feat: Smart Autonomous 3-Phase Optimizer — complete redesign

PROBLEM: Old system repeated identical params, all scores flat at 0.2500, user had zero control over symbol/TF/dates. Not smart, not dynamic. NEW ARCHITECTURE: optimizer/ (NEW package) ├── __init__.py ├── session_config.py User choices (EA, symbol, TF, dates, budget, objective) ├── lhs_sampler.py Latin Hypercube Sampling — diverse exploration ├── result_ranker.py Relative scoring (best in session=1.0, worst=0.0) ├── budget.py Time budget tracker └── pipeline.py 3-phase orchestrator Phase 1 — Broad Discovery (LHS, 20-26 runs): Samples FULL parameter space, not just defaults±tiny step LHS guarantees coverage: all 17 optimizable LEGSTECH params explored Relative ranking: profitable configs float top, losers score 0 Phase 2 — Refinement (9 runs): Neighbor search around top 3 configs at ±20% range (not ±0.5 step) Keeps best of Phase1 vs Phase2 — never regresses Phase 3 — Validation (5 runs): OOS backtest on unseen data period Sensitivity test: nudge params ±20%, detect fragility Verdict: RECOMMENDED / RISKY / NOT_RELIABLE Output: Clean downloadable .set file via /download_set/<run_id> UI REDESIGN: ui/templates/landing.html New / homepage (was old dashboard) ui/templates/setup.html New /setup — EA, symbol, TF, dates, budget, objective ui/templates/dashboard.html Updated /dashboard with: - 5-step phase indicator - Real progress bar per run - Phase 1 results table (top 5 after phase1) - Verdict banner with download button - No-profitable-config warning ui/static/js/dashboard.js Handles 8 new pipeline SocketIO events app.py New routes: /, /setup, /dashboard /api/start accepts full SessionConfig JSON /download_set/<id> serves optimized .set ea/registry.py +list_all() for setup page dropdown ui/templates/reports_index.html Back to Dashboard → /dashboard (was /) ui/static/css/style.css +dot-warn, dot-done, profit-pos/neg, aliases FIXES: Score no longer flat 0.2500 (was: absolute thresholds on losing EA) User now controls: symbol, timeframe, dates, budget, objective Parameters now span full range (was: tiny step from defaults) Verdict is actionable: RECOMMENDED / RISKY / NOT_RELIABLE with reason TESTED: 8/8 pre-flight checks pass Browser test: landing ✓, setup form ✓, /dashboard ✓, phase indicator active ✓, /reports ✓, back link ✓
2026-04-13 21:14:47 +00:00
parent c4b5fe0ad0
commit 47f6012eb2
14 changed files with 2675 additions and 364 deletions
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+"""
+optimizer/session_config.py
+Holds all user choices for one optimization session.
+Passed from the /setup form → /api/start → pipeline.
+"""
+from __future__ import annotations
+from dataclasses import dataclass, field, asdict
+from typing import Literal, Optional
+
+
+ObjectiveType = Literal["balanced", "max_profit", "min_drawdown"]
+BudgetMinutes = Literal[30, 60, 120]
+
+
+@dataclass
+class SessionConfig:
+    """Everything the user configures on the /setup page."""
+
+    # EA identity
+    ea_name:    str = "LEGSTECH_EA_V2"
+    symbol:     str = "XAUUSD"
+    timeframe:  str = "H1"
+
+    # Training period (in-sample)
+    train_start: str = "2022.01.01"
+    train_end:   str = "2023.12.31"
+
+    # Validation period (out-of-sample)
+    val_start:   str = "2024.01.01"
+    val_end:     str = "2024.06.30"
+
+    # Optimization objective
+    objective:   ObjectiveType = "balanced"
+
+    # Time budget
+    budget_minutes: int = 60       # 30 | 60 | 120
+
+    # Advanced: which params the user wants to optimize
+    # Empty list means "use profile's default optimize_params"
+    selected_params: list[str] = field(default_factory=list)
+
+    # Phase 1 sample count (derived from budget, not user-set directly)
+    phase1_samples: int = 20
+
+    # ── Derived helpers ───────────────────────────────────────────────────────
+
+    def derive_samples(self, seconds_per_run: float = 75.0) -> None:
+        """
+        Automatically set phase1_samples based on time budget.
+        Reserves ~40% of budget for Phase 2 + Phase 3.
+        """
+        total_seconds  = self.budget_minutes * 60
+        phase1_budget  = total_seconds * 0.55        # 55% for broad search
+        n              = int(phase1_budget / seconds_per_run)
+        self.phase1_samples = max(10, min(n, 50))    # clamp 10–50
+
+    @property
+    def phase2_samples(self) -> int:
+        """Refinement runs: top 3 configs × 3 neighbors each = 9."""
+        return 9
+
+    @property
+    def phase3_samples(self) -> int:
+        """Validation: 2 OOS runs + 3 sensitivity = 5."""
+        return 5
+
+    @property
+    def total_budget_runs(self) -> int:
+        return self.phase1_samples + self.phase2_samples + self.phase3_samples
+
+    # ── Scoring weights based on objective ───────────────────────────────────
+
+    @property
+    def scoring_weights(self) -> dict:
+        if self.objective == "max_profit":
+            return {"calmar": 0.3, "profit_factor": 0.3, "win_rate": 0.2, "net_profit": 0.2}
+        if self.objective == "min_drawdown":
+            return {"calmar": 0.6, "profit_factor": 0.25, "win_rate": 0.15, "net_profit": 0.0}
+        # balanced (default)
+        return {"calmar": 0.5, "profit_factor": 0.3, "win_rate": 0.2, "net_profit": 0.0}
+
+    # ── Serialization ─────────────────────────────────────────────────────────
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "SessionConfig":
+        known = {k: v for k, v in d.items() if k in cls.__dataclass_fields__}
+        return cls(**known)
+
+    @classmethod
+    def from_form(cls, form: dict) -> "SessionConfig":
+        """
+        Parse raw form POST data (all values are strings).
+        Handles type coercion, validation, and defaults.
+        """
+        def s(key, default=""): return str(form.get(key, default)).strip()
+        def i(key, default=0):
+            try: return int(form.get(key, default))
+            except (ValueError, TypeError): return default
+
+        cfg = cls(
+            ea_name         = s("ea_name", "LEGSTECH_EA_V2"),
+            symbol          = s("symbol", "XAUUSD").upper(),
+            timeframe       = s("timeframe", "H1").upper(),
+            train_start     = s("train_start", "2022.01.01").replace("-", "."),
+            train_end       = s("train_end", "2023.12.31").replace("-", "."),
+            val_start       = s("val_start", "2024.01.01").replace("-", "."),
+            val_end         = s("val_end", "2024.06.30").replace("-", "."),
+            objective       = s("objective", "balanced"),
+            budget_minutes  = i("budget_minutes", 60),
+            selected_params = form.get("selected_params", []),
+        )
+        cfg.derive_samples()
+        return cfg