ec937b8281e3fcc58687cb1d4c244499bbd3eea5
4 Commits
| Author | SHA1 | Message | Date | |
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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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05fe7ffa90 |
perf: bit-exact batch fast paths + streaming-first benchmark docs (#202)
## Summary - Dedicated batch fast paths for **EMA, RSI, Bollinger, MACD and ATR** (used by the Python bindings): one allocation filled in a single pass, warmup encoded as `NaN`, no per-element `Option` or input re-validation. Each is **bit-for-bit equal** to replaying `update` — SMA/Bollinger keep the drift-reseed cadence, the EMA-family keep the seed division and `mul_add` recurrences. Adds the `BatchNanExt` extension trait. - **Cross-library benchmark refresh**: `compare_libraries.py` reports the median across timing rounds (`--rounds` / `--streaming-rounds`), gains `--skip-batch` / `--skip-streaming`, and runs every peer through the streaming arena (recompute for batch-only libraries). `wickra-bench` drives the batch fast paths against `kand`. - **README** benchmark section reordered streaming-first (the order-of-magnitude result), with measured TA-Lib/tulipy/pandas-ta numbers in place of the CI-only placeholders. ## Impact - Python batch ~2× faster on EMA/RSI/MACD/ATR; streaming path unchanged. - The `batch == streaming` equivalence stays bit-exact. ## Verification - `cargo fmt` · `cargo clippy --workspace --all-targets --all-features -- -D warnings` (clean) - `cargo test --workspace --all-features` — 3782 unit + 420 doc tests pass - Python `pytest` — streaming-vs-batch, known-values, input-validation, smoke pass ## Notes - Node/WASM bindings keep their existing batch; the fast paths are Python-only for now. |
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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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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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