new: optimise.rs which handles optimisation. also linked into the python

examples/optimize.py

@@ -0,0 +1,58 @@
+"""
+Grid-search an SMA crossover over fast/slow periods.
+
+The strategy is written as a *signal function* rather than a Strategy subclass:
+it returns the target lot size for every bar in one vectorised pass. That runs
+once per parameter combination, so the per-bar work happens entirely in Rust
+and the whole grid runs on parallel threads.
+"""
+
+import time
+
+import numpy as np
+import pandas as pd
+
+from backtestingfx import Backtest
+
+LOT = 0.1
+
+
+def sma_cross(df, fast, slow):
+    """Long LOT lots while the fast SMA is above the slow one, otherwise flat."""
+    fast_sma = df["close"].rolling(fast).mean()
+    slow_sma = df["close"].rolling(slow).mean()
+    # NaN during warmup compares False, so the untradeable head comes out flat
+    return np.where(fast_sma > slow_sma, LOT, 0.0)
+
+
+df = pd.read_csv("data/EURUSD_1H.csv")
+
+backtest = Backtest(df, cash=10_000, commission=3.5, spread=0.00002)
+
+fast_range = range(5, 26)
+slow_range = range(30, 101, 5)
+combos = len(fast_range) * len(slow_range)
+
+start = time.perf_counter()
+results = backtest.optimize(
+    sma_cross,
+    maximize="total_return_pct",
+    fast=fast_range,
+    slow=slow_range,
+)
+elapsed = time.perf_counter() - start
+
+print(f"{combos} combinations over {len(df):,} bars in {elapsed:.2f}s "
+      f"({combos * len(df) / elapsed / 1e6:.1f}M bar-sims/sec)\n")
+
+print(f"{'fast':>6}{'slow':>6}{'return %':>12}{'trades':>9}{'win %':>8}{'max dd %':>10}")
+print("-" * 51)
+for params, stats in results[:10]:
+    print(
+        f"{params['fast']:>6}{params['slow']:>6}{stats.total_return_pct:>12.2f}"
+        f"{stats.num_trades:>9}{stats.win_rate_pct:>8.1f}{stats.max_drawdown_pct:>10.2f}"
+    )
+
+best_params, best_stats = results[0]
+print(f"\nBest: {best_params}")
+print(best_stats)