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:

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"?

The numbers below were measured on a single developer workstation and are not guaranteed to reproduce identically on different hardware — absolute µs values depend on CPU, memory clock and OS scheduler. Read them as relative speedups between libraries on identical input, not as a universal performance contract.

• Reproduced on: Windows 11 Pro 26200, AMD Ryzen 9 7950X3D, 64 GB DDR5, Rust 1.92 (release profile, lto = "fat", codegen-units = 1), Python 3.12, Node 20.
• Reproduce yourself: pip install -e bindings/python[bench] then python -m benchmarks.compare_libraries. The script auto-detects every installed peer library and runs them on the same generated inputs as Wickra. The CI job cross-library-bench runs the same script on every push and uploads the raw report as a build artefact.

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 20 000-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.

Streaming — per-tick latency after seeding with 5 000 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).

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.

Adding a new indicator means implementing one trait in Rust; all four bindings inherit it automatically.

Languages

Each binding ships several runnable examples (streaming, backtest, live feed); examples/README.md is the full cross-language index.

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/
│   └── 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/                examples/README.md indexes every language
│   ├── data/                real BTCUSDT OHLCV datasets, one per timeframe
│   ├── rust/                Rust workspace member (`wickra-examples`)
│   ├── python/              backtest, live trading, parallel assets, multi-tf
│   ├── node/                streaming, backtest, live trading (load `wickra`)
│   └── wasm/                browser demo for `wickra-wasm`
└── .github/workflows/       CI and release pipelines

Rust benchmarks live in crates/wickra/benches/; runnable Rust examples live in the workspace member crate at examples/rust/. 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.

Disclaimer

Wickra is an indicator toolkit, not a trading system. Values it computes are deterministic transforms of the input data — they are not financial advice and they do not predict the market. Any use of this library in a production trading context is at your own risk.

The library is provided as is, without warranty of any kind; see LICENSE for the full terms.