feat(calibration): Step 3 - Stategies preparado conceptualmente
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src/calibration/strategies_inspector.py
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326
src/calibration/strategies_inspector.py
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# src/calibration/strategies_inspector.py
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from __future__ import annotations
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from typing import Any, Dict, List
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import numpy as np
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import pandas as pd
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from src.data.storage import StorageManager
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from src.utils.logger import log
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from src.core.walk_forward import WalkForwardValidator
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from src.risk.stops.fixed_stop import FixedStop
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from src.risk.stops.trailing_stop import TrailingStop
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from src.risk.stops.atr_stop import ATRStop
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from src.risk.sizing.percent_risk import PercentRiskSizer
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# --------------------------------------------------
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# Strategy registry (con metadata de parámetros)
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# --------------------------------------------------
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from src.strategies.moving_average import MovingAverageCrossover
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from src.strategies.rsi_strategy import RSIStrategy
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from src.strategies.buy_and_hold import BuyAndHold
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STRATEGY_REGISTRY = {
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"moving_average": {
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"class": MovingAverageCrossover,
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"params": ["fast_period", "slow_period"],
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},
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"rsi": {
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"class": RSIStrategy,
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"params": ["rsi_period", "overbought", "oversold"],
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},
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"buy_and_hold": {
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"class": BuyAndHold,
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"params": [],
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},
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}
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# --------------------------------------------------
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# Helpers
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# --------------------------------------------------
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def list_available_strategies() -> List[Dict[str, Any]]:
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"""
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Devuelve metadata completa para UI.
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"""
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out = []
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for sid, entry in STRATEGY_REGISTRY.items():
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out.append({
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"strategy_id": sid,
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"name": entry["class"].__name__,
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"params": entry["params"],
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"tags": [], # puedes rellenar más adelante
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})
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return out
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def _build_stop_loss(stop_schema) -> object | None:
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if stop_schema.type == "fixed":
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return FixedStop(stop_fraction=float(stop_schema.stop_fraction))
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if stop_schema.type == "trailing":
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return TrailingStop(stop_fraction=float(stop_schema.stop_fraction))
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if stop_schema.type == "atr":
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return ATRStop(
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atr_period=int(stop_schema.atr_period),
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multiplier=float(stop_schema.atr_multiplier),
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)
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raise ValueError(f"Unknown stop type: {stop_schema.type}")
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def _build_position_sizer(risk_schema) -> PercentRiskSizer:
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return PercentRiskSizer(risk_fraction=float(risk_schema.risk_fraction))
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def _cap_units_by_max_position_fraction(units: float, capital: float, entry_price: float, max_position_fraction: float) -> float:
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max_units = (capital * max_position_fraction) / entry_price
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return float(min(units, max_units))
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def _accumulate_equity(initial: float, returns_pct: List[float]) -> List[float]:
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eq = [float(initial)]
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cur = float(initial)
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for r in returns_pct:
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cur *= (1.0 + float(r) / 100.0)
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eq.append(float(cur))
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return eq
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# --------------------------------------------------
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# Main
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# --------------------------------------------------
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def inspect_strategies_config(
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*,
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storage: StorageManager,
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payload,
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data_quality: Dict[str, Any],
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include_series: bool,
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progress_callback=None,
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) -> Dict[str, Any]:
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df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
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if df is None or df.empty:
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return {
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"valid": False,
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"status": "fail",
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"checks": {},
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"message": "No OHLCV data",
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"results": [],
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}
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checks: Dict[str, Any] = {}
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checks["data_quality"] = {
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"status": data_quality.get("status", "unknown"),
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"message": data_quality.get("message", ""),
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}
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if data_quality.get("status") == "fail":
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return {
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"valid": False,
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"status": "fail",
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"checks": checks,
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"message": "Step 1 data quality is FAIL. Strategies cannot be validated.",
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"results": [],
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"series": {} if include_series else None,
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}
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stop_loss = _build_stop_loss(payload.stop)
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base_sizer = _build_position_sizer(payload.risk)
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train_td = pd.Timedelta(days=int(payload.wf.train_days))
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test_td = pd.Timedelta(days=int(payload.wf.test_days))
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step_td = pd.Timedelta(days=int(payload.wf.step_days or payload.wf.test_days))
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overall_status = "ok"
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results: List[Dict[str, Any]] = []
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series: Dict[str, Any] = {"strategies": {}} if include_series else {}
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for sel in payload.strategies:
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sid = sel.strategy_id
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entry = STRATEGY_REGISTRY.get(sid)
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if entry is None:
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results.append({
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"strategy_id": sid,
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"status": "fail",
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"message": f"Unknown strategy_id: {sid}",
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"n_windows": 0,
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"oos_final_equity": payload.account_equity,
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"oos_total_return_pct": 0.0,
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"oos_max_dd_worst_pct": 0.0,
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"degradation_sharpe": None,
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"windows": [],
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})
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overall_status = "fail"
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continue
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strategy_class = entry["class"]
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valid_params = set(entry["params"])
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grid_params = set(sel.param_grid.keys())
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# 🔒 Validación estricta de parámetros
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if grid_params != valid_params:
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msg = f"Param grid keys {grid_params} do not match expected {valid_params}"
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results.append({
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"strategy_id": sid,
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"status": "fail",
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"message": msg,
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"n_windows": 0,
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"oos_final_equity": payload.account_equity,
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"oos_total_return_pct": 0.0,
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"oos_max_dd_worst_pct": 0.0,
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"degradation_sharpe": None,
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"windows": [],
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})
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overall_status = "fail"
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continue
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# Wrapper sizer
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class _CappedSizer(type(base_sizer)):
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def __init__(self, inner):
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self.inner = inner
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def calculate_size(self, *, capital, entry_price, stop_price=None, max_capital=None, volatility=None):
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u = self.inner.calculate_size(
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capital=capital,
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entry_price=entry_price,
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stop_price=stop_price,
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max_capital=max_capital,
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volatility=volatility,
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)
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return _cap_units_by_max_position_fraction(
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units=float(u),
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capital=float(capital),
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entry_price=float(entry_price),
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max_position_fraction=float(payload.risk.max_position_fraction),
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)
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capped_sizer = _CappedSizer(base_sizer)
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log.info(f"🧠 Step3 | WF run | strategy={sid}")
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try:
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wf = WalkForwardValidator(
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strategy_class=strategy_class,
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param_grid=sel.param_grid,
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data=df,
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train_window=train_td,
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test_window=test_td,
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step_size=step_td,
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initial_capital=float(payload.account_equity),
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commission=float(payload.commission),
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slippage=float(payload.slippage),
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optimizer_metric=str(payload.optimization.optimizer_metric),
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position_sizer=capped_sizer,
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stop_loss=stop_loss,
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max_combinations=int(payload.optimization.max_combinations),
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progress_callback=progress_callback,
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)
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wf_res = wf.run()
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win_df: pd.DataFrame = wf_res["windows"]
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if win_df is None or win_df.empty:
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status = "fail"
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msg = "WF produced no valid windows"
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overall_status = "fail"
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windows_out = []
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oos_returns = []
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else:
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trades = win_df["trades"].astype(int).tolist()
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too_few = sum(t < int(payload.optimization.min_trades_test) for t in trades)
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if too_few > 0:
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status = "warning"
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msg = f"{too_few} windows below min_trades_test"
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if overall_status == "ok":
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overall_status = "warning"
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else:
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status = "ok"
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msg = "WF OK"
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windows_out = []
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for _, r in win_df.iterrows():
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windows_out.append({
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"window": int(r["window"]),
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"train_start": str(r["train_start"]),
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"train_end": str(r["train_end"]),
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"test_start": str(r["test_start"]),
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"test_end": str(r["test_end"]),
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"return_pct": float(r["return_pct"]),
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"sharpe": float(r["sharpe"]),
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"max_dd_pct": float(r["max_dd_pct"]),
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"trades": int(r["trades"]),
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"params": dict(r["params"]) if isinstance(r["params"], dict) else r["params"],
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})
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oos_returns = win_df["return_pct"].astype(float).tolist()
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eq_curve = _accumulate_equity(float(payload.account_equity), oos_returns)
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oos_final = float(eq_curve[-1]) if eq_curve else float(payload.account_equity)
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oos_total_return = (oos_final / float(payload.account_equity) - 1.0) * 100.0
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oos_max_dd = float(np.min(win_df["max_dd_pct"])) if (win_df is not None and not win_df.empty) else 0.0
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results.append({
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"strategy_id": sid,
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"status": status,
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"message": msg,
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"n_windows": int(len(windows_out)),
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"oos_final_equity": oos_final,
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"oos_total_return_pct": float(oos_total_return),
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"oos_max_dd_worst_pct": float(oos_max_dd),
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"degradation_sharpe": None,
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"windows": windows_out,
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})
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if include_series:
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series["strategies"][sid] = {
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"window_returns_pct": oos_returns,
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"window_equity": eq_curve,
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}
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except Exception as e:
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log.error(f"❌ Step3 WF error | strategy={sid} | {e}")
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results.append({
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"strategy_id": sid,
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"status": "fail",
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"message": f"Exception: {e}",
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"n_windows": 0,
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"oos_final_equity": float(payload.account_equity),
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"oos_total_return_pct": 0.0,
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"oos_max_dd_worst_pct": 0.0,
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"degradation_sharpe": None,
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"windows": [],
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})
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overall_status = "fail"
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valid = overall_status != "fail"
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human_msg = {
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"ok": "Strategies validation OK",
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"warning": "Strategies validation has warnings",
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"fail": "Strategies validation FAILED",
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}[overall_status]
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out = {
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"valid": valid,
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"status": overall_status,
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"checks": checks,
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"message": human_msg,
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"results": results,
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}
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if include_series:
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out["series"] = series
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return out
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