kingchenc
395a2289f4
Version bump `0.8.3` → `0.8.4`. Ships the work merged via #254: ### Fixed - A single non-finite (NaN/inf) tick no longer poisons indicator state — 38 more scalar/pairwise indicators (linear-regression family, rolling quantiles/IQR, `Variance`/`StdDev`-derived stats, `Kurtosis`/`Skewness`, trailing stops, `KalmanHedgeRatio`, `SpreadBollingerBands`, …) now reject non-finite input and return `None`, joining the 16 pairwise indicators fixed earlier. ### Added - Catalogue-wide property-based invariant harness (`crates/wickra-core/tests/invariants.rs`) asserting `batch == streaming`, `reset == fresh`, and non-finite-input rejection for every indicator and bar-builder. ### Changed - CI: every job now has a runtime cap and the flaky Node test step auto-retries. - Documentation accuracy fixes in `SECURITY.md`, `ARCHITECTURE.md`, and `THREAT_MODEL.md`. Bump touches the manual release touchpoints only (`Cargo.toml`/`Cargo.lock`, Python/Node/Java/C#/R manifests, lockfiles, `SECURITY.md`, `CHANGELOG.md`). docs/webpage version strings are left to `sync-about.yml` on the tag.
Wickra — Python
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, plus a C ABI for C/C++, C#, Go, Java, R and any other C-capable language. 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
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
import numpy as np
import wickra as ta
# 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: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
batch(prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite.
Benchmark
Two benchmarks ship with the binding:
benchmarks/throughput.py— streaming and batch updates-per-second forSMA,ATRandMACD. This is per-binding FFI overhead (the same Rust core runs under every binding), not a cross-library ratio.benchmarks/compare_libraries.py— the cross-library comparison against TA-Lib, pandas-ta, tulipy and finta that backs the headline speedups.
maturin develop --release
python -m benchmarks.throughput
python -m benchmarks.compare_libraries # cross-library; auto-detects installed peers
See the repository BENCHMARKS.md.
Documentation
The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:
- Repository & full indicator list: https://github.com/wickra-lib/wickra
- Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
- Runnable examples:
examples/python/
Wickra ships native bindings for Python, Node.js, WebAssembly and Rust, plus a
C ABI hub that any C-capable language (C, C++, Go, C#, Java, R) links against —
all exposing the same indicators from the shared, unsafe-forbidden Rust core.
Disclaimer
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 either of Apache-2.0 or MIT at your option.