wickra 0.5.9

Streaming-first technical indicators: incremental, fast, install-free.

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")

Documentation

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 423 indicators; start at the indicators overview.
• Reference — warmup periods, streaming vs batch, indicator chaining, the data layer.
• Guides — Cookbook, TA-Lib migration, FAQ.

Why Wickra exists

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: 423 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.

Benchmarks

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] then python -m benchmarks.compare_libraries (auto-detects installed peers).

1. Rust core vs the other Rust TA 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.

2. Python vs the Python TA ecosystem — batch

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-bench workflow 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.

3. Python — streaming (per-tick latency)

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_libraries

Indicators

423 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
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
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
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
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
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
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.

Languages

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.

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 423 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.

Building everything from source

# 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 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/wickra-lib/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 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.

Contribution

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.

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.

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