Add blocker research and routing rename
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265
scripts/run_current_relaxed_oracle_analysis.py
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265
scripts/run_current_relaxed_oracle_analysis.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 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, 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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_overlay_weights,
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)
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OUT_JSON = Path("/tmp/strategy32_current_relaxed_oracle_analysis.json")
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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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core_ret = float(core_returns.get(execution_ts, 0.0))
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cap_ret = float(cap_returns.get(execution_ts, 0.0))
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chop_ret = float(chop_returns.get(execution_ts, 0.0))
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dist_ret = float(dist_returns.get(execution_ts, 0.0))
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portfolio_return = (
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core_ret
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+ cap_weight * cap_ret
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+ chop_weight * chop_ret
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+ dist_weight * dist_ret
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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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"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": portfolio_return,
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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 _window_metrics(curve: pd.Series, latest_bar: pd.Timestamp) -> dict[str, dict[str, float]]:
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return {
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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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def _year_metrics(curve: pd.Series, latest_bar: pd.Timestamp) -> dict[str, dict[str, float]]:
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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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return years
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def _regime_summary(detail: pd.DataFrame) -> list[dict[str, object]]:
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rows: list[dict[str, object]] = []
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for regime, chunk in detail.groupby("strategic_regime", dropna=False):
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rows.append(
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{
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"strategic_regime": regime or "",
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"bars": int(len(chunk)),
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"share": float(len(chunk) / len(detail)) if len(detail) else 0.0,
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"current_avg_return": float(chunk["current_return"].mean()),
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"relaxed_avg_return": float(chunk["relaxed_return"].mean()),
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"oracle_avg_return": float(chunk["oracle_bar_return"].mean()),
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"relaxed_win_rate": float((chunk["relaxed_return"] > chunk["current_return"]).mean()),
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"avg_diff_relaxed_minus_current": float((chunk["relaxed_return"] - chunk["current_return"]).mean()),
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}
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)
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return sorted(rows, key=lambda row: row["share"], reverse=True)
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def _winner_feature_summary(detail: pd.DataFrame, winner: str) -> dict[str, object]:
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if winner == "relaxed":
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mask = detail["relaxed_return"] > detail["current_return"]
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else:
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mask = detail["current_return"] >= detail["relaxed_return"]
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chunk = detail.loc[mask].copy()
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if chunk.empty:
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return {"bars": 0}
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return {
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"bars": int(len(chunk)),
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"share": float(len(chunk) / len(detail)) if len(detail) else 0.0,
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"avg_core_score": float(chunk["core_score"].mean()),
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"avg_breadth_persist": float(chunk["breadth_persist"].mean()),
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"avg_funding_persist": float(chunk["funding_persist"].mean()),
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"avg_panic_score": float(chunk["panic_score"].mean()),
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"avg_choppy_score": float(chunk["choppy_score"].mean()),
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"avg_distribution_score": float(chunk["distribution_score"].mean()),
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"avg_current_cash_pct": float(chunk["current_cash_pct"].mean()),
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"avg_relaxed_cash_pct": float(chunk["relaxed_cash_pct"].mean()),
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"top_regimes": (
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chunk["strategic_regime"].value_counts(normalize=True).head(5).rename_axis("regime").reset_index(name="share").to_dict(orient="records")
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),
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}
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def _oracle_block_returns(detail: pd.DataFrame, block_bars: int) -> pd.Series:
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rows: list[float] = []
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idx: list[pd.Timestamp] = []
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bar_count = len(detail)
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for start in range(0, bar_count, block_bars):
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end = min(start + block_bars, bar_count)
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chunk = detail.iloc[start:end]
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current_total = float((1.0 + chunk["current_return"]).prod() - 1.0)
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relaxed_total = float((1.0 + chunk["relaxed_return"]).prod() - 1.0)
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winner = "relaxed" if relaxed_total > current_total else "current"
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source_col = "relaxed_return" if winner == "relaxed" else "current_return"
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rows.extend(chunk[source_col].tolist())
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idx.extend(chunk["timestamp"].tolist())
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return pd.Series(rows, index=pd.DatetimeIndex(idx, name="timestamp"), dtype=float)
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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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merged = 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_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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merged["oracle_bar_return"] = merged[["current_return", "relaxed_return"]].max(axis=1)
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merged["winner"] = merged.apply(
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lambda row: "relaxed" if row["relaxed_return"] > row["current_return"] else "current",
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axis=1,
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)
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current_curve = _curve_from_returns(merged.set_index("timestamp")["current_return"])
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relaxed_curve = _curve_from_returns(merged.set_index("timestamp")["relaxed_return"])
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oracle_bar_curve = _curve_from_returns(merged.set_index("timestamp")["oracle_bar_return"])
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oracle_7d_curve = _curve_from_returns(_oracle_block_returns(merged, block_bars=42))
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oracle_30d_curve = _curve_from_returns(_oracle_block_returns(merged, block_bars=180))
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payload = {
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"analysis": "current_relaxed_oracle",
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"latest_bar": str(latest_bar),
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"bar_count": int(len(merged)),
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"relaxed_win_rate": float((merged["winner"] == "relaxed").mean()),
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"curves": {
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"current": {
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"windows": _window_metrics(current_curve, latest_bar),
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"years": _year_metrics(current_curve, latest_bar),
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},
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"relaxed": {
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"windows": _window_metrics(relaxed_curve, latest_bar),
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"years": _year_metrics(relaxed_curve, latest_bar),
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},
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"oracle_bar": {
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"windows": _window_metrics(oracle_bar_curve, latest_bar),
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"years": _year_metrics(oracle_bar_curve, latest_bar),
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},
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"oracle_7d": {
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"windows": _window_metrics(oracle_7d_curve, latest_bar),
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"years": _year_metrics(oracle_7d_curve, latest_bar),
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},
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"oracle_30d": {
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"windows": _window_metrics(oracle_30d_curve, latest_bar),
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"years": _year_metrics(oracle_30d_curve, latest_bar),
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},
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},
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"regime_summary": _regime_summary(merged),
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"winner_feature_summary": {
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"relaxed": _winner_feature_summary(merged, "relaxed"),
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"current": _winner_feature_summary(merged, "current"),
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},
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"latest_rows": merged.tail(10).assign(timestamp=lambda df: df["timestamp"].astype(str)).to_dict(orient="records"),
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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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