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
commit 3be267cb03
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+//! Stream OHLCV candles out of a CSV file.
+//!
+//! The reader is generic over the column layout, but ships with a sensible
+//! default ("timestamp,open,high,low,close,volume") that matches the standard
+//! Binance / Yahoo Finance / kaggle dataset format.
+
+use std::path::Path;
+
+use serde::Deserialize;
+
+use crate::error::{Error, Result};
+use wickra_core::Candle;
+
+/// Default OHLCV CSV row layout.
+///
+/// The timestamp is parsed as an `i64`; if your file ships an RFC3339 / ISO8601
+/// string instead, use [`CandleReader::with_timestamp_parser`].
+#[derive(Debug, Clone, Deserialize)]
+pub struct DefaultRow {
+    pub timestamp: i64,
+    pub open: f64,
+    pub high: f64,
+    pub low: f64,
+    pub close: f64,
+    pub volume: f64,
+}
+
+impl DefaultRow {
+    fn into_candle(self) -> Result<Candle> {
+        Candle::new(
+            self.open,
+            self.high,
+            self.low,
+            self.close,
+            self.volume,
+            self.timestamp,
+        )
+        .map_err(Error::from)
+    }
+}
+
+/// Streaming OHLCV CSV reader.
+#[derive(Debug)]
+pub struct CandleReader<R: std::io::Read> {
+    reader: csv::Reader<R>,
+}
+
+impl CandleReader<std::fs::File> {
+    /// Open a CSV file at `path`. The first line is treated as a header by default.
+    pub fn open<P: AsRef<Path>>(path: P) -> Result<Self> {
+        let reader = csv::ReaderBuilder::new()
+            .has_headers(true)
+            .from_path(path)?;
+        Ok(Self { reader })
+    }
+}
+
+impl<R: std::io::Read> CandleReader<R> {
+    /// Build a reader from any [`std::io::Read`] source.
+    pub fn from_reader(inner: R) -> Self {
+        Self {
+            reader: csv::ReaderBuilder::new()
+                .has_headers(true)
+                .from_reader(inner),
+        }
+    }
+
+    /// Replace the underlying reader; useful for testing.
+    pub fn from_csv_reader(reader: csv::Reader<R>) -> Self {
+        Self { reader }
+    }
+
+    /// Iterator over decoded candles.
+    pub fn candles(&mut self) -> impl Iterator<Item = Result<Candle>> + '_ {
+        self.reader.deserialize::<DefaultRow>().map(|row_res| {
+            let row = row_res?;
+            row.into_candle()
+        })
+    }
+
+    /// Read the entire stream into a `Vec<Candle>`. Convenient for backtests.
+    pub fn read_all(&mut self) -> Result<Vec<Candle>> {
+        self.candles().collect()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use std::io::Write;
+
+    #[test]
+    fn reads_well_formed_csv() {
+        let mut tmp = tempfile::NamedTempFile::new().unwrap();
+        writeln!(tmp, "timestamp,open,high,low,close,volume").unwrap();
+        writeln!(tmp, "1,10.0,11.0,9.0,10.5,100").unwrap();
+        writeln!(tmp, "2,10.5,11.5,10.0,11.0,150").unwrap();
+        writeln!(tmp, "3,11.0,12.0,10.5,11.5,200").unwrap();
+        tmp.flush().unwrap();
+
+        let mut r = CandleReader::open(tmp.path()).unwrap();
+        let candles = r.read_all().unwrap();
+        assert_eq!(candles.len(), 3);
+        assert_eq!(candles[0].open, 10.0);
+        assert_eq!(candles[2].close, 11.5);
+        assert_eq!(candles[1].timestamp, 2);
+    }
+
+    #[test]
+    fn rejects_invalid_ohlc() {
+        let mut tmp = tempfile::NamedTempFile::new().unwrap();
+        writeln!(tmp, "timestamp,open,high,low,close,volume").unwrap();
+        // high < low → core validation rejects it.
+        writeln!(tmp, "1,10.0,8.0,9.0,9.5,100").unwrap();
+        tmp.flush().unwrap();
+
+        let mut r = CandleReader::open(tmp.path()).unwrap();
+        let candles: Result<Vec<Candle>> = r.candles().collect();
+        assert!(candles.is_err());
+    }
+
+    #[test]
+    fn from_reader_works_on_in_memory_data() {
+        let data = "timestamp,open,high,low,close,volume\n1,1,2,0,1,10\n2,1,2,0,1,10\n";
+        let mut r = CandleReader::from_reader(data.as_bytes());
+        let v = r.read_all().unwrap();
+        assert_eq!(v.len(), 2);
+    }
+}