Add blocker research and routing rename
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217
scripts/run_current_cash_conditional_blocker.py
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217
scripts/run_current_cash_conditional_blocker.py
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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
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from strategy32.scripts.run_current_cash_learned_blocker import (
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CACHE_PATH,
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CURRENT_OVERHEAT_OVERRIDES,
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LearnedBlockerCandidate,
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_build_block_dataset,
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_build_regime_columns,
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_build_strategy_detail,
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_curve_from_returns,
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_metrics_for_curve,
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_ridge_predict,
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)
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OUT_JSON = Path("/tmp/strategy32_current_cash_conditional_blocker.json")
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@dataclass(frozen=True, slots=True)
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class ConditionalBlockerCandidate:
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blocker: LearnedBlockerCandidate
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max_trailing_total_21: float
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min_choppy_score: float
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min_distribution_score: float
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min_cash_pct: float
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@property
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def name(self) -> str:
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return (
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f"{self.blocker.name}"
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f"|trail<={self.max_trailing_total_21:.3f}"
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f"|chop>={self.min_choppy_score:.2f}"
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f"|dist>={self.min_distribution_score:.2f}"
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f"|cash>={self.min_cash_pct:.2f}"
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)
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def _conditional_active(block: pd.Series, candidate: ConditionalBlockerCandidate) -> bool:
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return (
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float(block["trailing_total_21"]) <= candidate.max_trailing_total_21
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and (
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float(block["choppy_score"]) >= candidate.min_choppy_score
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or float(block["distribution_score"]) >= candidate.min_distribution_score
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or float(block["cash_pct"]) >= candidate.min_cash_pct
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)
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)
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def _simulate_conditional_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: ConditionalBlockerCandidate,
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) -> pd.Series:
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rows = detail.reset_index(drop=True)
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features = [
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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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"cash_pct",
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"invested_pct",
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"trailing_total_21",
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"trailing_total_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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returns: list[float] = []
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idx: list[pd.Timestamp] = []
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blocker = candidate.blocker
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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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exposure_scale = 1.0
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if block_idx >= blocker.train_min_blocks:
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train_start = max(0, block_idx - blocker.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["block_total"].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, blocker.ridge_alpha)
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if pred <= blocker.prediction_threshold and _conditional_active(block, candidate):
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exposure_scale = blocker.blocked_scale
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for row in bar_block.itertuples(index=False):
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returns.append(float(getattr(row, "portfolio_return")) * exposure_scale)
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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[ConditionalBlockerCandidate]:
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candidates: list[ConditionalBlockerCandidate] = []
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for blocked_scale in (0.0, 0.25):
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blocker = LearnedBlockerCandidate(
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block_bars=42,
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train_min_blocks=12,
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lookback_blocks=24,
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ridge_alpha=1.0,
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prediction_threshold=-0.0025,
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blocked_scale=blocked_scale,
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)
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for max_trailing_total_21 in (0.0, -0.01, -0.02):
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for min_choppy_score in (0.20, 0.30, 0.40):
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for min_distribution_score in (0.10, 0.20, 0.30):
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for min_cash_pct in (0.20, 0.40, 0.60):
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candidates.append(
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ConditionalBlockerCandidate(
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blocker=blocker,
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max_trailing_total_21=max_trailing_total_21,
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min_choppy_score=min_choppy_score,
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min_distribution_score=min_distribution_score,
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min_cash_pct=min_cash_pct,
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)
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)
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return candidates
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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 baseline", flush=True)
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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=CURRENT_OVERHEAT_OVERRIDES,
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)
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detail = _build_strategy_detail(components)
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regime_columns = _build_regime_columns(detail)
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block_frame = _build_block_dataset(detail, 42, regime_columns)
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baseline_curve = _curve_from_returns(detail.set_index("timestamp")["portfolio_return"])
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baseline_windows, baseline_years, baseline_score, *_ = _metrics_for_curve(baseline_curve, latest_bar)
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top: list[dict[str, object]] = []
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candidates = _candidate_space()
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print(f"[phase] conditional blocker search {len(candidates)} candidates", flush=True)
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for idx, candidate in enumerate(candidates, start=1):
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sim_returns = _simulate_conditional_candidate(detail, block_frame, regime_columns, candidate)
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curve = _curve_from_returns(sim_returns)
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windows, years, score, negative_years, mdd_violations = _metrics_for_curve(curve, latest_bar)
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payload = {
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"candidate": {
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**asdict(candidate.blocker),
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"max_trailing_total_21": candidate.max_trailing_total_21,
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"min_choppy_score": candidate.min_choppy_score,
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"min_distribution_score": candidate.min_distribution_score,
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"min_cash_pct": candidate.min_cash_pct,
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},
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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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top.append(payload)
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top.sort(key=lambda item: float(item["score"]), reverse=True)
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top = top[:10]
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if idx % 50 == 0 or idx == len(candidates):
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print(f"[search] {idx}/{len(candidates)}", flush=True)
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output = {
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"analysis": "current_cash_conditional_blocker",
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"latest_bar": str(latest_bar),
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"baseline": {
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"score": baseline_score,
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"windows": baseline_windows,
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"years": baseline_years,
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},
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"top10": top,
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}
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OUT_JSON.write_text(json.dumps(output, indent=2), encoding="utf-8")
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print(json.dumps(top[:5], 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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