117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
|
"""
|
|||
|
|
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
|