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wickra/crates/wickra-core/src/indicators/trix.rs
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kingchenc 3be267cb03 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.
2026-05-21 17:50:45 +02:00

132 lines
3.3 KiB
Rust

//! TRIX: triple-smoothed EMA percent rate of change.
use crate::error::Result;
use crate::indicators::ema::Ema;
use crate::traits::Indicator;
/// TRIX: the 1-period percent rate of change of a triple-smoothed EMA.
///
/// `TRIX = 100 * (TR_t - TR_{t-1}) / TR_{t-1}` where
/// `TR_t = EMA(EMA(EMA(price)))`.
#[derive(Debug, Clone)]
pub struct Trix {
ema1: Ema,
ema2: Ema,
ema3: Ema,
prev_tr: Option<f64>,
period: usize,
}
impl Trix {
/// # Errors
/// Returns [`crate::Error::PeriodZero`] if `period == 0`.
pub fn new(period: usize) -> Result<Self> {
Ok(Self {
ema1: Ema::new(period)?,
ema2: Ema::new(period)?,
ema3: Ema::new(period)?,
prev_tr: None,
period,
})
}
/// Configured period.
pub const fn period(&self) -> usize {
self.period
}
}
impl Indicator for Trix {
type Input = f64;
type Output = f64;
fn update(&mut self, input: f64) -> Option<f64> {
let e1 = self.ema1.update(input)?;
let e2 = self.ema2.update(e1)?;
let e3 = self.ema3.update(e2)?;
match self.prev_tr {
Some(prev) if prev != 0.0 => {
let trix = 100.0 * (e3 - prev) / prev;
self.prev_tr = Some(e3);
Some(trix)
}
Some(_) => {
self.prev_tr = Some(e3);
Some(0.0)
}
None => {
self.prev_tr = Some(e3);
None
}
}
}
fn reset(&mut self) {
self.ema1.reset();
self.ema2.reset();
self.ema3.reset();
self.prev_tr = None;
}
fn warmup_period(&self) -> usize {
// Triple EMA seeds at 3*period-2; plus one extra for the rate of change.
3 * self.period - 1
}
fn is_ready(&self) -> bool {
self.prev_tr.is_some() && self.ema3.is_ready()
}
fn name(&self) -> &'static str {
"TRIX"
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::traits::BatchExt;
use approx::assert_relative_eq;
#[test]
fn constant_series_yields_zero_trix() {
let mut trix = Trix::new(5).unwrap();
let out = trix.batch(&[100.0_f64; 80]);
let last = out.iter().rev().flatten().next().unwrap();
assert_relative_eq!(*last, 0.0, epsilon = 1e-9);
}
#[test]
fn rising_series_eventually_positive_trix() {
let prices: Vec<f64> = (1..=200).map(f64::from).collect();
let mut trix = Trix::new(5).unwrap();
let last = trix.batch(&prices).into_iter().flatten().last().unwrap();
assert!(last > 0.0);
}
#[test]
fn batch_equals_streaming() {
let prices: Vec<f64> = (1..=80).map(|i| f64::from(i) * 1.3).collect();
let mut a = Trix::new(7).unwrap();
let mut b = Trix::new(7).unwrap();
assert_eq!(
a.batch(&prices),
prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
);
}
#[test]
fn reset_clears_state() {
let mut trix = Trix::new(5).unwrap();
trix.batch(&(1..=80).map(f64::from).collect::<Vec<_>>());
assert!(trix.is_ready());
trix.reset();
assert!(!trix.is_ready());
}
#[test]
fn rejects_zero_period() {
assert!(Trix::new(0).is_err());
}
}