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docs(P7): per-ecosystem binding READMEs + correct MSRV documentation (#85)

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* docs(contributing): correct MSRV to 1.86/1.88 and document the dep-forced floor (P7.1)

* docs(bindings): trim binding READMEs to per-ecosystem install + links (P7.2)

* docs(changelog): note per-ecosystem binding READMEs + MSRV doc fix (P7.1/P7.2)
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kingchenc
2026-05-31 05:30:34 +02:00
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commit 3f05342f72
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CHANGELOG.md
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@@ -13,6 +13,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
to `1`; every Node constructor now propagates the core's validation error
(e.g. `period must be greater than zero`), matching the Python and WASM
bindings and the Rust core. Constructing with a valid period is unaffected.
- **Binding package READMEs are now per-ecosystem.** The Python, Node.js, and
WebAssembly READMEs were byte-identical 314-line copies of the workspace
README and had drifted out of sync (stale indicator count, Python snippets
shown on the Node and WASM package pages). Each is now a focused landing page
with the correct install command, a language-correct quick-start snippet, and
links to the canonical documentation — removing the manual three-way sync
burden. No code or API changes.
- **CONTRIBUTING now states the correct MSRV (1.86 workspace / 1.88
`bindings/node`)** and documents that these are the dependency-forced floors,
kept minimal on purpose. The previous text claimed 1.75 / 1.77, which the
`msrv` CI job has enforced against since the criterion and napi-build bumps.
## [0.3.1] - 2026-05-30
CONTRIBUTING.md
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@@ -35,8 +35,13 @@ cargo test --workspace
cargo test -p wickra-data --features live-binance
```
The minimum supported Rust version is **1.75** for the workspace crates and
**1.77** for `bindings/node`; the `msrv` CI job enforces both.
The minimum supported Rust version is **1.86** for the workspace crates and
**1.88** for `bindings/node`; the `msrv` CI job enforces both. These floors are
not chosen freely — they are the lowest versions our dependencies allow
(criterion 0.8.2, the bench dev-dependency, requires 1.86; napi-build 2.3.2
requires 1.88). We keep the MSRV at that dependency-forced floor on purpose so
the library builds for the widest possible audience; please don't raise it
without a dependency that actually requires it.
### Python
bindings/node/README.md
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@@ -1,314 +1,73 @@
# Wickra
# Wickra — Node.js
[![CI](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/wickra-lib/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/wickra-lib/wickra)
[![crates.io](https://img.shields.io/crates/v/wickra.svg?logo=rust&color=orange)](https://crates.io/crates/wickra)
[![PyPI](https://img.shields.io/pypi/v/wickra.svg?logo=pypi&color=blue)](https://pypi.org/project/wickra/)
[![npm](https://img.shields.io/npm/v/wickra.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](LICENSE)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](https://github.com/wickra-lib/wickra/blob/main/LICENSE)
**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators for Node.js. `npm install wickra` —
prebuilt native binary, 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.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading bots and historical backtests share
the exact same implementation. This package is the Node.js binding (napi-rs);
it exposes 200+ streaming-first indicators across sixteen families.
```python
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 |
|------------------------|-----------------|-----------|----------------|--------|
| **★ Wickra** | **clean** | **yes** | **Python + Node + WASM + Rust** | **yes** |
| 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 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 9950X, 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.
| Indicator | **★ Wickra** | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### 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).
| Indicator | **★ Wickra (per tick)** | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× 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:
## Install
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
npm install wickra
```
## Indicators
The native addon ships as a prebuilt binary per platform (Linux, macOS,
Windows — x64 and arm64), selected automatically through optional
dependencies. There is nothing to compile.
214 streaming-first indicators across sixteen families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
## Quick start
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia |
| Trend & Directional | MACD, 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 |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC |
| 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, Detrended StdDev |
| 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, 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, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Spearman Correlation |
| Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, 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 |
| 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 |
| Market Profile | Value Area (POC / VAH / VAL), Initial Balance, Opening Range |
| 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) |
```js
const wickra = require('wickra');
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
// Batch: run an indicator over a whole array.
const prices = Array.from({ length: 1000 }, (_, i) => 100 + i * 0.1);
const values = new wickra.RSI(14).batch(prices); // null during warmup
## 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`](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
```rust
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`).
```rust
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}");
}
}
// Streaming: the same indicator, fed tick by tick in O(1).
const rsi = new wickra.RSI(14);
for (const price of liveFeed) {
const value = rsi.update(price); // no recomputation over history
if (value !== null && value > 70) {
console.log('overbought');
}
}
```
A Python live-trading example using the public `websockets` package lives at
`examples/python/live_trading.py`.
`batch(prices)` and feeding the same prices through `update()` produce
identical values — the equivalence is enforced by the test suite.
## Project layout
## Documentation
```
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
```
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and wiki:
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.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Wiki** (quickstarts, cookbook, TA-Lib migration): <https://github.com/wickra-lib/wickra/wiki>
- **Runnable examples:** [`examples/node/`](https://github.com/wickra-lib/wickra/tree/main/examples/node)
## Building everything from source
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
```bash
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
## Disclaimer
# 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](#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.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](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.
---
<p align="center">
<a href="https://github.com/wickra-lib/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/wickra-lib/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/wickra-lib/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
Licensed under the **PolyForm Noncommercial License 1.0.0**. Personal projects,
research, education, non-profits, and hobby trading bots are all fine; the one
thing not allowed is commercial sale of the software or of services built
around it. See [LICENSE](https://github.com/wickra-lib/wickra/blob/main/LICENSE).
bindings/python/README.md
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# Wickra
# Wickra — Python
[![CI](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/wickra-lib/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/wickra-lib/wickra)
[![crates.io](https://img.shields.io/crates/v/wickra.svg?logo=rust&color=orange)](https://crates.io/crates/wickra)
[![PyPI](https://img.shields.io/pypi/v/wickra.svg?logo=pypi&color=blue)](https://pypi.org/project/wickra/)
[![npm](https://img.shields.io/npm/v/wickra.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](LICENSE)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](https://github.com/wickra-lib/wickra/blob/main/LICENSE)
**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators for Python. `pip install wickra` — no
system dependencies, no C build tooling.**
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.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading bots and historical backtests share
the exact same implementation. This package is the Python binding (PyO3); it
exposes 200+ streaming-first indicators across sixteen families.
## Install
```bash
pip install wickra
```
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to
compile and no C library to track down.
## Quick start
```python
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage
# Batch: classic TA-Lib-style usage over a whole array.
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
# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # O(1) — no recomputation over history
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
```
## Why Wickra exists
`batch(prices)` and feeding the same prices through `update()` produce
identical values — the equivalence is enforced by the test suite.
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:
## Documentation
| Library | Install pain | Streaming | Multi-language | Active |
|------------------------|-----------------|-----------|----------------|--------|
| **★&nbsp;Wickra** | **clean** | **yes** | **Python + Node + WASM + Rust** | **yes** |
| 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 |
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and wiki:
Wickra is the only library that combines all of: clean install, streaming,
multi-language reach, and active maintenance.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Wiki** (quickstarts, cookbook, TA-Lib migration): <https://github.com/wickra-lib/wickra/wiki>
- **Runnable examples:** [`examples/python/`](https://github.com/wickra-lib/wickra/tree/main/examples/python)
## Benchmark: how much faster is "streaming-first"?
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
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.
## Disclaimer
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 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.
| Indicator | **★&nbsp;Wickra** | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### 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).
| Indicator | **★&nbsp;Wickra (per tick)** | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× 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:
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
```
## Indicators
214 streaming-first indicators across sixteen 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, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia |
| Trend & Directional | MACD, 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 |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC |
| 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, Detrended StdDev |
| 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, 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, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Spearman Correlation |
| Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, 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 |
| 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 |
| Market Profile | Value Area (POC / VAH / VAL), Initial Balance, Opening Range |
| 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) |
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`](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
```rust
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`).
```rust
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
```bash
# 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](#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.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](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.
---
<p align="center">
<a href="https://github.com/wickra-lib/wickra/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
</a>
<a href="https://github.com/wickra-lib/wickra/network/members">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
</a>
<a href="https://github.com/wickra-lib/wickra/issues">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/wickra-lib/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
</a>
</p>
<p align="center">
If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
</p>
Licensed under the **PolyForm Noncommercial License 1.0.0**. Personal projects,
research, education, non-profits, and hobby trading bots are all fine; the one
thing not allowed is commercial sale of the software or of services built
around it. See [LICENSE](https://github.com/wickra-lib/wickra/blob/main/LICENSE).
bindings/wasm/README.md
+45 -287
View File
@@ -1,314 +1,72 @@
# Wickra
# Wickra — WebAssembly
[![CI](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/wickra-lib/wickra/branch/main/graph/badge.svg)](https://codecov.io/gh/wickra-lib/wickra)
[![crates.io](https://img.shields.io/crates/v/wickra.svg?logo=rust&color=orange)](https://crates.io/crates/wickra)
[![PyPI](https://img.shields.io/pypi/v/wickra.svg?logo=pypi&color=blue)](https://pypi.org/project/wickra/)
[![npm](https://img.shields.io/npm/v/wickra.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](LICENSE)
[![npm](https://img.shields.io/npm/v/wickra-wasm.svg?logo=npm&color=red)](https://www.npmjs.com/package/wickra-wasm)
[![License: PolyForm-NC](https://img.shields.io/badge/license-PolyForm--NC--1.0.0-purple)](https://github.com/wickra-lib/wickra/blob/main/LICENSE)
**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
**Streaming-first technical indicators in the browser. `npm install
wickra-wasm` — pure WebAssembly, runs anywhere a modern JS engine does.**
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.
bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1)
streaming state machine, so live trading dashboards and historical backtests
share the exact same implementation. This package is the WebAssembly binding
(wasm-bindgen, built for the `web` target); it exposes 200+ streaming-first
indicators across sixteen families.
```python
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 |
|------------------------|-----------------|-----------|----------------|--------|
| **★&nbsp;Wickra** | **clean** | **yes** | **Python + Node + WASM + Rust** | **yes** |
| 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 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 9950X, 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.
| Indicator | **★&nbsp;Wickra** | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### 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).
| Indicator | **★&nbsp;Wickra (per tick)** | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× 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:
## Install
```bash
pip install -e bindings/python[bench]
python -m benchmarks.compare_libraries
npm install wickra-wasm
```
## Indicators
## Quick start
214 streaming-first indicators across sixteen families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
The module ships a default `init` export that loads the `.wasm` payload; await
it once before constructing indicators.
| Family | Indicators |
|--------|-----------|
| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA |
| Momentum Oscillators | RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia |
| Trend & Directional | MACD, 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 |
| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC |
| 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, Detrended StdDev |
| 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, 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, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Spearman Correlation |
| Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, 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 |
| 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 |
| Market Profile | Value Area (POC / VAH / VAL), Initial Balance, Opening Range |
| 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) |
```js
import init, { RSI } from 'wickra-wasm';
Adding a new indicator means implementing one trait in Rust; all four bindings
inherit it automatically.
await init(); // load the WebAssembly module once
## 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`](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
```rust
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`).
```rust
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}");
}
}
// Streaming: feed prices tick by tick in O(1).
const rsi = new RSI(14);
for (const price of liveFeed) {
const value = rsi.update(price); // null during warmup
if (value !== null && value > 70) {
console.log('overbought');
}
}
```
A Python live-trading example using the public `websockets` package lives at
`examples/python/live_trading.py`.
Constructors mirror the other bindings (`new SMA(20)`, `new MACD(12, 26, 9)`,
`new BollingerBands(20, 2.0)`, …); `update()` returns the latest value or
`null` while the indicator is still warming up.
## Project layout
## Documentation
```
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
```
The full indicator catalogue, guides, quickstarts, and API reference live in
the main repository and wiki:
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.
- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
- **Wiki** (quickstarts, cookbook, TA-Lib migration): <https://github.com/wickra-lib/wickra/wiki>
- **Runnable browser examples:** [`examples/wasm/`](https://github.com/wickra-lib/wickra/tree/main/examples/wasm)
## Building everything from source
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, `unsafe`-forbidden Rust core.
```bash
# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra
## Disclaimer
# 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](#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.
Wickra is an indicator toolkit, not a trading system. The values it computes
are deterministic transforms of the input data — they are not financial advice
and do not predict the market. Any use in a live trading context is at your own
risk. The library is provided **as is**, without warranty of any kind.
## License
Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](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.
</p>
Licensed under the **PolyForm Noncommercial License 1.0.0**. Personal projects,
research, education, non-profits, and hobby trading bots are all fine; the one
thing not allowed is commercial sale of the software or of services built
around it. See [LICENSE](https://github.com/wickra-lib/wickra/blob/main/LICENSE).