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# Wickra — Python
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---
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> **▶ Live demo:** all 514 indicators over real Binance market data, computed live in your browser — **[live.wickra.org](https://live.wickra.org)** · zero backend, powered by `wickra-wasm`.
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**Streaming-first technical indicators for Python. `pip install wickra` — zero
third-party dependencies (not even NumPy), no 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 WASM, 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
the exact same implementation. This package is the Python binding (PyO3); it
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exposes all 514 streaming-first indicators across twenty-four families.
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## Install
```bash
pip install wickra
```
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. `pip install wickra` pulls **zero**
third-party packages; NumPy is an optional extra (`pip install wickra[numpy]` )
for zero-copy interop.
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## Quick start
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```python
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import wickra as ta # zero third-party deps — not even NumPy
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# Batch: classic TA-Lib-style usage over a whole series.
prices = [ 100.0 + i * 0.1 for i in range ( 1000 )] # list, array.array or NumPy all work
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rsi = ta . RSI ( 14 )
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values = rsi . batch ( prices ) # array.array('d'), NaN during warmup
# np.asarray(values) wraps it zero-copy if you use NumPy
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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 :
print ( "overbought" )
```
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`batch(prices)` and feeding the same prices through `update()` produce
identical values — the equivalence is enforced by the test suite.
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## Benchmark
Two benchmarks ship with the binding:
- `benchmarks/throughput.py` — streaming and batch updates-per-second for `SMA` ,
`ATR` and `MACD` . 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.
```bash
maturin develop --release
python -m benchmarks.throughput
python -m benchmarks.compare_libraries # cross-library; auto-detects installed peers
```
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, WASM and Rust, plus a
C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against —
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all exposing the same indicators from the shared, `unsafe` -forbidden Rust core.
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## Security
Found a security issue? **Please don't open a public issue.** Report it privately
via the affected 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/blob/main/SECURITY.md>.
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## Disclaimer
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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.
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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 )
or [MIT ](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT ) at your option.