wickra/README.md

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

71 streaming-first indicators across eight families. Every one passes the batch == streaming equivalence test, reference-value tests, and reset semantics tests.

Family	Indicators
Moving Averages	SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA
Momentum Oscillators	RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator
Trend & Directional	MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter
Price Oscillators	PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power
Volatility & Bands	ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility
Trailing Stops	Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop
Volume	OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement
Price Statistics	Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle

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	bindings/node/examples/streaming.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.

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 71 indicators
│   ├── wickra/              top-level facade crate (publishes on crates.io)
│   │                        + benches/ and examples/backtest.rs
│   └── wickra-data/         CSV reader, tick aggregator, live exchange feeds
│                            + examples/live_binance.rs
├── bindings/
│   ├── python/              PyO3 + maturin (publishes on PyPI)
│   ├── node/                napi-rs (publishes on npm) + examples/
│   └── wasm/                wasm-bindgen (browsers, bundlers, Node) + examples/
├── examples/
│   └── python/              backtest, live trading, parallel assets, multi-tf
└── .github/workflows/       CI and release pipelines

Rust benchmarks and examples live inside their crate (crates/wickra/benches/, crates/<name>/examples/); there is no top-level benches/ directory.

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

Testing

Every layer is covered; run the suites with the commands in Building everything from source.

• wickra-core: unit tests per indicator — textbook reference values (Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), batch == streaming equivalence, reset semantics, NaN/Inf handling, and property tests.
• wickra-data: unit tests for CSV decoding, the tick aggregator, the resampler, and the Binance payload parser.
• bindings/python: pytest covering smoke checks, streaming/batch equivalence, reference values, lifecycle, input validation, and dict/tuple candle inputs.
• bindings/node: node --test cases for batch, streaming, and reference values across all indicators.
• bindings/wasm: wasm-bindgen-test cases for constructors, equivalence, and reference values.

Contributing

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 Indicator trait in crates/wickra-core/src/indicators/<name>.rs, wire it into indicators/mod.rs and the crate root, and add reference-value tests, a batch == streaming equivalence 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-core catch most regressions; please don't disable them.
• Improving a binding. Each binding lives under bindings/<lang> with its own tests; please keep the batch == streaming invariant.
• Style. cargo fmt --all + cargo clippy --workspace --all-targets -- -D warnings are 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.

License

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.

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If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.