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")Full documentation lives at docs.wickra.org:
- Quickstarts — Rust, Python, Node, WASM.
- Indicators — a per-indicator deep dive (formula, parameters, warmup) for every one of the 462 indicators; start at the indicators overview.
- Reference — warmup periods, streaming vs batch, indicator chaining, the data layer.
- Guides — Cookbook, TA-Lib migration, FAQ.
Wickra started as a personal itch. The existing TA libraries never quite fit the projects I was building, so I decided to build one from the ground up — partly to learn, partly because I genuinely enjoy taking something that already exists and trying to do it differently (and, ideally, better). It's open source because the useful version of that itch is the one other people can build on too.
Plenty of TA libraries are fast. Each one forces a trade-off Wickra does not:
| Library | Install | Streaming | Languages | Indicators | Active |
|---|---|---|---|---|---|
| ★ Wickra | clean | yes, O(1) | Python · Node · WASM · Rust | 423 | yes |
| kand | clean | yes | Python · WASM · Rust | ~60 | yes |
| ta-rs | clean | yes | Rust only | ~30 | stale |
| yata | clean | partial | Rust only | ~35 | yes |
| TA-Lib | yes (C deps) | no | many bindings | ~150 | barely |
| pandas-ta | clean | no | Python | ~130 | slow |
| finta | clean | no | Python | ~80 | stale |
| talipp | clean | yes | Python | ~40 | yes |
Wickra's edge is breadth with reach: 462 indicators that all update in O(1) per tick and ship natively to Python, Node.js, WebAssembly and Rust from a single engine.
On speed — and why Wickra isn't the fastest. It deliberately isn't. The
leaner Rust crates (kand, ta-rs) win several of the micro-benchmarks below, and
those losses are shown rather than hidden. The gap is a choice, not a ceiling:
every update validates its input, runs a real warmup before it emits a value,
and returns an Option so a single bad tick can't silently poison the state.
ta-rs, by contrast, hands back a bare f64 from the first tick with no
validation. If Wickra threw all of that away — raw f64 out, no checks, no
warmup contract — it would match or beat the leanest crate on every row. It
keeps the guarantees instead, and still wins RSI, Bollinger and ATR against kand.
What no other library matches is the combination: catalogue size, native O(1)
streaming, NaN-safety, and four first-class language targets at once.
Three comparisons, split by layer and mode. Read them as relative speedups on identical input — absolute µs depend on CPU, memory clock and OS scheduler, not a universal contract.
- Reproduced on: Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
Rust 1.92 (release:
lto = "fat",codegen-units = 1), Python 3.12. - Reproduce yourself:
- Rust core vs Rust crates:
cargo bench -p wickra-bench - Python vs Python libs:
pip install -e bindings/python[bench]thenpython -m benchmarks.compare_libraries(auto-detects installed peers).
- Rust core vs Rust crates:
Like-for-like, no language-binding overhead, over a 50 000-bar series (µs for the whole series, lower = faster). This is the honest engine comparison — Wickra wins some and loses some, and both are shown.
Streaming (one value fed per update):
| Indicator | ★ Wickra | kand | ta-rs | yata |
|---|---|---|---|---|
| SMA(20) | 50 | 38 | 47 | 38 |
| EMA(20) | 154 | 69 | 56 | 69 |
| RSI(14) | 164 | 216 | 74 | — |
| MACD(12, 26, 9) | 275 | 143 | 66 | — |
| Bollinger(20, 2) | 128 ★ | 248 | 168 | — |
| ATR(14) | 152 | 166 | 61 | — |
Batch (whole series at once). Only Wickra and kand expose a batch API; ta-rs and yata are streaming-only.
| Indicator | ★ Wickra | kand |
|---|---|---|
| SMA(20) | 82 | 42 |
| EMA(20) | 159 | 74 |
| RSI(14) | 253 ★ | 274 |
| MACD(12, 26, 9) | 681 | 283 |
| Bollinger(20, 2) | 445 ★ | 462 |
| ATR(14) | 175 | 173 |
ta-rs is the per-indicator speed champion on almost every row — it returns a
bare f64 with no warmup state and no input validation, trading away the
None-warmup and NaN-safety semantics Wickra keeps. Against kand, Wickra wins
streaming RSI, Bollinger and ATR (and batch RSI + Bollinger); Bollinger is the
one row where Wickra is the outright fastest of all four. The leaner crates
still win the pure recurrences (EMA, MACD) and SMA. yata exposes only SMA/EMA as
raw-value methods, so its other rows are omitted rather than faked.
Full pass over a 20 000-bar series, µs/op (lower = faster). ★ per row.
| Indicator | ★ Wickra | finta | TA-Lib | tulipy |
|---|---|---|---|---|
| SMA(20) | 59.6 ★ | 354.2 (5.9× slower) | ⧗ | ⧗ |
| EMA(20) | 88.4 ★ | 309.3 (3.5× slower) | ⧗ | ⧗ |
| RSI(14) | 77.3 ★ | 1 283 (16.6× slower) | ⧗ | ⧗ |
| MACD(12, 26, 9) | 116.4 ★ | 529.5 (4.6× slower) | ⧗ | ⧗ |
| Bollinger(20, 2) | 146.0 ★ | 1 246 (8.5× slower) | ⧗ | ⧗ |
| ATR(14) | 135.8 ★ | 3 812 (28× slower) | ⧗ | ⧗ |
⧗ = published by the CI Linux job. TA-Lib and tulipy ship C extensions that don't build cleanly on every desktop, so their canonical numbers come from the
cross-library-benchworkflow rather than this local table. pandas-ta needs Python ≥ 3.12 and isn't in the 3.11 CI matrix. The script auto-detects whichever peers are installed in your environment.
Seed 5 000 bars, then feed ticks one at a time. talipp is the only Python peer with a true incremental API; batch-only libraries like TA-Lib must recompute the entire history on every tick — Wickra updates in O(1).
| Indicator | ★ Wickra (per tick) | talipp (per tick) |
|---|---|---|
| SMA(20) | 0.067 µs ★ | 0.63 µs (9.4× slower) |
| EMA(20) | 0.051 µs ★ | 0.63 µs (12.2× slower) |
| RSI(14) | 0.053 µs ★ | 1.00 µs (19.1× slower) |
| MACD(12, 26, 9) | 0.071 µs ★ | 3.64 µs (51.5× slower) |
| Bollinger(20, 2) | 0.085 µs ★ | 4.87 µs (57.2× slower) |
Run the suite yourself:
cargo bench -p wickra-bench # Rust core vs kand / ta-rs / yata
pip install -e bindings/python[bench] # Python peers
python -m benchmarks.compare_libraries462 streaming-first indicators across twenty-four families. Every one passes the
batch == streaming equivalence test, reference-value tests, and reset
semantics tests. Each has a per-indicator deep dive (formula, parameters,
warmup) at docs.wickra.org.
| Family | Indicators |
|---|---|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA, SWMA, GMA, EHMA, Median MA, Adaptive Laguerre, GD, Holt-Winters |
| Momentum Oscillators | RSI (Wilder), Anchored RSI, Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia, ROC Percentage (ROCP), ROC Ratio (ROCR), ROC Ratio 100 (ROCR100), Disparity Index, Fisher RSI, RSX, Dynamic Momentum Index, Stochastic CCI, RMI, Derivative Oscillator, Elder Ray, Intraday Momentum Index, QQE |
| Trend & Directional | MACD, MACD Fixed (MACDFIX), MACD Extended (MACDEXT), ADX (+DI/-DI), ADXR, Aroon, TRIX, Aroon Oscillator, Vortex, Random Walk Index, Trend Intensity Index, Wave Trend Oscillator, Mass Index, Choppiness Index, Vertical Horizontal Filter, Plus DM, Minus DM, Plus DI, Minus DI, DX, TTM Trend, Trend Strength Index, Qstick, Polarized Fractal Efficiency, Wave PM, Gator Oscillator, Kase Permission Stochastic |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC, TSF Oscillator, MACD Histogram, PPO Histogram |
| Volatility & Bands | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility, RVI (Relative Volatility Index), Parkinson Volatility, Garman-Klass Volatility, Rogers-Satchell Volatility, Yang-Zhang Volatility, Volatility Cone |
| Bands & Channels | MA Envelope, Acceleration Bands, STARC Bands, ATR Bands, Hurst Channel, LinReg Channel, Standard Error Bands, Double Bollinger Bands, TTM Squeeze, Fractal Chaos Bands, VWAP StdDev Bands, Quartile Bands, Bomar Bands, Median Channel, Projection Bands, Projection Oscillator |
| Trailing Stops | Parabolic SAR, Parabolic SAR Extended (SAREXT), SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop, HiLo Activator, Volty Stop, Yo-Yo Exit, Donchian Channel Stop, Percentage Trailing Stop, Step Trailing Stop, Renko Trailing Stop, Kase DevStop, Elder SafeZone, ATR Ratchet, NRTR, Time-Based Stop, Modified MA Stop |
| Volume | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement, Klinger Volume Oscillator, Volume Oscillator, NVI, PVI, Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index, Volume RSI, Williams Accumulation/Distribution, Twiggs Money Flow, Trade Volume Index, Intraday Intensity Index, Better Volume, Volume-Weighted MACD |
| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle, Variance, Coefficient of Variation, Skewness, Kurtosis, Standard Error, Detrended StdDev, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Pairwise Beta, Pair Spread Z-Score, Lead-Lag Cross-Correlation, Cointegration, Relative Strength A-vs-B, Spearman Correlation, Mid Price, Mid Point, Average Price, Linear Regression Intercept, Time Series Forecast, Rolling Correlation, Rolling Covariance, OU Half-Life, Spread Hurst, Distance SSD, Beta-Neutral Spread, Variance Ratio, Granger Causality, Kalman Hedge Ratio, Spread Bollinger Bands, Spread AR(1) Coefficient, Jarque-Bera, Rolling Min-Max Scaler, Shannon Entropy, Sample Entropy, Kendall Tau |
| Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, Hilbert Phasor, Hilbert DC Phase, Hilbert Trend Mode, Sine Wave, Decycler, Decycler Oscillator, Roofing Filter, Center of Gravity, Cybernetic Cycle, Adaptive Cycle, Empirical Mode Decomposition, Ehlers Stochastic, Instantaneous Trendline, Highpass Filter, Reflex, Trendflex, Correlation Trend Indicator, Adaptive RSI, Universal Oscillator, Adaptive CCI, Bandpass Filter, Even Better Sinewave, Autocorrelation Periodogram |
| Pivots & S/R | Classic Pivots, Fibonacci Pivots, Camarilla, Woodie Pivots, DeMark Pivots, Williams Fractals, ZigZag |
| DeMark | TD Setup, TD Sequential, TD DeMarker, TD REI, TD Pressure, TD Combo, TD Countdown, TD Lines, TD Range Projection, TD Differential, TD Open, TD Risk Level |
| Ichimoku & Charts | Ichimoku Kinko Hyo (Tenkan, Kijun, Senkou A/B, Chikou), Heikin-Ashi |
| Alt-Chart Bars | Renko (box-size bricks), Kagi (reversal-amount lines), Point & Figure (X/O columns) |
| Candlestick Patterns | Doji, Hammer, Inverted Hammer, Hanging Man, Shooting Star, Engulfing, Harami, Morning/Evening Star, Three White Soldiers/Black Crows, Piercing Line/Dark Cloud Cover, Marubozu, Tweezer, Spinning Top, Three Inside Up/Down, Three Outside Up/Down, Two Crows, Upside Gap Two Crows, Identical Three Crows, Three Line Strike, Three Stars in the South, Abandoned Baby, Advance Block, Belt-hold, Breakaway, Counterattack, Doji Star, Dragonfly Doji, Gravestone Doji, Long-Legged Doji, Rickshaw Man, Evening Doji Star, Morning Doji Star, Gap Side-by-Side White, High-Wave, Hikkake, Modified Hikkake, Homing Pigeon, On-Neck, In-Neck, Thrusting, Separating Lines, Kicking, Kicking by Length, Ladder Bottom, Mat Hold, Matching Low, Long Line, Short Line, Rising Three Methods, Falling Three Methods, Upside Gap Three Methods, Downside Gap Three Methods, Stalled Pattern, Stick Sandwich, Takuri, Closing Marubozu, Opening Marubozu, Tasuki Gap, Unique Three River, Concealing Baby Swallow |
| Chart Patterns | Double Top / Bottom, Triple Top / Bottom, Head and Shoulders, Triangle (asc/desc/sym), Wedge (rising/falling), Flag / Pennant, Rectangle / Range, Cup and Handle |
| Harmonic Patterns | AB=CD, Gartley, Butterfly, Bat, Crab, Shark, Cypher, Three Drives |
| Fibonacci | Fibonacci Retracement, Fibonacci Extension, Fibonacci Projection, Auto-Fibonacci, Golden Pocket, Fibonacci Confluence, Fibonacci Fan, Fibonacci Arcs, Fibonacci Channel, Fibonacci Time Zones |
| Microstructure | Order-Book Imbalance (Top-1 / Top-N / Full), Microprice, Quoted Spread, Depth Slope, Signed Volume, Cumulative Volume Delta, Trade Imbalance, Effective Spread, Realized Spread, Kyle's Lambda, Footprint, Order Flow Imbalance, VPIN, Amihud Illiquidity, Roll Measure |
| Derivatives | Funding Rate, Funding Rate Mean, Funding Rate Z-Score, Funding Basis, Open-Interest Delta, OI / Price Divergence, OI-Weighted Price, Long/Short Ratio, Taker Buy/Sell Ratio, Liquidation Features, Term-Structure Basis, Calendar Spread |
| Market Profile | Value Area (POC / VAH / VAL), Volume Profile (histogram), TPO Profile, Initial Balance, Opening Range |
| Market Breadth | Advance/Decline Line, Advance/Decline Ratio, Advance/Decline Volume Line, McClellan Oscillator, McClellan Summation Index, TRIN / Arms Index, Breadth Thrust, New Highs - New Lows, High-Low Index, Percent Above Moving Average, Up/Down Volume Ratio, Bullish Percent Index, Cumulative Volume Index, Absolute Breadth Index, TICK Index |
| Risk / Performance | Sharpe Ratio, Sortino Ratio, Calmar Ratio, Omega Ratio, Max Drawdown, Average Drawdown, Drawdown Duration, Pain Index, Value at Risk, Conditional Value at Risk (CVaR), Profit Factor, Gain/Loss Ratio, Recovery Factor, Kelly Criterion, Treynor Ratio, Information Ratio, Alpha (Jensen) |
| Seasonality & Session | Session VWAP, Session High/Low, Session Range, Average Daily Range, Overnight Gap, Overnight/Intraday Return, Turn-of-Month, Seasonal Z-Score, Time-of-Day Return Profile, Day-of-Week Profile, Intraday Volatility Profile, Volume-by-Time Profile |
Every candlestick pattern emits a signed per-bar value — +1.0 bullish,
−1.0 bearish, 0.0 none — so the family drops straight into a feature matrix
as one column each. Doji is direction-less by default (+1.0 / 0.0);
construct it in signed mode (Doji::new().signed(), Doji(signed=True),
new Doji(true)) for a dragonfly / gravestone ±1 reading.
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 |
examples/node/backtest.js |
| Browser / WASM | npm install wickra-wasm |
examples/wasm/index.html |
| Rust | cargo add wickra |
examples/rust/src/bin/backtest.rs |
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.
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 462 indicators
│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
│ ├── wickra-data/ CSV reader, tick aggregator, live exchange feeds
│ └── wickra-bench/ internal cross-library benchmark harness (not published)
├── 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
Wickra's own regression benchmarks live in crates/wickra/benches/; the
cross-library comparison against kand, ta-rs and yata lives in the internal
crates/wickra-bench/ crate. Runnable Rust examples live in the workspace member
crate at examples/rust/. There is no top-level benches/ directory.
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra # Wickra's own regression benchmarks
cargo bench -p wickra-bench # cross-library comparison (kand, ta-rs, yata)
# 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 testEvery 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 == streamingequivalence,resetsemantics, 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 --testcases for batch, streaming, and reference values across all indicators.bindings/wasm:wasm-bindgen-testcases for constructors, equivalence, and reference values.
Contributions are very welcome — issues, bug reports, ideas, and pull requests all land in the same place: https://github.com/wickra-lib/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 either of
- Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
- MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)
at your option. Use it, fork it, modify it, redistribute it — commercially or not — file issues, send pull requests; all welcome.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
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.
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.