Batch: classic TA-Lib-style usage

June 24, 2026 · View on GitHub

Streaming-first technical indicators. Install with pip install wickra — no system dependencies, zero third-party packages.

▶ Live demo: all 514 indicators over real Binance market data, computed live in your browser — live.wickra.org · zero backend, powered by wickra-wasm.

Wickra is a multi-language technical-analysis library with a Rust core and native bindings for Python, Node.js and WASM, plus a C ABI that C, C++, C#, Go, Java, R and any other C-capable language links against. 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 wickra as ta                     # zero third-party deps — not even NumPy

# Batch: classic TA-Lib-style usage
prices = [100.0 + i * 0.1 for i in range(1000)]
rsi = ta.RSI(14)
values = rsi.batch(prices)              # array.array('d'), NaN during warmup
                                        # np.asarray(values) wraps it zero-copy if you use NumPy

# 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, C, C++, C#, Go, Java, R.
Indicators — a per-indicator deep dive (formula, parameters, warmup) for every one of the 514 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

Most TA libraries are fast, or multi-language, or broad. Wickra refuses to pick. It's the streaming-first engine built for the workload the others treat as an afterthought — live, tick-by-tick data — without giving up the breadth of a full batch library, and without making you reimplement your indicators four times to get there.

The biggest streaming-native catalogue, period. 514 indicators across 24 families — candlesticks, harmonic & chart patterns, market profile, market breadth, Renko/Kagi/Point&Figure bars, Ehlers DSP cycles, risk/performance metrics — every single one updating in O(1) per tick. TA-Lib ships ~150 and none of them stream.
One Rust core, five first-class targets. Native Rust · Python · Node.js · WASM plus a C ABI for C, C++, C#, Go, Java, R and any other C-capable language — identical math, identical results, zero per-language reimplementation and zero GIL bottleneck.
Correct by construction, not by hope. Every update validates its input, runs a real warmup, and returns an Option so a single bad tick can't silently poison state. batch == streaming is bit-exact, fuzzed and 100 %-line-covered for all 514 indicators.
Identical across every language — proven, not promised. All 514 indicators are replayed through all 10 languages (Rust · Python · Node.js · WASM · C · C++ · C# · Go · Java · R) and checked bit-for-bit against the Rust reference via shared golden fixtures in CI. The math is verifiably the same everywhere — this very check caught and fixed two real cross-language marshalling bugs.
Orders of magnitude faster where it counts. In streaming Wickra is 11–56× faster than the only other incremental peer and thousands of times faster than recompute-on-every-tick libraries. On batch it wins several rows outright and trades the simple recurrences (SMA, EMA, MACD) for its guarantees — and the losses are shown, not hidden.
Install in one line, anywhere. pip install wickra / npm install wickra — precompiled wheels and binaries, no C toolchain, none of TA-Lib's setup pain. macOS · Linux · Windows.
Batteries included — zero third-party deps, in every language. A full native data layer ships in the box: a CSV candle reader, a tick-to-candle aggregator, a timeframe resampler, a live Binance WebSocket feed and a historical Binance REST fetcher — in all 10 languages. Loading a CSV, rolling ticks into candles, resampling and streaming live data needs no foreign package — no pandas, no csv-parse, no ws/websockets, no jackson, no jsonlite, not even NumPy. pip install wickra / npm install wickra / go get / … pulls nothing else.
Truly permissive. MIT OR Apache-2.0 — drop it straight into commercial and closed-source work.

Every other library forces one of those compromises. Wickra doesn't:

Library	Install	Streaming	Languages	Indicators	Active
★ Wickra	clean	yes, O(1)	Rust · Python · Node.js · WASM · C · C++ · C# · Go · Java · R	514	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

Broad, multi-language, streaming-native and honest about its trade-offs — at the same time. That's the combination no one else ships.

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.

Benchmarks

Wickra updates every indicator in O(1) per tick. In streaming — the workload it is built for — it is 11–56× faster than the only other incremental peer and thousands of times faster than recompute-on-every-tick libraries. Batch is competitive: it wins several rows outright and trades a few µs elsewhere for None-warmup, NaN-safety and bit-exact batch == streaming.

Full tables (Rust + Python, streaming + batch) and how to reproduce them live in BENCHMARKS.md.

Pick your language with eyes open — per-binding throughput

Every binding calls the same Rust core, so this is not a speed claim — it is the raw cost of crossing each language's FFI boundary (SMA(20), 200 000 bars, Ryzen 9 9950X, million updates/sec). Batch stays high for most bindings; streaming is where the boundary shows — so if you stream tick-by-tick, the table tells you which binding keeps up and which to avoid for hot loops.

Language	streaming (Mupd/s)	batch (Mupd/s)
Rust (no FFI)	380	498
C / C++	365	358
C#	348	259
Python	31	46
Java	38	173
Go	23	394
WASM	21	169
Node.js	16	9
R	0.1	279

All ten share one verified implementation (see the verification badge above), so the numbers differ but the values are bit-for-bit identical. Methodology and the per-indicator breakdown are in BENCHMARKS.md.

Indicators

514 streaming-first indicators across twenty-four families. Every one passes the batch == streaming equivalence test, reference-value tests, and reset semantics tests — and is replayed through all 10 languages and checked bit-for-bit against the Rust reference (golden fixtures, in CI). 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 Oscillator, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index, Volume RSI, Williams Accumulation/Distribution, Twiggs Money Flow, Trade Volume Index, Intraday Intensity, 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, Central Pivot Range, Murrey Math Lines, Andrews Pitchfork, Volume-Weighted Support/Resistance, Pivot Reversal
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, TD Camouflage, TD Clop, TD Clopwin, TD Propulsion, TD Trap, TD D-Wave, TD Moving Averages
Ichimoku & Charts	Ichimoku Kinko Hyo (Tenkan, Kijun, Senkou A/B, Chikou), Heikin-Ashi, Heikin-Ashi Oscillator, Three Line Break, Smoothed Heikin-Ashi, Equivolume, CandleVolume
Alt-Chart Bars	Renko (box-size bricks), Kagi (reversal-amount lines), Point & Figure (X/O columns), Range, Tick, Volume, Dollar, Imbalance, Run, Three-Line Break
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, Tristar, Harami Cross, Tower Top/Bottom, Dumpling Top, New Price Lines, Frying Pan Bottom
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, Trade-Sign Autocorrelation, Hasbrouck Information Share
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, Estimated Leverage Ratio, OI-to-Volume Ratio, Perpetual Premium Index, Funding-Implied APR, Open-Interest Momentum
Market Profile	Value Area (POC / VAH / VAL), Volume Profile (histogram), TPO Profile, Initial Balance, Opening Range, Naked POC, Single Prints, Profile Shape, High/Low Volume Nodes, Composite Profile
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; every binding inherits it automatically (the C ABI — and the C#, Go, Java and R bindings generated from it — regenerate from the core).

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`
C / C++ (C ABI)	header + library, see `bindings/c`	`examples/c/streaming.c`
C# (C ABI)	`dotnet add package Wickra`, see `bindings/csharp`	`examples/csharp/streaming`
Go (cgo, C ABI)	`go get github.com/wickra-lib/wickra/bindings/go`, see `bindings/go`	`examples/go/streaming`
Java (FFM, C ABI)	Maven Central `org.wickra:wickra`, see `bindings/java`	`examples/java` (`Streaming`)
R (`.Call`, C ABI)	`R CMD INSTALL bindings/r`, see `bindings/r`	`examples/r/streaming.R`

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 the native bindings are memory-safe end to end. The C ABI runs the same safe core; only its thin FFI boundary uses unsafe, and the caller owns handle lifetimes (_new / _free).

Requirements

The minimum supported version per language. Prebuilt packages (Rust, Python, Node.js, WASM, C#) need only the runtime; the C-ABI bindings that compile on install — Go (cgo) and R (.Call) — also need a C compiler, and Java runs with --enable-native-access=ALL-UNNAMED.

Language	Package	Minimum supported
Rust	crates.io · `wickra`	1.86 (MSRV)
Python	PyPI · `wickra` (abi3 wheel)	3.9 (tested through 3.13)
Node.js	npm · `wickra` (N-API 8)	20 (tested on 22 · 24 LTS)
WASM	npm · `wickra-wasm`	any modern JS engine
C	`wickra.h` + library (releases)	C99 compiler
C++	`wickra.hpp` over the C ABI	C++14 compiler
C#	NuGet · `Wickra`	.NET 8 (`net8.0`)
Go	module · `wickra-lib/wickra-go`	Go 1.23 (cgo)
Java	Maven Central · `org.wickra:wickra`	Java 22 (FFM / Panama)
R	source package	R ≥ 2.10 (Rtools on Win.)

Full per-language detail (runtime vs. build-from-source) is on the Requirements page in the docs.

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 ships a complete, native data layer — exposed in all 10 languages, pulling zero third-party packages (no pandas / csv-parse / ws / jackson / jsonlite). In Rust it lives in the wickra-data crate; every binding exposes the same building blocks:

A streaming OHLCV CSV reader (CandleReader).
A tick-to-candle aggregator with arbitrary timeframes (TickAggregator).
A candle resampler for multi-timeframe analysis (1m → 5m → 1h on the fly, Resampler).
A live Binance Spot WebSocket kline feed (BinanceFeed, feature live-binance).
A historical Binance REST kline fetcher (fetch_binance_klines) — native HTTP + JSON, no third-party client.

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}");
        }
    }
}

Native live-feed and historical-fetch examples — using wickra.BinanceFeed and wickra.fetch_binance_klines, with no third-party HTTP/WebSocket client — live under examples/python/ (and the matching directory for every other language).

Project layout

wickra/
├── crates/
│   ├── wickra-core/         core engine + all 514 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)
│   ├── c/                   C ABI (cdylib + staticlib) + generated include/wickra.h
│   ├── csharp/              C# binding over the C ABI (publishes on NuGet)
│   ├── go/                  Go binding over the C ABI via cgo (module tag)
│   ├── r/                   R binding over the C ABI via .Call (R package)
│   └── java/                Java binding over the C ABI via the FFM API (Maven Central)
├── examples/                examples/README.md indexes every language
│   ├── data/                real BTCUSDT OHLCV datasets, one per timeframe
│   ├── rust/                Rust workspace member (`wickra-examples`)
│   ├── python/              backtest, live Binance feed, parallel assets, multi-tf
│   ├── node/                streaming, backtest, live Binance feed (load `wickra`)
│   ├── wasm/                browser demo for `wickra-wasm`
│   ├── c/                   C smoke + streaming, C++ RAII wrapper
│   ├── csharp/              streaming, backtest, strategies (load `Wickra`)
│   ├── go/                  streaming, backtest, strategies (cgo binding)
│   ├── r/                   streaming, backtest, strategies (.Call binding)
│   └── java/                streaming, backtest, strategies (FFM binding)
└── .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

# C ABI (cdylib + staticlib + generated header)
cargo build -p wickra-c --release
cmake -S examples/c -B examples/c/build -DWICKRA_LIB_DIR="$PWD/target/release"
cmake --build examples/c/build && ctest --test-dir examples/c/build --output-on-failure

# C# binding (requires the .NET 8 SDK; links the C ABI above)
dotnet test bindings/csharp/Wickra.Tests/Wickra.Tests.csproj

# Go binding (requires a C compiler for cgo; links the C ABI above)
cp target/release/libwickra.so bindings/go/lib/   # .dylib on macOS, wickra.dll on Windows
cd bindings/go && go test ./...

# R binding (requires a C toolchain / Rtools; links the C ABI above)
WICKRA_INCLUDE_DIR="$PWD/bindings/c/include" WICKRA_LIB_DIR="$PWD/target/release" \
  R CMD INSTALL bindings/r

# Java binding (requires JDK 22+ and Maven; links the C ABI above)
mvn -f bindings/java 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. A catalogue-wide property harness (tests/invariants.rs) additionally asserts batch == streaming, reset == fresh, and non-finite-input rejection for every indicator and bar-builder.
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.
bindings/c: Rust unit tests over the FFI boundary, plus C and C++ smoke tests and offline example ctests run on the three OSes.
bindings/csharp: dotnet test cases covering one indicator per FFI archetype (scalar/batch, multi-output, bars, profile, array input) plus SMA reference values.
bindings/go: go test cases covering one indicator per FFI archetype (scalar/batch, multi-output, bars, profile, array input), reset, and lifecycle.
bindings/r: testthat cases covering one indicator per FFI archetype (scalar/batch, multi-output, bars, profile, array input), reset, and validation.
bindings/java: JUnit cases covering one indicator per FFI archetype (scalar/batch, multi-output, bars, profile, array input) plus batch equivalence.

On top of those per-binding tests, all 10 languages (Rust, Python, Node.js, WASM, C, C++, C#, Go, Java, R) replay a shared, language-neutral golden fixture (testdata/golden/*.csv, generated by cargo run -p wickra-examples --bin gen_golden) and assert bit-for-bit parity with the Rust reference for every one of the 514 indicators across every archetype (scalar, multi-output, pairwise, derivatives-tick, cross-section, order-book, trade, profile, alt-chart bars, footprint). This catches FFI wiring bugs the math-only core tests cannot see — it has already found and fixed real cross-language marshalling bugs in the Java and R bindings.

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.

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