kingchencandGitHub
880a0e7430
* feat(kvo): add Klinger Volume Oscillator
Stephen J. Klinger's trend-aware volume-force MACD. Each bar produces a 'volume force' (vf) signed by the local trend (+1 / -1 / carry) and scaled by the ratio of the current accumulation horizon to its previous trend. KVO = EMA(vf, fast) - EMA(vf, slow), classic (34, 55).
Rust core (Kvo) with 7 unit tests (rejects zero / fast>=slow, accessors, constant series collapses to 0, warmup lands at slow+1, batch == streaming, reset clears state), plus Python (PyKvo + KVO export), Node (KvoNode), and WASM (WasmKvo) bindings. Fuzz target adds Kvo to the candle-input sweep, bench adds the candle-input KVO benchmark, README counter 71 -> 72 + family table row, CHANGELOG [Unreleased].
* feat(volume-oscillator): add Volume Oscillator (VO)
Percent difference between a fast and a slow SMA of the bar volume: 100 * (SMA(vol, fast) - SMA(vol, slow)) / SMA(vol, slow). Default (14, 28). The line stays near zero in stable conditions; positive readings show rising short-term participation, negative readings show waning interest.
Rust core (VolumeOscillator) with 8 unit tests (period validation, accessors, constant volume == 0, zero-volume window defensive branch, two reference values verified algebraically, batch == streaming, reset), plus Python (PyVolumeOscillator + VolumeOscillator export), Node (VolumeOscillatorNode), and WASM (WasmVolumeOscillator) bindings. Fuzz target adds VolumeOscillator to the candle-input sweep, bench adds the volume_oscillator benchmark, README counter 72 -> 73 + family table row, CHANGELOG [Unreleased].
* feat(nvi-pvi): add Negative & Positive Volume Index
Paul Dysart's cumulative volume-flow indices, popularised by Norman Fosback in 'Stock Market Logic'. Both run from a 1000.0 baseline and only update on a specific direction of volume change:
- NVI updates on volume-contraction bars (volume_t < volume_{t-1}), absorbing the percent close change. Tracks the 'smart money' leg per Fosback.
- PVI updates on volume-expansion bars (volume_t > volume_{t-1}). Tracks the 'crowd' leg.
Both expose with_baseline(f64) for custom starting indexes. The NVI/PVI pair is listed as a single line in indicator-ideas/families/07-volume.md and shares the same lifecycle/test/binding surface, so they ship as one commit.
Rust core (Nvi, Pvi) with 9 unit tests each (accessors, baseline seed, volume direction branches, zero-prev-close guard, custom baseline, batch == streaming, reset), plus Python (PyNvi/PyPvi + NVI/PVI exports), Node (NviNode/PviNode), and WASM (WasmNvi/WasmPvi) bindings. Fuzz target adds Nvi+Pvi to the candle-input sweep, bench adds nvi+pvi entries, README counter 73 -> 75 + family table row, CHANGELOG [Unreleased].
* feat(family-07): add Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index
Finishes the volume-flow family with the remaining (new) entries from
indicator-ideas/families/07-volume.md.
Indicators added:
- Williams A/D (`WilliamsAD`): Larry Williams' volume-less cumulative
accumulation/distribution line. Anchors each bar's contribution to
the previous close via true-high/true-low (gap-aware).
- Anchored VWAP (`AnchoredVwap`): cumulative VWAP whose accumulation
starts at a user-chosen anchor bar. Exposes `set_anchor()` (queued
to the next `update`) for click-to-anchor workflows. Reset clears
both state and pending-anchor flag.
- Demand Index (`DemandIndex`): James Sibbet's smoothed buying-vs-
selling pressure, in the streaming-friendly textbook form
`EMA(volume * close-return * (1 + range/close), period)`.
- Time Segmented Volume (`Tsv`): Don Worden's rolling window-sum of
`(close_t - close_{t-1}) * volume_t`. Default `period = 18`.
- Volume Zone Oscillator (`Vzo`): Walid Khalil's normalised volume-flow
oscillator bounded in `[-100, +100]`, defined as
`100 * EMA(signed_volume) / EMA(volume)`.
- Market Facilitation Index (`MarketFacilitationIndex`): Bill Williams'
per-bar `(high - low) / volume`. Returns `None` on zero-volume bars.
All six indicators ship with unit tests (`rejects_zero_period` where
applicable, `accessors_and_metadata`, constant-series behaviour,
batch == streaming equivalence, reset semantics, and reference-value
or saturation-extreme tests), Python / Node / WASM bindings, fuzz
coverage in `indicator_update_candle`, a `bench_candle_input` line per
indicator, README + CHANGELOG entries, and Python reference-value
tests in `test_new_indicators.py`.
The README indicator counter advances 75 -> 81.
* test(family-07): cover defensive cold paths + Default impls
- ad_oscillator: exercise `value()` after first emission.
- kvo: cover the `cm == 0.0` zero-OHLC defensive branch.
- nvi / pvi: exercise the Default impls.
318 lines
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Markdown
318 lines
15 KiB
Markdown
# Wickra
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[](https://github.com/kingchenc/wickra/actions/workflows/ci.yml)
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[](https://codecov.io/gh/kingchenc/wickra)
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[](https://crates.io/crates/wickra)
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[](https://pypi.org/project/wickra/)
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[](https://www.npmjs.com/package/wickra)
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[](LICENSE)
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**Streaming-first technical indicators. Install with `pip install wickra` — no system dependencies.**
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Wickra is a multi-language technical-analysis library with a Rust core and
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bindings for Python, Node.js, and WebAssembly. Every indicator is a state
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machine that updates in O(1) per new data point, so live trading bots and
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historical backtests share the exact same implementation.
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```python
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import numpy as np
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import wickra as ta
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# Batch: classic TA-Lib-style usage
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prices = np.linspace(100, 200, 1000)
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rsi = ta.RSI(14)
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values = rsi.batch(prices) # numpy array, NaN during warmup
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# Streaming: same indicator, fed tick by tick
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rsi = ta.RSI(14)
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for price in live_feed:
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value = rsi.update(price) # O(1) — no recomputation over history
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if value is not None and value > 70:
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print("overbought")
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```
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## Why Wickra exists
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The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
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talipp, tulipy — and every one of them shares the same blind spot:
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| Library | Install pain | Streaming | Multi-language | Active |
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|------------------------|-----------------|-----------|----------------|--------|
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| **★ Wickra** | **clean** | **yes** | **Python + Node + WASM + Rust** | **yes** |
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| TA-Lib (Python) | yes (C deps) | no | no | barely |
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| pandas-ta | clean | no | no | slow |
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| finta | clean | no | no | stale |
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| ta-lib-python | yes (C deps) | no | no | barely |
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| talipp | clean | yes | no | yes |
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| Tulip Indicators | yes (C deps) | no | partial | stale |
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| ooples (C#) | clean | no | C# only | yes |
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Wickra is the only library that combines all of: clean install, streaming,
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multi-language reach, and active maintenance.
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## Benchmark: how much faster is "streaming-first"?
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The numbers below were measured on a single developer workstation and are not
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guaranteed to reproduce identically on different hardware — absolute µs values
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depend on CPU, memory clock and OS scheduler. Read them as **relative
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speedups** between libraries on identical input, not as a universal
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performance contract.
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- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
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Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
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Python 3.12, Node 20.
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- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
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`python -m benchmarks.compare_libraries`. The script auto-detects every
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installed peer library and runs them on the same generated inputs as
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Wickra. The CI job `cross-library-bench` runs the same script on every
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push and uploads the raw report as a build artefact.
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Lower µs/op = faster. Wickra wins every batch category outright, and the
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streaming gap widens linearly with how much history a batch-only library has
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to recompute on every tick.
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### Batch — single full pass over a 20 000-bar series
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Reading the table: each cell shows that library's runtime, plus how many times
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slower it is than Wickra in parentheses. **★** marks the winner per row.
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| Indicator | **★ Wickra** | finta | talipp |
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|---------------------|---------------------|-----------------------------|-------------------------------|
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| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
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| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
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| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
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| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
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| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
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| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
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### Streaming — per-tick latency after seeding with 5 000 historical bars
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A batch-only library has to re-run its full indicator over the entire history on
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every new tick; Wickra updates state in O(1).
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| Indicator | **★ Wickra (per tick)** | talipp (per tick) |
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|-----------|---------------------|---------------------------|
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| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
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> TA-Lib and pandas-ta are not included here because both fail to install
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> cleanly on Windows without C build tooling — which is precisely the install
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> pain Wickra was built to remove. The benchmark script auto-detects every
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> peer library it can find and runs them on the same inputs as Wickra; install
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> them in your environment to see those rows light up too.
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Run the suite yourself:
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```bash
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pip install -e bindings/python[bench]
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python -m benchmarks.compare_libraries
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```
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## Indicators
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121 streaming-first indicators across nine families. Every one passes the
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`batch == streaming` equivalence test, reference-value tests, and reset
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semantics tests.
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| Family | Indicators |
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|--------|-----------|
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| Moving Averages | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA |
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| 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 |
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| 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 |
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| Price Oscillators | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC |
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| 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 |
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| 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 |
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| Trailing Stops | Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
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| 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 |
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| Price Statistics | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle |
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Adding a new indicator means implementing one trait in Rust; all four bindings
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inherit it automatically.
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## Languages
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| Binding | Install | Example |
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|-------------------|-----------------------------------------------|---------|
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| Python (PyO3) | `pip install wickra` | `examples/python/backtest.py` |
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| Node.js (napi-rs) | `npm install wickra` | `examples/node/backtest.js` |
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| Browser / WASM | `npm install wickra-wasm` | `examples/wasm/index.html` |
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| Rust | `cargo add wickra` | `examples/rust/src/bin/backtest.rs` |
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Each binding ships several runnable examples (streaming, backtest, live feed);
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[`examples/README.md`](examples/README.md) is the full cross-language index.
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The wickra-core crate is `unsafe`-forbidden, so every binding inherits a
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memory-safe implementation.
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## Rust API
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```rust
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use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};
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// Streaming or batch — same trait, same code.
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let mut sma = Sma::new(14)?;
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let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
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let mut rsi = Rsi::new(14)?;
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for price in live_feed {
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if let Some(v) = rsi.update(price) {
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println!("RSI = {v}");
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}
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}
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// Compose indicators: RSI(7) on top of EMA(14).
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let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
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chain.update(price);
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```
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## Live data sources
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`wickra-data` (separate crate, opt-in) ships:
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- A streaming OHLCV **CSV reader**.
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- A **tick-to-candle aggregator** with arbitrary timeframes.
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- A **candle resampler** for multi-timeframe analysis (1m → 5m → 1h on the fly).
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- A **Binance Spot WebSocket** kline adapter (feature `live-binance`).
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```rust
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use wickra::{Indicator, Rsi};
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use wickra_data::live::binance::{BinanceKlineStream, Interval};
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let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
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let mut rsi = Rsi::new(14)?;
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while let Some(event) = stream.next_event().await? {
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if event.is_closed {
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if let Some(v) = rsi.update(event.candle.close) {
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println!("RSI = {v:.2}");
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}
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}
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}
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```
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A Python live-trading example using the public `websockets` package lives at
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`examples/python/live_trading.py`.
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## Project layout
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```
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wickra/
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├── crates/
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│ ├── wickra-core/ core engine + all 121 indicators
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│ ├── wickra/ top-level facade crate (publishes on crates.io) + benches/
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│ └── wickra-data/ CSV reader, tick aggregator, live exchange feeds
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├── bindings/
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│ ├── python/ PyO3 + maturin (publishes on PyPI)
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│ ├── node/ napi-rs (publishes on npm)
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│ └── wasm/ wasm-bindgen (browsers, bundlers, Node)
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├── examples/ examples/README.md indexes every language
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│ ├── data/ real BTCUSDT OHLCV datasets, one per timeframe
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│ ├── rust/ Rust workspace member (`wickra-examples`)
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│ ├── python/ backtest, live trading, parallel assets, multi-tf
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│ ├── node/ streaming, backtest, live trading (load `wickra`)
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│ └── wasm/ browser demo for `wickra-wasm`
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└── .github/workflows/ CI and release pipelines
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```
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Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
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in the workspace member crate at `examples/rust/`. There is no top-level
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`benches/` directory.
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## Building everything from source
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```bash
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# Rust core + tests
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cargo test --workspace
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cargo clippy --workspace --all-targets -- -D warnings
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cargo bench -p wickra
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# Python binding (requires Rust toolchain + maturin)
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cd bindings/python
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maturin develop --release
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pytest
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# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
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wasm-pack build bindings/wasm --target web --release --features panic-hook
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# Node binding (requires @napi-rs/cli)
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cd bindings/node && npm install && npm run build && npm test
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```
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## Testing
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Every layer is covered; run the suites with the commands in
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[Building everything from source](#building-everything-from-source).
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- `wickra-core`: unit tests per indicator — textbook reference values
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(Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), `batch == streaming`
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equivalence, `reset` semantics, NaN/Inf handling, and property tests.
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- `wickra-data`: unit tests for CSV decoding, the tick aggregator, the
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resampler, and the Binance payload parser.
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- `bindings/python`: pytest covering smoke checks, streaming/batch
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equivalence, reference values, lifecycle, input validation, and
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dict/tuple candle inputs.
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- `bindings/node`: `node --test` cases for batch, streaming, and reference
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values across all indicators.
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- `bindings/wasm`: `wasm-bindgen-test` cases for constructors, equivalence,
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and reference values.
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## Contributing
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Contributions are very welcome — issues, bug reports, ideas, and pull requests
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all land in the same place: <https://github.com/kingchenc/wickra>.
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A short orientation for first-time contributors:
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- **Adding an indicator.** Implement the `Indicator` trait in
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`crates/wickra-core/src/indicators/<name>.rs`, wire it into
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`indicators/mod.rs` and the crate root, and add reference-value tests,
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a `batch == streaming` equivalence test, and (where it makes sense) a
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proptest. The four bindings inherit your indicator automatically once
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you expose it in the language wrappers.
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- **Fixing a numeric bug.** Add a failing test that pins the textbook value
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first, then fix the math. Property tests in `crates/wickra-core` catch
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most regressions; please don't disable them.
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- **Improving a binding.** Each binding lives under `bindings/<lang>` with
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its own tests; please keep the `batch == streaming` invariant.
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- **Style.** `cargo fmt --all` + `cargo clippy --workspace --all-targets -- -D warnings`
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are CI gates; running them locally before pushing keeps reviews short.
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For larger architectural changes, open an issue first so we can sketch the
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shape together before you invest the time.
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## License
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Licensed under the **PolyForm Noncommercial License 1.0.0**. See [LICENSE](LICENSE).
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In plain English: use it, fork it, modify it, redistribute it, file issues, send
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pull requests — all welcome. Personal projects, research, education, non-profits,
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government, hobby trading bots: all fine. The one thing that's not allowed is
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commercial sale of the software or of services built around it. If you want to
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use Wickra commercially, get in touch about a license.
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## Disclaimer
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Wickra is an indicator toolkit, not a trading system. Values it computes are
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deterministic transforms of the input data — they are not financial advice and
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they do not predict the market. Any use of this library in a production
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trading context is at your own risk.
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The library is provided **as is**, without warranty of any kind; see
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[LICENSE](LICENSE) for the full terms.
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---
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<p align="center">
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<a href="https://github.com/kingchenc/wickra/stargazers">
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<img alt="GitHub stars" src="https://img.shields.io/github/stars/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ffd866">
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</a>
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<a href="https://github.com/kingchenc/wickra/network/members">
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<img alt="GitHub forks" src="https://img.shields.io/github/forks/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=78dce8">
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</a>
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<a href="https://github.com/kingchenc/wickra/issues">
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<img alt="GitHub issues" src="https://img.shields.io/github/issues/kingchenc/wickra?style=for-the-badge&logo=github&logoColor=white&color=ff6188">
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</a>
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</p>
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<p align="center">
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If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
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</p>
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