429 lines
19 KiB
Python
429 lines
19 KiB
Python
from __future__ import annotations
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import json
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import sys
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from dataclasses import asdict, dataclass
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PACKAGE_PARENT = Path(__file__).resolve().parents[2]
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if str(PACKAGE_PARENT) not in sys.path:
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sys.path.insert(0, str(PACKAGE_PARENT))
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from strategy32.live.runtime import BEST_CASH_OVERLAY
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from strategy32.research.soft_router import build_cash_overlay_period_components, load_component_bundle, score_candidate, segment_metrics
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from strategy32.scripts.run_current_relaxed_hybrid_experiment import (
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CACHE_PATH,
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CURRENT_OVERHEAT_OVERRIDES,
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RELAXED_OVERHEAT_OVERRIDES,
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WINDOWS,
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YEAR_PERIODS,
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YTD_START,
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_baseline_summary,
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_overlay_weights,
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)
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OUT_JSON = Path("/tmp/strategy32_current_relaxed_learned_entry_overlay.json")
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@dataclass(frozen=True, slots=True)
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class LearnedOverlayCandidate:
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block_bars: int
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train_min_blocks: int
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lookback_blocks: int
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ridge_alpha: float
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prediction_threshold: float
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overlay_scale: float
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@property
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def name(self) -> str:
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return (
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f"block:{self.block_bars}"
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f"|train:{self.train_min_blocks}"
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f"|lookback:{self.lookback_blocks}"
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f"|alpha:{self.ridge_alpha:.2f}"
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f"|th:{self.prediction_threshold:.4f}"
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f"|scale:{self.overlay_scale:.2f}"
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)
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def _build_strategy_detail(components: dict[str, object]) -> pd.DataFrame:
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timestamps = list(components["timestamps"])
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score_map = components["score_frame"].set_index("timestamp").sort_index()
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cash_map = components["core_exposure_frame"].set_index("timestamp")["cash_pct"].sort_index()
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core_returns = components["core_returns"]
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cap_returns = components["cap_returns"]
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chop_returns = components["chop_returns"]
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dist_returns = components["dist_returns"]
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rows: list[dict[str, object]] = []
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for i in range(1, len(timestamps)):
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signal_ts = pd.Timestamp(timestamps[i - 1])
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execution_ts = pd.Timestamp(timestamps[i])
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score_row = score_map.loc[signal_ts].to_dict() if signal_ts in score_map.index else {}
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core_cash_pct = float(cash_map.get(signal_ts, cash_map.iloc[-1] if not cash_map.empty else 1.0))
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cap_weight, chop_weight, dist_weight = _overlay_weights(BEST_CASH_OVERLAY, score_row, core_cash_pct)
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rows.append(
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{
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"timestamp": execution_ts,
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"strategic_regime": str(score_row.get("strategic_regime", "")),
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"core_score": float(score_row.get("core_score", 0.0)),
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"panic_score": float(score_row.get("panic_score", 0.0)),
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"choppy_score": float(score_row.get("choppy_score", 0.0)),
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"distribution_score": float(score_row.get("distribution_score", 0.0)),
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"breadth_persist": float(score_row.get("breadth_persist", 0.0) or 0.0),
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"funding_persist": float(score_row.get("funding_persist", 0.0) or 0.0),
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"taker_persist": float(score_row.get("taker_persist", 0.0) or 0.0),
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"volume_accel_persist": float(score_row.get("volume_accel_persist", 0.0) or 0.0),
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"mean_taker_imbalance": float(score_row.get("mean_taker_imbalance", 0.0) or 0.0),
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"taker_imbalance_dispersion": float(score_row.get("taker_imbalance_dispersion", 0.0) or 0.0),
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"positive_taker_ratio": float(score_row.get("positive_taker_ratio", 0.0) or 0.0),
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"mean_alt_volume_accel": float(score_row.get("mean_alt_volume_accel", 0.0) or 0.0),
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"positive_volume_accel_ratio": float(score_row.get("positive_volume_accel_ratio", 0.0) or 0.0),
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"funding_dispersion": float(score_row.get("funding_dispersion", 0.0) or 0.0),
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"basis_dispersion": float(score_row.get("basis_dispersion", 0.0) or 0.0),
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"alt_return_dispersion_7d": float(score_row.get("alt_return_dispersion_7d", 0.0) or 0.0),
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"mean_funding_acceleration": float(score_row.get("mean_funding_acceleration", 0.0) or 0.0),
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"mean_basis_trend": float(score_row.get("mean_basis_trend", 0.0) or 0.0),
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"cash_pct": core_cash_pct,
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"invested_pct": max(0.0, 1.0 - core_cash_pct),
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"cap_weight": cap_weight,
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"chop_weight": chop_weight,
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"dist_weight": dist_weight,
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"portfolio_return": (
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float(core_returns.get(execution_ts, 0.0))
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+ cap_weight * float(cap_returns.get(execution_ts, 0.0))
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+ chop_weight * float(chop_returns.get(execution_ts, 0.0))
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+ dist_weight * float(dist_returns.get(execution_ts, 0.0))
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),
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}
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)
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return pd.DataFrame(rows)
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def _curve_from_returns(returns: pd.Series) -> pd.Series:
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equity = 1000.0
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vals = [equity]
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idx = [returns.index[0] - pd.Timedelta(hours=4)]
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for ts, ret in returns.items():
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equity *= max(0.0, 1.0 + float(ret))
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idx.append(pd.Timestamp(ts))
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vals.append(equity)
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return pd.Series(vals, index=pd.DatetimeIndex(idx, name="timestamp"), dtype=float)
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def _metrics_for_curve(curve: pd.Series, latest_bar: pd.Timestamp) -> tuple[dict[str, dict[str, float]], dict[str, dict[str, float]], float, int, int]:
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windows = {
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label: segment_metrics(curve, latest_bar - pd.Timedelta(days=days), latest_bar)
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for days, label in WINDOWS
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}
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years = {
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label: segment_metrics(curve, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1)))
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for label, start, end_exclusive in YEAR_PERIODS
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}
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years["2026_YTD"] = segment_metrics(curve, YTD_START, latest_bar)
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score, negative_years, mdd_violations = score_candidate(
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{label: windows[label] for _, label in WINDOWS},
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{label: years[label] for label, _, _ in YEAR_PERIODS},
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)
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return windows, years, score, negative_years, mdd_violations
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def _ridge_predict(train_x: np.ndarray, train_y: np.ndarray, test_x: np.ndarray, alpha: float) -> float:
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if len(train_x) == 0:
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return 0.0
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train_x = np.nan_to_num(train_x, nan=0.0, posinf=0.0, neginf=0.0)
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train_y = np.nan_to_num(train_y, nan=0.0, posinf=0.0, neginf=0.0)
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test_x = np.nan_to_num(test_x, nan=0.0, posinf=0.0, neginf=0.0)
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mean = train_x.mean(axis=0)
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std = train_x.std(axis=0)
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std[std < 1e-9] = 1.0
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x_train = (train_x - mean) / std
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x_test = (test_x - mean) / std
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x_train = np.clip(x_train, -8.0, 8.0)
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x_test = np.clip(x_test, -8.0, 8.0)
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train_y = np.clip(train_y, -0.50, 0.50)
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x_train = np.column_stack([np.ones(len(x_train)), x_train])
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x_test = np.concatenate([[1.0], x_test])
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penalty = np.eye(x_train.shape[1]) * alpha
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penalty[0, 0] = 0.0
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lhs = x_train.T @ x_train + penalty
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rhs = x_train.T @ train_y
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try:
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beta = np.linalg.solve(lhs, rhs)
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except np.linalg.LinAlgError:
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beta = np.linalg.pinv(lhs) @ rhs
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return float(x_test @ beta)
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def _build_regime_columns(detail: pd.DataFrame) -> list[str]:
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regime_dummies = pd.get_dummies(detail["strategic_regime"], prefix="regime", dtype=float)
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for column in regime_dummies.columns:
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detail[column] = regime_dummies[column]
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return sorted(regime_dummies.columns.tolist())
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def _build_block_dataset(detail: pd.DataFrame, block_bars: int, regime_columns: list[str]) -> pd.DataFrame:
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rows: list[dict[str, object]] = []
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frame = detail.copy()
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frame["trailing_current_42"] = frame["current_return"].shift(1).rolling(42, min_periods=6).sum()
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frame["trailing_relaxed_42"] = frame["relaxed_return"].shift(1).rolling(42, min_periods=6).sum()
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frame["trailing_diff_42"] = frame["trailing_relaxed_42"] - frame["trailing_current_42"]
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frame["trailing_core_score_21"] = frame["core_score"].shift(1).rolling(21, min_periods=6).mean()
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frame["trailing_breadth_21"] = frame["breadth_persist"].shift(1).rolling(21, min_periods=6).mean()
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frame["trailing_choppy_21"] = frame["choppy_score"].shift(1).rolling(21, min_periods=6).mean()
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frame["extra_raw"] = np.minimum(frame["current_cash_pct"], np.maximum(frame["relaxed_invested_pct"] - frame["current_invested_pct"], 0.0))
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relaxed_unit = np.where(frame["relaxed_invested_pct"] > 1e-9, frame["relaxed_return"] / frame["relaxed_invested_pct"], 0.0)
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frame["overlay_add_return_full"] = frame["extra_raw"] * relaxed_unit
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for start in range(0, len(frame), block_bars):
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block = frame.iloc[start : start + block_bars]
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if block.empty:
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continue
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trigger = block.iloc[0]
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current_total = float((1.0 + block["current_return"]).prod() - 1.0)
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relaxed_total = float((1.0 + block["relaxed_return"]).prod() - 1.0)
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overlay_total = float((1.0 + (block["current_return"] + block["overlay_add_return_full"])).prod() / (1.0 + current_total) - 1.0)
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row = {
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"timestamp": trigger["timestamp"],
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"current_total": current_total,
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"relaxed_total": relaxed_total,
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"overlay_total_full": overlay_total,
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"current_cash_pct": float(trigger["current_cash_pct"]),
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"relaxed_invested_pct": float(trigger["relaxed_invested_pct"]),
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"core_score": float(trigger["core_score"]),
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"breadth_persist": float(trigger["breadth_persist"]),
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"funding_persist": float(trigger["funding_persist"]),
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"taker_persist": float(trigger["taker_persist"]),
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"volume_accel_persist": float(trigger["volume_accel_persist"]),
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"mean_taker_imbalance": float(trigger["mean_taker_imbalance"]),
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"taker_imbalance_dispersion": float(trigger["taker_imbalance_dispersion"]),
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"positive_taker_ratio": float(trigger["positive_taker_ratio"]),
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"mean_alt_volume_accel": float(trigger["mean_alt_volume_accel"]),
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"positive_volume_accel_ratio": float(trigger["positive_volume_accel_ratio"]),
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"funding_dispersion": float(trigger["funding_dispersion"]),
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"basis_dispersion": float(trigger["basis_dispersion"]),
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"alt_return_dispersion_7d": float(trigger["alt_return_dispersion_7d"]),
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"mean_funding_acceleration": float(trigger["mean_funding_acceleration"]),
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"mean_basis_trend": float(trigger["mean_basis_trend"]),
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"panic_score": float(trigger["panic_score"]),
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"choppy_score": float(trigger["choppy_score"]),
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"distribution_score": float(trigger["distribution_score"]),
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"trailing_current_42": float(trigger["trailing_current_42"]) if pd.notna(trigger["trailing_current_42"]) else 0.0,
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"trailing_relaxed_42": float(trigger["trailing_relaxed_42"]) if pd.notna(trigger["trailing_relaxed_42"]) else 0.0,
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"trailing_diff_42": float(trigger["trailing_diff_42"]) if pd.notna(trigger["trailing_diff_42"]) else 0.0,
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"trailing_core_score_21": float(trigger["trailing_core_score_21"]) if pd.notna(trigger["trailing_core_score_21"]) else 0.0,
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"trailing_breadth_21": float(trigger["trailing_breadth_21"]) if pd.notna(trigger["trailing_breadth_21"]) else 0.0,
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"trailing_choppy_21": float(trigger["trailing_choppy_21"]) if pd.notna(trigger["trailing_choppy_21"]) else 0.0,
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"block_start_index": int(start),
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"block_end_index": int(block.index[-1]),
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}
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for column in regime_columns:
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row[column] = float(trigger.get(column, 0.0))
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rows.append(row)
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return pd.DataFrame(rows)
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def _feature_columns(regime_columns: list[str]) -> list[str]:
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return [
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"core_score",
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"breadth_persist",
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"funding_persist",
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"taker_persist",
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"volume_accel_persist",
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"mean_taker_imbalance",
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"taker_imbalance_dispersion",
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"positive_taker_ratio",
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"mean_alt_volume_accel",
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"positive_volume_accel_ratio",
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"funding_dispersion",
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"basis_dispersion",
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"alt_return_dispersion_7d",
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"mean_funding_acceleration",
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"mean_basis_trend",
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"panic_score",
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"choppy_score",
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"distribution_score",
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"current_cash_pct",
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"relaxed_invested_pct",
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"trailing_current_42",
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"trailing_relaxed_42",
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"trailing_diff_42",
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"trailing_core_score_21",
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"trailing_breadth_21",
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"trailing_choppy_21",
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*regime_columns,
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]
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def _simulate_candidate(
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detail: pd.DataFrame,
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block_frame: pd.DataFrame,
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regime_columns: list[str],
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candidate: LearnedOverlayCandidate,
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) -> pd.Series:
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rows = detail.reset_index(drop=True)
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features = _feature_columns(regime_columns)
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returns: list[float] = []
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idx: list[pd.Timestamp] = []
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for block_idx, block in block_frame.iterrows():
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start_idx = int(block["block_start_index"])
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end_idx = int(block["block_end_index"])
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bar_block = rows.iloc[start_idx : end_idx + 1]
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use_overlay = False
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if block_idx >= candidate.train_min_blocks:
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train_start = max(0, block_idx - candidate.lookback_blocks)
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train = block_frame.iloc[train_start:block_idx]
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train_x = train[features].to_numpy(dtype=float)
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train_y = train["overlay_total_full"].to_numpy(dtype=float)
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test_x = block[features].to_numpy(dtype=float)
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pred = _ridge_predict(train_x, train_y, test_x, candidate.ridge_alpha)
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use_overlay = pred > candidate.prediction_threshold
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for row in bar_block.itertuples(index=False):
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extra_add = 0.0
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if use_overlay:
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extra_add = float(getattr(row, "overlay_add_return_full")) * candidate.overlay_scale
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returns.append(float(getattr(row, "current_return")) + extra_add)
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idx.append(pd.Timestamp(getattr(row, "timestamp")))
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return pd.Series(returns, index=pd.DatetimeIndex(idx, name="timestamp"), dtype=float)
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def _candidate_space() -> list[LearnedOverlayCandidate]:
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space: list[LearnedOverlayCandidate] = []
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for block_bars in (42, 84):
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for train_min_blocks in (12, 18, 24):
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for lookback_blocks in (24, 60):
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if lookback_blocks < train_min_blocks:
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continue
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for ridge_alpha in (0.5, 1.0, 5.0, 20.0):
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for prediction_threshold in (0.0, 0.0010, 0.0025, 0.0050):
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for overlay_scale in (0.25, 0.50, 0.75, 1.00):
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space.append(
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LearnedOverlayCandidate(
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block_bars=block_bars,
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train_min_blocks=train_min_blocks,
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lookback_blocks=lookback_blocks,
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ridge_alpha=ridge_alpha,
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prediction_threshold=prediction_threshold,
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overlay_scale=overlay_scale,
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)
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)
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return space
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def main() -> None:
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bundle, latest_bar = load_component_bundle(CACHE_PATH)
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eval_start = latest_bar - pd.Timedelta(days=1825)
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print("[phase] build current", flush=True)
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current = build_cash_overlay_period_components(
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bundle=bundle,
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eval_start=eval_start,
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eval_end=latest_bar,
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profile_name=BEST_CASH_OVERLAY.regime_profile,
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core_filter=BEST_CASH_OVERLAY.core_filter,
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cap_engine=BEST_CASH_OVERLAY.cap_engine,
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chop_engine=BEST_CASH_OVERLAY.chop_engine,
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dist_engine=BEST_CASH_OVERLAY.dist_engine,
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core_config_overrides=CURRENT_OVERHEAT_OVERRIDES,
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)
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print("[phase] build relaxed", flush=True)
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relaxed = build_cash_overlay_period_components(
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bundle=bundle,
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eval_start=eval_start,
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eval_end=latest_bar,
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profile_name=BEST_CASH_OVERLAY.regime_profile,
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core_filter=BEST_CASH_OVERLAY.core_filter,
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cap_engine=BEST_CASH_OVERLAY.cap_engine,
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chop_engine=BEST_CASH_OVERLAY.chop_engine,
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dist_engine=BEST_CASH_OVERLAY.dist_engine,
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core_config_overrides=RELAXED_OVERHEAT_OVERRIDES,
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)
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current_detail = _build_strategy_detail(current).rename(
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columns={
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"cash_pct": "current_cash_pct",
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"invested_pct": "current_invested_pct",
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"portfolio_return": "current_return",
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}
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)
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relaxed_detail = _build_strategy_detail(relaxed).rename(
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columns={
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"cash_pct": "relaxed_cash_pct",
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"invested_pct": "relaxed_invested_pct",
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"portfolio_return": "relaxed_return",
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}
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)
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detail = current_detail.merge(
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relaxed_detail[
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[
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"timestamp",
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"relaxed_cash_pct",
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"relaxed_invested_pct",
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"relaxed_return",
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]
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],
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on="timestamp",
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how="inner",
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)
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detail["extra_raw"] = np.minimum(detail["current_cash_pct"], np.maximum(detail["relaxed_invested_pct"] - detail["current_invested_pct"], 0.0))
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relaxed_unit = np.where(detail["relaxed_invested_pct"] > 1e-9, detail["relaxed_return"] / detail["relaxed_invested_pct"], 0.0)
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detail["overlay_add_return_full"] = detail["extra_raw"] * relaxed_unit
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regime_columns = _build_regime_columns(detail)
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candidates = _candidate_space()
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rows: list[dict[str, object]] = []
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print(f"[phase] learned search {len(candidates)} candidates", flush=True)
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block_cache: dict[int, pd.DataFrame] = {}
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|
for idx, candidate in enumerate(candidates, start=1):
|
|
block_frame = block_cache.get(candidate.block_bars)
|
|
if block_frame is None:
|
|
block_frame = _build_block_dataset(detail, candidate.block_bars, regime_columns)
|
|
block_cache[candidate.block_bars] = block_frame
|
|
returns = _simulate_candidate(detail, block_frame, regime_columns, candidate)
|
|
curve = _curve_from_returns(returns)
|
|
windows, years, score, negative_years, mdd_violations = _metrics_for_curve(curve, latest_bar)
|
|
rows.append(
|
|
{
|
|
"candidate": asdict(candidate),
|
|
"name": candidate.name,
|
|
"score": score,
|
|
"negative_years": negative_years,
|
|
"mdd_violations": mdd_violations,
|
|
"windows": windows,
|
|
"years": years,
|
|
}
|
|
)
|
|
if idx % 96 == 0 or idx == len(candidates):
|
|
print(f"[search] {idx}/{len(candidates)}", flush=True)
|
|
|
|
rows.sort(key=lambda row: float(row["score"]), reverse=True)
|
|
best = rows[0]
|
|
payload = {
|
|
"analysis": "current_relaxed_learned_entry_overlay",
|
|
"latest_bar": str(latest_bar),
|
|
"candidate": best["candidate"],
|
|
"score": best["score"],
|
|
"negative_years": best["negative_years"],
|
|
"mdd_violations": best["mdd_violations"],
|
|
"windows": best["windows"],
|
|
"years": best["years"],
|
|
"baselines": _baseline_summary(),
|
|
"search_top": rows[:10],
|
|
}
|
|
OUT_JSON.write_text(json.dumps(payload, indent=2), encoding="utf-8")
|
|
print(json.dumps(payload, indent=2))
|
|
print(f"[saved] {OUT_JSON}", flush=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|