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 +1,189 @@
|
||||
"""
|
||||
analysis/reversal.py
|
||||
Detects trades that went into significant profit but ultimately closed as losses.
|
||||
Measures profit giveback and proposes trailing / TP tightening adjustments.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from data.models import Finding, RunMetrics
|
||||
from analysis.base import BaseAnalyzer
|
||||
|
||||
|
||||
class ReversalAnalyzer(BaseAnalyzer):
|
||||
"""
|
||||
Profit Reversal Detector.
|
||||
|
||||
A "reversal" trade is one that:
|
||||
- Closed at a loss (net_money < 0)
|
||||
- Had MFE >= threshold pips (i.e. was at significant unrealised profit at some point)
|
||||
|
||||
Also flags trades that won but captured less than X% of their MFE (partial giveback).
|
||||
"""
|
||||
|
||||
name = "reversal"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mfe_threshold_pips: float = 15.0,
|
||||
min_reversal_rate: float = 0.15,
|
||||
poor_capture_rate: float = 0.55,
|
||||
permutation_n: int = 500,
|
||||
):
|
||||
self.mfe_threshold = mfe_threshold_pips
|
||||
self.min_reversal_rate = min_reversal_rate
|
||||
self.poor_capture_rate = poor_capture_rate
|
||||
self.permutation_n = permutation_n
|
||||
|
||||
def analyze(self, trades: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
|
||||
findings = []
|
||||
|
||||
has_mfe = "mfe_pips" in trades.columns and trades["mfe_pips"].notna().sum() > 10
|
||||
|
||||
if has_mfe:
|
||||
findings += self._check_reversals(trades, metrics)
|
||||
findings += self._check_capture_rate(trades, metrics)
|
||||
else:
|
||||
# Without MFE, do a simpler check using result_class if available
|
||||
if "result_class" in trades.columns:
|
||||
findings += self._check_result_classes(trades, metrics)
|
||||
|
||||
return sorted(findings, key=lambda f: f.confidence, reverse=True)
|
||||
|
||||
# ── Reversal rate check ───────────────────────────────────────────────────
|
||||
|
||||
def _check_reversals(self, df: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
|
||||
"""Find losing trades that had significant unrealised profit."""
|
||||
losers = df[df["net_money"] < 0]
|
||||
if len(losers) == 0:
|
||||
return []
|
||||
|
||||
reversals = losers[losers["mfe_pips"] >= self.mfe_threshold]
|
||||
rate = len(reversals) / len(losers)
|
||||
|
||||
if rate < self.min_reversal_rate or len(reversals) < 5:
|
||||
return []
|
||||
|
||||
avg_giveback_pips = float(reversals["mfe_pips"].mean())
|
||||
total_lost = float(losers["net_money"].sum())
|
||||
reversal_lost = float(reversals["net_money"].sum())
|
||||
|
||||
# Permutation test: is the reversal rate unusually high?
|
||||
all_mfe = df[df["net_money"] < 0]["mfe_pips"].dropna().values
|
||||
if len(all_mfe) > 0:
|
||||
p_val = self._permutation_pvalue(
|
||||
reversals["mfe_pips"].values, all_mfe,
|
||||
n_permutations=self.permutation_n, alternative="greater"
|
||||
)
|
||||
else:
|
||||
p_val = 0.01 # assume significant
|
||||
|
||||
confidence = max(0.0, min(1.0, 1 - p_val))
|
||||
impact_pnl = abs(reversal_lost) # upper bound on recoverable PnL
|
||||
|
||||
# Compute median reversal time for trailing stop suggestion
|
||||
if "duration_minutes" in df.columns:
|
||||
median_dur = float(reversals["duration_minutes"].median())
|
||||
else:
|
||||
median_dur = 0
|
||||
|
||||
# Compute 25th percentile of MFE at reversal — suggest TrailStart at this level
|
||||
mfe_p25 = float(reversals["mfe_pips"].quantile(0.25))
|
||||
|
||||
suggested = {
|
||||
"InpUseTrailing": True,
|
||||
"InpTrailStartPips": round(max(10.0, mfe_p25 * 0.85), 1),
|
||||
"InpTrailStepPips": 10.0,
|
||||
}
|
||||
|
||||
return [Finding(
|
||||
run_id=self.run_id,
|
||||
analyzer=self.name,
|
||||
description=(
|
||||
f"{rate*100:.0f}% of losing trades had MFE ≥ {self.mfe_threshold:.0f} pips "
|
||||
f"before reversing ({len(reversals)} trades). "
|
||||
f"Avg giveback: {avg_giveback_pips:.1f} pips. "
|
||||
f"Estimated recoverable PnL: ${impact_pnl:.0f}."
|
||||
),
|
||||
severity=self._severity(confidence, impact_pnl, metrics.net_profit),
|
||||
confidence=confidence,
|
||||
impact_estimate_pnl=impact_pnl,
|
||||
suggested_params=suggested,
|
||||
evidence={
|
||||
"reversal_count": len(reversals),
|
||||
"reversal_rate": round(rate, 4),
|
||||
"avg_giveback_pips": round(avg_giveback_pips, 2),
|
||||
"mfe_p25": round(mfe_p25, 2),
|
||||
"median_duration_min": round(median_dur, 0),
|
||||
"p_value": round(p_val, 4),
|
||||
},
|
||||
)]
|
||||
|
||||
# ── MFE Capture rate check ────────────────────────────────────────────────
|
||||
|
||||
def _check_capture_rate(self, df: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
|
||||
"""Check if winning trades are capturing enough of their MFE."""
|
||||
winners = df[(df["net_money"] > 0) & df["mfe_pips"].notna() & (df["mfe_pips"] > 0)]
|
||||
if len(winners) < 10:
|
||||
return []
|
||||
|
||||
if "exit_quality" not in df.columns or df["exit_quality"].isna().all():
|
||||
winners = winners.copy()
|
||||
winners["exit_quality"] = winners["net_pips"] / winners["mfe_pips"].clip(lower=0.01)
|
||||
|
||||
mean_capture = float(winners["exit_quality"].mean())
|
||||
if mean_capture >= self.poor_capture_rate:
|
||||
return []
|
||||
|
||||
potential_gain = float(
|
||||
(winners["mfe_pips"] - winners["net_pips"]).clip(lower=0).mean()
|
||||
) * float(winners["lot_size"].mean()) * 100 # rough $
|
||||
|
||||
confidence = self._confidence_from_z(
|
||||
(self.poor_capture_rate - mean_capture) / max(0.01, winners["exit_quality"].std())
|
||||
)
|
||||
|
||||
return [Finding(
|
||||
run_id=self.run_id,
|
||||
analyzer=self.name,
|
||||
description=(
|
||||
f"Winners capture only {mean_capture*100:.0f}% of their MFE on average. "
|
||||
f"Potential gain with better exits: ~${potential_gain*len(winners):.0f}."
|
||||
),
|
||||
severity=self._severity(confidence, potential_gain * len(winners), metrics.net_profit),
|
||||
confidence=min(0.95, confidence),
|
||||
impact_estimate_pnl=potential_gain * len(winners),
|
||||
suggested_params={
|
||||
"InpUseTrailing": True,
|
||||
"InpTrailStartPips": round(float(winners["mfe_pips"].quantile(0.30)), 1),
|
||||
},
|
||||
evidence={
|
||||
"mean_capture_ratio": round(mean_capture, 4),
|
||||
"winner_count": len(winners),
|
||||
},
|
||||
)]
|
||||
|
||||
# ── Fallback: result_class based ─────────────────────────────────────────
|
||||
|
||||
def _check_result_classes(self, df: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
|
||||
"""Simple reversal check using pre-classified result_class column."""
|
||||
reversals = df[df["result_class"] == "reversal"]
|
||||
losers = df[df["net_money"] < 0]
|
||||
if len(losers) == 0 or len(reversals) == 0:
|
||||
return []
|
||||
|
||||
rate = len(reversals) / len(losers)
|
||||
if rate < self.min_reversal_rate:
|
||||
return []
|
||||
|
||||
return [Finding(
|
||||
run_id=self.run_id,
|
||||
analyzer=self.name,
|
||||
description=f"{rate*100:.0f}% of losers classified as reversals (MFE-based).",
|
||||
severity="medium",
|
||||
confidence=0.65,
|
||||
impact_estimate_pnl=abs(float(reversals["net_money"].sum())),
|
||||
suggested_params={"InpUseTrailing": True},
|
||||
evidence={"reversal_rate": round(rate, 4)},
|
||||
)]
|
||||
Reference in New Issue
Block a user