Initial commit: multi-symbol bot with backtest engine and RSI trend strategy

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

engine/optimizer.py

@@ -0,0 +1,62 @@
+"""
+Grid-search optimizer.
+
+Iterates over a parameter grid, re-instantiates the strategy for each
+combination, runs the backtest on the in-sample slice, and ranks by a
+composite score (Sharpe × expectancy × return).
+"""
+
+from __future__ import annotations
+
+import itertools
+from dataclasses import dataclass
+from typing import Any, Type
+
+import numpy as np
+import pandas as pd
+
+from .backtest import BacktestEngine
+from .metrics  import Metrics, compute
+
+
+@dataclass
+class OptResult:
+    params: dict[str, Any]
+    metrics: Metrics
+    score: float
+
+
+def grid_search(
+    StrategyClass: Type,
+    param_grid: dict[str, list],
+    df: pd.DataFrame,
+    engine: BacktestEngine,
+    *,
+    verbose: bool = False,
+) -> list[OptResult]:
+    """
+    Run every combination of parameters in `param_grid`.
+    Returns results sorted best-first by composite score.
+    """
+    keys   = list(param_grid.keys())
+    values = list(param_grid.values())
+    combos = list(itertools.product(*values))
+
+    results: list[OptResult] = []
+
+    for idx, combo in enumerate(combos):
+        params   = dict(zip(keys, combo))
+        strategy = StrategyClass(**params)
+
+        trades, equity = engine.run(df, strategy)
+        m = compute(trades, equity, engine.initial_balance)
+        s = m.score()
+
+        results.append(OptResult(params=params, metrics=m, score=s))
+
+        if verbose and (idx + 1) % 50 == 0:
+            print(f"  [{idx+1}/{len(combos)}]  best so far: "
+                  f"{max(results, key=lambda r: r.score).score:.4f}")
+
+    results.sort(key=lambda r: r.score, reverse=True)
+    return results