perf: cache backtests by input signature

backtesting/engine.py

@@ -1,9 +1,11 @@
 """Historical backtest engine for Bitcoin Accumulation Zone scoring."""
 
+import copy
 import json
 import logging
 import os
 import sys
+import threading
 from collections import defaultdict
 from datetime import datetime, timedelta
 
@@ -17,6 +19,10 @@ sys.path.insert(0, BASE_DIR)
 
 HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
 CACHE_PATH = os.path.join(BASE_DIR, "data", "cache.json")
+ML_WEIGHTS_PATH = os.path.join(BASE_DIR, "config", "ml_weights.json")
+
+_BACKTEST_CACHE = {}
+_BACKTEST_CACHE_LOCK = threading.Lock()
 
 # Score brackets matching the dashboard assessment levels
 BRACKETS = SCORE_BRACKETS
@@ -398,7 +404,43 @@ def compute_max_drawdown_forward(price_lookup, date, window=90):
     return round(max_dd, 2) if max_dd > 0 else 0
 
 
+def _file_signature(path):
+    """Return a cheap signature that invalidates when an input file changes."""
+    try:
+        stat = os.stat(path)
+        return path, stat.st_mtime_ns, stat.st_size
+    except OSError:
+        return path, None, None
+
+
+def clear_backtest_cache():
+    """Clear memoized backtest results (primarily for explicit refreshes/tests)."""
+    with _BACKTEST_CACHE_LOCK:
+        _BACKTEST_CACHE.clear()
+
+
 def run_backtest(ml_mode=False):
+    """Return an isolated cached result keyed by all material input files."""
+    signature = (
+        bool(ml_mode),
+        _file_signature(HISTORY_PATH),
+        _file_signature(_THRESH_PATH),
+        _file_signature(ML_WEIGHTS_PATH),
+        _file_signature(CACHE_PATH),
+    )
+    with _BACKTEST_CACHE_LOCK:
+        cached = _BACKTEST_CACHE.get(signature)
+    if cached is not None:
+        return copy.deepcopy(cached)
+
+    result = _compute_backtest(ml_mode=ml_mode)
+    with _BACKTEST_CACHE_LOCK:
+        _BACKTEST_CACHE.clear()
+        _BACKTEST_CACHE[signature] = copy.deepcopy(result)
+    return copy.deepcopy(result)
+
+
+def _compute_backtest(ml_mode=False):
     """Run the full backtest and return comprehensive results.
 
     If ml_mode=True, uses ML-optimized metric weights instead of equal weights.