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/tema.rs

@@ -0,0 +1,107 @@
+//! Triple Exponential Moving Average (TEMA).
+
+use crate::error::Result;
+use crate::indicators::ema::Ema;
+use crate::traits::Indicator;
+
+/// Triple Exponential Moving Average: `3 * EMA1 - 3 * EMA2 + EMA3`,
+/// where `EMA2 = EMA(EMA1)` and `EMA3 = EMA(EMA2)`.
+///
+/// Reduces lag further than DEMA at the cost of more responsiveness to noise.
+#[derive(Debug, Clone)]
+pub struct Tema {
+    ema1: Ema,
+    ema2: Ema,
+    ema3: Ema,
+    period: usize,
+}
+
+impl Tema {
+    /// # 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)?,
+            period,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for Tema {
+    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)?;
+        Some(3.0 * e1 - 3.0 * e2 + e3)
+    }
+
+    fn reset(&mut self) {
+        self.ema1.reset();
+        self.ema2.reset();
+        self.ema3.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        3 * self.period - 2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.ema3.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "TEMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn constant_series_yields_constant_tema() {
+        let mut tema = Tema::new(5).unwrap();
+        let out = tema.batch(&[42.0_f64; 80]);
+        let last = out.iter().rev().flatten().next().unwrap();
+        assert_relative_eq!(*last, 42.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=80)
+            .map(|i| (f64::from(i) * 0.3).sin() * 10.0)
+            .collect();
+        let mut a = Tema::new(5).unwrap();
+        let mut b = Tema::new(5).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut tema = Tema::new(5).unwrap();
+        tema.batch(&(1..=80).map(f64::from).collect::<Vec<_>>());
+        assert!(tema.is_ready());
+        tema.reset();
+        assert!(!tema.is_ready());
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(Tema::new(0).is_err());
+    }
+}