297 lines
12 KiB
Python
297 lines
12 KiB
Python
from __future__ import annotations
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import json
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import os
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import sys
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from dataclasses import asdict
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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 strategy29.backtest.window_analysis import slice_bundle
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from strategy32.live.runtime import BEST_CASH_OVERLAY, LIVE_STRATEGY_OVERRIDES
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from strategy32.research.soft_router import (
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MacroScaleSpec,
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build_cash_overlay_period_components,
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compose_cash_overlay_curve,
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load_component_bundle,
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score_candidate,
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segment_metrics,
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)
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CACHE_PATH = "/tmp/strategy32_fixed66_bundle.pkl"
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OUT_JSON = Path("/tmp/strategy32_relaxed_macro_scaling_search.json")
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RELAXED_OVERHEAT_OVERRIDES = {
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**LIVE_STRATEGY_OVERRIDES,
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"momentum_min_score": 0.58,
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"momentum_min_relative_strength": -0.03,
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"momentum_min_7d_return": 0.00,
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"universe_min_avg_dollar_volume": 75_000_000.0,
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"hard_filter_refresh_cadence": "1d",
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"hard_filter_min_history_bars": 120,
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"hard_filter_lookback_bars": 30,
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"hard_filter_min_avg_dollar_volume": 50_000_000.0,
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}
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CURRENT_OVERHEAT_OVERRIDES = {
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**LIVE_STRATEGY_OVERRIDES,
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"hard_filter_refresh_cadence": "1d",
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"hard_filter_min_history_bars": 120,
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"hard_filter_lookback_bars": 30,
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"hard_filter_min_avg_dollar_volume": 50_000_000.0,
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}
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WINDOWS = (
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(365, "1y"),
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(730, "2y"),
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(1095, "3y"),
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(1460, "4y"),
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(1825, "5y"),
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)
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YEAR_PERIODS = (
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("2021", pd.Timestamp("2021-03-16 04:00:00+00:00"), pd.Timestamp("2022-01-01 00:00:00+00:00")),
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("2022", pd.Timestamp("2022-01-01 00:00:00+00:00"), pd.Timestamp("2023-01-01 00:00:00+00:00")),
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("2023", pd.Timestamp("2023-01-01 00:00:00+00:00"), pd.Timestamp("2024-01-01 00:00:00+00:00")),
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("2024", pd.Timestamp("2024-01-01 00:00:00+00:00"), pd.Timestamp("2025-01-01 00:00:00+00:00")),
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("2025", pd.Timestamp("2025-01-01 00:00:00+00:00"), pd.Timestamp("2026-01-01 00:00:00+00:00")),
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)
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YTD_START = pd.Timestamp("2026-01-01 00:00:00+00:00")
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def _clip01(value: float) -> float:
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return min(max(float(value), 0.0), 1.0)
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def _ramp(value: float, start: float, end: float) -> float:
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if end == start:
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return 1.0 if value >= end else 0.0
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if value <= start:
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return 0.0
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if value >= end:
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return 1.0
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return (value - start) / (end - start)
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def _build_macro_scale_map(sliced_bundle, *, timestamps: list[pd.Timestamp], spec: MacroScaleSpec) -> pd.Series:
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btc_prices = sliced_bundle.prices["BTC"]
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closes = btc_prices.set_index("timestamp")["close"].astype(float).sort_index()
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daily = closes.resample("1D").last().dropna()
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weekly = daily.resample("W-SUN").last().dropna()
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fast = weekly.ewm(span=spec.fast_weeks, adjust=False).mean()
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slow = weekly.ewm(span=spec.slow_weeks, adjust=False).mean()
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close_scale = (weekly / slow - 1.0).apply(lambda value: _ramp(float(value), spec.close_gap_start, spec.close_gap_full))
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fast_scale = (fast / slow - 1.0).apply(lambda value: _ramp(float(value), spec.fast_gap_start, spec.fast_gap_full))
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blended = spec.close_weight * close_scale + (1.0 - spec.close_weight) * fast_scale
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macro_scale = spec.floor + (1.0 - spec.floor) * blended.clip(0.0, 1.0)
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aligned = macro_scale.reindex(pd.DatetimeIndex(timestamps, name="timestamp"), method="ffill")
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return aligned.fillna(1.0).clip(spec.floor, 1.0).astype(float)
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def _candidate_specs() -> list[MacroScaleSpec]:
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specs: list[MacroScaleSpec] = []
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for floor in (0.25, 0.35, 0.45):
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for close_gap_start, close_gap_full in ((-0.08, 0.02), (-0.06, 0.02), (-0.05, 0.04)):
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for fast_gap_start, fast_gap_full in ((-0.04, 0.01), (-0.03, 0.02)):
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for close_weight in (0.55, 0.65):
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specs.append(
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MacroScaleSpec(
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floor=floor,
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close_gap_start=close_gap_start,
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close_gap_full=close_gap_full,
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fast_gap_start=fast_gap_start,
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fast_gap_full=fast_gap_full,
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close_weight=close_weight,
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)
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)
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return specs
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def _collect_metrics(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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window_results: dict[str, dict[str, float]] = {}
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for days, label in WINDOWS:
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start = latest_bar - pd.Timedelta(days=days)
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window_results[label] = segment_metrics(curve, start, latest_bar)
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year_results: dict[str, dict[str, float]] = {}
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for label, start, end_exclusive in YEAR_PERIODS:
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year_results[label] = segment_metrics(curve, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1)))
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year_results["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: window_results[label] for _, label in WINDOWS},
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{label: year_results[label] for label, _, _ in YEAR_PERIODS},
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)
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return window_results, year_results, score, negative_years, mdd_violations
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def _evaluate_exact_sequential(
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bundle,
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latest_bar: pd.Timestamp,
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*,
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core_overrides: dict[str, object],
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macro_scale_spec: MacroScaleSpec | None,
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) -> dict[str, object]:
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window_results: dict[str, dict[str, float]] = {}
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year_results: dict[str, dict[str, float]] = {}
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periods = [
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*(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar) for days, label in WINDOWS),
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*(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))) for label, start, end_exclusive in YEAR_PERIODS),
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("year", "2026_YTD", YTD_START, latest_bar),
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]
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latest_weights: list[dict[str, object]] = []
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for kind, label, start, end in periods:
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components = build_cash_overlay_period_components(
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bundle=bundle,
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eval_start=start,
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eval_end=end,
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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=core_overrides,
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macro_scale_spec=macro_scale_spec,
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)
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curve, weights = compose_cash_overlay_curve(candidate=BEST_CASH_OVERLAY, **components)
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metrics = segment_metrics(curve, start, end)
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if kind == "window":
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window_results[label] = metrics
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else:
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year_results[label] = metrics
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if label == "2026_YTD":
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latest_weights = weights.tail(1).assign(timestamp=lambda df: df["timestamp"].astype(str)).to_dict(orient="records")
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score, negative_years, mdd_violations = score_candidate(
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{label: window_results[label] for _, label in WINDOWS},
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{label: year_results[label] for label, _, _ in YEAR_PERIODS},
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)
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return {
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"candidate": asdict(BEST_CASH_OVERLAY),
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"core_overrides": core_overrides,
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"macro_scale_spec": asdict(macro_scale_spec) if macro_scale_spec is not None else None,
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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": window_results,
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"years": year_results,
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"latest_weights": latest_weights,
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"validation": "exact_independent_periods_cash_overlay_sequential",
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}
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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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sliced = slice_bundle(bundle, eval_start - pd.Timedelta(days=365), latest_bar)
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print("[phase] build relaxed core components", flush=True)
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relaxed_components = 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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print("[phase] search macro specs", flush=True)
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search_rows: list[dict[str, object]] = []
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specs = _candidate_specs()
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for idx, spec in enumerate(specs, start=1):
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macro_scale_map = _build_macro_scale_map(sliced, timestamps=relaxed_components["timestamps"][:-1], spec=spec)
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curve, _weights = compose_cash_overlay_curve(
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candidate=BEST_CASH_OVERLAY,
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timestamps=relaxed_components["timestamps"],
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score_frame=relaxed_components["score_frame"],
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core_returns=relaxed_components["core_returns"],
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core_exposure_frame=relaxed_components["core_exposure_frame"],
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cap_returns=relaxed_components["cap_returns"],
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chop_returns=relaxed_components["chop_returns"],
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dist_returns=relaxed_components["dist_returns"],
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macro_scale_map=macro_scale_map,
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)
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windows, years, score, negative_years, mdd_violations = _collect_metrics(curve, latest_bar)
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search_rows.append(
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{
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"macro_scale_spec": asdict(spec),
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"windows": windows,
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"years": years,
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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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}
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)
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if idx % 6 == 0 or idx == len(specs):
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print(f"[search] {idx}/{len(specs)}", flush=True)
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search_rows.sort(key=lambda row: float(row["score"]), reverse=True)
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top_search = search_rows[:5]
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search_only = os.getenv("STRATEGY32_SEARCH_ONLY", "").strip().lower() in {"1", "true", "yes", "on"}
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if search_only:
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payload = {
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"analysis": "relaxed_overheat_macro_scaling_search",
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"mode": "search_only",
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"latest_bar": str(latest_bar),
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"core_filter": "relaxed_overheat",
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"candidate": asdict(BEST_CASH_OVERLAY),
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"search_top": top_search,
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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}")
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return
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print("[phase] exact baselines", flush=True)
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baselines = {
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"current_overheat": _evaluate_exact_sequential(
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bundle,
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latest_bar,
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core_overrides=CURRENT_OVERHEAT_OVERRIDES,
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macro_scale_spec=None,
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),
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"relaxed_overheat": _evaluate_exact_sequential(
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bundle,
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latest_bar,
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core_overrides=RELAXED_OVERHEAT_OVERRIDES,
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macro_scale_spec=None,
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),
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}
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best_spec = MacroScaleSpec(**top_search[0]["macro_scale_spec"])
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print(f"[phase] exact best spec {best_spec.name}", flush=True)
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best_exact = _evaluate_exact_sequential(
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bundle,
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latest_bar,
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core_overrides=RELAXED_OVERHEAT_OVERRIDES,
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macro_scale_spec=best_spec,
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)
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payload = {
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"analysis": "relaxed_overheat_macro_scaling_search",
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"latest_bar": str(latest_bar),
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"core_filter": "relaxed_overheat",
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"candidate": asdict(BEST_CASH_OVERLAY),
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"baselines": baselines,
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"search_top": top_search,
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"best_exact": best_exact,
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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}")
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if __name__ == "__main__":
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main()
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