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# Wickra — Python
---
> **▶ 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`.
**Streaming-first technical indicators for Python. `pip install wickra` — zero
third-party dependencies (not even NumPy), 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 WASM, 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 all 514 streaming-first indicators across twenty-four families.
## Install
```bash
pip install wickra
```
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to
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.
## Quick start
```python
import wickra as ta # zero third-party deps — not even NumPy
# 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
rsi = ta.RSI(14)
values = rsi.batch(prices) # array.array('d'), NaN during warmup
# np.asarray(values) wraps it zero-copy if you use NumPy
# 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 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).
## Documentation
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and documentation site:
- **Repository & full indicator list:**
- **Docs** (quickstarts, cookbook, TA-Lib migration):
- **Runnable examples:** [`examples/python/`](https://github.com/wickra-lib/wickra/tree/main/examples/python)
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 —
all exposing the same indicators from the shared, `unsafe`-forbidden Rust core.
## 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: .
## 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](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.