strategy32/scripts/run_current_cash_learned_blocker_exact_plateau.py

from __future__ import annotations

import json
import sys
from dataclasses import asdict
from pathlib import Path

import pandas as pd

PACKAGE_PARENT = Path(__file__).resolve().parents[2]
if str(PACKAGE_PARENT) not in sys.path:
    sys.path.insert(0, str(PACKAGE_PARENT))

from strategy32.live.runtime import BEST_CASH_OVERLAY
from strategy32.research.soft_router import (
    build_cash_overlay_period_components,
    load_component_bundle,
    score_candidate,
    segment_metrics,
)
from strategy32.scripts.run_current_cash_learned_blocker import (
    CACHE_PATH,
    CURRENT_OVERHEAT_OVERRIDES,
    LearnedBlockerCandidate,
    _build_block_dataset,
    _build_regime_columns,
    _build_strategy_detail,
    _curve_from_returns,
    _simulate_candidate,
)
from strategy32.scripts.run_current_relaxed_hybrid_experiment import WINDOWS, YEAR_PERIODS, YTD_START


OUT_JSON = Path("/tmp/strategy32_current_cash_blocker_exact_plateau.json")
BASELINE_JSON = Path("/tmp/strategy32_live_combo_backtest.json")


def _candidate_space() -> list[LearnedBlockerCandidate]:
    candidates: list[LearnedBlockerCandidate] = []
    for block_bars in (21, 42):
        for train_min_blocks in (8, 12, 18):
            for ridge_alpha in (0.5, 1.0, 5.0, 20.0):
                for prediction_threshold in (-0.0050, -0.0025, 0.0):
                    for blocked_scale in (0.0, 0.25, 0.50):
                        candidates.append(
                            LearnedBlockerCandidate(
                                block_bars=block_bars,
                                train_min_blocks=train_min_blocks,
                                lookback_blocks=24,
                                ridge_alpha=ridge_alpha,
                                prediction_threshold=prediction_threshold,
                                blocked_scale=blocked_scale,
                            )
                        )
    return candidates


def main() -> None:
    bundle, latest_bar = load_component_bundle(CACHE_PATH)
    period_specs: list[tuple[str, str, pd.Timestamp, pd.Timestamp]] = []
    for days, label in WINDOWS:
        period_specs.append(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar))
    for label, start, end_exclusive in YEAR_PERIODS:
        period_specs.append(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))))
    period_specs.append(("year", "2026_YTD", YTD_START, latest_bar))

    period_cache: dict[str, dict[str, object]] = {}
    for kind, label, start, end in period_specs:
        components = build_cash_overlay_period_components(
            bundle=bundle,
            eval_start=start,
            eval_end=end,
            profile_name=BEST_CASH_OVERLAY.regime_profile,
            core_filter=BEST_CASH_OVERLAY.core_filter,
            cap_engine=BEST_CASH_OVERLAY.cap_engine,
            chop_engine=BEST_CASH_OVERLAY.chop_engine,
            dist_engine=BEST_CASH_OVERLAY.dist_engine,
            core_config_overrides=CURRENT_OVERHEAT_OVERRIDES,
        )
        detail = _build_strategy_detail(components)
        regime_columns = _build_regime_columns(detail)
        period_cache[label] = {
            "kind": kind,
            "start": start,
            "end": end,
            "detail": detail,
            "regime_columns": regime_columns,
            "block_frames": {},
        }
    print(f"[periods] cached {len(period_cache)} periods", flush=True)

    candidates = _candidate_space()
    print(f"[candidates] {len(candidates)} exact candidates", flush=True)

    rows: list[dict[str, object]] = []
    for idx, candidate in enumerate(candidates, start=1):
        windows: dict[str, dict[str, float]] = {}
        years: dict[str, dict[str, float]] = {}
        for label, cached in period_cache.items():
            detail = cached["detail"]
            regime_columns = cached["regime_columns"]
            block_frames = cached["block_frames"]
            if candidate.block_bars not in block_frames:
                block_frames[candidate.block_bars] = _build_block_dataset(detail, candidate.block_bars, regime_columns)
            block_frame = block_frames[candidate.block_bars]
            simulated_returns = _simulate_candidate(detail, block_frame, regime_columns, candidate)
            curve = _curve_from_returns(simulated_returns)
            metrics = segment_metrics(curve, cached["start"], cached["end"])
            if cached["kind"] == "window":
                windows[label] = metrics
            else:
                years[label] = metrics

        score, negative_years, mdd_violations = score_candidate(
            {label: windows[label] for _, label in WINDOWS},
            {label: years[label] for label, _, _ in YEAR_PERIODS},
        )
        rows.append(
            {
                "candidate": asdict(candidate),
                "name": candidate.name,
                "score": score,
                "negative_years": negative_years,
                "mdd_violations": mdd_violations,
                "windows": windows,
                "years": years,
            }
        )
        if idx % 24 == 0 or idx == len(candidates):
            print(f"[progress] {idx}/{len(candidates)}", flush=True)

    rows.sort(key=lambda item: float(item["score"]), reverse=True)
    output = {
        "analysis": "current_cash_blocker_exact_plateau",
        "latest_bar": str(latest_bar),
        "all_count": len(rows),
        "top10": rows[:10],
        "baseline_exact": json.loads(BASELINE_JSON.read_text(encoding="utf-8")) if BASELINE_JSON.exists() else None,
    }
    OUT_JSON.write_text(json.dumps(output, indent=2), encoding="utf-8")
    print(json.dumps(output["top10"], indent=2))
    print(f"[saved] {OUT_JSON}", flush=True)


if __name__ == "__main__":
    main()