13b2ee52315eaed28ec8dcf5d4ed67292264fa4e
17 Commits
| Author | SHA1 | Message | Date | |
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 kingchenc
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d362ae26a3 |
feat(data): expose CandleReader (CSV) natively in all 10 languages (#311)
Add the data-layer CSV candle reader to every binding so loading OHLCV candles from a CSV no longer needs a per-language CSV/dataframe dependency. - C ABI: wickra_candle_reader_new(bytes, len) / _count / _read / _free over an opaque CandleReader handle (parse the whole buffer up front, then drain). - Native: Node/WASM CandleReader.read() -> Candle[], Python read() -> list[tuple]. - C-ABI languages: Go Read() []Candle, C# Candle[] Read(), Java Candle[] read(), R read() S3 generic (n x 6 matrix); C / C++ call the C ABI directly. - Cross-language golden testdata/golden/data_csv*.csv pins the parsed candles bit-for-bit across every binding. Verified locally across Rust (test+clippy+fmt), Node, WASM, Python, C#, Go, Java, R, and the C/C++ cmake parity suite. |
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kingchenc
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cb6da4d737 |
feat(data-layer): Resampler (candle resampling) in all 10 languages (#310)
* feat(data-layer): Resampler (candle resampling) in all 10 languages Second data-layer feature (F3): resample candles into a higher timeframe. - Native (Node.js/WASM): new Resampler(timeframe) -> update(o,h,l,c,v,ts): Candle|null + flush(): Candle|null. Python the same -> tuple|None. - C ABI: wickra_resampler_new/update/flush/free (update has the multi-output shape so the generators auto-emit it; flush is bespoke). Go Update -> (Candle, bool) + Flush; C# Candle? Update/Flush; Java Candle update/flush; R update() generic + a flush() S3 method (extends base::flush); C/C++ direct. - Cross-language golden (testdata/golden/data_resampled.csv): the shared input candles resampled into 5-unit buckets, the final partial bucket via flush, pinned bit-for-bit across every binding. Verified locally in all 10 (3 candles for the 5-unit smoke; 16 for the golden). The WickraCandle output record is shared with the tick aggregator (deduped). * test(node): exclude data-layer types from the indicator completeness contract The Resampler exposes update(), so the completeness test flagged it as an indicator and required batch/reset/isReady/warmupPeriod, which a data-layer type does not have. Exclude TickAggregator and Resampler like the bar builders. |
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kingchenc
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8a103ef920 |
feat(data-layer): TickAggregator (tick-to-candle) in all 10 languages (#309)
* feat(data-layer): TickAggregator in Node, WASM, Python + C ABI hub First data-layer feature (F2): roll trade ticks up into fixed-timeframe OHLCV candles, exposed natively and over the C ABI. - wickra-data wired as a binding dependency (workspace dep; its wickra-core dep is default-features=false so it never forces rayon into the rayon-free WASM build — native bindings re-enable parallel through their own dependency). - Node `TickAggregator(bucket, gapFill?)` -> `push(price, size, ts): Candle[]`; WASM the same (array of objects); Python `push(...) -> list[tuple]`. - C ABI: `WickraCandle` struct + `wickra_tick_aggregator_new/push/free` (push writes candles into a caller buffer and returns the count), generated via the capi generator's new DATA_LAYER section; cbindgen now parses wickra-data so `TickAggregator` is a forward-declared opaque; header vendored to bindings/go. Verified bit-identical across Node/WASM/Python/C/C++ (o=100 h=101 l=100 c=101 v=3 ts=0 for the shared 3-tick probe). WIP: Go/C#/Java/R generated bindings and the cross-language golden are still pending. * feat(data-layer): TickAggregator in Go, C#, Java, R (lossless push/drain) Complete F2 across all 10 languages: the C-ABI tick aggregator now uses a two-step push/drain so gap-fill candles are never lost, and the four generated bindings expose it idiomatically. - C ABI redesigned: opaque TickAggregator handle (inner aggregator + pending buffer); push consumes a tick and returns the closed-candle count, drain copies them into a count-sized caller buffer. - Go: NewTickAggregator + Push(price,size,ts) []Candle; C#: TickAggregator + Candle[] Push(...); Java: TickAggregator + Candle[] push(...); R: TickAggregator constructor + push() S3 generic returning an (n x 6) numeric matrix. - Candle output record generated per language from WickraCandle. Verified bit-identical to the native bindings (o=100 h=101 l=100 c=101 v=3 ts=0) in Go, C#, Java, and R at runtime; R passes R CMD check (pre-existing doc warnings only). WIP: cross-language data-layer golden + CHANGELOG still pending. * test(data-layer): cross-language golden for the tick aggregator + CHANGELOG gen_golden emits a deterministic tick stream (testdata/golden/data_ticks.csv) and the reference candle streams with and without gap filling (data_candles.csv, data_candles_gap.csv). Every binding replays the shared ticks through its TickAggregator and checks the candles bit-for-bit (fp tolerance) against the Rust reference: - Node / WASM / Python / Go / C# / Java / R: a dedicated parity test each. - C / C++: data_layer_test.c (compiled as both, run as ctest). The gap-fill fixture closes several candles from a single push, exercising the lossless push/drain path. Records the feature under CHANGELOG [Unreleased]. * fix(examples): rename the CSV-loader candle to WickraBar The example CSV helper (wickra_csv.h) defined its own struct WickraCandle, which now collides with the public C ABI WickraCandle (the tick aggregator output) in any example that includes both headers (backtest, multi_timeframe, the strategy examples). The public type owns the name; rename the example loader's bar to WickraBar. The generated golden_test.c is untouched (its only match was the unrelated WickraCandleVolumeOutput). |
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kingchenc
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4f708d410d |
test: golden-pin the four de-duplicated indicators across all bindings (#305)
* test: golden-pin the four de-duplicated indicators across all C-ABI bindings Extend gen_golden to emit reference fixtures for AdOscillator (ADOSC), IntradayIntensity, AwesomeOscillatorHistogram and AverageDrawdown, and replay them through the Go / C# / Java / R golden harnesses so their corrected definitions stay bit-identical to the Rust core in every binding. Go suite verified locally (gcc 13 + cgo): all 9 golden tests pass; C#/Java/R use the same fixtures and harness pattern (CI-verified). First step of extending the golden coverage beyond the seven archetype representatives. * test: golden-pin the scalar-output tranche (308 indicators) against Rust Extend gen_golden with a generated emit_scalar that writes reference fixtures for every single-f64-output indicator (scalar / candle / pairwise input) using valid constructor params, and add a manifest-driven generic Python golden replay that reconstructs each by its native name and checks it bit-for-bit against the Rust output. 308 indicators now value-tied to the Rust core in Python (pytest: 308/308). Takes golden coverage from the 7 archetype representatives to 308+ of the catalogue. 22 scalar indicators with non-default constructor constraints are skipped by gen_golden for now (logged), as are non-f64-output ones; multi-output, exotic inputs and the per-indicator arg arities of the C-ABI/Node replays follow. Generated + verified locally with the full toolchain. * test: golden-pin the multi-output tranche (70 indicators) in Python Add a generated emit_multi to gen_golden (per-indicator Output-field access, one CSV column per field) and a manifest-driven generic Python replay that checks every field of each multi-output indicator against the Rust reference. 70 multi-output indicators now value-tied to Rust in Python; combined with the scalar tranche, 378 indicators are golden-pinned. 8 multi with non-default param constraints and 5 with non-f64 Output fields (Option/Vec/i64) are deferred. pytest green. * test(golden): add 30 constraint-tuned indicators to scalar/multi golden suite Emit golden fixtures for 22 scalar-output and 8 multi-output indicators whose constructors need non-default parameters (Alma, Jma, Psar, T3, Mama, DoubleBollinger, ZigZag, ...). All 408 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 36 missed scalar/multi indicators Add 26 single-output (LinearRegression family, HT cycle, Candle volatility estimators, DrawdownDuration) and 10 multi-output (BollingerBands, MACD/MACDEXT/MACDFIX, Camarilla, VWAP bands, ...) indicators to the golden suite. 444 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 50 exotic-input indicators Add deterministic synthetic feeders for the DerivativesTick (17), CrossSection (15), Trade (8), TradeQuote (3) and OrderBook (7) families, derived from the shared OHLCV input series in both gen_golden and a new Python replay harness (test_golden_exotic). All 494 fixtures replay bit-for-bit through the Python binding. * test(golden): complete 514-indicator golden coverage Add the final tranches: 3 mixed multi-output indicators (Ichimoku, WilliamsFractals, LeadLagCrossCorrelation), 6 histogram profiles (time/volume seasonality + TPO/volume price profiles), 10 alt-chart bar builders and the footprint. Every one of the 514 distinct indicators now has a Rust-generated g_<Canonical>.csv fixture and a generic Python replay (scalar/multi/exotic/profile/bars), all passing bit-for-bit. * test(golden): add generic Node replay for all 514 indicators A manifest-driven node:test harness reconstructs every indicator by its native class, feeds the same synthetic stream derived from the shared golden input, and checks output bit-for-bit against the Rust reference fixtures (scalar/multi/exotic/profile/bars). node_manifest.json is generated from index.d.ts plus the Python-side manifests. 514/514 pass. * test(golden): add generated Go replay for all 514 indicators golden_all_test.go (generated by gen_golden_test.py) reconstructs every Go indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. A reflection-based comparator flattens multi-output structs, profiles and bar slices so one path covers all archetypes. This is the first C-ABI binding verified across the full catalogue. 514/514 pass. * test(golden): add generated C# replay for all 514 indicators GoldenAllTests.g.cs (generated by gen_golden_test.py) reconstructs every C# indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures via a reflection-based flatten covering scalar/multi/profile/bar archetypes. 514/514 pass. Also add the '#nullable enable' directive the compiler requires to the generated Indicators.g.cs, clearing the four CS8669 warnings on the nullable double[] profile return types. * fix(java): marshal C ABI bool params correctly; add 514 golden replay The Java FFM binding marshalled the cross-section state flags (newHigh, newLow, aboveMa, onBuySignal) as JAVA_DOUBLE arrays, but the C ABI takes them as const bool* (one byte each), so the native side read the low byte of each 8-byte double and saw every flag as false. Add WickraNative. boolSegment and use it across the 15 cross-section indicators. Also pass the MacdExt MaType arguments as byte to match the uint8_t downcall descriptor (was int, throwing WrongMethodTypeException). Add GoldenAllTest.java (generated by gen_golden_test.py): a reflection runner replaying all 514 indicators against the Rust reference fixtures. The bugs above were found by this test; 514/514 now pass. * fix(r): marshal C ABI bool flags correctly; add 514 golden replay The R wrapper passed the cross-section state flags as (bool *)REAL(x), reinterpreting the 8-byte doubles as 1-byte bools so the native side read every flag as false. Add wk_bool_vec to convert each flag vector into a real C bool buffer and use it for all 15 cross-section update wrappers. Add test-golden-all.R + generated golden_specs.R: a reflective runner replaying all 514 indicators against the Rust reference fixtures. The bug above was found by this test; verified 514/514 pass locally. * test(golden): add WASM replay for all 514 indicators A manifest-driven node:test harness loads the nodejs-target wasm-pack build, reconstructs every indicator by its JS class, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. wasm_manifest.json is generated from the wasm .d.ts plus the shared manifests; a recursive flattener covers scalar, multi (Reflect objects), profile and bar shapes. 514/514 pass locally (wasm-pack build --target nodejs, then node --test). * test(golden): add C and C++ replay for all 514 indicators golden_test.c (generated by gen_golden_test.py) drives every indicator through the C ABI (wickra.h) and checks output bit-for-bit against the Rust reference fixtures. golden_test.cpp #includes the same source so the identical runner is compiled and run under both gcc (C) and g++ (C++) via the CMake targets golden_test / golden_test_cpp — proving the extern "C" header is consumable from each language. Both 514/514 (verified via ctest). * test(golden): gofmt the generated Go golden replay * test(golden): make the Node fixture reader CRLF-safe and pin fixtures to LF |
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kingchenc
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2ef60874b9 |
fix(examples): satisfy clippy -D warnings in gen_golden (#291)
Replace `i as i64` with `i64::try_from(i)` (cast_possible_wrap) and rename the OHLCV destructure to descriptive names (many_single_char_names) so `cargo clippy --all-targets --all-features -- -D warnings` passes on the 1.95 toolchain. Dev-tool only; no library change. |
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kingchenc
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8bfe24ac1d |
test(golden): language-neutral fixtures + per-binding parity runners (#255)
**Task 5 — golden-fixture parity for the C-ABI bindings.** Lifts the C#/Go/Java/R tests from *one indicator per archetype* toward reference-value parity, catching FFI wiring bugs (swapped params, wrong multi-output field) the math-only core tests cannot see. ## What's here - **`examples/rust/src/bin/gen_golden.rs`** + **`testdata/golden/*.csv`** — a Rust generator computing a deterministic OHLCV series plus the core's reference outputs for a curated archetype-spanning set: scalar (`Sma`/`Ema`/`Rsi`), candle (`Atr`), scalar multi-output (`MACD`), candle multi-output (`ADX`), pairwise (`Beta`). `nan` marks warmup. Regenerate with `cargo run -p wickra-examples --bin gen_golden`. - **Parity runners** replaying the identical fixtures through each FFI (rel-tol 1e-6), each a standard test in the binding's existing suite (no `ci.yml` change — rides `dotnet test` / `go test` / `mvn install` / `R CMD`+testthat). A walk-up search locates `testdata/golden` regardless of run dir. - **C#** (`bindings/csharp/.../GoldenTests.cs`) — ✅ validated locally, 7/7 pass. - **Go** (`bindings/go/golden_test.go`) — ✅ validated locally, pass. - **Java** (`bindings/java/.../GoldenTests.java`) — modeled on the archetype API; validated by CI (no local mvn). - **R** (`bindings/r/tests/testthat/test-golden.R`) — modeled on the archetype API; validated by CI (no local Rscript). ## Notes - The curated set spans every marshalling archetype; extending the indicator list is mechanical (add to the generator + regenerate). Bars/profile archetypes can be added next. - No new CI jobs. |
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kingchenc
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3ebcb3f758 |
Per-binding throughput benchmarks + test-coverage gaps (#246)
Adds a `throughput` benchmark to every target and closes two small test-coverage documentation/QA gaps. One PR, no merge of binding code beyond the additive benchmarks and one C test. ## 1. Per-binding throughput benchmarks (all 9 targets) Each benchmark feeds a deterministic synthetic OHLCV series through three indicators chosen by **FFI call-signature archetype** (not algorithm — the same Rust core runs underneath all bindings): - `SMA(20)` — 1-in → 1-out (baseline boundary cost) - `ATR(14)` — multi-in → 1-out (input marshalling) - `MACD(12,26,9)` — 1-in → multi-out (output marshalling) Streaming is timed for all three; batch for the single-output SMA and ATR (median of 3 runs, after a warmup pass). New: Python (PyO3), WASM, C (CMake), C# (Stopwatch), Go, Java (FFM), R, and the Rust core baseline (`examples/rust/.../throughput.rs`, **no FFI** — the ceiling the bindings are measured against and the value their batch paths converge towards). Node already had `throughput.js`. **Not a speed claim:** there is no comparable streaming TA library for C, C#, Go, Java, R or WASM to compare against, so these are raw per-binding throughput numbers documenting each language's FFI overhead — see BENCHMARKS.md §3. The "Wickra is fast" claim still lives in §1/§2 (Rust core + the Python/Rust cross-library runs). ## 2. README `## Testing`: C# and C bullets The section listed every layer except C# and C, even though both have suites. Adds the two missing bullets. ## 3. C archetype ctest `examples/c/archetypes.c` drives one indicator per FFI archetype through the real C boundary (scalar + batch==streaming, multi-output, bars, profile, array input) plus reset, invalid-parameter and NULL-safety — the C counterpart of the Go/R/Java archetype suites. Runs on three OSes via the existing CMake/ctest. ## Notes - Benchmarks are not CI-gated (manual-run scripts, like the existing `throughput.js`); no `ci.yml`/`release.yml` changes. - Docs: BENCHMARKS.md §3, a `## Benchmark` section in every binding README, a CHANGELOG entry. - Verified locally by running: Rust, Python, C, C#, Go, Java (real numbers); the C archetype ctest with `-Wall -Wextra -Wpedantic -Werror`. WASM and R are API-correct and syntax-checked but need their own toolchains to run. |
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kingchenc
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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. |
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kingchenc
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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. |
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kingchenc
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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 |
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kingchenc
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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. |
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kingchenc
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43b0b26736 |
examples: add parallel-assets demos for Rust and Node
Python's parallel_assets.py demoed GIL-release multi-core throughput; Rust and Node both lacked a sibling that shows their own native parallelism. Close the gap with two real, runnable examples. * examples/rust/src/bin/parallel_assets.rs — synthesises an (assets, bars) panel with a deterministic per-asset LCG, runs a serial baseline, then `Sma::batch_parallel` / `Rsi::batch_parallel` via rayon, asserts the two outputs are element-wise identical and prints the speedup. Toggle indicator with `--indicator sma|rsi`. * examples/node/parallel_assets.js — same shape, but the parallel run is a `worker_threads` pool that re-loads the native binding in each worker. Each worker computes the last non-null indicator value for its slice; the main thread aggregates and verifies serial == parallel per asset. Both examples report timings and the serial-vs-parallel sanity check passes. Defaults (200 × 5000) keep the example fast on dev hardware; larger `--assets`/`--bars` is where the speedup numbers move (Node's worker spawn cost dominates the smallest sizes, which is honest and educational). examples/README.md gains the two new rows. |
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kingchenc
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962ced0712 |
examples: add multi-timeframe demos for Rust and Node
Python's examples/python/multi_timeframe.py had no Rust or Node sibling. Add both — the Rust version uses wickra-data's `Resampler` / `resample_all` (the canonical path; no manual roll-up), the Node version mirrors the Python one's inline aggregation because wickra-data's resampler is currently Rust-only. * examples/rust/src/bin/multi_timeframe.rs — reads the bundled 1m CSV via `CandleReader`, resamples to 5m / 15m / 1h / 4h / 1d via `resample_all`, prints last RSI(14), MACD(12,26,9) histogram and ADX(14) per timeframe. * examples/node/multi_timeframe.js — same outputs from a hand-rolled bucket aggregator; reuses the new examples/data/ default path. * examples/README.md gains the new rows. Run side by side: the Rust and Node summaries are bit-identical at every timeframe (50000 / 10000 / 3334 / 834 / 209 / 35 bars; same RSI, MACD histogram and ADX to two decimals) — confirming both the Rust resampler and the inline Node aggregator produce the same OHLC buckets. |
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kingchenc
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5a4cf66022 |
examples: add streaming demos for Python and Rust
Python and Rust both lacked a standalone "streaming indicators" example that mirrors examples/node/streaming.js — the quickstart docs cover the pattern, but a runnable file makes the parity visible across all four languages. * examples/python/streaming.py — argparse-driven synthetic streaming demo feeding SMA(20) / EMA(20) / RSI(14) / MACD(12,26,9), tagging BUY?/SELL? candidates when RSI extremes and MACD-histogram direction agree. * examples/rust/src/bin/streaming.rs — same demo as a wickra-examples binary, reusing the seeded LCG so its first 40 rows are bit-identical to the Python (and Node) sibling — a strong cross-language consistency signal verified by running both side by side. * examples/README.md gains a `streaming` row in the Rust and Python tables. |
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kingchenc
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747d1a5b1b |
examples: move Rust examples into a top-level examples/rust/ crate
The three Rust examples (backtest, fetch_btcusdt, live_binance) used to
live each in their own crate's examples/ dir, splitting the example set
across crates and burying it inside the source tree. Move them into a new
workspace member crate at `examples/rust/` (package `wickra-examples`,
`publish = false`) so all language examples sit under one top-level
`examples/<lang>/` tree.
* `examples/rust/Cargo.toml` declares the per-binary deps (wickra,
wickra-data with the `live-binance` feature always on, serde_json, tokio
for the macro and current-thread runtime).
* `examples/rust/src/bin/{backtest,fetch_btcusdt,live_binance}.rs` are the
three migrated binaries; their doc-comments and the fetch_btcusdt output
path are updated for the new location and run command
(`cargo run -p wickra-examples --bin <name>`).
* Workspace `Cargo.toml` lists the new member; the now-empty
`[dev-dependencies]` extras (`wickra`, `tokio` in wickra-data and
`serde_json` in wickra) that existed only for these examples are dropped.
* The `[[example]] live_binance` table is removed from wickra-data's
manifest since the file moved out.
* README "Languages" + project-layout, examples/README.md, Quickstart-Rust
and Data-Layer are pointed at the new paths and commands.
`cargo build -p wickra-examples` and `cargo run --release -p wickra-examples
--bin backtest -- examples/data/btcusdt-1d.csv` both succeed; the rest of
the workspace (core, data, wickra) builds, clippies (`--all-targets -D
warnings`) and tests (508 core + 28 data + 1 integration + 74+3+1
doctests) all stay green.
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kingchenc
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06f67c8c0e |
Move Rust examples into their crates so cargo publish picks them up
The previous `[[example]] path = "../../examples/rust/..."` entries pointed at files outside the crate root. cargo only packages files inside the crate directory, so the examples would have been silently dropped on publish. Moving them to crates/wickra/examples/ and crates/wickra-data/examples/ keeps them as part of the published package and lets cargo discover them automatically (no [[example]] override needed). README and project-layout section updated. |
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kingchenc
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3be267cb03 |
Wickra 0.1.0: streaming-first technical indicators
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
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