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fix: remove leakage from legacy ML evaluation

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This commit is contained in:
Hermes Agent
2026-07-26 22:59:21 +00:00
parent aef714d6c7
commit 81654b5743
6 changed files with 240 additions and 34 deletions
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orchestrator.py
+15 -7
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@@ -28,7 +28,7 @@ MAC_MINI_HOST = "bizzle@bizzles-mac-mini-1"
MAX_ITERATIONS = 50
CONVERGENCE_WINDOW = 5
CONVERGENCE_THRESHOLD = 0.01 # 1% improvement
TARGET_COST_IMPROVEMENT = 20.0 # 20% cost basis improvement = exceptional
TARGET_COST_IMPROVEMENT = 20.0 # Backward-compatible name: terminal wealth objective
MIN_SIGNAL_COUNT = 30 # Minimum strong buy signals for valid results
ML_TIMEOUT = 600 # 10 minutes
@@ -49,6 +49,11 @@ def log(msg, color=""):
print(f"{C.DIM}[{ts}]{C.RESET} {color}{msg}{C.RESET}")
def objective_score(results):
"""Return the equal-capital portfolio objective used for model selection."""
return float(results.get("terminal_wealth_improvement_pct", 0.0))
def run_cmd(cmd, timeout=120, check=True):
"""Run a shell command and return stdout."""
result = subprocess.run(
@@ -160,11 +165,12 @@ def print_header():
def print_results(results, iteration):
cost_imp = results.get("cost_basis_improvement_pct", 0)
color = C.GREEN if cost_imp > 15 else C.YELLOW if cost_imp > 10 else C.RED
objective = objective_score(results)
color = C.GREEN if objective > 15 else C.YELLOW if objective > 10 else C.RED
print(f"""
{C.BOLD}--- Iteration {iteration} Results ---{C.RESET}
Cost Improvement: {color}{C.BOLD}{cost_imp:.1f}%{C.RESET}
Terminal Wealth vs DCA: {color}{C.BOLD}{objective:.1f}%{C.RESET}
Legacy Cost Basis Delta: {results.get('cost_basis_improvement_pct', 0):.1f}%
Avg Cost (Model): ${results.get('avg_cost_basis_model', 0):,.2f}
Avg Cost (DCA): ${results.get('avg_cost_basis_dca', 0):,.2f}
Strong Signals: {results.get('strong_buy_signal_count', 0)}
@@ -244,7 +250,7 @@ def main():
print_results(results, iteration)
current_score = results.get("cost_basis_improvement_pct", 0)
current_score = objective_score(results)
signal_count = results.get("strong_buy_signal_count", 0)
is_best = current_score > best_score and signal_count >= MIN_SIGNAL_COUNT
@@ -252,12 +258,14 @@ def main():
best_score = current_score
with open(best_config_path, "w") as f:
json.dump(config, f, indent=2)
log(f"NEW BEST! Cost Improvement: {best_score:.1f}%", f"{C.BOLD}{C.GREEN}")
log(f"NEW BEST! Terminal Wealth Improvement: {best_score:.1f}%", f"{C.BOLD}{C.GREEN}")
iter_data = {
"iteration": iteration,
"timestamp": datetime.now(timezone.utc).isoformat(),
"cost_improvement": current_score,
"objective_improvement": current_score,
"objective": "equal_periodic_contribution_terminal_wealth",
"avg_30d_return": results.get("avg_quality_score_strong_buy", 0),
"avg_90d_return": results.get("pct_quality_strong_buy", 0),
"signal_count": signal_count,
@@ -312,7 +320,7 @@ def main():
========================================================{C.RESET}
Total Iterations: {len(history)}
Best Cost Improvement: {C.BOLD}{best_score:.1f}%{C.RESET}
Best Terminal Wealth Improvement: {C.BOLD}{best_score:.1f}%{C.RESET}
Best Config: {best_config_path}
Iteration Log: {ITERATIONS_LOG}
""")