3be267cb03
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.
288 lines
8.5 KiB
Rust
288 lines
8.5 KiB
Rust
//! Core traits: the [`Indicator`] state machine and the [`BatchExt`] blanket extension.
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/// A streaming technical indicator.
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///
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/// Every indicator in Wickra implements this trait. The contract is:
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///
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/// - [`update`](Indicator::update) is called once per input point and must be O(1) in
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/// the input length. Pre-existing buffered state may be touched, but no full
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/// recomputation over the entire series is permitted.
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/// - The returned `Option<Output>` is `None` while the indicator is still in its
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/// *warmup* phase (insufficient inputs to produce a defined value), and `Some`
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/// once it is ready.
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/// - [`reset`](Indicator::reset) clears all state, returning the indicator to the
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/// exact configuration it had immediately after construction.
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///
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/// Implementors that consume scalar prices use `Input = f64` so they automatically
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/// gain access to chaining via [`Chain`].
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pub trait Indicator {
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/// Type of one input data point (typically `f64` for a price, or `Candle` / `Tick`).
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type Input;
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/// Type of one output value.
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type Output;
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/// Feed one new data point into the indicator and return the freshly computed
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/// output, or `None` if the indicator is still warming up.
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fn update(&mut self, input: Self::Input) -> Option<Self::Output>;
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/// Reset all internal state, leaving the indicator equivalent to a freshly
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/// constructed instance with the same parameters.
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fn reset(&mut self);
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/// Number of inputs required before the first non-`None` output can be produced.
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fn warmup_period(&self) -> usize;
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/// Whether the indicator has emitted at least one value since the last reset.
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fn is_ready(&self) -> bool;
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/// Stable, human-readable indicator name. Used by chaining and diagnostics.
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fn name(&self) -> &'static str;
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}
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/// Blanket extension that adds batch evaluation to every [`Indicator`].
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///
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/// The naive `batch` simply replays `update` over a slice, which is always correct
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/// because `update` is the only state transition. Concrete indicators may override
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/// `batch` if they have a faster vectorized path; the default keeps the contract
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/// `batch == repeated update`.
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pub trait BatchExt: Indicator {
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/// Run the indicator over a slice of inputs in order, returning one output (or
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/// `None` during warmup) per input.
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fn batch(&mut self, inputs: &[Self::Input]) -> Vec<Option<Self::Output>>
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where
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Self::Input: Clone,
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{
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let mut out = Vec::with_capacity(inputs.len());
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for x in inputs {
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out.push(self.update(x.clone()));
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}
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out
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}
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/// Run an independent copy of the indicator over each input series in parallel.
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///
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/// Each asset is processed by its own fresh instance built via `make`, so state
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/// never leaks across assets. Requires the `parallel` feature (enabled by
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/// default), which pulls in `rayon`.
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#[cfg(feature = "parallel")]
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fn batch_parallel<F>(
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inputs_per_asset: &[Vec<Self::Input>],
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make: F,
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) -> Vec<Vec<Option<Self::Output>>>
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where
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Self: Sized + Send,
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Self::Input: Sync + Clone,
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Self::Output: Send,
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F: Fn() -> Self + Sync + Send,
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{
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use rayon::prelude::*;
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inputs_per_asset
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.par_iter()
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.map(|series| {
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let mut ind = make();
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ind.batch(series)
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})
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.collect()
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}
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}
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impl<T: Indicator> BatchExt for T {}
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/// Chain two indicators so the output of the first becomes the input of the second.
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///
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/// Both indicators must agree on `f64` as the bridging type, which is the common
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/// case for price-in/value-out indicators. The chain itself is an indicator, so
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/// chains can be nested arbitrarily.
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///
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/// # Example
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///
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/// ```
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/// use wickra_core::{Chain, Ema, Indicator, Rsi};
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///
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/// // RSI(7) on top of EMA(14). EMA seeds at input 14, then RSI needs 7+1 more
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/// // valid inputs to emit, so the chain becomes ready at input 21.
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/// let mut chain = Chain::new(Ema::new(14).unwrap(), Rsi::new(7).unwrap());
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/// for i in 1..=21 {
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/// chain.update(f64::from(i));
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/// }
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/// assert!(chain.is_ready());
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/// ```
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#[derive(Debug, Clone)]
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pub struct Chain<A, B>
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where
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A: Indicator<Input = f64, Output = f64>,
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B: Indicator<Input = f64>,
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{
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first: A,
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second: B,
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}
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impl<A, B> Chain<A, B>
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where
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A: Indicator<Input = f64, Output = f64>,
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B: Indicator<Input = f64>,
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{
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/// Construct a chain whose inputs flow through `first` and then `second`.
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pub const fn new(first: A, second: B) -> Self {
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Self { first, second }
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}
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/// Add a third stage on top.
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pub fn then<C>(self, third: C) -> Chain<Self, C>
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where
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C: Indicator<Input = f64>,
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Self: Indicator<Input = f64, Output = f64>,
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{
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Chain::new(self, third)
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}
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/// Borrow the upstream indicator.
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pub const fn first(&self) -> &A {
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&self.first
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}
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/// Borrow the downstream indicator.
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pub const fn second(&self) -> &B {
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&self.second
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}
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}
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impl<A, B> Indicator for Chain<A, B>
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where
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A: Indicator<Input = f64, Output = f64>,
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B: Indicator<Input = f64>,
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{
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type Input = f64;
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type Output = B::Output;
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fn update(&mut self, input: f64) -> Option<Self::Output> {
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self.first.update(input).and_then(|v| self.second.update(v))
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}
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fn reset(&mut self) {
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self.first.reset();
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self.second.reset();
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}
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fn warmup_period(&self) -> usize {
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// Conservative upper bound: both stages must warm up.
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self.first.warmup_period() + self.second.warmup_period()
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}
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fn is_ready(&self) -> bool {
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self.first.is_ready() && self.second.is_ready()
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}
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fn name(&self) -> &'static str {
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"Chain"
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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/// A trivial test indicator: identity (passes input through).
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#[derive(Debug, Default)]
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struct Identity {
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seen: bool,
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}
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impl Indicator for Identity {
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type Input = f64;
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type Output = f64;
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fn update(&mut self, input: f64) -> Option<f64> {
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self.seen = true;
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Some(input)
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}
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fn reset(&mut self) {
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self.seen = false;
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}
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fn warmup_period(&self) -> usize {
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0
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}
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fn is_ready(&self) -> bool {
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self.seen
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}
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fn name(&self) -> &'static str {
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"Identity"
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}
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}
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/// Another trivial test indicator: scales input by 2.
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#[derive(Debug, Default)]
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struct Doubler {
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seen: bool,
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}
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impl Indicator for Doubler {
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type Input = f64;
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type Output = f64;
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fn update(&mut self, input: f64) -> Option<f64> {
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self.seen = true;
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Some(input * 2.0)
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}
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fn reset(&mut self) {
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self.seen = false;
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}
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fn warmup_period(&self) -> usize {
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0
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}
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fn is_ready(&self) -> bool {
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self.seen
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}
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fn name(&self) -> &'static str {
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"Doubler"
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}
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}
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#[test]
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fn batch_replays_update() {
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let mut id = Identity::default();
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let out = id.batch(&[1.0, 2.0, 3.0]);
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assert_eq!(out, vec![Some(1.0), Some(2.0), Some(3.0)]);
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}
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#[test]
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fn chain_pipes_first_into_second() {
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let mut c = Chain::new(Doubler::default(), Doubler::default());
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// 5 -> 10 -> 20
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assert_eq!(c.update(5.0), Some(20.0));
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}
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#[test]
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fn chain_is_ready_only_after_both_stages_emit() {
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let mut c = Chain::new(Doubler::default(), Doubler::default());
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assert!(!c.is_ready());
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c.update(1.0);
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assert!(c.is_ready());
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}
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#[test]
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fn chain_reset_propagates() {
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let mut c = Chain::new(Doubler::default(), Doubler::default());
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c.update(1.0);
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assert!(c.is_ready());
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c.reset();
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assert!(!c.is_ready());
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}
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#[test]
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fn chain_three_levels_via_then() {
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let c = Chain::new(Doubler::default(), Doubler::default()).then(Doubler::default());
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let mut c = c;
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// 1 -> 2 -> 4 -> 8
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assert_eq!(c.update(1.0), Some(8.0));
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}
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#[cfg(feature = "parallel")]
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#[test]
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fn batch_parallel_runs_independent_instances() {
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let series: Vec<Vec<f64>> = vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]];
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let out = Doubler::batch_parallel(&series, Doubler::default);
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assert_eq!(out.len(), 2);
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assert_eq!(out[0], vec![Some(2.0), Some(4.0), Some(6.0)]);
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assert_eq!(out[1], vec![Some(8.0), Some(10.0), Some(12.0)]);
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}
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}
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