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perf: bit-exact batch fast paths + streaming-first benchmark docs (#202)

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## Summary
- Dedicated batch fast paths for **EMA, RSI, Bollinger, MACD and ATR** (used by the Python bindings): one allocation filled in a single pass, warmup encoded as `NaN`, no per-element `Option` or input re-validation. Each is **bit-for-bit equal** to replaying `update` — SMA/Bollinger keep the drift-reseed cadence, the EMA-family keep the seed division and `mul_add` recurrences. Adds the `BatchNanExt` extension trait.
- **Cross-library benchmark refresh**: `compare_libraries.py` reports the median across timing rounds (`--rounds` / `--streaming-rounds`), gains `--skip-batch` / `--skip-streaming`, and runs every peer through the streaming arena (recompute for batch-only libraries). `wickra-bench` drives the batch fast paths against `kand`.
- **README** benchmark section reordered streaming-first (the order-of-magnitude result), with measured TA-Lib/tulipy/pandas-ta numbers in place of the CI-only placeholders.

## Impact
- Python batch ~2× faster on EMA/RSI/MACD/ATR; streaming path unchanged.
- The `batch == streaming` equivalence stays bit-exact.

## Verification
- `cargo fmt` · `cargo clippy --workspace --all-targets --all-features -- -D warnings` (clean)
- `cargo test --workspace --all-features` — 3782 unit + 420 doc tests pass
- Python `pytest` — streaming-vs-batch, known-values, input-validation, smoke pass

## Notes
- Node/WASM bindings keep their existing batch; the fast paths are Python-only for now.
This commit is contained in:
kingchenc
2026-06-08 00:17:58 +02:00
committed by GitHub
parent e97c3389fe
commit 05fe7ffa90
14 changed files with 1400 additions and 296 deletions
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crates/wickra-bench/benches/cross_lib.rs
+11 -7
View File
@@ -25,7 +25,7 @@
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use std::hint::black_box;
use wickra::{Atr, BatchExt, BollingerBands, Candle, Ema, Indicator, MacdIndicator, Rsi, Sma};
use wickra::{Atr, BollingerBands, Candle, Ema, Indicator, MacdIndicator, Rsi, Sma};
use wickra_data::csv::CandleReader;
use yata::prelude::Method;
@@ -82,7 +82,7 @@ fn sma_group(crit: &mut Criterion, closes: &[f64]) {
|bencher, &series| {
bencher.iter(|| {
let mut ind = Sma::new(SMA_PERIOD).unwrap();
black_box(ind.batch(series));
black_box(ind.batch_nan(series));
});
},
);
@@ -170,7 +170,7 @@ fn ema_group(crit: &mut Criterion, closes: &[f64]) {
|bencher, &series| {
bencher.iter(|| {
let mut ind = Ema::new(EMA_PERIOD).unwrap();
black_box(ind.batch(series));
black_box(ind.batch_nan(series));
});
},
);
@@ -253,7 +253,7 @@ fn rsi_group(crit: &mut Criterion, closes: &[f64]) {
|bencher, &series| {
bencher.iter(|| {
let mut ind = Rsi::new(RSI_PERIOD).unwrap();
black_box(ind.batch(series));
black_box(ind.batch_nan(series));
});
},
);
@@ -352,7 +352,7 @@ fn macd_group(crit: &mut Criterion, closes: &[f64]) {
|bencher, &series| {
bencher.iter(|| {
let mut ind = MacdIndicator::classic();
black_box(ind.batch(series));
black_box(ind.batch_macd(series));
});
},
);
@@ -478,7 +478,7 @@ fn bbands_group(crit: &mut Criterion, closes: &[f64]) {
|bencher, &series| {
bencher.iter(|| {
let mut ind = BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
black_box(ind.batch(series));
black_box(ind.batch_bands(series));
});
},
);
@@ -604,9 +604,13 @@ fn atr_group(crit: &mut Criterion, candles: &[Candle]) {
BenchmarkId::new("wickra/batch", len),
&series,
|bencher, &series| {
// Column extraction is outside the timed loop, mirroring kand's arm.
let high: Vec<f64> = series.iter().map(|candle| candle.high).collect();
let low: Vec<f64> = series.iter().map(|candle| candle.low).collect();
let close: Vec<f64> = series.iter().map(|candle| candle.close).collect();
bencher.iter(|| {
let mut ind = Atr::new(ATR_PERIOD).unwrap();
black_box(ind.batch(series));
black_box(ind.batch_atr(&high, &low, &close));
});
},
);