279 lines
11 KiB
Python
279 lines
11 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 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_block_router.json")
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@dataclass(frozen=True, slots=True)
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class BlockRouterCandidate:
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positive_regimes: tuple[str, ...]
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core_score_min: float
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breadth_persist_min: float
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funding_persist_min: float
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panic_max: float
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choppy_max: float
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distribution_max: float
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current_cash_min: float
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block_bars: int
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@property
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def name(self) -> str:
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regimes = ",".join(self.positive_regimes)
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return (
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f"regimes:{regimes}"
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f"|core>={self.core_score_min:.2f}"
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f"|breadth>={self.breadth_persist_min:.2f}"
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f"|funding>={self.funding_persist_min:.2f}"
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f"|panic<={self.panic_max:.2f}"
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f"|choppy<={self.choppy_max:.2f}"
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f"|dist<={self.distribution_max:.2f}"
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f"|cash>={self.current_cash_min:.2f}"
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f"|block:{self.block_bars}"
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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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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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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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"core_cash_pct": core_cash_pct,
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"portfolio_return": portfolio_return,
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}
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)
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return pd.DataFrame(rows)
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def _pick_relaxed(row: pd.Series, candidate: BlockRouterCandidate) -> bool:
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return (
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str(row.get("strategic_regime", "")) in candidate.positive_regimes
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and float(row.get("core_score", 0.0)) >= candidate.core_score_min
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and float(row.get("breadth_persist", 0.0)) >= candidate.breadth_persist_min
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and float(row.get("funding_persist", 0.0)) >= candidate.funding_persist_min
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and float(row.get("panic_score", 0.0)) <= candidate.panic_max
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and float(row.get("choppy_score", 0.0)) <= candidate.choppy_max
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and float(row.get("distribution_score", 0.0)) <= candidate.distribution_max
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and float(row.get("current_cash_pct", 0.0)) >= candidate.current_cash_min
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)
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def _compose_block_returns(detail: pd.DataFrame, candidate: BlockRouterCandidate) -> pd.Series:
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returns: list[float] = []
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idx: list[pd.Timestamp] = []
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rows = detail.reset_index(drop=True)
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for start in range(0, len(rows), candidate.block_bars):
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end = min(start + candidate.block_bars, len(rows))
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block = rows.iloc[start:end]
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trigger = block.iloc[0]
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use_relaxed = _pick_relaxed(trigger, candidate)
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source_col = "relaxed_return" if use_relaxed else "current_return"
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returns.extend(block[source_col].tolist())
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idx.extend(block["timestamp"].tolist())
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return pd.Series(returns, index=pd.DatetimeIndex(idx, name="timestamp"), dtype=float)
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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 _candidate_space() -> list[BlockRouterCandidate]:
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space: list[BlockRouterCandidate] = []
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positive_sets = (
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("MOMENTUM_EXPANSION",),
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("MOMENTUM_EXPANSION", "EUPHORIC_BREAKOUT"),
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("CHOPPY_ROTATION", "MOMENTUM_EXPANSION"),
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)
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for positive_regimes in positive_sets:
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for core_score_min in (0.50, 0.55, 0.60):
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for breadth_persist_min in (0.45, 0.50, 0.55):
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for funding_persist_min in (0.50, 0.55, 0.60):
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for panic_max in (0.20, 0.30):
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for choppy_max in (0.20, 0.30, 0.40):
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for distribution_max in (0.20, 0.30, 0.40):
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for current_cash_min in (0.50, 0.65, 0.80):
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for block_bars in (42, 84, 180):
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space.append(
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BlockRouterCandidate(
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positive_regimes=positive_regimes,
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core_score_min=core_score_min,
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breadth_persist_min=breadth_persist_min,
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funding_persist_min=funding_persist_min,
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panic_max=panic_max,
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choppy_max=choppy_max,
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distribution_max=distribution_max,
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current_cash_min=current_cash_min,
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block_bars=block_bars,
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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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"core_cash_pct": "current_cash_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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"core_cash_pct": "relaxed_cash_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[["timestamp", "relaxed_cash_pct", "relaxed_return"]],
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on="timestamp",
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how="inner",
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)
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rows: list[dict[str, object]] = []
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candidates = _candidate_space()
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print(f"[phase] search {len(candidates)} block-router candidates", flush=True)
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for idx, candidate in enumerate(candidates, start=1):
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returns = _compose_block_returns(detail, candidate)
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curve = _curve_from_returns(returns)
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windows, years, score, negative_years, mdd_violations = _metrics_for_curve(curve, latest_bar)
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rows.append(
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{
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"candidate": asdict(candidate),
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"name": candidate.name,
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"score": score,
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"negative_years": negative_years,
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"mdd_violations": mdd_violations,
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"windows": windows,
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"years": years,
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}
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)
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if idx % 96 == 0 or idx == len(candidates):
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print(f"[search] {idx}/{len(candidates)}", flush=True)
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rows.sort(key=lambda row: float(row["score"]), reverse=True)
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best = BlockRouterCandidate(**rows[0]["candidate"])
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best_returns = _compose_block_returns(detail, best)
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best_curve = _curve_from_returns(best_returns)
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windows, years, score, negative_years, mdd_violations = _metrics_for_curve(best_curve, latest_bar)
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payload = {
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"analysis": "current_relaxed_block_router",
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"latest_bar": str(latest_bar),
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"candidate": asdict(best),
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"score": score,
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"negative_years": negative_years,
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"mdd_violations": mdd_violations,
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"windows": windows,
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"years": years,
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"baselines": _baseline_summary(),
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"search_top": rows[:10],
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}
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OUT_JSON.write_text(json.dumps(payload, indent=2), encoding="utf-8")
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print(json.dumps(payload, indent=2))
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print(f"[saved] {OUT_JSON}", flush=True)
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if __name__ == "__main__":
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main()
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