Wickra Feature Store — Python

Turn OHLCV and microstructure event streams into ML-ready feature matrices over 497 O(1) streaming indicators — for Python. pip install wickra-feature-store — prebuilt wheels for Linux, macOS and Windows, nothing to compile.

Python bindings for wickra-feature-store, the data-driven feature-matrix core. Build a FeatureStore from a spec JSON, drive it with command JSONs, and read back feature matrices — the same command protocol every language binding speaks.

Install

pip install wickra-feature-store

Pre-built wheels ship for Linux, macOS and Windows — there is nothing to compile and no C library to track down.

Building from this repository (contributors)

maturin develop --release
pytest -q

Quick start

import json
from wickra_feature_store import FeatureStore

spec = json.dumps({
    "universe": ["AAA"],
    "features": [
        {"kind": "indicator", "name": "Sma", "params": [2]},
        {"kind": "price", "field": "close"},
    ],
    "labels": [{"kind": "forward_return", "horizon": 1}],
})

store = FeatureStore(spec)

def candle(ts, close):
    return {"ts": ts, "open": close, "high": close,
            "low": close, "close": close, "volume": 1.0}

response = store.command(json.dumps({
    "cmd": "build_batch",
    "data": {"AAA": [candle(0, 100.0), candle(1, 110.0), candle(2, 121.0)]},
}))

matrix = json.loads(response)
print(matrix["columns"])  # ['Sma(2)', 'price.close', 'fwd_return(1)']

# Straight into pandas:
#   import pandas as pd
#   df = pd.DataFrame(matrix["data"], columns=matrix["columns"])

API

Arrow / Parquet output is a binary file format and is not available over this JSON surface; use the wickra-feature-store CLI for columnar output.

Benchmark

Every binding forwards to the same data-driven Rust core, so what this one adds is the call overhead of PyO3, not a different result. The core's throughput is measured by the repository's benchmark suite and the nightly bench.yml run; the numbers, the machine and how to reproduce them are in the repository BENCHMARKS.md.

Documentation

The full guide, the spec reference and the API documentation live in the main repository and the documentation site:

• Repository: https://github.com/wickra-lib/wickra-feature-store
• Docs (guides, spec reference, cookbook): https://feature-store.wickra.org
• Runnable example: examples/python/

Wickra Feature Store ships native bindings for Python, Node.js, WASM and Rust, plus a C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against — all forwarding to the same data-driven, unsafe-forbidden Rust core.

Security

Found a security issue? Please don't open a public issue. Report it privately via the repository's Security tab ("Report a vulnerability") or email support@wickra.org with a subject line starting [wickra security]. Full policy: https://github.com/wickra-lib/wickra-feature-store/blob/main/SECURITY.md.

Disclaimer

wickra-feature-store is research and engineering tooling, not financial advice. A feature matrix describes historical data under the spec you provide; it makes no claim about the profitability or future performance of any model trained on it. Trading carries risk; you are responsible for your own decisions.

License

Licensed under either of Apache-2.0 or MIT at your option.