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 ✓
This commit is contained in:
LEGSTECH Optimizer
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"""optimizer/ — Smart 3-Phase EA Optimization Pipeline"""
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"""
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optimizer/budget.py
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Time budget manager — tracks elapsed time and estimates remaining runs.
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Updates its average-run-time estimate after every completed run.
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"""
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from __future__ import annotations
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import time
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from loguru import logger
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class BudgetManager:
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"""
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Tracks the time budget for an optimization session.
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Usage:
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budget = BudgetManager(budget_minutes=60)
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budget.start()
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...after each run:
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budget.record_run(elapsed_seconds)
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if budget.is_exhausted():
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break
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remaining = budget.estimated_runs_remaining()
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"""
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def __init__(self, budget_minutes: int, initial_seconds_per_run: float = 75.0):
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self.budget_seconds = budget_minutes * 60
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self.avg_seconds_per_run = initial_seconds_per_run
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self._start_ts: float = None
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self._run_times: list[float] = []
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def start(self) -> None:
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self._start_ts = time.time()
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logger.info(f"BudgetManager: started. Budget = {self.budget_seconds/60:.0f} min")
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@property
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def elapsed_seconds(self) -> float:
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if self._start_ts is None:
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return 0.0
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return time.time() - self._start_ts
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@property
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def remaining_seconds(self) -> float:
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return max(0.0, self.budget_seconds - self.elapsed_seconds)
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@property
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def elapsed_pct(self) -> float:
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return min(100.0, self.elapsed_seconds / self.budget_seconds * 100)
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def record_run(self, seconds: float) -> None:
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"""Call after each run completes. Updates rolling average."""
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self._run_times.append(seconds)
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# Rolling average (last 5 runs for responsiveness)
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recent = self._run_times[-5:]
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self.avg_seconds_per_run = sum(recent) / len(recent)
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def estimated_runs_remaining(self) -> int:
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"""How many more runs fit in the remaining budget."""
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if self.avg_seconds_per_run <= 0:
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return 0
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return max(0, int(self.remaining_seconds / self.avg_seconds_per_run))
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def is_exhausted(self) -> bool:
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"""True when less than 1 average run's time remains."""
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return self.remaining_seconds < self.avg_seconds_per_run
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def can_fit(self, n_runs: int) -> bool:
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"""True if n_runs more runs fit in remaining budget."""
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return self.remaining_seconds >= n_runs * self.avg_seconds_per_run
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def summary(self) -> str:
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return (
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f"Budget: {self.elapsed_seconds/60:.1f}/{self.budget_seconds/60:.0f} min used. "
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f"Avg run: {self.avg_seconds_per_run:.0f}s. "
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f"Est. remaining: {self.estimated_runs_remaining()} runs."
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)
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"""
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optimizer/lhs_sampler.py
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Latin Hypercube Sampling — generates diverse parameter sets that
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span the FULL optimization range with minimum samples.
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Why LHS instead of random:
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With 20 samples and 8 parameters:
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- Pure random: clusters near the center, misses extremes
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- LHS: divides each parameter into 20 equal bands, samples exactly
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one value per band — GUARANTEED coverage of the full space.
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Result: 20 LHS samples cover the space better than 200 random samples.
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"""
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from __future__ import annotations
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import random
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import math
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from typing import Any
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from loguru import logger
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from ea.schema import ParameterSchema, ParameterDef
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class LatinHypercubeSampler:
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"""
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Generates n_samples diverse parameter dicts from a ParameterSchema.
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Usage:
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sampler = LatinHypercubeSampler(seed=42)
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samples = sampler.sample(schema, n_samples=20)
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# → list of 20 dicts, each covering different regions
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Each sample is a complete param dict (all params, optimizable + fixed).
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Fixed params always take their automation-safe default value.
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"""
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def __init__(self, seed: int = None):
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self.rng = random.Random(seed)
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def sample(self, schema: ParameterSchema, n_samples: int) -> list[dict[str, Any]]:
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"""
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Generate n_samples diverse parameter sets.
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Returns list of complete param dicts (ready for IniBuilder).
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"""
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opts = schema.optimizable()
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if not opts:
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logger.warning("LHSampler: no optimizable params — returning n_samples copies of defaults")
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return [schema.defaults() for _ in range(n_samples)]
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logger.info(
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f"LHSampler: generating {n_samples} samples over "
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f"{len(opts)} optimizable params: {[p.name for p in opts]}"
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)
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# Build LHS columns: one per optimizable param
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# Each column is a shuffled list of n_samples values — each from a different band
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columns: dict[str, list[Any]] = {}
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for param in opts:
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columns[param.name] = self._lhs_column(param, n_samples)
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# Assemble rows: combine column values
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base = schema.defaults()
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samples = []
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for i in range(n_samples):
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row = dict(base) # start with all defaults (includes fixed params)
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for param in opts:
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row[param.name] = columns[param.name][i]
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samples.append(row)
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return samples
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# ── Internal ─────────────────────────────────────────────────────────────
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def _lhs_column(self, param: ParameterDef, n: int) -> list[Any]:
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"""
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Generate n values for one parameter using Latin Hypercube spacing.
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Each value comes from a different equally-sized band of the range.
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The list is shuffled so rows don't correlate across parameters.
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"""
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if param.type == "bool":
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# Alternate True/False, shuffled
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vals = [True if i < n // 2 else False for i in range(n)]
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# Ensure roughly 50/50
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if n % 2 == 1:
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vals.append(self.rng.choice([True, False]))
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vals = vals[:n]
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self.rng.shuffle(vals)
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return vals
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if param.type == "enum":
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# Cycle through enum values with shuffled repetition
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enum_vals = param.enum_values
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vals = []
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while len(vals) < n:
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cycle = list(enum_vals)
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self.rng.shuffle(cycle)
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vals.extend(cycle)
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self.rng.shuffle(vals)
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return vals[:n]
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if param.type in ("int", "float"):
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return self._lhs_continuous(param, n)
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# fixed: return default repeated
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return [param.default] * n
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def _lhs_continuous(self, param: ParameterDef, n: int) -> list[Any]:
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"""LHS for continuous (float) or discrete (int) parameters."""
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lo = float(param.min)
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hi = float(param.max)
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step = float(param.step) if param.step else (hi - lo) / n
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band_size = (hi - lo) / n
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values = []
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for i in range(n):
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band_lo = lo + i * band_size
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band_hi = band_lo + band_size
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# Random point within the band
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raw = self.rng.uniform(band_lo, band_hi)
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# Snap to valid step grid
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if step > 0:
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steps_from_lo = round((raw - lo) / step)
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snapped = lo + steps_from_lo * step
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# Clamp to [lo, hi]
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snapped = max(lo, min(hi, snapped))
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else:
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snapped = raw
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values.append(param.clamp(snapped))
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# Shuffle so the ordering isn't correlated with other params
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self.rng.shuffle(values)
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return values
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def sample_neighbors(
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self,
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base_params: dict[str, Any],
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schema: ParameterSchema,
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n_neighbors: int,
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step_pct: float = 0.20,
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) -> list[dict[str, Any]]:
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"""
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Generate n_neighbors variants of base_params by nudging
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the most impactful optimizable params by ±step_pct of their range.
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Used in Phase 2 refinement.
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step_pct=0.20 means ±20% of (max - min). Much larger
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than the old ±0.5 step — actually explores the space.
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"""
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opts = schema.optimizable()
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if not opts:
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return [dict(base_params)]
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neighbors = []
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for _ in range(n_neighbors):
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candidate = dict(base_params)
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# Perturb 2–4 random optimizable params
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n_perturb = min(len(opts), self.rng.randint(2, 4))
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to_perturb = self.rng.sample(opts, n_perturb)
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for param in to_perturb:
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current = candidate[param.name]
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if param.type == "bool":
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# 50% chance to flip
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if self.rng.random() < 0.5:
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candidate[param.name] = not current
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continue
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if param.type == "enum":
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candidate[param.name] = self.rng.choice(param.enum_values)
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continue
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# float / int: nudge by ±step_pct of range
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span = float(param.max) - float(param.min)
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delta = span * step_pct * self.rng.choice([-1, 1])
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raw = float(current) + delta
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candidate[param.name] = param.clamp(raw)
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neighbors.append(candidate)
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return neighbors
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"""
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optimizer/pipeline.py
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The Smart 3-Phase Optimization Pipeline.
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Replaces optimizer_loop.py as the core orchestration engine.
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Receives a SessionConfig → runs Phase 1, 2, 3 → emits SocketIO events.
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Architecture:
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Phase 1: Broad Discovery (LHS samples, 20–30 runs)
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Phase 2: Deep Refinement (neighbor search around top 3)
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Phase 3: Validation (OOS backtest + sensitivity)
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Decision: RECOMMENDED / RISKY / NOT_RELIABLE + .set file download
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"""
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from __future__ import annotations
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import time
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import uuid
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import threading
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Optional, Callable
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import yaml
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from loguru import logger
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from ea.registry import EARegistry, EAProfile
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from ea.schema import ParameterSchema
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from mt5.ini_builder import IniBuilder
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from mt5.runner import MT5Runner
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from mt5.report_parser import ReportParser
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from data.models import Run
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from data.store import DataStore
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from reports.writer import ReportWriter
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from optimizer.session_config import SessionConfig
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from optimizer.lhs_sampler import LatinHypercubeSampler
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from optimizer.result_ranker import ResultRanker, RankedResult
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from optimizer.budget import BudgetManager
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import pandas as pd
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BASE_DIR = Path(__file__).parent.parent
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RUNS_DIR = BASE_DIR / "runs"
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DB_PATH = BASE_DIR / "optimizer.db"
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class OptimizationPipeline:
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"""
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3-phase autonomous optimization pipeline. Run in a background thread.
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Usage:
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pipeline = OptimizationPipeline("config.yaml", socketio, reports_dir)
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pipeline.configure(session_config)
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thread = threading.Thread(target=pipeline.run, daemon=True)
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thread.start()
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"""
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def __init__(self, config_path: str, socketio, reports_dir: Path):
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self.config_path = config_path
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self.socketio = socketio
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self.reports_dir = reports_dir
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with open(config_path) as f:
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self.cfg = yaml.safe_load(f)
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# Runtime state
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self.session: Optional[SessionConfig] = None
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self.running = False
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self._stop_flag = False
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self._phase = "idle"
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self._run_count = 0
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self._total_runs = 0
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self.best_result: Optional[RankedResult] = None
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self.phase1_results: list[RankedResult] = []
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self.phase2_results: list[RankedResult] = []
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self.final_result: Optional[RankedResult] = None
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self.verdict: Optional[str] = None
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self.best_set_path: Optional[Path] = None
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self.run_start_ts: Optional[float] = None
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# ── Public API ────────────────────────────────────────────────────────────
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def configure(self, session: SessionConfig) -> None:
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self.session = session
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def stop(self) -> None:
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self._stop_flag = True
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self._emit("status_change", {"state": "stopping"})
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def get_status(self) -> dict:
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elapsed = int(time.time() - self.run_start_ts) if self.run_start_ts else 0
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return {
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"state": "running" if self.running else "idle",
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"phase": self._phase,
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"run_count": self._run_count,
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"total_runs": self._total_runs,
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"best_score": round(self.best_result.score, 4) if self.best_result else 0.0,
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"verdict": self.verdict,
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"elapsed_s": elapsed,
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"ea_name": self.session.ea_name if self.session else "",
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"symbol": self.session.symbol if self.session else "",
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"timeframe": self.session.timeframe if self.session else "",
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}
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# ── Main entry point ──────────────────────────────────────────────────────
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def run(self) -> None:
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self.running = True
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self._stop_flag = False
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self.run_start_ts = time.time()
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try:
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self._run_pipeline()
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except Exception as e:
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logger.exception(f"Pipeline crashed: {e}")
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self._emit("error", {"msg": f"Pipeline error: {e}"})
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finally:
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self.running = False
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self._phase = "idle"
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self._emit("status_change", {"state": "idle"})
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def _run_pipeline(self) -> None:
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cfg = self.session
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self._total_runs = cfg.total_budget_runs
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self._emit("status_change", {"state": "running", "phase": "setup"})
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self._log("info", f"🚀 Smart Optimizer started — {cfg.ea_name} | {cfg.symbol} | {cfg.timeframe}")
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self._log("info", f"📋 Budget: {cfg.budget_minutes} min | Samples: {cfg.phase1_samples} Phase1 + {cfg.phase2_samples} Phase2 + {cfg.phase3_samples} Phase3")
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# ── Build components ─────────────────────────────────────────────────
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reg = EARegistry(self.config_path)
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profile = reg.get(cfg.ea_name)
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schema = reg.get_schema(profile)
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# Apply user's param selection if specified
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if cfg.selected_params:
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for p in schema.parameters.values():
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if p.type != "fixed":
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p.optimize = p.name in cfg.selected_params
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builder = IniBuilder(self.config_path, schema=schema)
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runner = MT5Runner(self.config_path)
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parser = ReportParser()
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store = DataStore(DB_PATH, RUNS_DIR)
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writer = ReportWriter(self.reports_dir)
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sampler = LatinHypercubeSampler(seed=int(time.time()))
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ranker = ResultRanker(weights=cfg.scoring_weights)
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budget = BudgetManager(cfg.budget_minutes)
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budget.start()
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# ── Phase 1: Broad Discovery ─────────────────────────────────────────
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if self._stop_flag:
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return
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self._phase = "phase1"
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self._emit("phase_start", {"phase": "phase1", "total": cfg.phase1_samples})
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self._log("info", f"━━ Phase 1: Broad Discovery ({cfg.phase1_samples} configurations) ━━")
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samples = sampler.sample(schema, cfg.phase1_samples)
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phase1_raw: list[RankedResult] = []
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for i, params in enumerate(samples):
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if self._stop_flag:
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break
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run_id = f"p1_{i+1:02d}_{datetime.utcnow().strftime('%H%M%S')}"
|
||||
self._log("info", f"[{i+1}/{cfg.phase1_samples}] Testing configuration {i+1}...")
|
||||
|
||||
t0 = time.time()
|
||||
result = self._execute_run(
|
||||
run_id, params, cfg.train_start, cfg.train_end,
|
||||
"phase1", builder, runner, parser, store, writer, ranker, profile
|
||||
)
|
||||
budget.record_run(time.time() - t0)
|
||||
phase1_raw.append(result)
|
||||
self._run_count += 1
|
||||
|
||||
# Emit progress after each run
|
||||
self._emit("run_complete", {
|
||||
"run_id": run_id,
|
||||
"phase": "phase1",
|
||||
"run_number": i + 1,
|
||||
"total": cfg.phase1_samples,
|
||||
"net_profit": round(result.net_profit, 2),
|
||||
"calmar": round(result.calmar, 3),
|
||||
"profit_factor": round(result.profit_factor, 3),
|
||||
"win_rate": round(result.win_rate, 1),
|
||||
"max_drawdown": round(result.max_drawdown, 2),
|
||||
"total_trades": result.total_trades,
|
||||
"passing": result.passing,
|
||||
"progress_pct": round((i + 1) / cfg.phase1_samples * 100),
|
||||
"budget_summary": budget.summary(),
|
||||
})
|
||||
|
||||
if budget.is_exhausted():
|
||||
self._log("warning", "⏱ Time budget reached during Phase 1")
|
||||
break
|
||||
|
||||
# Rank Phase 1 results
|
||||
self.phase1_results = ranker.rank(phase1_raw)
|
||||
top5 = ranker.top_n(self.phase1_results, 5)
|
||||
|
||||
n_passing = sum(1 for r in self.phase1_results if r.passing)
|
||||
self._log("info" if n_passing > 0 else "warning",
|
||||
f"Phase 1 complete: {n_passing}/{len(self.phase1_results)} profitable configurations found"
|
||||
)
|
||||
|
||||
# Emit Phase 1 summary for checkpoint UI
|
||||
self._emit("phase1_complete", {
|
||||
"total_tested": len(self.phase1_results),
|
||||
"n_passing": n_passing,
|
||||
"top_results": [self._result_to_dict(r) for r in top5],
|
||||
})
|
||||
|
||||
if not top5:
|
||||
self._emit("no_profitable_config", {
|
||||
"msg": (
|
||||
"Phase 1 found no profitable configuration. "
|
||||
"Suggestions: try a different date range, check EA settings, "
|
||||
"or try a different timeframe."
|
||||
)
|
||||
})
|
||||
self._log("error", "❌ No profitable configuration found in Phase 1. Stopping.")
|
||||
return
|
||||
|
||||
if self._stop_flag:
|
||||
return
|
||||
|
||||
# ── Phase 2: Deep Refinement ─────────────────────────────────────────
|
||||
if not budget.can_fit(3):
|
||||
self._log("warning", "⏱ Not enough budget for Phase 2 — using Phase 1 winner directly")
|
||||
self.final_result = top5[0]
|
||||
else:
|
||||
self._phase = "phase2"
|
||||
self._emit("phase_start", {"phase": "phase2", "total": cfg.phase2_samples})
|
||||
self._log("info", f"━━ Phase 2: Deep Refinement (refining top {min(3, len(top5))} configs) ━━")
|
||||
|
||||
top3 = top5[:3]
|
||||
phase2_raw = []
|
||||
neighbors_per = cfg.phase2_samples // max(1, len(top3))
|
||||
|
||||
for rank_i, base_result in enumerate(top3):
|
||||
if self._stop_flag:
|
||||
break
|
||||
|
||||
self._log("info", f" Refining config #{rank_i+1}: {base_result.run_id}")
|
||||
neighbors = sampler.sample_neighbors(
|
||||
base_result.params, schema,
|
||||
n_neighbors=neighbors_per, step_pct=0.20
|
||||
)
|
||||
|
||||
for j, params in enumerate(neighbors):
|
||||
if self._stop_flag or budget.is_exhausted():
|
||||
break
|
||||
|
||||
run_id = f"p2_{rank_i+1}_{j+1:02d}_{datetime.utcnow().strftime('%H%M%S')}"
|
||||
t0 = time.time()
|
||||
result = self._execute_run(
|
||||
run_id, params, cfg.train_start, cfg.train_end,
|
||||
"phase2", builder, runner, parser, store, writer, ranker, profile
|
||||
)
|
||||
budget.record_run(time.time() - t0)
|
||||
phase2_raw.append(result)
|
||||
self._run_count += 1
|
||||
|
||||
self._emit("run_complete", {
|
||||
"run_id": run_id,
|
||||
"phase": "phase2",
|
||||
"net_profit": round(result.net_profit, 2),
|
||||
"calmar": round(result.calmar, 3),
|
||||
"passing": result.passing,
|
||||
"progress_pct": round(self._run_count / self._total_runs * 100),
|
||||
})
|
||||
|
||||
# Best from Phase 1 + Phase 2 combined
|
||||
all_results = list(self.phase1_results) + ranker.rank(phase2_raw)
|
||||
all_ranked = ranker.rank(
|
||||
[r for r in all_results if r.passing]
|
||||
or list(self.phase1_results) # fallback to Phase 1 if P2 all fail
|
||||
)
|
||||
self.phase2_results = ranker.rank(phase2_raw)
|
||||
self.final_result = all_ranked[0] if all_ranked else top5[0]
|
||||
|
||||
self._emit("phase2_complete", {
|
||||
"best_run_id": self.final_result.run_id,
|
||||
"best_score": round(self.final_result.score, 4),
|
||||
"best_profit": round(self.final_result.net_profit, 2),
|
||||
"best_calmar": round(self.final_result.calmar, 3),
|
||||
})
|
||||
self._log("info",
|
||||
f"Phase 2 complete. Best config: {self.final_result.run_id} "
|
||||
f"(profit=${self.final_result.net_profit:.0f}, calmar={self.final_result.calmar:.2f})"
|
||||
)
|
||||
|
||||
if self._stop_flag:
|
||||
return
|
||||
|
||||
# ── Phase 3: Validation ───────────────────────────────────────────────
|
||||
if not budget.can_fit(2):
|
||||
self._log("warning", "⏱ Not enough budget for Phase 3 validation — skipping OOS test")
|
||||
self.verdict = "RISKY"
|
||||
oos_result = None
|
||||
else:
|
||||
self._phase = "phase3"
|
||||
self._emit("phase_start", {"phase": "phase3", "total": cfg.phase3_samples})
|
||||
self._log("info", "━━ Phase 3: Validation (out-of-sample + sensitivity) ━━")
|
||||
|
||||
# OOS test
|
||||
oos_id = f"oos_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}"
|
||||
self._log("info", f" OOS test: {cfg.val_start} → {cfg.val_end}")
|
||||
t0 = time.time()
|
||||
oos_result = self._execute_run(
|
||||
oos_id, self.final_result.params,
|
||||
cfg.val_start, cfg.val_end,
|
||||
"phase3_oos", builder, runner, parser, store, writer, ranker, profile
|
||||
)
|
||||
budget.record_run(time.time() - t0)
|
||||
self._run_count += 1
|
||||
|
||||
self._emit("run_complete", {
|
||||
"run_id": oos_id,
|
||||
"phase": "phase3_oos",
|
||||
"net_profit": round(oos_result.net_profit, 2),
|
||||
"calmar": round(oos_result.calmar, 3),
|
||||
"passing": oos_result.passing,
|
||||
})
|
||||
|
||||
# Sensitivity test (2 runs: nudge top param up and down)
|
||||
sens_results = []
|
||||
opts = schema.optimizable()
|
||||
if opts and budget.can_fit(2):
|
||||
top_param = opts[0] # first optimizable param
|
||||
for direction in [1, -1]:
|
||||
if budget.is_exhausted() or self._stop_flag:
|
||||
break
|
||||
nudged = dict(self.final_result.params)
|
||||
current = float(nudged.get(top_param.name, top_param.default))
|
||||
span = float(top_param.max) - float(top_param.min)
|
||||
nudged[top_param.name] = top_param.clamp(current + direction * span * 0.20)
|
||||
|
||||
sens_id = f"sens_{direction}_{datetime.utcnow().strftime('%H%M%S')}"
|
||||
t0 = time.time()
|
||||
sr = self._execute_run(
|
||||
sens_id, nudged, cfg.train_start, cfg.train_end,
|
||||
"phase3_sens", builder, runner, parser, store, writer, ranker, profile
|
||||
)
|
||||
budget.record_run(time.time() - t0)
|
||||
sens_results.append(sr)
|
||||
self._run_count += 1
|
||||
|
||||
# Determine verdict
|
||||
self.verdict = self._determine_verdict(self.final_result, oos_result, sens_results)
|
||||
|
||||
# ── Generate .set output ─────────────────────────────────────────────
|
||||
self.best_set_path = self._write_set_file(self.final_result, schema, cfg)
|
||||
|
||||
# ── Final emit ───────────────────────────────────────────────────────
|
||||
self._emit("optimization_complete", {
|
||||
"verdict": self.verdict,
|
||||
"best_run_id": self.final_result.run_id,
|
||||
"net_profit": round(self.final_result.net_profit, 2),
|
||||
"calmar": round(self.final_result.calmar, 3),
|
||||
"profit_factor": round(self.final_result.profit_factor, 3),
|
||||
"win_rate": round(self.final_result.win_rate, 1),
|
||||
"max_drawdown": round(self.final_result.max_drawdown, 2),
|
||||
"total_trades": self.final_result.total_trades,
|
||||
"oos_profit": round(oos_result.net_profit, 2) if oos_result else None,
|
||||
"oos_calmar": round(oos_result.calmar, 3) if oos_result else None,
|
||||
"set_file_url": f"/download_set/{self.final_result.run_id}" if self.best_set_path else None,
|
||||
"total_runs": self._run_count,
|
||||
"elapsed_min": round((time.time() - self.run_start_ts) / 60, 1),
|
||||
})
|
||||
|
||||
verdict_icon = {"RECOMMENDED": "✅", "RISKY": "⚠️", "NOT_RELIABLE": "❌"}.get(self.verdict, "?")
|
||||
self._log("success" if self.verdict == "RECOMMENDED" else "warning",
|
||||
f"{verdict_icon} VERDICT: {self.verdict} | "
|
||||
f"Profit: ${self.final_result.net_profit:.0f} | "
|
||||
f"Calmar: {self.final_result.calmar:.2f}"
|
||||
)
|
||||
|
||||
# ── Single run executor ───────────────────────────────────────────────────
|
||||
|
||||
def _execute_run(
|
||||
self, run_id, params, period_start, period_end, phase,
|
||||
builder, runner, parser, store, writer, ranker, profile
|
||||
) -> RankedResult:
|
||||
"""Execute one MT5 backtest and return a RankedResult."""
|
||||
run_dir = RUNS_DIR / run_id
|
||||
run_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
try:
|
||||
ini_path = builder.build(
|
||||
run_id=run_id, params=params,
|
||||
period_start=period_start, period_end=period_end,
|
||||
output_dir=run_dir, phase=phase,
|
||||
ea_file=profile.ex5_file,
|
||||
ea_symbol=profile.symbol,
|
||||
ea_timeframe=profile.timeframe,
|
||||
)
|
||||
|
||||
run = Run(
|
||||
run_id=run_id,
|
||||
ea_name=profile.name, symbol=profile.symbol,
|
||||
timeframe=profile.timeframe,
|
||||
period_start=period_start, period_end=period_end,
|
||||
params=params, phase=phase,
|
||||
tester_model=self.cfg["mt5"]["tester_model"],
|
||||
ini_snapshot=ini_path.read_text(),
|
||||
)
|
||||
store.save_run(run)
|
||||
|
||||
result = runner.run(
|
||||
run_id, ini_path, run_dir / "report",
|
||||
log_csv_search_dir=Path(self.cfg["mt5"]["mql5_files_path"]),
|
||||
profile=profile,
|
||||
)
|
||||
|
||||
if not result.success:
|
||||
return ranker.make_result(run_id, params, phase, None,
|
||||
error=result.error_message)
|
||||
|
||||
metrics, _ = parser.parse(result.report_xml, result.report_html)
|
||||
if metrics is None:
|
||||
return ranker.make_result(run_id, params, phase, None,
|
||||
error="parse_failed")
|
||||
|
||||
metrics.run_id = run_id
|
||||
writer.write(run_id, metrics, pd.DataFrame(), [], params)
|
||||
|
||||
return ranker.make_result(run_id, params, phase, metrics)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"[{run_id}] Run error: {e}")
|
||||
return ranker.make_result(run_id, params, phase, None, error=str(e))
|
||||
|
||||
# ── Verdict logic ─────────────────────────────────────────────────────────
|
||||
|
||||
def _determine_verdict(
|
||||
self,
|
||||
best: RankedResult,
|
||||
oos: Optional[RankedResult],
|
||||
sens: list[RankedResult],
|
||||
) -> str:
|
||||
"""
|
||||
RECOMMENDED: IS profitable + OOS profitable + not fragile
|
||||
RISKY: IS profitable but OOS weak OR fragile
|
||||
NOT_RELIABLE: IS marginal or OOS loss
|
||||
"""
|
||||
if best.calmar < 0.1 or best.net_profit <= 0:
|
||||
return "NOT_RELIABLE"
|
||||
|
||||
oos_ok = False
|
||||
if oos and oos.net_profit > 0:
|
||||
# OOS degradation: acceptable if OOS calmar ≥ 50% of IS calmar
|
||||
oos_ratio = oos.calmar / max(best.calmar, 0.001)
|
||||
oos_ok = oos_ratio >= 0.50
|
||||
elif oos is None:
|
||||
oos_ok = True # No OOS test — can't penalize
|
||||
|
||||
# Sensitivity: fragile if any nudge drops Calmar by >50%
|
||||
fragile = any(
|
||||
s.passing and s.calmar < best.calmar * 0.50
|
||||
for s in sens
|
||||
) if sens else False
|
||||
|
||||
if oos_ok and not fragile and best.calmar >= 0.30:
|
||||
return "RECOMMENDED"
|
||||
if oos_ok or (not fragile and best.calmar >= 0.20):
|
||||
return "RISKY"
|
||||
return "NOT_RELIABLE"
|
||||
|
||||
# ── .set file output ──────────────────────────────────────────────────────
|
||||
|
||||
def _write_set_file(
|
||||
self, result: RankedResult, schema: ParameterSchema, cfg: SessionConfig
|
||||
) -> Optional[Path]:
|
||||
"""Write a clean .set file for MT5 import."""
|
||||
try:
|
||||
out_dir = self.reports_dir / result.run_id
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
out_path = out_dir / f"{cfg.ea_name}_optimized_{cfg.symbol}_{cfg.timeframe}.set"
|
||||
|
||||
header = (
|
||||
f"Optimized by MT5 Smart Optimizer\n"
|
||||
f"EA: {cfg.ea_name} | Symbol: {cfg.symbol} | TF: {cfg.timeframe}\n"
|
||||
f"Training: {cfg.train_start} – {cfg.train_end}\n"
|
||||
f"Verdict: {self.verdict}\n"
|
||||
f"Net Profit: ${result.net_profit:.2f} | Calmar: {result.calmar:.2f} | "
|
||||
f"Win Rate: {result.win_rate:.1f}%\n"
|
||||
f"Generated: {datetime.utcnow().strftime('%Y-%m-%d %H:%M UTC')}"
|
||||
)
|
||||
|
||||
content = schema.to_set_file(result.params, header_comment=header)
|
||||
out_path.write_text(content, encoding="utf-8")
|
||||
logger.info(f"Optimized .set file written: {out_path}")
|
||||
return out_path
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not write .set file: {e}")
|
||||
return None
|
||||
|
||||
# ── Helpers ───────────────────────────────────────────────────────────────
|
||||
|
||||
def _result_to_dict(self, r: RankedResult) -> dict:
|
||||
return {
|
||||
"run_id": r.run_id,
|
||||
"rank": r.rank,
|
||||
"score": round(r.score, 4),
|
||||
"net_profit": round(r.net_profit, 2),
|
||||
"calmar": round(r.calmar, 3),
|
||||
"profit_factor": round(r.profit_factor, 3),
|
||||
"win_rate": round(r.win_rate, 1),
|
||||
"max_drawdown": round(r.max_drawdown, 2),
|
||||
"total_trades": r.total_trades,
|
||||
"passing": r.passing,
|
||||
"params_summary": self._params_summary(r.params),
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _params_summary(params: dict) -> str:
|
||||
"""Show a few key params for display."""
|
||||
keys = ["InpRiskPercent", "InpRRRatio", "InpMaxDailyLossPct",
|
||||
"InpTrailStartPips", "InpMinScore", "InpSessionStart", "InpSessionEnd"]
|
||||
parts = []
|
||||
for k in keys:
|
||||
if k in params:
|
||||
short = k.replace("Inp", "")
|
||||
parts.append(f"{short}={params[k]}")
|
||||
return " | ".join(parts[:4])
|
||||
|
||||
def _log(self, level: str, msg: str) -> None:
|
||||
getattr(logger, level, logger.info)(msg)
|
||||
self._emit("log", {"level": level, "msg": msg})
|
||||
|
||||
def _emit(self, event: str, data: dict = {}) -> None:
|
||||
try:
|
||||
self.socketio.emit(event, data)
|
||||
except Exception as e:
|
||||
logger.debug(f"Emit error ({event}): {e}")
|
||||
@@ -0,0 +1,178 @@
|
||||
"""
|
||||
optimizer/result_ranker.py
|
||||
Relative scoring and ranking of backtest results within a session.
|
||||
|
||||
KEY DESIGN: Scores are relative to the current session — the best run
|
||||
in the session = 1.0, worst passing = 0.0. This eliminates the flat
|
||||
0.2500 problem from absolute thresholds.
|
||||
|
||||
Failing runs (unprofitable or < 30 trades) always score 0.0 and
|
||||
float to the bottom of the ranking.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
MIN_TRADES = 30 # Runs with fewer trades have no statistical meaning
|
||||
|
||||
|
||||
@dataclass
|
||||
class RankedResult:
|
||||
"""One backtest run's result after scoring and ranking."""
|
||||
run_id: str
|
||||
params: dict[str, Any]
|
||||
phase: str # "phase1" | "phase2" | "phase3_oos" | "phase3_sens"
|
||||
|
||||
# Raw metrics from ReportParser
|
||||
net_profit: float = 0.0
|
||||
calmar: float = 0.0
|
||||
profit_factor: float = 0.0
|
||||
win_rate: float = 0.0
|
||||
max_drawdown: float = 0.0
|
||||
total_trades: int = 0
|
||||
|
||||
# Computed by ranker
|
||||
raw_score: float = 0.0 # weighted before normalization
|
||||
score: float = 0.0 # normalized 0–1 within session
|
||||
passing: bool = False # True if profitable + enough trades
|
||||
rank: int = 0 # 1 = best
|
||||
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
class ResultRanker:
|
||||
"""
|
||||
Ranks a list of RankedResult objects using relative scoring.
|
||||
|
||||
Usage:
|
||||
ranker = ResultRanker(weights={"calmar": 0.5, "profit_factor": 0.3, "win_rate": 0.2})
|
||||
ranker.rank(results) # modifies in-place: sets .raw_score, .score, .passing, .rank
|
||||
"""
|
||||
|
||||
def __init__(self, weights: dict[str, float] = None):
|
||||
self.weights = weights or {
|
||||
"calmar": 0.50,
|
||||
"profit_factor": 0.30,
|
||||
"win_rate": 0.20,
|
||||
}
|
||||
|
||||
def rank(self, results: list[RankedResult]) -> list[RankedResult]:
|
||||
"""
|
||||
Score, classify (passing/failing), and rank all results.
|
||||
Returns the same list sorted by rank (best first).
|
||||
Modifies results in-place.
|
||||
"""
|
||||
# Step 1: Classify each result
|
||||
for r in results:
|
||||
r.passing = self._is_passing(r)
|
||||
r.raw_score = self._raw_score(r) if r.passing else 0.0
|
||||
|
||||
# Step 2: Normalize scores within passing runs
|
||||
passing = [r for r in results if r.passing]
|
||||
failing = [r for r in results if not r.passing]
|
||||
|
||||
if passing:
|
||||
max_raw = max(r.raw_score for r in passing)
|
||||
min_raw = min(r.raw_score for r in passing)
|
||||
span = max_raw - min_raw
|
||||
|
||||
for r in passing:
|
||||
if span > 1e-9:
|
||||
r.score = (r.raw_score - min_raw) / span
|
||||
else:
|
||||
r.score = 1.0 # all passing runs scored identically
|
||||
|
||||
for r in failing:
|
||||
r.score = 0.0
|
||||
|
||||
# Step 3: Sort (passing by score desc, failing at bottom)
|
||||
passing.sort(key=lambda r: r.score, reverse=True)
|
||||
failing.sort(key=lambda r: r.raw_score, reverse=True)
|
||||
ranked = passing + failing
|
||||
|
||||
for i, r in enumerate(ranked):
|
||||
r.rank = i + 1
|
||||
|
||||
n_pass = len(passing)
|
||||
n_fail = len(failing)
|
||||
logger.info(
|
||||
f"ResultRanker: {len(results)} runs — "
|
||||
f"{n_pass} passing, {n_fail} failing. "
|
||||
f"Best score: {passing[0].score:.3f} ({passing[0].run_id})"
|
||||
if passing else
|
||||
f"ResultRanker: {len(results)} runs — 0 passing (EA found no profitable config)"
|
||||
)
|
||||
|
||||
return ranked
|
||||
|
||||
def top_n(self, results: list[RankedResult], n: int) -> list[RankedResult]:
|
||||
"""Return top n passing results."""
|
||||
return [r for r in results if r.passing][:n]
|
||||
|
||||
# ── Internal ─────────────────────────────────────────────────────────────
|
||||
|
||||
def _is_passing(self, r: RankedResult) -> bool:
|
||||
"""A result passes if it's profitable AND has enough trades."""
|
||||
if r.error:
|
||||
return False
|
||||
if r.total_trades < MIN_TRADES:
|
||||
return False
|
||||
if r.net_profit <= 0:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _raw_score(self, r: RankedResult) -> float:
|
||||
"""Weighted composite score (before normalization)."""
|
||||
w = self.weights
|
||||
|
||||
# Calmar: cap at 5.0 to prevent wild outliers dominating
|
||||
calmar_capped = min(max(r.calmar, 0.0), 5.0) / 5.0
|
||||
|
||||
# Profit factor: cap at 3.0
|
||||
pf_capped = min(max(r.profit_factor, 0.0), 3.0) / 3.0
|
||||
|
||||
# Win rate: 0–1 already
|
||||
wr = max(0.0, min(1.0, r.win_rate / 100.0 if r.win_rate > 1 else r.win_rate))
|
||||
|
||||
score = (
|
||||
w.get("calmar", 0.5) * calmar_capped +
|
||||
w.get("profit_factor", 0.3) * pf_capped +
|
||||
w.get("win_rate", 0.2) * wr
|
||||
)
|
||||
|
||||
# Optional net_profit boost (for max_profit objective)
|
||||
if "net_profit" in w and w["net_profit"] > 0:
|
||||
# Normalize profit to ~$10k scale
|
||||
profit_norm = min(max(r.net_profit / 10000.0, 0.0), 1.0)
|
||||
score += w["net_profit"] * profit_norm
|
||||
|
||||
return score
|
||||
|
||||
def make_result(
|
||||
self,
|
||||
run_id: str,
|
||||
params: dict,
|
||||
phase: str,
|
||||
metrics, # RunMetrics from report_parser — or None on failure
|
||||
error: str = None,
|
||||
) -> RankedResult:
|
||||
"""Convenience constructor from RunMetrics."""
|
||||
if metrics is None or error:
|
||||
return RankedResult(
|
||||
run_id=run_id, params=params, phase=phase,
|
||||
error=error or "run_failed",
|
||||
)
|
||||
return RankedResult(
|
||||
run_id = run_id,
|
||||
params = params,
|
||||
phase = phase,
|
||||
net_profit = getattr(metrics, "net_profit", 0.0) or 0.0,
|
||||
calmar = getattr(metrics, "calmar_ratio", 0.0) or 0.0,
|
||||
profit_factor = getattr(metrics, "profit_factor", 0.0) or 0.0,
|
||||
win_rate = getattr(metrics, "win_rate", 0.0) or 0.0,
|
||||
max_drawdown = getattr(metrics, "max_drawdown_pct", 0.0) or 0.0,
|
||||
total_trades = getattr(metrics, "total_trades", 0) or 0,
|
||||
)
|
||||
@@ -0,0 +1,116 @@
|
||||
"""
|
||||
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
|
||||
Reference in New Issue
Block a user