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/kama.rs

@@ -0,0 +1,157 @@
+//! Kaufman's Adaptive Moving Average (KAMA).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Kaufman's Adaptive Moving Average.
+///
+/// KAMA adapts its smoothing constant to volatility: efficient (trending) markets
+/// get a fast smoothing constant, choppy markets get a slow one. Parameters are
+/// the efficiency-ratio lookback (`er_period`, default 10), the fast EMA period
+/// (`fast`, default 2) and the slow EMA period (`slow`, default 30).
+#[derive(Debug, Clone)]
+pub struct Kama {
+    er_period: usize,
+    fast_sc: f64,
+    slow_sc: f64,
+    window: VecDeque<f64>,
+    state: Option<f64>,
+}
+
+impl Kama {
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] / [`Error::InvalidPeriod`] for bad parameters.
+    pub fn new(er_period: usize, fast: usize, slow: usize) -> Result<Self> {
+        if er_period == 0 || fast == 0 || slow == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if fast >= slow {
+            return Err(Error::InvalidPeriod {
+                message: "KAMA fast period must be strictly less than slow",
+            });
+        }
+        let fast_sc = 2.0 / (fast as f64 + 1.0);
+        let slow_sc = 2.0 / (slow as f64 + 1.0);
+        Ok(Self {
+            er_period,
+            fast_sc,
+            slow_sc,
+            window: VecDeque::with_capacity(er_period + 1),
+            state: None,
+        })
+    }
+
+    /// Classic Kaufman parameters: (10, 2, 30).
+    pub fn classic() -> Self {
+        Self::new(10, 2, 30).expect("classic KAMA parameters are valid")
+    }
+
+    /// Configured `(er_period, fast, slow)` periods.
+    pub fn periods(&self) -> (usize, f64, f64) {
+        (self.er_period, self.fast_sc, self.slow_sc)
+    }
+}
+
+impl Indicator for Kama {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.state;
+        }
+        if self.window.len() == self.er_period + 1 {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+
+        if self.window.len() < self.er_period + 1 {
+            return None;
+        }
+
+        let first = *self.window.front().expect("non-empty");
+        let last = *self.window.back().expect("non-empty");
+        let direction = (last - first).abs();
+        let volatility: f64 = self
+            .window
+            .iter()
+            .zip(self.window.iter().skip(1))
+            .map(|(a, b)| (b - a).abs())
+            .sum();
+
+        let er = if volatility == 0.0 {
+            0.0
+        } else {
+            direction / volatility
+        };
+        let sc = (er * (self.fast_sc - self.slow_sc) + self.slow_sc).powi(2);
+
+        let prev = self.state.unwrap_or(first);
+        let new = prev + sc * (input - prev);
+        self.state = Some(new);
+        Some(new)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.state = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.er_period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.state.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "KAMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn constant_series_yields_constant_kama() {
+        let mut k = Kama::classic();
+        let out = k.batch(&[100.0_f64; 100]);
+        let last = out.iter().rev().flatten().next().unwrap();
+        assert_relative_eq!(*last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn rejects_invalid_periods() {
+        assert!(Kama::new(0, 2, 30).is_err());
+        assert!(Kama::new(10, 30, 2).is_err()); // fast >= slow
+        assert!(Kama::new(10, 2, 2).is_err()); // fast == slow
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| (f64::from(i) * 0.2).sin() * 5.0 + f64::from(i) * 0.1)
+            .collect();
+        let mut a = Kama::classic();
+        let mut b = Kama::classic();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut k = Kama::classic();
+        k.batch(&(1..=50).map(f64::from).collect::<Vec<_>>());
+        assert!(k.is_ready());
+        k.reset();
+        assert!(!k.is_ready());
+    }
+}