kingchenc
3ebcb3f758
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.
91 lines
3.7 KiB
Markdown
91 lines
3.7 KiB
Markdown
# Wickra — Python
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[](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
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[](https://codecov.io/gh/wickra-lib/wickra)
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[](https://pypi.org/project/wickra/)
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[](https://github.com/wickra-lib/wickra#license)
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**Streaming-first technical indicators for Python. `pip install wickra` — no
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system dependencies, no C build tooling.**
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Wickra is a multi-language technical-analysis library with a Rust core and
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bindings for Python, Node.js and WebAssembly, plus a C ABI for C/C++, C#, Go, Java, R and any
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other C-capable language. Every indicator is an O(1)
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streaming state machine, so live trading bots and historical backtests share
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the exact same implementation. This package is the Python binding (PyO3); it
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exposes 200+ streaming-first indicators across sixteen families.
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## Install
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```bash
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pip install wickra
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```
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Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to
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compile and no C library to track down.
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## Quick start
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```python
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import numpy as np
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import wickra as ta
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# Batch: classic TA-Lib-style usage over a whole array.
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prices = np.linspace(100, 200, 1000)
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rsi = ta.RSI(14)
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values = rsi.batch(prices) # numpy array, NaN during warmup
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# Streaming: the same indicator, fed tick by tick in O(1).
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rsi = ta.RSI(14)
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for price in live_feed:
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value = rsi.update(price) # no recomputation over history
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if value is not None and value > 70:
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print("overbought")
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```
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`batch(prices)` and feeding the same prices through `update()` produce
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identical values — the equivalence is enforced by the test suite.
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## Benchmark
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Two benchmarks ship with the binding:
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- `benchmarks/throughput.py` — streaming and batch updates-per-second for `SMA`,
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`ATR` and `MACD`. This is per-binding FFI overhead (the same Rust core runs
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under every binding), not a cross-library ratio.
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- `benchmarks/compare_libraries.py` — the cross-library comparison against
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TA-Lib, pandas-ta, tulipy and finta that backs the headline speedups.
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```bash
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maturin develop --release
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python -m benchmarks.throughput
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python -m benchmarks.compare_libraries # cross-library; auto-detects installed peers
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```
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See the repository [BENCHMARKS.md](https://github.com/wickra-lib/wickra/blob/main/BENCHMARKS.md).
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## Documentation
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The full indicator catalogue, guides, quickstarts, and API reference live in
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the main repository and documentation site:
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- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
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- **Docs** (quickstarts, cookbook, TA-Lib migration): <https://docs.wickra.org>
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- **Runnable examples:** [`examples/python/`](https://github.com/wickra-lib/wickra/tree/main/examples/python)
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Wickra ships native bindings for Python, Node.js, WebAssembly and Rust, plus a
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C ABI hub that any C-capable language (C, C++, Go, C#, Java, R) links against —
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all exposing the same indicators from the shared, `unsafe`-forbidden Rust core.
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## Disclaimer
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Wickra is an indicator toolkit, not a trading system. The values it computes
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are deterministic transforms of the input data — they are not financial advice
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and do not predict the market. Any use in a live trading context is at your own
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risk. The library is provided **as is**, without warranty of any kind.
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## License
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Licensed under either of [Apache-2.0](https://github.com/wickra-lib/wickra/blob/main/LICENSE-APACHE)
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or [MIT](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT) at your option.
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