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/keltner.rs

@@ -0,0 +1,142 @@
+//! Keltner Channels.
+
+use crate::error::{Error, Result};
+use crate::indicators::atr::Atr;
+use crate::indicators::ema::Ema;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Keltner Channels output.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct KeltnerOutput {
+    /// Upper band = middle + multiplier * ATR.
+    pub upper: f64,
+    /// Middle band = EMA of typical price.
+    pub middle: f64,
+    /// Lower band = middle - multiplier * ATR.
+    pub lower: f64,
+}
+
+/// Keltner Channels: an EMA centerline with bands sized by ATR.
+#[derive(Debug, Clone)]
+pub struct Keltner {
+    ema: Ema,
+    atr: Atr,
+    multiplier: f64,
+    ema_period: usize,
+    atr_period: usize,
+}
+
+impl Keltner {
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] / [`Error::NonPositiveMultiplier`] on invalid inputs.
+    pub fn new(ema_period: usize, atr_period: usize, multiplier: f64) -> Result<Self> {
+        if !multiplier.is_finite() || multiplier <= 0.0 {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            ema: Ema::new(ema_period)?,
+            atr: Atr::new(atr_period)?,
+            multiplier,
+            ema_period,
+            atr_period,
+        })
+    }
+
+    /// Classic configuration: EMA(20), ATR(10), 2.0x multiplier.
+    pub fn classic() -> Self {
+        Self::new(20, 10, 2.0).expect("classic Keltner parameters are valid")
+    }
+
+    /// Configured `(ema_period, atr_period, multiplier)`.
+    pub const fn periods(&self) -> (usize, usize, f64) {
+        (self.ema_period, self.atr_period, self.multiplier)
+    }
+}
+
+impl Indicator for Keltner {
+    type Input = Candle;
+    type Output = KeltnerOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<KeltnerOutput> {
+        let mid = self.ema.update(candle.typical_price())?;
+        let atr = self.atr.update(candle)?;
+        Some(KeltnerOutput {
+            upper: mid + self.multiplier * atr,
+            middle: mid,
+            lower: mid - self.multiplier * atr,
+        })
+    }
+
+    fn reset(&mut self) {
+        self.ema.reset();
+        self.atr.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.ema_period.max(self.atr_period)
+    }
+
+    fn is_ready(&self) -> bool {
+        self.ema.is_ready() && self.atr.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "KeltnerChannels"
+    }
+}
+
+#[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_market_collapses_bands() {
+        let candles: Vec<Candle> = (0..50).map(|_| c(10.0, 10.0, 10.0)).collect();
+        let mut k = Keltner::new(20, 10, 2.0).unwrap();
+        let last = k.batch(&candles).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last.upper, last.middle, epsilon = 1e-9);
+        assert_relative_eq!(last.lower, last.middle, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn upper_above_middle_above_lower() {
+        let candles: Vec<Candle> = (0..100)
+            .map(|i| {
+                let m = 100.0 + (f64::from(i) * 0.2).sin() * 5.0;
+                c(m + 1.0, m - 1.0, m)
+            })
+            .collect();
+        let mut k = Keltner::classic();
+        for o in k.batch(&candles).into_iter().flatten() {
+            assert!(o.upper >= o.middle);
+            assert!(o.middle >= o.lower);
+        }
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..50)
+            .map(|i| c(f64::from(i) + 1.0, f64::from(i) - 1.0, f64::from(i)))
+            .collect();
+        let mut a = Keltner::classic();
+        let mut b = Keltner::classic();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn rejects_invalid_input() {
+        assert!(Keltner::new(0, 10, 2.0).is_err());
+        assert!(Keltner::new(20, 10, 0.0).is_err());
+        assert!(Keltner::new(20, 10, -1.0).is_err());
+    }
+}