"""Diagnose exactly why the scripted test and workflow give different results. Compares the same pred.pkl through: 1. Script logic (weekly rebalance, equal-weight, hold-through-week, zero cost) 2. Workflow logic (PortAnaRecord daily backtest, TopkDropout-like) Isolates the effect of: A. Weekly vs daily position evaluation B. Equal weight vs risk_degree sizing C. Hold-through-week vs daily top-k re-ranking """ from __future__ import annotations import json, pathlib import numpy as np import pandas as pd LAKE_ROOT = "/home/data/lake" OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf") WINDOWS = [ {"label": "2026", "start": "2026-01-04", "end": "2026-08-19", "pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"}, {"label": "2025", "start": "2025-01-02", "end": "2025-12-31", "pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"}, {"label": "2024", "start": "2024-01-02", "end": "2024-12-31", "pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"}, {"label": "2023", "start": "2023-01-03", "end": "2023-12-29", "pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"}, {"label": "2021", "start": "2021-01-04", "end": "2021-12-31", "pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"}, ] SYMS = [ "SPY","QQQ","DIA","IWM","MDY","VTI","VOO","VEA","VWO","VT","EFA","EEM", "TLT","IEF","SHY","AGG","BND","LQD","HYG","JNK","EMB","GLD","SLV", "USO","UNG","DBA","DBC","XLK","XLF","XLE","XLV","XLI","XLY","XLP", "XLU","XLB","XLRE","ARKK","SMH","SOXX","IBB","XBI","ITA","XAR", "ICLN","TAN","FDN","IGV","ESPO","REM", ] def load_pred(path): df = pd.read_pickle(path) s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df idx = s.index new_dt = pd.to_datetime(idx.get_level_values(0)).normalize() s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names) return s def load_closes(start, end): from tac_qlib.data.config import LakeConfig, resolve_lake_root cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US") closes = {} for sym in SYMS: p = cfg.bar_path("1d", sym) if not p.exists(): continue try: df = pd.read_parquet(p) except: continue if not len(df): continue tcol = df["t"] if "t" in df.columns else df["date"] ts = pd.to_datetime(tcol) df = df.assign(_t=ts).set_index("_t").sort_index() warmup = pd.Timestamp(start) - pd.Timedelta(days=60) df = df.loc[warmup:end] if len(df) >= 22: closes[sym] = df["c"] return pd.DataFrame(closes) def strategy_script(pred, closes, start, end, topk=10, risk_degree=1.0): """Mimics the scripted test: weekly rebalance, hold all week.""" ret_df = closes.pct_change() ret_df.index = pd.to_datetime(ret_df.index).normalize() dt_idx = pred.index.get_level_values(0) trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique()) equity = 1_000_000.0 holdings = [] prev_week = None daily_eq = [] for d in trade_dates: try: day_scores = pred.loc[d] except KeyError: daily_eq.append(equity) prev_scores = None continue if isinstance(day_scores, pd.DataFrame): day_scores = day_scores.iloc[:, 0] day_scores = day_scores.dropna().sort_values(ascending=False) cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if cur_week != prev_week or not holdings: holdings = list(day_scores.index[:topk]) ret_row = ret_df.loc[d] if d in ret_df.index else None if ret_row is not None and holdings: wts = np.array([risk_degree / len(holdings)] * len(holdings)) rets = ret_row.reindex(holdings).fillna(0).values equity *= (1 + (wts * rets).sum()) daily_eq.append(equity) prev_week = cur_week return pd.Series(daily_eq, index=trade_dates) def strategy_daily_topk(pred, closes, start, end, topk=10, risk_degree=1.0): """Mimics PortAnaRecord: re-rank every day, hold top-k.""" ret_df = closes.pct_change() ret_df.index = pd.to_datetime(ret_df.index).normalize() dt_idx = pred.index.get_level_values(0) trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique()) equity = 1_000_000.0 daily_eq = [] for d in trade_dates: try: day_scores = pred.loc[d] except KeyError: daily_eq.append(equity) continue if isinstance(day_scores, pd.DataFrame): day_scores = day_scores.iloc[:, 0] day_scores = day_scores.dropna().sort_values(ascending=False) holdings = list(day_scores.index[:topk]) ret_row = ret_df.loc[d] if d in ret_df.index else None if ret_row is not None and holdings: wts = np.array([risk_degree / len(holdings)] * len(holdings)) rets = ret_row.reindex(holdings).fillna(0).values equity *= (1 + (wts * rets).sum()) daily_eq.append(equity) return pd.Series(daily_eq, index=trade_dates) def metrics(eq): if len(eq) < 2: return {"ann_ret": 0, "sharpe": 0, "maxDD": 0} rets = eq.pct_change().dropna() ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1) vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0 sharpe = ann_ret / vol if vol > 0 else 0 peak = eq.cummax() dd = (eq - peak) / peak return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)} def main(): OUT.mkdir(parents=True, exist_ok=True) results = [] for w in WINDOWS: print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===") pred = load_pred(w["pred"]) closes = load_closes(w["start"], w["end"]) print(f" pred dates: {pred.index.get_level_values(0).min()} to {pred.index.get_level_values(0).max()}") print(f" close dates: {closes.index.min()} to {closes.index.max()}") print(f" symbols in close: {closes.shape[1]}") # Script: weekly, equal weight (risk_degree=1.0) eq_weekly_100 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0) m_weekly_100 = metrics(eq_weekly_100) # Script: weekly, 95% risk degree eq_weekly_95 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95) m_weekly_95 = metrics(eq_weekly_95) # Daily top-k: re-rank daily, equal weight eq_daily_100 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0) m_daily_100 = metrics(eq_daily_100) # Daily top-k: re-rank daily, 95% eq_daily_95 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95) m_daily_95 = metrics(eq_daily_95) row = { "year": w["label"], "script_weekly_100": m_weekly_100, "script_weekly_95": m_weekly_95, "daily_topk_100": m_daily_100, "daily_topk_95": m_daily_95, } results.append(row) print(f" Script weekly 100%: ann={m_weekly_100['ann_ret']:+.1%} sharpe={m_weekly_100['sharpe']:.2f} maxDD={m_weekly_100['maxDD']:.1%}") print(f" Script weekly 95%: ann={m_weekly_95['ann_ret']:+.1%} sharpe={m_weekly_95['sharpe']:.2f} maxDD={m_weekly_95['maxDD']:.1%}") print(f" Daily topk 100%: ann={m_daily_100['ann_ret']:+.1%} sharpe={m_daily_100['sharpe']:.2f} maxDD={m_daily_100['maxDD']:.1%}") print(f" Daily topk 95%: ann={m_daily_95['ann_ret']:+.1%} sharpe={m_daily_95['sharpe']:.2f} maxDD={m_daily_95['maxDD']:.1%}") with open(OUT / "diagnosis.json", "w") as f: json.dump(results, f, indent=2, default=str) print(f"\nSaved to {OUT / 'diagnosis.json'}") if __name__ == "__main__": main()