Streaming-first technical indicators. Install with pip install wickra — no system dependencies.
Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js, and WebAssembly. Every indicator is a state machine that updates in O(1) per new data point, so live trading bots and historical backtests share the exact same implementation.
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: same indicator, fed tick by tick
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # O(1) — no recomputation over history
if value is not None and value > 70:
print("overbought")The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta, talipp, tulipy — and every one of them shares the same blind spot:
| Library | Install pain | Streaming | Multi-language | Active |
|---|---|---|---|---|
| TA-Lib (Python) | yes (C deps) | no | no | barely |
| pandas-ta | clean | no | no | slow |
| finta | clean | no | no | stale |
| ta-lib-python | yes (C deps) | no | no | barely |
| talipp | clean | yes | no | yes |
| Tulip Indicators | yes (C deps) | no | partial | stale |
| ooples (C#) | clean | no | C# only | yes |
| Wickra | clean | yes | Python+Node+WASM+Rust | yes |
Wickra is the only library that combines all of: clean install, streaming, multi-language reach, and active maintenance.
Reproduced on this machine with python -m benchmarks.compare_libraries.
Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
Reading the table: each cell shows that library's runtime, plus how many times slower it is than Wickra in parentheses. ★ marks the winner per row.
| Indicator | Wickra | finta | talipp |
|---|---|---|---|
| SMA(20) | 26.0 µs ★ | 295.3 µs (11.4× slower) | 1 812.8 µs (69.7× slower) |
| EMA(20) | 16.8 µs ★ | 205.5 µs (12.2× slower) | 2 534.4 µs (150.9× slower) |
| RSI(14) | 31.2 µs ★ | 714.1 µs (22.9× slower) | 3 751.7 µs (120.2× slower) |
| MACD(12, 26, 9) | 30.8 µs ★ | 359.5 µs (11.7× slower) | 11 642.2 µs (378.0× slower) |
| Bollinger(20, 2.0) | 26.7 µs ★ | 690.6 µs (25.9× slower) | 27 482.4 µs (1 030.1× slower) |
| ATR(14) | 40.6 µs ★ | 1 120.3 µs (27.6× slower) | 3 760.2 µs (92.7× slower) |
A batch-only library has to re-run its full indicator over the entire history on every new tick; Wickra updates state in O(1).
| Indicator | Wickra (per tick) | talipp (per tick) |
|---|---|---|
| RSI(14) | 0.07 µs ★ | 1.16 µs (17.5× slower) |
TA-Lib and pandas-ta are not included here because both fail to install cleanly on Windows without C build tooling — which is precisely the install pain Wickra was built to remove. The benchmark script auto-detects every peer library it can find and runs them on the same inputs as Wickra; install them in your environment to see those rows light up too.
Run the suite yourself:
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries25 streaming-first indicators across four families. Every one passes the
batch == streaming equivalence test, reference-value tests, and reset
semantics tests.
| Family | Indicators |
|---|---|
| Trend | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA |
| Momentum | RSI (Wilder), MACD, Stochastic, CCI, ROC, Williams %R, ADX (+DI/-DI), MFI, TRIX, Awesome Oscillator, Aroon |
| Volatility | Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Parabolic SAR |
| Volume | OBV, VWAP (cumulative + rolling) |
Adding a new indicator means implementing one trait in Rust; all four bindings inherit it automatically.
| Binding | Install | Example |
|---|---|---|
| Python (PyO3) | pip install wickra |
examples/python/backtest.py |
| Node.js (napi-rs) | npm install wickra |
bindings/node/__tests__/smoke.test.js |
| Browser / WASM | npm install wickra-wasm |
bindings/wasm/examples/index.html |
| Rust | cargo add wickra |
crates/wickra/examples/backtest.rs |
The wickra-core crate is unsafe-forbidden, so every binding inherits a
memory-safe implementation.
use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};
// Streaming or batch — same trait, same code.
let mut sma = Sma::new(14)?;
let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
let mut rsi = Rsi::new(14)?;
for price in live_feed {
if let Some(v) = rsi.update(price) {
println!("RSI = {v}");
}
}
// Compose indicators: RSI(7) on top of EMA(14).
let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
chain.update(price);wickra-data (separate crate, opt-in) ships:
- A streaming OHLCV CSV reader.
- A tick-to-candle aggregator with arbitrary timeframes.
- A candle resampler for multi-timeframe analysis (1m → 5m → 1h on the fly).
- A Binance Spot WebSocket kline adapter (feature
live-binance).
use wickra::{Indicator, Rsi};
use wickra_data::live::binance::{BinanceKlineStream, Interval};
let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
let mut rsi = Rsi::new(14)?;
while let Some(event) = stream.next_event().await? {
if event.is_closed {
if let Some(v) = rsi.update(event.candle.close) {
println!("RSI = {v:.2}");
}
}
}A Python live-trading example using the public websockets package lives at
examples/python/live_trading.py.
wickra/
├── crates/
│ ├── wickra-core/ core engine + all 25 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io)
│ └── wickra-data/ CSV reader, tick aggregator, live exchange feeds
├── bindings/
│ ├── python/ PyO3 + maturin (publishes on PyPI)
│ ├── node/ napi-rs (publishes on npm)
│ └── wasm/ wasm-bindgen (browsers, bundlers, Node)
├── examples/
│ └── python/ backtest, live trading, parallel assets, multi-tf
│ (Rust examples live inside their crate at crates/<name>/examples/)
├── benches/ cargo bench targets
└── .github/workflows/ CI and release pipelines
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
# Python binding (requires Rust toolchain + maturin)
cd bindings/python
maturin develop --release
pytest
# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
wasm-pack build bindings/wasm --target web --release --features panic-hook
# Node binding (requires @napi-rs/cli)
cd bindings/node && npm install && npm run build && npm testwickra-core: 171 unit tests + 2 doctests, including textbook-value tests for Wilder RSI, Bollinger Bands, MACD, ATR, and Stochastic.wickra-data: 11 unit tests + 1 doctest, covers CSV decoding, the tick aggregator, the resampler, and the Binance payload parser.bindings/python: 56 pytest tests covering smoke checks, streaming==batch equivalence, reference values, lifecycle, and dict/tuple candle inputs.bindings/node: 7 Node test-runner cases vianode --test.
Contributions are very welcome — issues, bug reports, ideas, and pull requests all land in the same place: https://github.com/kingchenc/wickra.
A short orientation for first-time contributors:
- Adding an indicator. Implement the
Indicatortrait incrates/wickra-core/src/indicators/<name>.rs, wire it intoindicators/mod.rsand the crate root, and add reference-value tests, abatch == streamingequivalence test, and (where it makes sense) a proptest. The four bindings inherit your indicator automatically once you expose it in the language wrappers. - Fixing a numeric bug. Add a failing test that pins the textbook value
first, then fix the math. Property tests in
crates/wickra-corecatch most regressions; please don't disable them. - Improving a binding. Each binding lives under
bindings/<lang>with its own tests; please keep thebatch == streaminginvariant. - Style.
cargo fmt --all+cargo clippy --workspace --all-targets -- -D warningsare CI gates; running them locally before pushing keeps reviews short.
For larger architectural changes, open an issue first so we can sketch the shape together before you invest the time.
Licensed under the PolyForm Noncommercial License 1.0.0. See LICENSE.
In plain English: use it, fork it, modify it, redistribute it, file issues, send pull requests — all welcome. Personal projects, research, education, non-profits, government, hobby trading bots: all fine. The one thing that's not allowed is commercial sale of the software or of services built around it. If you want to use Wickra commercially, get in touch about a license.
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.