Gahow Wang
c4ae944345
V3's output only includes {SPY, TQQQ, UPRO, GLD, DBC}. When PT
triggered, park_col resolved to "" (cash at 0%) instead of SHY.
Now injects SHY column before the PT loop if present in data.
Impact: ~0 in 2016-2026 (rising rates made SHY slightly negative),
but fixes ~0.6%/yr drag in normal rate environments (SHY ~4%/yr,
14.3% of days in PT-park).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
192 lines
6.5 KiB
Python
192 lines
6.5 KiB
Python
"""TrendRider V7 — V3 + vol-target + profit-take on leveraged ETFs.
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Architecture
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------------
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Three sequential layers, each PIT-safe:
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Layer 1 — TrendRiderV3 regime engine (MA150)
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SPY technicals → risk-on (TQQQ/UPRO) vs risk-off (GLD/DBC).
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Single momentum-leader pick within each basket.
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Terminal shift(1) for execution lag.
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Layer 2 — Vol-target overlay (28% target, 60-100% scale)
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scale = clip(target_vol / realized_vol_60d, 0.6, 1.0)
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Applied with shift(1) so today's scale uses yesterday's vol.
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Layer 3 — Profit-take with hysteresis (30% threshold, clear to SHY)
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When the held asset gains ≥30% from entry: clear position → SHY.
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Restore when gain drops below 20% (hysteresis band = 10%).
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Entry price = yesterday's close at symbol change (PIT-safe).
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Why profit-take works on leveraged ETFs
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---------------------------------------
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3x ETFs (TQQQ, UPRO) suffer from volatility drag: daily rebalancing
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erodes multi-day compound returns proportional to variance. After a
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+30% gain, the position sits on a large base where subsequent
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volatility causes disproportionate drag. Clearing the position:
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1. Locks in geometric gains before vol drag erodes them.
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2. Reduces the base on which the drag operates.
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3. Forces rebalancing from an asset that has become "overweight."
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This is not alpha from prediction — it is alpha from mechanical
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rebalancing of a negatively-convex instrument, a structural effect
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documented in the leveraged ETF literature.
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Empirical 10y (2016-05-16 to 2026-05-13, 10bps one-way cost):
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Ann 54.7%, Vol 24.2%, Sharpe(rf=5%) 1.72, MaxDD -25.7%,
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Sortino 2.23, Calmar 2.13.
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16 CLEAR events, 9 RESTORE events, avg ~1.6 clears/year.
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from strategies.base import Strategy
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from strategies.permanent import TrendRiderV3
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class TrendRiderV7(Strategy):
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"""V3 + vol-target + profit-take for leveraged ETF portfolios.
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Parameters
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----------
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ma_long : int
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MA window for the V3 regime signal on SPY. Default 150
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(validated: smooth Sharpe surface across MA100-200).
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target_vol : float
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Annualized vol target for the vol-scaling overlay.
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vol_window : int
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Trailing window (trading days) for realized-vol estimate.
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min_lev, max_lev : float
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Clip bounds for the vol-target scale factor.
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pt_threshold : float
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Profit-take threshold: clear position when held asset's gain
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from entry reaches this level (e.g. 0.30 = +30%).
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pt_band : float
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Hysteresis band: restore position only when gain drops below
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(pt_threshold - pt_band). Prevents oscillation near threshold.
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pt_park : str
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Symbol to allocate cleared capital to (default "SHY").
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Set to "" to hold cash (0% return) instead.
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"""
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def __init__(
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self,
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# V3 regime engine
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ma_long: int = 150,
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signal: str = "SPY",
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risk_on: tuple[str, ...] = ("TQQQ", "UPRO"),
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risk_off: tuple[str, ...] = ("GLD", "DBC"),
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# Vol-target overlay
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target_vol: float = 0.28,
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vol_window: int = 60,
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min_lev: float = 0.6,
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max_lev: float = 1.0,
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# Profit-take overlay
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pt_threshold: float = 0.30,
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pt_band: float = 0.10,
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pt_park: str = "SHY",
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# V3 passthrough
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**v3_kwargs,
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) -> None:
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self.target_vol = target_vol
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self.vol_window = vol_window
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self.min_lev = min_lev
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self.max_lev = max_lev
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self.pt_threshold = pt_threshold
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self.pt_band = pt_band
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self.pt_park = pt_park
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self.v3 = TrendRiderV3(
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signal=signal,
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risk_on=risk_on,
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risk_off=risk_off,
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ma_long=ma_long,
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**v3_kwargs,
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)
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def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
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# --- Layer 1: V3 regime weights (already shift(1)'d) ---
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w = self.v3.generate_signals(data)
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# Ensure pt_park column exists so PT can allocate to it
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if self.pt_park and self.pt_park in data.columns and self.pt_park not in w.columns:
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w[self.pt_park] = 0.0
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# --- Layer 2: Vol-target overlay ---
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daily_ret = data.pct_change(fill_method=None).fillna(0.0)
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port_rets = (w * daily_ret).sum(axis=1)
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realized_vol = (
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port_rets.rolling(self.vol_window, min_periods=21).std()
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* np.sqrt(252)
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)
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scale = (self.target_vol / realized_vol).clip(
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lower=self.min_lev, upper=self.max_lev,
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)
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scale = scale.shift(1).fillna(1.0)
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w = w.mul(scale, axis=0)
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# --- Layer 3: Profit-take with hysteresis ---
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if self.pt_threshold <= 0:
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return w
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held = w.idxmax(axis=1)
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max_w = w.max(axis=1)
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held[max_w < 1e-8] = ""
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park_col = self.pt_park if self.pt_park in w.columns else ""
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entry_price: float | None = None
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current_sym: str | None = None
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is_stopped = False
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restore_level = self.pt_threshold - self.pt_band
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for i in range(len(w)):
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sym = held.iloc[i]
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if not sym or max_w.iloc[i] < 1e-8:
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current_sym = None
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entry_price = None
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is_stopped = False
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continue
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# New position (V3 switched symbol)
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if sym != current_sym:
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current_sym = sym
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entry_price = (
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float(data[sym].iloc[i - 1])
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if i > 0 and sym in data.columns
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else None
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)
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is_stopped = False
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continue
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if (
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entry_price is None
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or entry_price <= 0
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or sym not in data.columns
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):
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continue
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# PIT-safe: use yesterday's close for the gain check
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yesterday = float(data[sym].iloc[i - 1]) if i > 0 else float(data[sym].iloc[i])
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gain = yesterday / entry_price - 1.0
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if is_stopped:
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if gain < restore_level:
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is_stopped = False
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else:
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w.iloc[i] = 0.0
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if park_col:
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w.at[w.index[i], park_col] = scale.iloc[i]
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else:
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if gain >= self.pt_threshold:
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is_stopped = True
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w.iloc[i] = 0.0
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if park_col:
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w.at[w.index[i], park_col] = scale.iloc[i]
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return w
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__all__ = ["TrendRiderV7"]
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