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.

crates/wickra-core/src/indicators/cci.rs

@@ -0,0 +1,150 @@
+//! Commodity Channel Index (CCI).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Commodity Channel Index.
+///
+/// `CCI = (TP - SMA(TP)) / (0.015 * mean absolute deviation of TP)`, where
+/// `TP = (high + low + close) / 3`.
+#[derive(Debug, Clone)]
+pub struct Cci {
+    period: usize,
+    factor: f64,
+    window: VecDeque<f64>,
+    sum: f64,
+}
+
+impl Cci {
+    /// Construct a new CCI with the canonical 0.015 scaling factor.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Self::with_factor(period, 0.015)
+    }
+
+    /// Construct a CCI with a custom scaling factor (the standard literature
+    /// uses 0.015 to put roughly 70 % of values inside ±100).
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0` and
+    /// [`Error::NonPositiveMultiplier`] if `factor <= 0`.
+    pub fn with_factor(period: usize, factor: f64) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !factor.is_finite() || factor <= 0.0 {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            period,
+            factor,
+            window: VecDeque::with_capacity(period),
+            sum: 0.0,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for Cci {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let tp = candle.typical_price();
+        if self.window.len() == self.period {
+            let old = self.window.pop_front().expect("non-empty");
+            self.sum -= old;
+        }
+        self.window.push_back(tp);
+        self.sum += tp;
+        if self.window.len() < self.period {
+            return None;
+        }
+        let n = self.period as f64;
+        let mean = self.sum / n;
+        let mad: f64 = self.window.iter().map(|v| (v - mean).abs()).sum::<f64>() / n;
+        if mad == 0.0 {
+            return Some(0.0);
+        }
+        Some((tp - mean) / (self.factor * mad))
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.sum = 0.0;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "CCI"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn c(h: f64, l: f64, cl: f64) -> Candle {
+        Candle::new(cl, h, l, cl, 1.0, 0).unwrap()
+    }
+
+    #[test]
+    fn flat_candles_yield_zero() {
+        let candles: Vec<Candle> = (0..30).map(|_| c(10.0, 10.0, 10.0)).collect();
+        let mut cci = Cci::new(20).unwrap();
+        for v in cci.batch(&candles).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn rejects_invalid_input() {
+        assert!(Cci::new(0).is_err());
+        assert!(Cci::with_factor(20, 0.0).is_err());
+        assert!(Cci::with_factor(20, -1.0).is_err());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                let m = 50.0 + (f64::from(i) * 0.2).sin() * 10.0;
+                c(m + 1.0, m - 1.0, m)
+            })
+            .collect();
+        let mut a = Cci::new(20).unwrap();
+        let mut b = Cci::new(20).unwrap();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let candles: Vec<Candle> = (0..30).map(|_| c(10.0, 10.0, 10.0)).collect();
+        let mut cci = Cci::new(20).unwrap();
+        cci.batch(&candles);
+        assert!(cci.is_ready());
+        cci.reset();
+        assert!(!cci.is_ready());
+    }
+}