ch11: signal-quality gate REFUTED — walk-forward workflow shows gate harmful (exp 61-67, EVIDENCE#053)
This commit is contained in:
zhaoli
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"""Diagnose script-vs-workflow gap v3: replicate workflow execution mechanics exactly.
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Replicates the WeeklyRebalanceDropoutStrategy execution:
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1. Weekly rebalance (first trading day of ISO week only)
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2. TopkDropout selection: sell bottom n_drop, buy top fill
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3. Cash-after-sells sizing: sell first, then cash * risk_degree / len(buy)
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4. Whole-share rounding (floor)
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5. Asymmetric costs: open_cost=5bp, close_cost=15bp, min_cost=$5 per order
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6. Optional SQ gate (hit-rate threshold)
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Compares against the idealized script (fractional shares, symmetric cost).
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"""
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from __future__ import annotations
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import json, pathlib
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import numpy as np
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import pandas as pd
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LAKE_ROOT = "/home/data/lake"
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OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
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WINDOWS = [
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{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
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"pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
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{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
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"pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
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{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
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"pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
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{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
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"pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
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{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
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"pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
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]
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SYMS = [
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"SPY","QQQ","DIA","IWM","MDY","VTI","VOO","VEA","VWO","VT","EFA","EEM",
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"TLT","IEF","SHY","AGG","BND","LQD","HYG","JNK","EMB","GLD","SLV",
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"USO","UNG","DBA","DBC","XLK","XLF","XLE","XLV","XLI","XLY","XLP",
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"XLU","XLB","XLRE","ARKK","SMH","SOXX","IBB","XBI","ITA","XAR",
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"ICLN","TAN","FDN","IGV","ESPO","REM",
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]
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OPEN_COST = 0.0005 # 5bp
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CLOSE_COST = 0.0015 # 15bp
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MIN_COST = 5.0 # $5 minimum per order
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def load_pred(path):
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df = pd.read_pickle(path)
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s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
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idx = s.index
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new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
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s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
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return s
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def load_closes(start, end):
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from tac_qlib.data.config import LakeConfig, resolve_lake_root
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cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
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closes = {}
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for sym in SYMS:
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p = cfg.bar_path("1d", sym)
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if not p.exists(): continue
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try:
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df = pd.read_parquet(p)
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except: continue
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if not len(df): continue
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tcol = df["t"] if "t" in df.columns else df["date"]
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ts = pd.to_datetime(tcol)
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df = df.assign(_t=ts).set_index("_t").sort_index()
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df.index = pd.to_datetime(df.index).normalize()
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warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
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df = df.loc[warmup:end]
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if len(df) >= 22:
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closes[sym] = df["c"]
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return pd.DataFrame(closes)
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def load_opens(start, end):
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from tac_qlib.data.config import LakeConfig, resolve_lake_root
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cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
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opens = {}
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for sym in SYMS:
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p = cfg.bar_path("1d", sym)
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if not p.exists(): continue
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try:
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df = pd.read_parquet(p)
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except: continue
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if not len(df): continue
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tcol = df["t"] if "t" in df.columns else df["date"]
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ts = pd.to_datetime(tcol)
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df = df.assign(_t=ts).set_index("_t").sort_index()
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df.index = pd.to_datetime(df.index).normalize()
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warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
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df = df.loc[warmup:end]
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if len(df) >= 22:
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opens[sym] = df["o"]
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return pd.DataFrame(opens)
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def load_vwap(start, end):
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from tac_qlib.data.config import LakeConfig, resolve_lake_root
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cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
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vwaps = {}
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for sym in SYMS:
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p = cfg.bar_path("1d", sym)
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if not p.exists(): continue
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try:
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df = pd.read_parquet(p)
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except: continue
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if not len(df): continue
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tcol = df["t"] if "t" in df.columns else df["date"]
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ts = pd.to_datetime(tcol)
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df = df.assign(_t=ts).set_index("_t").sort_index()
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warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
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df = df.loc[warmup:end]
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if len(df) >= 22:
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vwaps[sym] = df["vw"]
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return pd.DataFrame(vwaps)
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def compute_gate(pred, closes, start, end, gate_topk=10, gate_lookback=5, gate_threshold=0.5):
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"""Compute the SQ gate: rolling average hit-rate of topk predictions."""
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ret_df = closes.pct_change()
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ret_df.index = pd.to_datetime(ret_df.index).normalize()
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dt_idx = pred.index.get_level_values(0)
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pred_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
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if len(pred_dates) < 2:
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return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
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hit_rates = {}
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for i in range(1, len(pred_dates)):
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day = pred_dates[i]
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prev_day = pred_dates[i - 1]
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try:
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prev_scores = pred.loc[prev_day]
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except KeyError:
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continue
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if isinstance(prev_scores, pd.DataFrame):
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prev_scores = prev_scores.iloc[:, 0]
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prev_scores = prev_scores.dropna().sort_values(ascending=False)
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topk_syms = list(prev_scores.index[:gate_topk])
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if day not in ret_df.index:
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continue
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today_ret = ret_df.loc[day]
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topk_rets = today_ret.reindex(topk_syms).dropna()
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if len(topk_rets) == 0:
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continue
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hit_rates[day] = (topk_rets > 0).sum() / len(topk_rets)
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if not hit_rates:
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return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
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hr_series = pd.Series(hit_rates).sort_index()
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rolling_hr = hr_series.rolling(gate_lookback, min_periods=1).mean()
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gate = rolling_hr >= gate_threshold
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gate.iloc[:gate_lookback] = True
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return gate
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def strategy_idealized(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
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"""Idealized script: fractional shares, symmetric cost, no gate."""
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ret_df = closes.pct_change(fill_method=None)
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ret_df.index = pd.to_datetime(ret_df.index).normalize()
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dt_idx = pred.index.get_level_values(0)
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trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
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equity = 1_000_000.0
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holdings = []
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prev_week = None
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daily_eq = []
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for d in trade_dates:
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try:
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day_scores = pred.loc[d]
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except KeyError:
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daily_eq.append(equity)
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continue
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if isinstance(day_scores, pd.DataFrame):
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day_scores = day_scores.iloc[:, 0]
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day_scores = day_scores.dropna().sort_values(ascending=False)
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cur_week = (d.isocalendar()[0], d.isocalendar()[1])
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if cur_week != prev_week:
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new_holdings = list(day_scores.index[:topk])
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if holdings and cost_bps > 0:
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sold = set(holdings) - set(new_holdings)
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bought = set(new_holdings) - set(holdings)
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turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
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equity *= (1 - turnover * cost_bps / 10000)
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holdings = new_holdings
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ret_row = ret_df.loc[d] if d in ret_df.index else None
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if ret_row is not None and holdings:
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wts = np.array([risk_degree / len(holdings)] * len(holdings))
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rets = ret_row.reindex(holdings).fillna(0).values
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equity *= (1 + (wts * rets).sum())
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daily_eq.append(equity)
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prev_week = cur_week
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return pd.Series(daily_eq, index=trade_dates)
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def strategy_workflow_exact(pred, closes, opens, start, end,
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topk=10, n_drop=1, risk_degree=0.95,
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use_gate=False, gate_series=None):
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"""Exact replication of WeeklyRebalanceDropoutStrategy execution mechanics.
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- Sells first (all shares of dropped positions)
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- Sizes buys as: cash * risk_degree / len(buy)
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- Rounds to whole shares (floor)
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- Asymmetric costs: open_cost on buys, close_cost on sells, $5 min per order
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- Tracks position values for daily equity
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"""
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ret_df = closes.pct_change(fill_method=None)
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ret_df.index = pd.to_datetime(ret_df.index).normalize()
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open_df = opens.copy()
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open_df.index = pd.to_datetime(open_df.index).normalize()
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dt_idx = pred.index.get_level_values(0)
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trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
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cash = 1_000_000.0
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positions = {} # {sym: num_shares}
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prev_week = None
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daily_eq = []
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for d in trade_dates:
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# Skip non-trading days (pred may include weekends)
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if d not in closes.index:
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daily_eq.append(daily_eq[-1] if daily_eq else cash)
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continue
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try:
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day_scores = pred.loc[d]
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except KeyError:
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daily_eq.append(daily_eq[-1] if daily_eq else cash)
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continue
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if isinstance(day_scores, pd.DataFrame):
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day_scores = day_scores.iloc[:, 0]
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day_scores = day_scores.dropna().sort_values(ascending=False)
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cur_week = (d.isocalendar()[0], d.isocalendar()[1])
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if cur_week != prev_week:
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# === REBALANCE DAY ===
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# Check gate
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if use_gate and gate_series is not None:
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known = gate_series[gate_series.index <= d]
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if len(known) and not bool(known.iloc[-1]):
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# gate closed: sell everything, go to cash
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for sym in list(positions.keys()):
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shares = positions[sym]
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if shares <= 0:
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continue
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sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
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if sell_price is None or pd.isna(sell_price):
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continue
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trade_val = shares * sell_price
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trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
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cash += trade_val - trade_cost
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positions[sym] = 0
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positions = {s: v for s, v in positions.items() if v > 0}
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daily_eq.append(cash)
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prev_week = cur_week
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continue
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# TopkDropout selection (matching WeeklyRebalanceDropoutStrategy exactly)
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current_syms = [s for s, v in positions.items() if v > 0]
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last = pred.loc[d].reindex(current_syms).sort_values(ascending=False).index if current_syms else pd.Index([])
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# buy candidates: top stocks NOT in current holdings, take n_drop + topk - len(last)
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buy_cands = day_scores[~day_scores.index.isin(last)].sort_values(ascending=False).index
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buy_list = list(buy_cands[:n_drop + topk - len(last)])
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# comb = union of current holdings + buy candidates (actual strategy line 132)
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comb = pred.loc[d].reindex(last.union(pd.Index(buy_list))).sort_values(ascending=False).index
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# sell: items from current holdings that are in the bottom n_drop of comb
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sell_list = list(last[last.isin(comb[-n_drop:])]) if n_drop > 0 and len(comb) >= n_drop else []
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# --- SELL FIRST ---
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for sym in sell_list:
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if sym not in positions or positions[sym] <= 0:
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continue
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shares = positions[sym]
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sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
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if sell_price is None or pd.isna(sell_price):
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continue
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trade_val = shares * sell_price
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trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
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cash += trade_val - trade_cost
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positions[sym] = 0
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# --- BUY ---
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n_buy = len(buy_list)
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if n_buy > 0:
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buy_budget = cash * risk_degree / n_buy
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for sym in buy_list:
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buy_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
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if buy_price is None or pd.isna(buy_price) or buy_price <= 0:
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continue
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shares_to_buy = int(buy_budget / buy_price) # floor to whole shares
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if shares_to_buy <= 0:
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continue
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trade_val = shares_to_buy * buy_price
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trade_cost = max(trade_val * OPEN_COST, MIN_COST) if trade_val > 0 else 0
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total_cost = trade_val + trade_cost
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if total_cost > cash:
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shares_to_buy = int((cash - MIN_COST) / buy_price)
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if shares_to_buy <= 0:
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continue
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trade_val = shares_to_buy * buy_price
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trade_cost = max(trade_val * OPEN_COST, MIN_COST)
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total_cost = trade_val + trade_cost
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cash -= total_cost
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positions[sym] = positions.get(sym, 0) + shares_to_buy
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positions = {s: v for s, v in positions.items() if v > 0}
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# === DAILY EQUITY ===
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eq = cash
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if d in closes.index:
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for sym, shares in positions.items():
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if sym in closes.columns:
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px = closes.loc[d, sym]
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if not pd.isna(px):
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eq += shares * px
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daily_eq.append(eq)
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prev_week = cur_week
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return pd.Series(daily_eq, index=trade_dates)
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def metrics(eq):
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if len(eq) < 2:
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return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
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rets = eq.pct_change().dropna()
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ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
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vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
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sharpe = ann_ret / vol if vol > 0 else 0
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peak = eq.cummax()
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dd = (eq - peak) / peak
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return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
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def main():
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OUT.mkdir(parents=True, exist_ok=True)
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results = []
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for w in WINDOWS:
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print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
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pred = load_pred(w["pred"])
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closes = load_closes(w["start"], w["end"])
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opens = load_opens(w["start"], w["end"])
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print(f" pred: {pred.index.get_level_values(0).min().date()} to {pred.index.get_level_values(0).max().date()}, "
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f"{pred.index.get_level_values(1).nunique()} syms")
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print(f" close: {closes.index.min().date()} to {closes.index.max().date()}, {closes.shape[1]} syms")
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row = {"year": w["label"]}
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# A. Idealized: fractional shares, 10bp symmetric, no gate (diag v2 baseline)
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eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
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m = metrics(eq); row["ideal_100_zc"] = m
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print(f" A. Ideal 100% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
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# B. Idealized: 95% invested, 10bp symmetric
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eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=0)
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m = metrics(eq); row["ideal_95_zc"] = m
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print(f" B. Ideal 95% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
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# C. Idealized: 95%, 10bp cost
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eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=10)
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m = metrics(eq); row["ideal_95_10bp"] = m
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print(f" C. Ideal 95% 10bp: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
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# D. Workflow-exact: whole shares, 5/15bp, $5 min, no gate
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eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
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topk=10, n_drop=1, risk_degree=0.95,
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use_gate=False)
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m = metrics(eq); row["wf_exact_95_nogate"] = m
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print(f" D. WF exact 95% nogate: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
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# E. Workflow-exact: whole shares, 5/15bp, $5 min, WITH SQ gate
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gate = compute_gate(pred, closes, w["start"], w["end"],
|
||||
gate_topk=10, gate_lookback=5, gate_threshold=0.5)
|
||||
gate_open_pct = gate.sum() / len(gate) if len(gate) > 0 else 1.0
|
||||
eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
|
||||
topk=10, n_drop=1, risk_degree=0.95,
|
||||
use_gate=True, gate_series=gate)
|
||||
m = metrics(eq); row["wf_exact_95_gate"] = m
|
||||
print(f" E. WF exact 95% gate: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%} gate_open={gate_open_pct:.0%}")
|
||||
|
||||
# Gap analysis
|
||||
ideal = row["ideal_95_zc"]["ann_ret"]
|
||||
wf_nogate = row["wf_exact_95_nogate"]["ann_ret"]
|
||||
wf_gate = row["wf_exact_95_gate"]["ann_ret"]
|
||||
print(f"\n Gap analysis:")
|
||||
print(f" Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, nogate): {ideal:+.1%} → {wf_nogate:+.1%} (gap: {wf_nogate - ideal:+.1%})")
|
||||
print(f" Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, gate): {ideal:+.1%} → {wf_gate:+.1%} (gap: {wf_gate - ideal:+.1%})")
|
||||
|
||||
results.append(row)
|
||||
|
||||
with open(OUT / "diagnosis_v3.json", "w") as f:
|
||||
json.dump(results, f, indent=2, default=str)
|
||||
print(f"\nSaved to {OUT / 'diagnosis_v3.json'}")
|
||||
|
||||
# Summary table
|
||||
print("\n" + "=" * 80)
|
||||
print("SUMMARY: Ideal vs Workflow-Exact")
|
||||
print("=" * 80)
|
||||
print(f"{'Year':<6} {'Ideal%zc':>10} {'WF nogate':>10} {'WF gate':>10} {'Gap(nogate)':>12} {'Gap(gate)':>12}")
|
||||
for r in results:
|
||||
y = r["year"]
|
||||
i = r["ideal_95_zc"]["ann_ret"]
|
||||
wn = r["wf_exact_95_nogate"]["ann_ret"]
|
||||
wg = r["wf_exact_95_gate"]["ann_ret"]
|
||||
print(f"{y:<6} {i:>+10.1%} {wn:>+10.1%} {wg:>+10.1%} {wn-i:>+12.1%} {wg-i:>+12.1%}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user