Files
wickra/docs/wiki/Quickstart-Rust.md
T
kingchenc 39a252ea66 release(0.2.0): bump version from 0.1.5 to 0.2.0
This release carries the full post-audit work — 46 new indicators
(25 → 71), an eight-family taxonomy restructure, new bindings for
RollingVWAP, the WASM streaming-update parity, the pyo3/numpy CVE
fix, the SMA/Bollinger drift bound, the O(1) LinearRegression
refactor, the UlcerIndex deque and the PSAR is_ready/reset fixes
plus a refreshed example suite and wiki. The earlier 0.1.5 number
was never published; jumping straight to 0.2.0 is the cleaner signal
for the scope of the change.

Bumped:
- Cargo.toml workspace + wickra-core workspace-dep version
- bindings/python/pyproject.toml
- bindings/node/package.json + optionalDependencies (six platform pins)
- 6 x bindings/node/npm/<target>/package.json
- Cargo.lock regenerated
- CHANGELOG.md [0.2.0] header + compare-link
- docs/wiki/Home.md published-versions table
- docs/wiki/Quickstart-{Rust,Node,WASM}.md + Warmup-Periods.md
  version-pinned narrative lines

Verified locally:
- cargo fmt/clippy/test (628 passed, 0 failed)
- cargo deny check (no suppression)
- bindings/node node --test (92/92)
- bindings/python pytest (118/118)
- import wickra reports 0.2.0 with 72 indicator classes
2026-05-23 19:58:02 +02:00

4.6 KiB

Quickstart: Rust

A five-minute tour of the Wickra Rust crate. By the end you will have run a batch SMA, fed an RSI tick by tick, and composed two indicators with Chain.

Install

cargo add wickra

The default features pull in parallel (rayon-based batch_parallel); turn them off with cargo add wickra --no-default-features if you want a leaner build. The wickra crate is a thin façade that re-exports everything from wickra-core; you can also depend on wickra-core directly if you want to skip the façade.

The published crate is at version 0.2.0 on crates.io.

The Indicator trait in 30 seconds

Every indicator implements the same trait:

pub trait Indicator {
    type Input;
    type Output;
    fn update(&mut self, input: Self::Input) -> Option<Self::Output>;
    fn reset(&mut self);
    fn warmup_period(&self) -> usize;
    fn is_ready(&self) -> bool;
    fn name(&self) -> &'static str;
}

update is O(1) in the input length. The companion trait BatchExt is a blanket extension that adds a batch(&[Self::Input]) method to every indicator — its default implementation is literally a loop over update, so batch and streaming results are bit-for-bit identical.

Batch and streaming side by side

use wickra::{BatchExt, Indicator, Rsi, Sma};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // 1. Batch: SMA(3) over five prices.
    let mut sma = Sma::new(3)?;
    let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
    println!("{:?}", out);
    // -> [None, None, Some(2.0), Some(3.0), Some(4.0)]

    // 2. Streaming: feed Wilder's textbook example into RSI(14).
    let mut rsi = Rsi::new(14)?;
    let prices = [
        44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08,
        45.89, 46.03, 45.61, 46.28, 46.28, 46.00, 46.03, 46.41,
    ];
    for (tick, price) in prices.iter().enumerate() {
        if let Some(v) = rsi.update(*price) {
            println!("tick {:2}  close={:.2}  rsi={:.4}", tick + 1, price, v);
        }
    }
    Ok(())
}

The streaming loop prints:

tick 15  close=46.28  rsi=70.4641
tick 16  close=46.00  rsi=66.2496
tick 17  close=46.03  rsi=66.4809
tick 18  close=46.41  rsi=69.3469

The first value lands on tick 15 because Rsi::new(14)?.warmup_period() == 15 (14 diffs to seed Wilder's smoothing, so the 15th input emits the first RSI). The 70.4641 value matches the textbook value pinned by the unit test classic_wilder_textbook_values in crates/wickra-core/src/indicators/rsi.rs.

Composing indicators with Chain

Chain<A, B> wires the output of A straight into the input of B, provided both stages agree on f64 as the bridging type. The chain itself is an Indicator, so you can stack three stages with .then(c), or four with .then(c).then(d).

use wickra::{Chain, Ema, Indicator, Rsi};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // RSI(7) computed on the output of EMA(14).
    let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
    for i in 1..=22 {
        if let Some(v) = chain.update(f64::from(i)) {
            println!("chain emitted at input #{i}: {v}");
        }
    }
    println!("chain.warmup_period() = {}", chain.warmup_period());
    Ok(())
}

Output:

chain emitted at input #21: 100
chain emitted at input #22: 100
chain.warmup_period() = 22

Ema::new(14) needs 14 inputs to seed and Rsi::new(7) needs 8 more once the EMA starts flowing, so the chain emits its first value at input 21. The warmup_period() reported by Chain is a conservative first + second sum (here 14 + 8 = 22); see Indicator Chaining for the exact contract.

A deeper example

examples/rust/src/bin/backtest.rs (in the wickra-examples workspace crate) computes a panel of indicators (RSI, EMA, Bollinger, MACD, ATR, ADX, OBV) over an OHLCV CSV by way of wickra-data:

cargo run --release -p wickra-examples --bin backtest -- path/to/ohlcv.csv

For live-data work, wickra-data ships a streaming CSV reader, a tick-to-candle aggregator, a candle resampler, and a Binance kline WebSocket adapter under the live-binance feature. examples/rust/src/bin/live_binance.rs is the canonical example for the latter.

See also