Wickra

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")

Why Wickra exists

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.

Benchmark: how much faster is "streaming-first"?

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.

Batch — single full pass over a 5 000-bar series

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)

Streaming — per-tick latency after seeding with 2 000 historical bars

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_libraries

Indicators in 0.1.0

25 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.

Languages

Binding	Install	Example
Python (PyO3)	pip install wickra	examples/python/backtest.py
Node.js (napi-rs)	npm install @wickra/wickra	bindings/node/__tests__/smoke.test.js
Browser / WASM	wasm-pack build bindings/wasm --target web	bindings/wasm/examples/index.html
Rust	cargo add wickra	examples/rust/backtest.rs

The wickra-core crate is unsafe-forbidden, so every binding inherits a memory-safe implementation.

Rust API

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);

Live data sources

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.

Project layout

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/                backtest, live Binance
├── benches/                 cargo bench targets
└── .github/workflows/       CI and release pipelines

Building everything from source

# 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 test

Test counts

• wickra-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 via node --test.

License

Licensed under the Apache License, Version 2.0. See LICENSE.