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
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LEGSTECH Optimizer
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"""
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analysis/entry_exit_quality.py
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Scores trade entries and exits using MAE/MFE ratios.
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from data.models import Finding, RunMetrics
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from analysis.base import BaseAnalyzer
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class EntryExitQualityAnalyzer(BaseAnalyzer):
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"""
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Entry/Exit Quality Scorer.
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Uses MAE and MFE to independently assess:
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- Entry quality: how much did price move against us before moving our way?
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- Exit quality : what fraction of the available move did we capture?
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Diagnosis matrix:
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┌─────────────┬──────────────┬──────────────────────────────────────┐
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│entry_quality│ exit_quality │ Diagnosis │
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├─────────────┼──────────────┼──────────────────────────────────────┤
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│ High │ High │ Healthy — no action │
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│ High │ Low │ Good entries, poor exits → trail/TP │
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│ Low │ High │ Bad entries, recovering → entry filt │
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│ Low │ Low │ Systematic issue → both sides broken │
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└─────────────┴──────────────┴──────────────────────────────────────┘
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"""
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name = "entry_exit_quality"
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min_trades = 20
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def __init__(
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self,
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poor_exit_threshold: float = 0.55,
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poor_entry_threshold: float = 0.40,
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good_entry_threshold: float = 0.65,
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):
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self.poor_exit = poor_exit_threshold
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self.poor_entry = poor_entry_threshold
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self.good_entry = good_entry_threshold
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def analyze(self, trades: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
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if "mfe_pips" not in trades.columns or trades["mfe_pips"].isna().all():
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return []
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df = trades[trades["mfe_pips"].notna() & (trades["mfe_pips"] > 0)].copy()
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if len(df) < self.min_trades:
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return []
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# Compute quality scores if not already present
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if "exit_quality" not in df.columns or df["exit_quality"].isna().all():
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df["exit_quality"] = (df["net_pips"] / df["mfe_pips"].clip(lower=0.01)).clip(0, 1)
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if "entry_quality" not in df.columns or df["entry_quality"].isna().all():
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denom = df["mfe_pips"] + df["mae_pips"].fillna(0) + 0.01
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df["entry_quality"] = (1 - df["mae_pips"].fillna(0) / denom).clip(0, 1)
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mean_exit = float(df["exit_quality"].mean())
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mean_entry = float(df["entry_quality"].mean())
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findings = []
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# ── Case 1: Good entries, poor exits ──────────────────────────────
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if mean_exit < self.poor_exit and mean_entry >= self.good_entry:
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z = (self.poor_exit - mean_exit) / max(0.01, df["exit_quality"].std())
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confidence = min(0.95, self._confidence_from_z(z))
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potential = float((df["mfe_pips"] - df["net_pips"].clip(lower=0)).clip(lower=0).mean())
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impact = potential * len(df) * 0.1 # rough dollar estimate
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findings.append(Finding(
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run_id=self.run_id,
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analyzer=self.name,
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description=(
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f"Good entries (quality={mean_entry:.2f}) but poor exits "
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f"(quality={mean_exit:.2f}). "
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f"Capturing only {mean_exit*100:.0f}% of available MFE. "
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f"Consider trailing stop or tighter TP."
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),
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severity=self._severity(confidence, impact, metrics.net_profit),
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confidence=confidence,
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impact_estimate_pnl=impact,
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suggested_params={
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"InpUseTrailing": True,
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"InpTrailStartPips": round(float(df["mfe_pips"].quantile(0.30)), 1),
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"InpTrailStepPips": 10.0,
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},
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evidence={
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"mean_entry_quality": round(mean_entry, 4),
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"mean_exit_quality": round(mean_exit, 4),
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"diagnosis": "good_entry_poor_exit",
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"sample_size": len(df),
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},
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))
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# ── Case 2: Poor entries ───────────────────────────────────────────
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elif mean_entry < self.poor_entry:
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z = (self.poor_entry - mean_entry) / max(0.01, df["entry_quality"].std())
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confidence = min(0.95, self._confidence_from_z(z))
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# Trades with high MAE but positive result still suggest entry timing issue
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high_mae_losers = df[(df["mae_pips"] > df["mfe_pips"]) & (df["net_money"] < 0)]
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impact = abs(float(high_mae_losers["net_money"].sum()))
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findings.append(Finding(
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run_id=self.run_id,
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analyzer=self.name,
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description=(
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f"Poor entry quality ({mean_entry:.2f}): "
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f"significant adverse move before trades become profitable. "
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f"{len(high_mae_losers)} trades had MAE > MFE and closed at a loss. "
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f"Consider tightening entry filters (ATR, EMA slope, score gate)."
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),
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severity=self._severity(confidence, impact, metrics.net_profit),
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confidence=confidence,
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impact_estimate_pnl=impact,
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suggested_params={
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"InpUseATRFilter": True, # placeholder name; map to actual param
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"InpATRMultiplier": round(float(df["mae_pips"].quantile(0.70)) / 100, 1),
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"InpMinScore": 9, # tighten quality gate
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},
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evidence={
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"mean_entry_quality": round(mean_entry, 4),
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"mean_exit_quality": round(mean_exit, 4),
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"diagnosis": "poor_entry",
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"high_mae_loser_count": int(len(high_mae_losers)),
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"sample_size": len(df),
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},
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))
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# ── Case 3: Both broken ────────────────────────────────────────────
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elif mean_exit < self.poor_exit and mean_entry < self.poor_entry:
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confidence = 0.75
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impact = abs(metrics.net_profit) * 0.5 # rough
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findings.append(Finding(
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run_id=self.run_id,
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analyzer=self.name,
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description=(
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f"Both entry ({mean_entry:.2f}) and exit ({mean_exit:.2f}) quality are poor. "
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f"This suggests a systematic issue with the strategy logic. "
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f"Consider testing a different BotMode or EntryMode."
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),
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severity="high",
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confidence=confidence,
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impact_estimate_pnl=impact,
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suggested_params={"InpBotMode": 2}, # conservative mode
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evidence={
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"mean_entry_quality": round(mean_entry, 4),
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"mean_exit_quality": round(mean_exit, 4),
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"diagnosis": "both_broken",
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"sample_size": len(df),
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},
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))
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# ── Always report summary stats as a LOW finding for visibility ───
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findings.append(Finding(
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run_id=self.run_id,
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analyzer=self.name,
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description=(
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f"Entry quality: {mean_entry:.2f} | Exit quality: {mean_exit:.2f} | "
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f"Sample: {len(df)} trades with MFE data."
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),
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severity="low",
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confidence=0.99,
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impact_estimate_pnl=0.0,
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suggested_params={},
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evidence={
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"mean_entry_quality": round(mean_entry, 4),
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"mean_exit_quality": round(mean_exit, 4),
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"sample_size": len(df),
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"diagnosis": "summary",
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},
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))
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return sorted(findings, key=lambda f: f.confidence, reverse=True)
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