Gahow Wang
541f7bcf5b
Add 28 research scripts covering DCA simulation, momentum evaluation, Sharpe optimization, trend rider analysis, and US fundamentals exploration.
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
"""Vol-targeting overlay on V5/V6 blends — tests if dynamic exposure scaling
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can lift realized Sharpe past 1.30 toward 1.50+.
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The vol-target post-processor scales total weights by min(1, target_vol /
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realized_vol_20d) using the strategy's *own* realized 20-day vol from the
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prior backtest output. It shrinks exposure (toward cash) in high-vol
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regimes — same effect as a deleveraging manager.
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"""
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from __future__ import annotations
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import os
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import sys
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import numpy as np
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import pandas as pd
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from research.trend_rider_robustness import (
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buy_hold_weights,
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evaluate_weights,
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portfolio_returns,
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)
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from research.trend_rider_v6_eval import load_combined_panel
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from strategies.permanent import ETF_UNIVERSE
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from strategies.trend_rider_v5 import TrendRiderV5
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from strategies.trend_rider_v6 import TrendRiderV6
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IS_START = "2015-01-02"
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IS_END = "2020-12-31"
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OOS_START = "2021-01-01"
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OOS_END = "2026-05-07"
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def _fmt(x):
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return f"{x*100:7.2f}%"
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def vol_target_overlay(weights: pd.DataFrame, prices: pd.DataFrame,
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target_vol: float, vol_window: int = 20,
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lookback_lag: int = 1) -> pd.DataFrame:
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"""Scale weights so realized 20-day portfolio vol ≈ target_vol.
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`lookback_lag` ensures PIT-safety: scaling at row t uses vol estimate
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available at end of row t-1.
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"""
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rets = portfolio_returns(weights, prices, transaction_cost=0.0)
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realized = rets.rolling(vol_window).std(ddof=1) * np.sqrt(252)
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realized = realized.shift(lookback_lag)
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realized = realized.fillna(target_vol) # warmup: no scaling
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scale = (target_vol / realized.replace(0.0, np.nan)).clip(upper=1.0).fillna(1.0)
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out = weights.mul(scale, axis=0)
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return out
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def evaluate_blend(name, blend, panel, label_prefix="", txn=0.001):
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rows = []
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for window_name, (s, e) in {"FULL": (IS_START, OOS_END),
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"IS": (IS_START, IS_END),
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"OOS": (OOS_START, OOS_END)}.items():
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ev = evaluate_weights(name, blend, panel[blend.columns], txn, s, e)
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print(f" [{window_name}] {label_prefix}{name:<28s} "
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f"CAGR {_fmt(ev.cagr)} Vol {_fmt(ev.volatility)} "
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f"Sharpe {ev.sharpe:5.2f} MDD {_fmt(ev.max_drawdown)} "
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f"Calmar {ev.calmar:5.2f} X {ev.final_multiple:6.2f}")
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rows.append({"window": window_name, "name": name, **ev.__dict__})
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return rows
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def main() -> None:
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panel = load_combined_panel()
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etf_set = (set(ETF_UNIVERSE)
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| {"QQQ", "TQQQ", "UPRO", "GLD", "DBC", "SHY", "SPY",
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"YINN", "CHAU", "7200.HK", "7500.HK"})
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stock_universe = [c for c in panel.columns if c not in etf_set]
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v5 = TrendRiderV5()
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v6_best = TrendRiderV6(
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signal_name="rec_mfilt+deep_upvol", top_n=10,
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tier2_leverage_overlay=0.50,
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stock_universe=stock_universe,
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)
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v5_w = v5.generate_signals(panel)
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v6_w = v6_best.generate_signals(panel)
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# Align columns
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cols = sorted(set(v5_w.columns) | set(v6_w.columns))
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v5_a = v5_w.reindex(columns=cols).fillna(0.0)
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v6_a = v6_w.reindex(index=v5_a.index, columns=cols).fillna(0.0)
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print(f"V5 vs V6 corr = {portfolio_returns(v5_a, panel[cols], 0.001).corr(portfolio_returns(v6_a, panel[cols], 0.001)):.3f}")
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print("\n=== V5 + V6 blends WITH vol targeting ===")
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blend_ratios = [(0.50, 0.50), (0.70, 0.30), (0.40, 0.60), (0.30, 0.70)]
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targets = [0.20, 0.22, 0.25, 0.30]
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for w5, w6 in blend_ratios:
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blend = v5_a * w5 + v6_a * w6
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for tgt in targets:
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sized = vol_target_overlay(blend, panel[blend.columns], target_vol=tgt)
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evaluate_blend(f"V5={w5:.0%}+V6={w6:.0%} vt{tgt:.2f}", sized, panel,
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label_prefix="")
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print()
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# Vol target on pure V5 / V6 too
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print("\n=== Pure strategies WITH vol targeting ===")
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for tgt in targets:
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for nm, w in [("V5", v5_a), ("V6best", v6_a)]:
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sized = vol_target_overlay(w, panel[w.columns], target_vol=tgt)
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evaluate_blend(f"{nm} vt{tgt:.2f}", sized, panel)
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if __name__ == "__main__":
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main()
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