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

analysis/base.py

@@ -0,0 +1,86 @@
+"""
+analysis/base.py
+Abstract base class for all analyzer modules.
+"""
+from __future__ import annotations
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from data.models import Finding, RunMetrics
+
+
+class BaseAnalyzer(ABC):
+    """
+    Every analyzer receives a trades DataFrame and RunMetrics,
+    and returns a list of Finding objects sorted by confidence descending.
+    """
+
+    name: str = "base"
+    min_trades: int = 30          # refuse to analyze below this count
+    run_id: str = ""
+
+    def run(
+        self,
+        trades: pd.DataFrame,
+        metrics: RunMetrics,
+        run_id: str,
+    ) -> list[Finding]:
+        """Entry point — enforces minimum trade count gate."""
+        self.run_id = run_id
+        if len(trades) < self.min_trades:
+            return []
+        return self.analyze(trades, metrics)
+
+    @abstractmethod
+    def analyze(self, trades: pd.DataFrame, metrics: RunMetrics) -> list[Finding]:
+        """Implement analysis logic. Return list of findings."""
+        ...
+
+    # ── Statistical helpers ────────────────────────────────────────────────────
+
+    def _confidence_from_z(self, z: float) -> float:
+        """Map a Z-score to a [0,1] confidence value using normal CDF."""
+        from scipy.stats import norm
+        return float(min(1.0, max(0.0, 2 * norm.cdf(abs(z)) - 1)))
+
+    def _permutation_pvalue(
+        self,
+        group_values: np.ndarray,
+        all_values: np.ndarray,
+        n_permutations: int = 500,
+        alternative: str = "less",   # 'less' = testing if group mean < overall mean
+    ) -> float:
+        """
+        Non-parametric permutation test.
+        Returns p-value: probability that observed group mean is due to chance.
+        Lower p-value = more statistically significant.
+        """
+        if len(group_values) == 0 or len(all_values) == 0:
+            return 1.0
+
+        observed_stat = np.mean(group_values)
+        n_group = len(group_values)
+        count_extreme = 0
+
+        rng = np.random.default_rng(seed=42)   # deterministic
+        for _ in range(n_permutations):
+            sample = rng.choice(all_values, size=n_group, replace=False)
+            sample_stat = np.mean(sample)
+            if alternative == "less" and sample_stat <= observed_stat:
+                count_extreme += 1
+            elif alternative == "greater" and sample_stat >= observed_stat:
+                count_extreme += 1
+
+        return count_extreme / n_permutations
+
+    def _severity(self, confidence: float, impact_pnl: float, total_pnl: float) -> str:
+        """Derive severity from confidence and relative $ impact."""
+        impact_fraction = abs(impact_pnl) / max(abs(total_pnl), 1)
+        if confidence >= 0.80 or impact_fraction >= 0.15:
+            return "high"
+        elif confidence >= 0.60 or impact_fraction >= 0.07:
+            return "medium"
+        return "low"