扩展指标

benches/backtest_benchmark.rs

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+//! Benchmark for RaptorBT backtesting performance.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use raptorbt::core::types::{BacktestConfig, CompiledSignals, Direction, OhlcvData};
+use raptorbt::indicators::trend::{ema, sma};
+use raptorbt::portfolio::engine::PortfolioEngine;
+
+/// Generate sample OHLCV data.
+fn generate_sample_data(n: usize) -> OhlcvData {
+    let mut open = vec![100.0; n];
+    let mut high = vec![101.0; n];
+    let mut low = vec![99.0; n];
+    let mut close = vec![100.0; n];
+
+    // Create a trending pattern
+    for i in 1..n {
+        let change = (i as f64 * 0.1).sin() * 2.0;
+        close[i] = close[i - 1] + change;
+        open[i] = close[i - 1];
+        high[i] = close[i].max(open[i]) + 1.0;
+        low[i] = close[i].min(open[i]) - 1.0;
+    }
+
+    OhlcvData {
+        timestamps: (0..n as i64).collect(),
+        open,
+        high,
+        low,
+        close,
+        volume: vec![1000.0; n],
+    }
+}
+
+/// Generate sample trading signals based on SMA crossover.
+fn generate_sample_signals(
+    close: &[f64],
+    fast_period: usize,
+    slow_period: usize,
+) -> CompiledSignals {
+    let n = close.len();
+    let fast_sma = sma(close, fast_period).unwrap_or_else(|_| vec![0.0; n]);
+    let slow_sma = sma(close, slow_period).unwrap_or_else(|_| vec![0.0; n]);
+
+    let mut entries = vec![false; n];
+    let mut exits = vec![false; n];
+
+    for i in 1..n {
+        // Entry: fast crosses above slow
+        if fast_sma[i] > slow_sma[i] && fast_sma[i - 1] <= slow_sma[i - 1] {
+            entries[i] = true;
+        }
+        // Exit: fast crosses below slow
+        if fast_sma[i] < slow_sma[i] && fast_sma[i - 1] >= slow_sma[i - 1] {
+            exits[i] = true;
+        }
+    }
+
+    CompiledSignals {
+        symbol: "BENCH".to_string(),
+        entries,
+        exits,
+        position_sizes: None,
+        direction: Direction::Long,
+        weight: 1.0,
+    }
+}
+
+fn bench_single_backtest(c: &mut Criterion) {
+    let mut group = c.benchmark_group("single_backtest");
+
+    for size in [1000, 5000, 10000, 50000].iter() {
+        group.bench_with_input(BenchmarkId::new("bars", size), size, |b, &size| {
+            let ohlcv = generate_sample_data(size);
+            let signals = generate_sample_signals(&ohlcv.close, 10, 30);
+            let config = BacktestConfig::default();
+            let engine = PortfolioEngine::new(config);
+
+            b.iter(|| {
+                let result = engine.run_single(black_box(&ohlcv), black_box(&signals));
+                black_box(result)
+            });
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_sma(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sma");
+
+    for size in [1000, 5000, 10000, 50000].iter() {
+        group.bench_with_input(BenchmarkId::new("data_size", size), size, |b, &size| {
+            let ohlcv = generate_sample_data(size);
+
+            b.iter(|| {
+                let result = sma(black_box(&ohlcv.close), black_box(20));
+                black_box(result)
+            });
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_ema(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ema");
+
+    for size in [1000, 5000, 10000, 50000].iter() {
+        group.bench_with_input(BenchmarkId::new("data_size", size), size, |b, &size| {
+            let ohlcv = generate_sample_data(size);
+
+            b.iter(|| {
+                let result = ema(black_box(&ohlcv.close), black_box(20));
+                black_box(result)
+            });
+        });
+    }
+
+    group.finish();
+}
+
+criterion_group!(benches, bench_single_backtest, bench_sma, bench_ema);
+criterion_main!(benches);