Wickra 0.1.0: streaming-first technical indicators
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
This commit is contained in:
kingchenc
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[package]
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name = "wickra"
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description = "Streaming-first technical analysis library: incremental indicators, drop-in TA-Lib replacement, multi-language."
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version.workspace = true
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authors.workspace = true
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edition.workspace = true
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rust-version.workspace = true
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license.workspace = true
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repository.workspace = true
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homepage.workspace = true
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readme.workspace = true
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keywords.workspace = true
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categories.workspace = true
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[lints]
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workspace = true
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[dependencies]
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wickra-core = { workspace = true }
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[features]
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default = ["parallel"]
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parallel = ["wickra-core/parallel"]
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[dev-dependencies]
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approx = { workspace = true }
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criterion = { workspace = true }
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proptest = { workspace = true }
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wickra-data = { path = "../wickra-data" }
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[[bench]]
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name = "indicators"
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harness = false
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[[example]]
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name = "backtest"
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path = "../../examples/rust/backtest.rs"
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required-features = []
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@@ -0,0 +1,142 @@
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//! Microbenchmarks for every built-in indicator.
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//!
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//! Run with:
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//! ```text
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//! cargo bench -p wickra
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//! ```
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//!
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//! Each benchmark feeds a deterministic synthetic price series through both the
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//! streaming (`update` loop) and batch APIs of an indicator. Sizes cover small
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//! (1 000), medium (10 000), and large (100 000) workloads.
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
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use wickra::{
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Atr, BatchExt, BollingerBands, Candle, Ema, Indicator, MacdIndicator, Obv, Rsi, Sma,
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Stochastic, Wma,
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};
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/// Deterministic synthetic price series of length `n`.
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fn price_series(n: usize) -> Vec<f64> {
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(0..n)
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.map(|i| {
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let t = i as f64;
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100.0 + (t * 0.013).sin() * 12.0 + (t * 0.071).cos() * 4.0 + (t * 0.003).sin() * 30.0
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})
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.collect()
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}
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/// Synthetic OHLC candle series.
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fn candle_series(n: usize) -> Vec<Candle> {
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let closes = price_series(n);
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closes
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.iter()
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.enumerate()
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.map(|(i, c)| {
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let t = i as f64;
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let spread = 0.5 + (t * 0.05).sin().abs();
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// Benchmark synthetic data: i originates from a usize counter capped at 100_000,
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// well within i64::MAX. The wrap-around lint does not apply here.
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#[allow(clippy::cast_possible_wrap)]
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let ts = i as i64;
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Candle::new_unchecked(*c, c + spread, c - spread, *c, 1_000.0, ts)
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})
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.collect()
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}
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fn bench_scalar<I, F>(c: &mut Criterion, name: &str, sizes: &[usize], make: F)
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where
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F: Fn() -> I,
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I: Indicator<Input = f64, Output = f64> + BatchExt,
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{
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let mut group = c.benchmark_group(name);
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for &n in sizes {
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let series = price_series(n);
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group.throughput(Throughput::Elements(n as u64));
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group.bench_with_input(BenchmarkId::new("streaming", n), &series, |b, prices| {
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b.iter(|| {
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let mut ind = make();
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for p in prices {
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black_box(ind.update(*p));
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}
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});
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});
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group.bench_with_input(BenchmarkId::new("batch", n), &series, |b, prices| {
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b.iter(|| {
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let mut ind = make();
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black_box(ind.batch(prices));
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});
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});
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}
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group.finish();
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}
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fn bench_macd(c: &mut Criterion, sizes: &[usize]) {
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let mut group = c.benchmark_group("macd");
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for &n in sizes {
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let series = price_series(n);
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group.throughput(Throughput::Elements(n as u64));
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group.bench_with_input(BenchmarkId::new("streaming", n), &series, |b, prices| {
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b.iter(|| {
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let mut ind = MacdIndicator::classic();
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for p in prices {
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black_box(ind.update(*p));
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}
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});
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});
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}
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group.finish();
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}
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fn bench_bollinger(c: &mut Criterion, sizes: &[usize]) {
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let mut group = c.benchmark_group("bollinger");
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for &n in sizes {
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let series = price_series(n);
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group.throughput(Throughput::Elements(n as u64));
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group.bench_with_input(BenchmarkId::new("streaming", n), &series, |b, prices| {
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b.iter(|| {
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let mut ind = BollingerBands::classic();
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for p in prices {
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black_box(ind.update(*p));
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}
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});
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});
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}
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group.finish();
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}
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fn bench_candle_input<I, F, O>(c: &mut Criterion, name: &str, sizes: &[usize], make: F)
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where
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F: Fn() -> I,
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I: Indicator<Input = Candle, Output = O>,
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{
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let mut group = c.benchmark_group(name);
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for &n in sizes {
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let candles = candle_series(n);
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group.throughput(Throughput::Elements(n as u64));
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group.bench_with_input(BenchmarkId::new("streaming", n), &candles, |b, candles| {
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b.iter(|| {
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let mut ind = make();
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for c in candles {
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black_box(ind.update(*c));
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}
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});
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});
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}
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group.finish();
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}
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fn benches(c: &mut Criterion) {
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let sizes = [1_000_usize, 10_000, 100_000];
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bench_scalar(c, "sma", &sizes, || Sma::new(14).unwrap());
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bench_scalar(c, "ema", &sizes, || Ema::new(14).unwrap());
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bench_scalar(c, "wma", &sizes, || Wma::new(14).unwrap());
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bench_scalar(c, "rsi", &sizes, || Rsi::new(14).unwrap());
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bench_macd(c, &sizes);
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bench_bollinger(c, &sizes);
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bench_candle_input(c, "atr", &sizes, || Atr::new(14).unwrap());
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bench_candle_input(c, "stochastic", &sizes, Stochastic::classic);
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bench_candle_input(c, "obv", &sizes, Obv::new);
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}
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criterion_group!(name = wickra_benches; config = Criterion::default(); targets = benches);
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criterion_main!(wickra_benches);
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@@ -0,0 +1,21 @@
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//! Wickra: streaming-first technical analysis.
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//!
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//! This crate is a thin re-export of [`wickra_core`] so downstream users can depend on
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//! a single `wickra` package without thinking about the internal split. Every public
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//! item lives in `wickra_core`; only the names re-exported here are part of the stable
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//! public API.
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//!
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//! # Example
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//!
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//! ```
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//! use wickra::{Indicator, Sma};
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//!
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//! let mut sma = Sma::new(3).unwrap();
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//! let prices = [1.0, 2.0, 3.0, 4.0, 5.0];
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//! let out: Vec<Option<f64>> = prices.iter().map(|p| sma.update(*p)).collect();
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//! assert_eq!(out, vec![None, None, Some(2.0), Some(3.0), Some(4.0)]);
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//! ```
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#![cfg_attr(docsrs, feature(doc_auto_cfg))]
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pub use wickra_core::*;
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