices, 10);
for (i, (g, w)) in got.iter().zip(want.iter()).enumerate() {
assert_eq!(g.is_some(), w.is_some(), "readiness mismatch at {i}");
if let (Some(a), Some(b)) = (g, w) {
assert_relative_eq!(*a, *b, epsilon = 1e-9);
}
}
}
#[test]
fn reset_clears_state() {
let mut alf = AdaptiveLaguerreFilter::new(5).unwrap();
alf.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
assert!(alf.is_ready());
alf.reset();
assert!(!alf.is_ready());
assert_eq!(alf.update(1.0), None);
}
#[test]
fn batch_equals_streaming() {
let prices: Vec<f64> = (1..=50).map(|i| f64::from(i) * 0.7).collect();
let mut a = AdaptiveLaguerreFilter::new(7).unwrap();
let mut b = AdaptiveLaguerreFilter::new(7).unwrap();
assert_eq!(
a.batch(&prices),
prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
);
}
#[test]
fn ignores_non_finite_input() {
let mut alf = AdaptiveLaguerreFilter::new(3).unwrap();
alf.update(10.0);
alf.update(11.0);
let ready = alf.update(12.0).expect("ready after three inputs");
assert_eq!(alf.update(f64::NAN), Some(ready));
assert_eq!(alf.update(f64::INFINITY), Some(ready));
}
}
@@ -0,0 +1,296 @@
//! Adaptive RSI — an RSI whose up/down averaging adapts to the efficiency ratio.
use std::collections::VecDeque;
use crate::error::{Error, Result};
use crate::traits::Indicator;
/// Adaptive RSI — Wilder's RSI in which the smoothing of the average gain and
/// average loss **adapts to trendiness** via Kaufman's efficiency ratio, so the
/// oscillator reacts fast in a clean move and smooths through chop.
///
/// ```text
/// ER = |price_t price_{tperiod}| / Σ |Δprice| over the window (efficiency ratio, 0..1)
/// sc = ( ER·(2/3 2/31) + 2/31 )² (KAMA smoothing constant)
/// avg_gain += sc·(gain avg_gain), avg_loss += sc·(loss avg_loss)
/// RSI = 100 · avg_gain / (avg_gain + avg_loss)
/// ```
///
/// A fixed-period [`Rsi`](crate::Rsi) is a compromise: short periods whip in
/// ranges, long ones lag in trends. This adaptive form borrows Kaufman's
/// efficiency ratio (`directional move / total path`) to set the smoothing each
/// bar — near `1` (a clean trend) the averages track gains and losses almost
/// immediately; near `0` (noise) they barely move, filtering the chop. The result
/// is an RSI that is responsive when it should be and quiet when it should be. It
/// is the efficiency-ratio cousin of Ehlers' cycle-adaptive RSI, which instead
/// sets the lookback from the measured dominant cycle.
///
/// Output is bounded in `[0, 100]`; a flat market returns the neutral `50`. The
/// first value lands after `period + 1` inputs. Each `update` is O(1).
///
/// # Example
///
/// ```
/// use wickra_core::{Indicator, AdaptiveRsi};
///
/// let mut indicator = AdaptiveRsi::new(14).unwrap();
/// let mut last = None;
/// for i in 0..60 {
/// last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
/// }
/// assert!(last.is_some());
/// ```
#[derive(Debug, Clone)]
pub struct AdaptiveRsi {
period: usize,
prices: VecDeque<f64>,
abs_changes: VecDeque<f64>,
abs_sum: f64,
prev: Option<f64>,
seed_gain: f64,
seed_loss: f64,
seed_count: usize,
avg_gain: Option<f64>,
avg_loss: Option<f64>,
last: Option<f64>,
}
impl AdaptiveRsi {
/// Construct an adaptive RSI with the given efficiency-ratio `period`.
///
/// # Errors
///
/// Returns [`Error::PeriodZero`] if `period == 0`.
pub fn new(period: usize) -> Result<Self> {
if period == 0 {
return Err(Error::PeriodZero);
}
Ok(Self {
period,
prices: VecDeque::with_capacity(period + 1),
abs_changes: VecDeque::with_capacity(period),
abs_sum: 0.0,
prev: None,
seed_gain: 0.0,
seed_loss: 0.0,
seed_count: 0,
avg_gain: None,
avg_loss: None,
last: None,
})
}
/// Configured efficiency-ratio period.
pub const fn period(&self) -> usize {
self.period
}
/// Current value if available.
pub const fn value(&self) -> Option<f64> {
self.last
}
fn rsi_from_avgs(avg_gain: f64, avg_loss: f64) -> f64 {
let denom = avg_gain + avg_loss;
if denom == 0.0 {
50.0
} else {
100.0 * (avg_gain / denom)
}
}
fn efficiency_ratio(&self, price: f64) -> f64 {
let oldest = *self.prices.front().expect("window non-empty");
let direction = (price - oldest).abs();
if self.abs_sum == 0.0 {
0.0
} else {
(direction / self.abs_sum).clamp(0.0, 1.0)
}
}
}
impl Indicator for AdaptiveRsi {
type Input = f64;
type Output = f64;
fn update(&mut self, price: f64) -> Option<f64> {
if !price.is_finite() {
return self.last;
}
let Some(prev) = self.prev else {
self.prev = Some(price);
self.prices.push_back(price);
return None;
};
let change = price - prev;
self.prev = Some(price);
let gain = if change > 0.0 { change } else { 0.0 };
let loss = if change < 0.0 { -change } else { 0.0 };
// Maintain the price window (period + 1) and the |Δ| window (period).
self.prices.push_back(price);
if self.prices.len() > self.period + 1 {
self.prices.pop_front();
}
if self.abs_changes.len() == self.period {
self.abs_sum -= self.abs_changes.pop_front().expect("non-empty");
}
self.abs_changes.push_back(change.abs());
self.abs_sum += change.abs();
if let (Some(ag), Some(al)) = (self.avg_gain, self.avg_loss) {
let er = self.efficiency_ratio(price);
let fast = 2.0 / 3.0;
let slow = 2.0 / 31.0;
let sc = (er * (fast - slow) + slow).powi(2);
let new_ag = ag + sc * (gain - ag);
let new_al = al + sc * (loss - al);
self.avg_gain = Some(new_ag);
self.avg_loss = Some(new_al);
let v = Self::rsi_from_avgs(new_ag, new_al);
self.last = Some(v);
return Some(v);
}
self.seed_gain += gain;
self.seed_loss += loss;
self.seed_count += 1;
if self.seed_count == self.period {
let ag = self.seed_gain / self.period as f64;
let al = self.seed_loss / self.period as f64;
self.avg_gain = Some(ag);
self.avg_loss = Some(al);
let v = Self::rsi_from_avgs(ag, al);
self.last = Some(v);
return Some(v);
}
None
}
fn reset(&mut self) {
self.prices.clear();
self.abs_changes.clear();
self.abs_sum = 0.0;
self.prev = None;
self.seed_gain = 0.0;
self.seed_loss = 0.0;
self.seed_count = 0;
self.avg_gain = None;
self.avg_loss = None;
self.last = None;
}
fn warmup_period(&self) -> usize {
self.period + 1
}
fn is_ready(&self) -> bool {
self.last.is_some()
}
fn name(&self) -> &'static str {
"AdaptiveRsi"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
#[test]
fn rejects_zero_period() {
assert!(matches!(AdaptiveRsi::new(0), Err(Error::PeriodZero)));
}
#[test]
fn accessors_and_metadata() {
let r = AdaptiveRsi::new(14).unwrap();
assert_eq!(r.period(), 14);
assert_eq!(r.warmup_period(), 15);
assert_eq!(r.name(), "AdaptiveRsi");
assert!(!r.is_ready());
assert_eq!(r.value(), None);
}
#[test]
fn first_emission_at_warmup_period() {
let mut r = AdaptiveRsi::new(4).unwrap();
let out = r.batch(&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
for v in out.iter().take(4) {
assert!(v.is_none());
}
assert!(out[4].is_some());
}
#[test]
fn pure_uptrend_is_one_hundred() {
let mut r = AdaptiveRsi::new(5).unwrap();
let last = r
.batch(&(1..=40).map(f64::from).collect::<Vec<_>>())
.into_iter()
.flatten()
.last()
.unwrap();
assert_relative_eq!(last, 100.0, epsilon = 1e-9);
}
#[test]
fn flat_market_is_neutral() {
let mut r = AdaptiveRsi::new(4).unwrap();
let last = r.batch(&[7.0; 20]).into_iter().flatten().last().unwrap();
assert_relative_eq!(last, 50.0, epsilon = 1e-9);
}
#[test]
fn output_in_range() {
let mut r = AdaptiveRsi::new(14).unwrap();
for v in r
.batch(
&(0..200)
.map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 8.0)
.collect::<Vec<_>>(),
)
.into_iter()
.flatten()
{
assert!((0.0..=100.0).contains(&v));
}
}
#[test]
fn ignores_non_finite() {
let mut r = AdaptiveRsi::new(4).unwrap();
let ready = r
.batch(&[1.0, 2.0, 3.0, 4.0, 5.0])
.into_iter()
.flatten()
.last()
.unwrap();
assert_eq!(r.update(f64::NAN), Some(ready));
}
#[test]
fn reset_clears_state() {
let mut r = AdaptiveRsi::new(4).unwrap();
r.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
assert!(r.is_ready());
r.reset();
assert!(!r.is_ready());
assert_eq!(r.value(), None);
assert_eq!(r.update(1.0), None);
}
#[test]
fn batch_equals_streaming() {
let xs: Vec<f64> = (0..120)
.map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 9.0)
.collect();
let batch = AdaptiveRsi::new(14).unwrap().batch(&xs);
let mut b = AdaptiveRsi::new(14).unwrap();
let streamed: Vec<_> = xs.iter().map(|x| b.update(*x)).collect();
assert_eq!(batch, streamed);
}
}
@@ -0,0 +1,192 @@
//! Advance Block candlestick pattern.
use crate::ohlcv::Candle;
use crate::traits::Indicator;
/// Advance Block — a 3-bar bearish warning: three green candles still pushing to
/// higher closes, but visibly running out of steam — each real body shrinks while
/// the upper shadows lengthen, hinting the advance is about to stall.
///
/// ```text
/// all three green & higher closes
/// each opens inside the prior body
/// shrinking bodies (body3 < body2 < body1)
/// upper shadow of bar3 >= upper shadow of bar2 and bar3 has an upper shadow
/// ```
///
/// Output is `1.0` when the pattern completes and `0.0` otherwise. Advance Block
/// is a single-direction (bearish-only) warning, so it never emits `+1.0`. The
/// first two bars always return `0.0` because the three-bar window is not yet
/// filled. Pattern-shape check only — no trend filter is applied; combine with a
/// trend indicator for actionable signals.
///
/// # Signed ±1 encoding
///
/// This detector emits the uniform candlestick sign convention shared across the
/// pattern family — `1.0` bearish, `0.0` no pattern — so it drops straight into
/// a machine-learning feature matrix as a single dimension.
///
/// # Example
///
/// ```
/// use wickra_core::{AdvanceBlock, Candle, Indicator};
///
/// let mut indicator = AdvanceBlock::new();
/// indicator.update(Candle::new(10.0, 13.1, 9.9, 13.0, 1.0, 0).unwrap());
/// indicator.update(Candle::new(12.0, 14.3, 11.9, 14.0, 1.0, 1).unwrap());
/// let out = indicator
/// .update(Candle::new(13.5, 15.0, 13.4, 14.5, 1.0, 2).unwrap());
/// assert_eq!(out, Some(-1.0));
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdvanceBlock {
prev: Option<Candle>,
prev_prev: Option<Candle>,
has_emitted: bool,
}
impl AdvanceBlock {
/// Construct a new Advance Block detector.
pub const fn new() -> Self {
Self {
prev: None,
prev_prev: None,
has_emitted: false,
}
}
}
impl Indicator for AdvanceBlock {
type Input = Candle;
type Output = f64;
fn update(&mut self, candle: Candle) -> Option<f64> {
self.has_emitted = true;
let pp = self.prev_prev;
let p = self.prev;
self.prev_prev = self.prev;
self.prev = Some(candle);
let (Some(bar1), Some(bar2)) = (pp, p) else {
return Some(0.0);
};
let body1 = bar1.close - bar1.open;
let body2 = bar2.close - bar2.open;
let body3 = candle.close - candle.open;
let upper2 = bar2.high - bar2.close;
let upper3 = candle.high - candle.close;
if bar1.close > bar1.open
&& bar2.close > bar2.open
&& candle.close > candle.open
&& bar2.close > bar1.close
&& candle.close > bar2.close
&& bar2.open >= bar1.open
&& bar2.open <= bar1.close
&& candle.open >= bar2.open
&& candle.open <= bar2.close
&& body2 < body1
&& body3 < body2
&& upper3 >= upper2
&& upper3 > 0.0
{
return Some(-1.0);
}
Some(0.0)
}
fn reset(&mut self) {
self.prev = None;
self.prev_prev = None;
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
3
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AdvanceBlock"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
fn c(open: f64, high: f64, low: f64, close: f64, ts: i64) -> Candle {
Candle::new(open, high, low, close, 1.0, ts).unwrap()
}
#[test]
fn accessors_and_metadata() {
let t = AdvanceBlock::new();
assert_eq!(t.name(), "AdvanceBlock");
assert_eq!(t.warmup_period(), 3);
assert!(!t.is_ready());
}
#[test]
fn advance_block_is_minus_one() {
let mut t = AdvanceBlock::new();
assert_eq!(t.update(c(10.0, 13.1, 9.9, 13.0, 0)), Some(0.0));
assert_eq!(t.update(c(12.0, 14.3, 11.9, 14.0, 1)), Some(0.0));
assert_eq!(t.update(c(13.5, 15.0, 13.4, 14.5, 2)), Some(-1.0));
}
#[test]
fn strong_advance_yields_zero() {
let mut t = AdvanceBlock::new();
// Bodies grow instead of shrinking -> a strong advance, not blocked.
assert_eq!(t.update(c(10.0, 11.1, 9.9, 11.0, 0)), Some(0.0));
assert_eq!(t.update(c(10.5, 12.6, 10.4, 12.5, 1)), Some(0.0));
assert_eq!(t.update(c(11.5, 14.1, 11.4, 14.0, 2)), Some(0.0));
}
#[test]
fn no_upper_shadow_growth_yields_zero() {
let mut t = AdvanceBlock::new();
t.update(c(10.0, 13.1, 9.9, 13.0, 0));
t.update(c(12.0, 14.3, 11.9, 14.0, 1));
// bar3 shrinking body but no upper shadow -> not blocked.
assert_eq!(t.update(c(13.5, 14.5, 13.4, 14.5, 2)), Some(0.0));
}
#[test]
fn first_two_bars_return_zero() {
let mut t = AdvanceBlock::new();
assert_eq!(t.update(c(10.0, 13.1, 9.9, 13.0, 0)), Some(0.0));
assert_eq!(t.update(c(12.0, 14.3, 11.9, 14.0, 1)), Some(0.0));
}
#[test]
fn batch_equals_streaming() {
let candles: Vec<Candle> = (0..40)
.map(|i| {
let base = 100.0 + i as f64;
c(base, base + 2.0, base - 0.2, base + 1.5, i)
})
.collect();
let mut a = AdvanceBlock::new();
let mut b = AdvanceBlock::new();
assert_eq!(
a.batch(&candles),
candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
);
}
#[test]
fn reset_clears_state() {
let mut t = AdvanceBlock::new();
t.update(c(10.0, 13.1, 9.9, 13.0, 0));
t.update(c(12.0, 14.3, 11.9, 14.0, 1));
t.update(c(13.5, 15.0, 13.4, 14.5, 2));
assert!(t.is_ready());
t.reset();
assert!(!t.is_ready());
assert_eq!(t.update(c(10.0, 13.1, 9.9, 13.0, 0)), Some(0.0));
}
}
@@ -0,0 +1,168 @@
//! Advance/Decline Line — cumulative net advancing-minus-declining issues.
use crate::cross_section::CrossSection;
use crate::traits::Indicator;
/// Advance/Decline Line (A/D Line) — the running cumulative sum of net advancing
/// issues across a universe.
///
/// On each [`CrossSection`] tick the net breadth is `advancers - decliners`:
/// the number of symbols with a positive price change minus the number with a
/// negative change (unchanged symbols are ignored). The line accumulates this
/// net value over time, so a rising line means advancers have persistently
/// outnumbered decliners — broad participation — while a falling line warns that
/// a rally is being carried by fewer and fewer names (a breadth divergence when
/// the index itself is still rising).
///
/// `Input = CrossSection`, `Output = f64`. The line is defined from the very
/// first tick, so `warmup_period == 1` and the indicator is ready after one
/// update.
///
/// # Example
///
/// ```
/// use wickra_core::{AdvanceDecline, CrossSection, Indicator, Member};
///
/// let mut ad = AdvanceDecline::new();
/// // 3 advancers, 1 decliner -> net +2.
/// let tick = CrossSection::new(
/// vec![
/// Member::new(1.0, 10.0, false, false),
/// Member::new(0.5, 10.0, false, false),
/// Member::new(2.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// ],
/// 0,
/// )
/// .unwrap();
/// assert_eq!(ad.update(tick), Some(2.0));
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdvanceDecline {
line: f64,
has_emitted: bool,
}
impl AdvanceDecline {
/// Construct a new Advance/Decline Line indicator.
#[must_use]
pub const fn new() -> Self {
Self {
line: 0.0,
has_emitted: false,
}
}
}
impl Indicator for AdvanceDecline {
type Input = CrossSection;
type Output = f64;
fn update(&mut self, section: CrossSection) -> Option<f64> {
let net = section.advancers() as f64 - section.decliners() as f64;
self.line += net;
self.has_emitted = true;
Some(self.line)
}
fn reset(&mut self) {
self.line = 0.0;
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
1
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AdvanceDecline"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::cross_section::Member;
use crate::traits::BatchExt;
/// Build a cross-section with `up` advancers, `down` decliners and `flat`
/// unchanged symbols.
fn section(up: usize, down: usize, flat: usize) -> CrossSection {
let mut members = Vec::new();
for _ in 0..up {
members.push(Member::new(1.0, 10.0, false, false));
}
for _ in 0..down {
members.push(Member::new(-1.0, 10.0, false, false));
}
for _ in 0..flat {
members.push(Member::new(0.0, 10.0, false, false));
}
CrossSection::new(members, 0).unwrap()
}
#[test]
fn accessors_and_metadata() {
let ad = AdvanceDecline::new();
assert_eq!(ad.name(), "AdvanceDecline");
assert_eq!(ad.warmup_period(), 1);
assert!(!ad.is_ready());
}
#[test]
fn first_tick_emits_net_breadth() {
let mut ad = AdvanceDecline::new();
assert_eq!(ad.update(section(3, 1, 0)), Some(2.0));
assert!(ad.is_ready());
}
#[test]
fn line_accumulates_across_ticks() {
let mut ad = AdvanceDecline::new();
assert_eq!(ad.update(section(3, 1, 0)), Some(2.0)); // +2 -> 2
assert_eq!(ad.update(section(1, 4, 0)), Some(-1.0)); // -3 -> -1
assert_eq!(ad.update(section(2, 0, 0)), Some(1.0)); // +2 -> 1
}
#[test]
fn unchanged_symbols_are_ignored() {
let mut ad = AdvanceDecline::new();
// 2 up, 2 down, 5 unchanged -> net 0, line stays flat.
assert_eq!(ad.update(section(2, 2, 5)), Some(0.0));
assert_eq!(ad.update(section(2, 2, 5)), Some(0.0));
}
#[test]
fn reset_clears_state() {
let mut ad = AdvanceDecline::new();
ad.update(section(5, 0, 0));
assert!(ad.is_ready());
ad.reset();
assert!(!ad.is_ready());
// Line restarts from zero, not from the pre-reset value.
assert_eq!(ad.update(section(1, 0, 0)), Some(1.0));
}
#[test]
fn batch_equals_streaming() {
let sections = vec![
section(3, 1, 2),
section(1, 4, 0),
section(2, 2, 1),
section(5, 0, 3),
];
let mut a = AdvanceDecline::new();
let mut b = AdvanceDecline::new();
assert_eq!(
a.batch(&sections),
sections
.iter()
.map(|s| b.update(s.clone()))
.collect::<Vec<_>>()
);
}
}
@@ -0,0 +1,151 @@
//! Advance/Decline Ratio — advancing issues divided by declining issues.
use crate::cross_section::CrossSection;
use crate::traits::Indicator;
/// Advance/Decline Ratio (ADR) — the number of advancing symbols divided by the
/// number of declining symbols across a universe.
///
/// On each [`CrossSection`] tick the ratio is `advancers / decliners`: a reading
/// above one means advancing issues outnumber declining ones (broad strength),
/// while a reading below one signals broad weakness. Because it is a ratio rather
/// than a difference, the ADR is comparable across universes of different sizes.
///
/// When a tick has no declining symbols the denominator is floored to one, so the
/// ratio degrades gracefully to the advancer count instead of dividing by zero.
///
/// `Input = CrossSection`, `Output = f64`. The ratio is defined from the first
/// tick, so `warmup_period == 1` and the indicator is ready after one update.
///
/// # Example
///
/// ```
/// use wickra_core::{AdvanceDeclineRatio, CrossSection, Indicator, Member};
///
/// let mut adr = AdvanceDeclineRatio::new();
/// // 3 advancers, 1 decliner -> ratio 3.0.
/// let tick = CrossSection::new(
/// vec![
/// Member::new(1.0, 10.0, false, false),
/// Member::new(0.5, 10.0, false, false),
/// Member::new(2.0, 10.0, false, false),
/// Member::new(-1.0, 10.0, false, false),
/// ],
/// 0,
/// )
/// .unwrap();
/// assert_eq!(adr.update(tick), Some(3.0));
/// ```
#[derive(Debug, Clone, Default)]
pub struct AdvanceDeclineRatio {
has_emitted: bool,
}
impl AdvanceDeclineRatio {
/// Construct a new Advance/Decline Ratio indicator.
#[must_use]
pub const fn new() -> Self {
Self { has_emitted: false }
}
}
impl Indicator for AdvanceDeclineRatio {
type Input = CrossSection;
type Output = f64;
fn update(&mut self, section: CrossSection) -> Option<f64> {
let advancers = section.advancers() as f64;
let decliners = section.decliners().max(1) as f64;
self.has_emitted = true;
Some(advancers / decliners)
}
fn reset(&mut self) {
self.has_emitted = false;
}
fn warmup_period(&self) -> usize {
1
}
fn is_ready(&self) -> bool {
self.has_emitted
}
fn name(&self) -> &'static str {
"AdvanceDeclineRatio"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::cross_section::Member;
use crate::traits::BatchExt;
fn section(up: usize, down: usize) -> CrossSection {
let mut members = Vec::new();
for _ in 0..up {
members.push(Member::new(1.0, 10.0, false, false));
}
for _ in 0..down {
members.push(Member::new(-1.0, 10.0, false, false));
}
// A non-empty unchanged member guarantees a valid universe when both
// counts are zero.
members.push(Member::new(0.0, 10.0, false, false));
CrossSection::new(members, 0).unwrap()
}
#[test]
fn accessors_and_metadata() {
let adr = AdvanceDeclineRatio::new();
assert_eq!(adr.name(), "AdvanceDeclineRatio");
assert_eq!(adr.warmup_period(), 1);
assert!(!adr.is_ready());
}
#[test]
fn first_tick_emits_ratio() {
let mut adr = AdvanceDeclineRatio::new();
assert_eq!(adr.update(section(3, 1)), Some(3.0));
assert!(adr.is_ready());
}
#[test]
fn zero_decliners_floors_denominator() {
let mut adr = AdvanceDeclineRatio::new();
// 4 advancers, 0 decliners -> 4 / max(0, 1) = 4.0.
assert_eq!(adr.update(section(4, 0)), Some(4.0));
}
#[test]
fn no_advancers_yields_zero() {
let mut adr = AdvanceDeclineRatio::new();
assert_eq!(adr.update(section(0, 5)), Some(0.0));
}
#[test]
fn reset_clears_state() {
let mut adr = AdvanceDeclineRatio::new();
adr.update(section(3, 1));
assert!(adr.is_ready());
adr.reset();
assert!(!adr.is_ready());
assert_eq!(adr.update(section(2, 1)), Some(2.0));
}
#[test]
fn batch_equals_streaming() {
let sections = vec![section(3, 1), section(4, 0), section(0, 5), section(2, 2)];
let mut a = AdvanceDeclineRatio::new();
let mut b = AdvanceDeclineRatio::new();
assert_eq!(
a.batch(&sections),
sections
.iter()
.map(|s| b.update(s.clone()))
.collect::<Vec<_>>()
);
}
}
+1 -1
View File
@@ -91,7 +91,7 @@ impl Adx {
}
}
fn directional_movement(prev: &Candle, current: &Candle) -> (f64, f64) {
pub(crate) fn directional_movement(prev: &Candle, current: &Candle) -> (f64, f64) {
let up = current.high - prev.high;
let down = prev.low - current.low;
let plus_dm = if up > down && up > 0.0 { up } else { 0.0 };

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