Initial commit: MT5 EA Optimizer v1.0
Full optimization system for LEGSTECH_EA_V2: - Flask + SocketIO live dashboard (dark premium UI) - MT5 process control (auto-kill, clean launch, retry) - HTML report parser (UTF-16 LE, 597 trades, metrics) - Pre-run validation and actionable error messages - Analysis engines: Reversal, TimePerfomance, EntryExit, EquityCurve - Composite scoring (Calmar-primary) - Mutation engine with knowledge_base.yaml - Validation gate: IS + Walk-Forward - Reports folder with HTML/CSV per run - Double-click launcher batch file
This commit is contained in:
LEGSTECH Optimizer
@@ -0,0 +1,174 @@
|
||||
"""
|
||||
scoring/composite.py
|
||||
Composite score function for LEGSTECH_EA_V2 / XAUUSD optimization.
|
||||
Primary: Calmar Ratio | Secondary: PF, MFE capture, session stability, recovery.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
from typing import Optional
|
||||
import numpy as np
|
||||
import yaml
|
||||
from loguru import logger
|
||||
|
||||
from data.models import RunMetrics
|
||||
|
||||
|
||||
def _clip_normalize(value: float, lo: float, hi: float) -> float:
|
||||
"""Normalize value to [0, 1] by clipping to [lo, hi]."""
|
||||
if hi <= lo:
|
||||
return 0.0
|
||||
return float(np.clip((value - lo) / (hi - lo), 0.0, 1.0))
|
||||
|
||||
|
||||
def _session_stability(session_stats: dict) -> float:
|
||||
"""
|
||||
Compute session stability as 1 - (std of per-session Calmar / mean Calmar).
|
||||
Returns 1.0 (perfectly stable) if only one session or no variance.
|
||||
session_stats: {session_name: {"calmar": float, "trade_count": int}}
|
||||
"""
|
||||
calmars = [
|
||||
v["calmar"] for v in session_stats.values()
|
||||
if v.get("trade_count", 0) >= 10 and "calmar" in v
|
||||
]
|
||||
if len(calmars) < 2:
|
||||
return 1.0 # not enough sessions to measure stability
|
||||
mean_c = np.mean(calmars)
|
||||
std_c = np.std(calmars)
|
||||
if abs(mean_c) < 0.01:
|
||||
return 0.0
|
||||
cv = std_c / abs(mean_c) # coefficient of variation
|
||||
return float(np.clip(1.0 - cv, 0.0, 1.0))
|
||||
|
||||
|
||||
class CompositeScorer:
|
||||
"""
|
||||
Weighted composite score calculator.
|
||||
|
||||
All sub-scores are independently normalized to [0, 1].
|
||||
Two multiplicative penalties are applied after weighting:
|
||||
- Significance penalty: reduces weight for low trade counts
|
||||
- Reversal penalty : reduces score if reversal rate is high
|
||||
"""
|
||||
|
||||
DEFAULT_WEIGHTS = {
|
||||
"calmar": 0.35,
|
||||
"profit_factor": 0.20,
|
||||
"mfe_capture": 0.20,
|
||||
"session_stability": 0.15,
|
||||
"recovery_factor": 0.10,
|
||||
}
|
||||
|
||||
DEFAULT_NORMALIZATION = {
|
||||
"calmar": {"lo": 0.0, "hi": 4.0},
|
||||
"profit_factor": {"lo": 1.0, "hi": 3.5},
|
||||
"mfe_capture": {"lo": 0.0, "hi": 1.0},
|
||||
"session_stability": {"lo": 0.0, "hi": 1.0},
|
||||
"recovery_factor": {"lo": 0.0, "hi": 6.0},
|
||||
}
|
||||
|
||||
def __init__(self, config_path: str = "config.yaml"):
|
||||
try:
|
||||
with open(config_path) as f:
|
||||
cfg = yaml.safe_load(f)
|
||||
scoring_cfg = cfg.get("scoring", {})
|
||||
self.weights = scoring_cfg.get("weights", self.DEFAULT_WEIGHTS)
|
||||
norm_cfg = scoring_cfg.get("normalization", {})
|
||||
self.norm = {
|
||||
k: norm_cfg.get(k, self.DEFAULT_NORMALIZATION.get(k, {"lo": 0, "hi": 1}))
|
||||
for k in self.DEFAULT_WEIGHTS
|
||||
}
|
||||
self.min_trades = cfg["thresholds"]["min_trades"]
|
||||
self.significance_at = cfg["scoring"].get("significance_trades", 150)
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load scoring config: {e}. Using defaults.")
|
||||
self.weights = self.DEFAULT_WEIGHTS
|
||||
self.norm = self.DEFAULT_NORMALIZATION
|
||||
self.min_trades = 50
|
||||
self.significance_at = 150
|
||||
|
||||
def score(
|
||||
self,
|
||||
metrics: RunMetrics,
|
||||
session_stats: Optional[dict] = None,
|
||||
) -> float:
|
||||
"""
|
||||
Compute composite score for a RunMetrics object.
|
||||
Returns 0.0 if minimum trade count is not met.
|
||||
|
||||
Args:
|
||||
metrics: RunMetrics from the parsed backtest report
|
||||
session_stats: Optional {session: {calmar, trade_count, ...}} for stability
|
||||
"""
|
||||
if metrics.total_trades < self.min_trades:
|
||||
logger.debug(
|
||||
f"Score=0 (trades {metrics.total_trades} < min {self.min_trades})"
|
||||
)
|
||||
return 0.0
|
||||
|
||||
# ── Sub-scores ────────────────────────────────────────────────────────
|
||||
calmar_score = _clip_normalize(
|
||||
metrics.calmar_ratio,
|
||||
**self.norm["calmar"]
|
||||
)
|
||||
pf_score = _clip_normalize(
|
||||
metrics.profit_factor,
|
||||
**self.norm["profit_factor"]
|
||||
)
|
||||
capture_score = _clip_normalize(
|
||||
metrics.avg_mfe_capture if metrics.avg_mfe_capture is not None else 0.5,
|
||||
**self.norm["mfe_capture"]
|
||||
)
|
||||
stab_score = _clip_normalize(
|
||||
_session_stability(session_stats or {}),
|
||||
**self.norm["session_stability"]
|
||||
)
|
||||
recovery_score = _clip_normalize(
|
||||
metrics.recovery_factor,
|
||||
**self.norm["recovery_factor"]
|
||||
)
|
||||
|
||||
# ── Weighted sum ──────────────────────────────────────────────────────
|
||||
raw = (
|
||||
self.weights["calmar"] * calmar_score +
|
||||
self.weights["profit_factor"] * pf_score +
|
||||
self.weights["mfe_capture"] * capture_score +
|
||||
self.weights["session_stability"] * stab_score +
|
||||
self.weights["recovery_factor"] * recovery_score
|
||||
)
|
||||
|
||||
# ── Penalties ─────────────────────────────────────────────────────────
|
||||
# Significance: scale up to 1.0 as trade count reaches significance_at
|
||||
significance = min(1.0, metrics.total_trades / self.significance_at)
|
||||
|
||||
# Reversal penalty: each 10% reversal rate removes 20% of score
|
||||
reversal_penalty = 1.0
|
||||
if metrics.reversal_rate is not None:
|
||||
reversal_penalty = max(0.0, 1.0 - metrics.reversal_rate * 2.0)
|
||||
|
||||
final = raw * significance * reversal_penalty
|
||||
|
||||
logger.debug(
|
||||
f"Score breakdown: calmar={calmar_score:.3f} pf={pf_score:.3f} "
|
||||
f"capture={capture_score:.3f} stab={stab_score:.3f} "
|
||||
f"recovery={recovery_score:.3f} → raw={raw:.3f} "
|
||||
f"× sig={significance:.3f} × rev={reversal_penalty:.3f} = {final:.4f}"
|
||||
)
|
||||
return round(final, 6)
|
||||
|
||||
def breakdown(
|
||||
self,
|
||||
metrics: RunMetrics,
|
||||
session_stats: Optional[dict] = None,
|
||||
) -> dict[str, float]:
|
||||
"""Return the full breakdown of sub-scores for display."""
|
||||
return {
|
||||
"calmar_score": _clip_normalize(metrics.calmar_ratio, **self.norm["calmar"]),
|
||||
"pf_score": _clip_normalize(metrics.profit_factor, **self.norm["profit_factor"]),
|
||||
"capture_score": _clip_normalize(
|
||||
metrics.avg_mfe_capture or 0.5, **self.norm["mfe_capture"]),
|
||||
"stability_score": _clip_normalize(
|
||||
_session_stability(session_stats or {}), **self.norm["session_stability"]),
|
||||
"recovery_score": _clip_normalize(metrics.recovery_factor, **self.norm["recovery_factor"]),
|
||||
"significance": min(1.0, metrics.total_trades / self.significance_at),
|
||||
"reversal_penalty":max(0.0, 1.0 - (metrics.reversal_rate or 0) * 2),
|
||||
"final": self.score(metrics, session_stats),
|
||||
}
|
||||
Reference in New Issue
Block a user