Gahow Wang
df0a051403
Three-layer strategy for leveraged ETF portfolios: Layer 1: V3 regime engine (MA150) — SPY technicals for risk-on/off Layer 2: Vol-target overlay (28%, clip 0.6-1.0) — scale by realized vol Layer 3: Profit-take with hysteresis (+30% → clear to SHY, restore <20%) The profit-take exploits a structural property of 3x leveraged ETFs: after large gains, volatility drag on the inflated base erodes compound returns. Clearing the position locks in geometric gains before the drag takes effect — this is rebalancing alpha, not prediction alpha. 10y backtest (2016-2026, 10bps one-way cost): Ann 54.7%, Sharpe(rf=5%) 1.72, MaxDD -25.7%, Sortino 2.23 Also registers trend_rider_v7, trend_rider_v7_vt24, trend_rider_v7_vt32 in the trader strategy registry and ETF_STRATEGY_UNIVERSES. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
188 lines
6.3 KiB
Python
188 lines
6.3 KiB
Python
"""TrendRider V7 — V3 + vol-target + profit-take on leveraged ETFs.
|
|
|
|
Architecture
|
|
------------
|
|
Three sequential layers, each PIT-safe:
|
|
|
|
Layer 1 — TrendRiderV3 regime engine (MA150)
|
|
SPY technicals → risk-on (TQQQ/UPRO) vs risk-off (GLD/DBC).
|
|
Single momentum-leader pick within each basket.
|
|
Terminal shift(1) for execution lag.
|
|
|
|
Layer 2 — Vol-target overlay (28% target, 60-100% scale)
|
|
scale = clip(target_vol / realized_vol_60d, 0.6, 1.0)
|
|
Applied with shift(1) so today's scale uses yesterday's vol.
|
|
|
|
Layer 3 — Profit-take with hysteresis (30% threshold, clear to SHY)
|
|
When the held asset gains ≥30% from entry: clear position → SHY.
|
|
Restore when gain drops below 20% (hysteresis band = 10%).
|
|
Entry price = yesterday's close at symbol change (PIT-safe).
|
|
|
|
Why profit-take works on leveraged ETFs
|
|
---------------------------------------
|
|
3x ETFs (TQQQ, UPRO) suffer from volatility drag: daily rebalancing
|
|
erodes multi-day compound returns proportional to variance. After a
|
|
+30% gain, the position sits on a large base where subsequent
|
|
volatility causes disproportionate drag. Clearing the position:
|
|
|
|
1. Locks in geometric gains before vol drag erodes them.
|
|
2. Reduces the base on which the drag operates.
|
|
3. Forces rebalancing from an asset that has become "overweight."
|
|
|
|
This is not alpha from prediction — it is alpha from mechanical
|
|
rebalancing of a negatively-convex instrument, a structural effect
|
|
documented in the leveraged ETF literature.
|
|
|
|
Empirical 10y (2016-05-16 to 2026-05-13, 10bps one-way cost):
|
|
Ann 54.7%, Vol 24.2%, Sharpe(rf=5%) 1.72, MaxDD -25.7%,
|
|
Sortino 2.23, Calmar 2.13.
|
|
16 CLEAR events, 9 RESTORE events, avg ~1.6 clears/year.
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from strategies.base import Strategy
|
|
from strategies.permanent import TrendRiderV3
|
|
|
|
|
|
class TrendRiderV7(Strategy):
|
|
"""V3 + vol-target + profit-take for leveraged ETF portfolios.
|
|
|
|
Parameters
|
|
----------
|
|
ma_long : int
|
|
MA window for the V3 regime signal on SPY. Default 150
|
|
(validated: smooth Sharpe surface across MA100-200).
|
|
target_vol : float
|
|
Annualized vol target for the vol-scaling overlay.
|
|
vol_window : int
|
|
Trailing window (trading days) for realized-vol estimate.
|
|
min_lev, max_lev : float
|
|
Clip bounds for the vol-target scale factor.
|
|
pt_threshold : float
|
|
Profit-take threshold: clear position when held asset's gain
|
|
from entry reaches this level (e.g. 0.30 = +30%).
|
|
pt_band : float
|
|
Hysteresis band: restore position only when gain drops below
|
|
(pt_threshold - pt_band). Prevents oscillation near threshold.
|
|
pt_park : str
|
|
Symbol to allocate cleared capital to (default "SHY").
|
|
Set to "" to hold cash (0% return) instead.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
# V3 regime engine
|
|
ma_long: int = 150,
|
|
signal: str = "SPY",
|
|
risk_on: tuple[str, ...] = ("TQQQ", "UPRO"),
|
|
risk_off: tuple[str, ...] = ("GLD", "DBC"),
|
|
# Vol-target overlay
|
|
target_vol: float = 0.28,
|
|
vol_window: int = 60,
|
|
min_lev: float = 0.6,
|
|
max_lev: float = 1.0,
|
|
# Profit-take overlay
|
|
pt_threshold: float = 0.30,
|
|
pt_band: float = 0.10,
|
|
pt_park: str = "SHY",
|
|
# V3 passthrough
|
|
**v3_kwargs,
|
|
) -> None:
|
|
self.target_vol = target_vol
|
|
self.vol_window = vol_window
|
|
self.min_lev = min_lev
|
|
self.max_lev = max_lev
|
|
self.pt_threshold = pt_threshold
|
|
self.pt_band = pt_band
|
|
self.pt_park = pt_park
|
|
self.v3 = TrendRiderV3(
|
|
signal=signal,
|
|
risk_on=risk_on,
|
|
risk_off=risk_off,
|
|
ma_long=ma_long,
|
|
**v3_kwargs,
|
|
)
|
|
|
|
def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
|
|
# --- Layer 1: V3 regime weights (already shift(1)'d) ---
|
|
w = self.v3.generate_signals(data)
|
|
|
|
# --- Layer 2: Vol-target overlay ---
|
|
daily_ret = data.pct_change(fill_method=None).fillna(0.0)
|
|
port_rets = (w * daily_ret).sum(axis=1)
|
|
realized_vol = (
|
|
port_rets.rolling(self.vol_window, min_periods=21).std()
|
|
* np.sqrt(252)
|
|
)
|
|
scale = (self.target_vol / realized_vol).clip(
|
|
lower=self.min_lev, upper=self.max_lev,
|
|
)
|
|
scale = scale.shift(1).fillna(1.0)
|
|
w = w.mul(scale, axis=0)
|
|
|
|
# --- Layer 3: Profit-take with hysteresis ---
|
|
if self.pt_threshold <= 0:
|
|
return w
|
|
|
|
held = w.idxmax(axis=1)
|
|
max_w = w.max(axis=1)
|
|
held[max_w < 1e-8] = ""
|
|
|
|
park_col = self.pt_park if self.pt_park in w.columns else ""
|
|
entry_price: float | None = None
|
|
current_sym: str | None = None
|
|
is_stopped = False
|
|
restore_level = self.pt_threshold - self.pt_band
|
|
|
|
for i in range(len(w)):
|
|
sym = held.iloc[i]
|
|
if not sym or max_w.iloc[i] < 1e-8:
|
|
current_sym = None
|
|
entry_price = None
|
|
is_stopped = False
|
|
continue
|
|
|
|
# New position (V3 switched symbol)
|
|
if sym != current_sym:
|
|
current_sym = sym
|
|
entry_price = (
|
|
float(data[sym].iloc[i - 1])
|
|
if i > 0 and sym in data.columns
|
|
else None
|
|
)
|
|
is_stopped = False
|
|
continue
|
|
|
|
if (
|
|
entry_price is None
|
|
or entry_price <= 0
|
|
or sym not in data.columns
|
|
):
|
|
continue
|
|
|
|
# PIT-safe: use yesterday's close for the gain check
|
|
yesterday = float(data[sym].iloc[i - 1]) if i > 0 else float(data[sym].iloc[i])
|
|
gain = yesterday / entry_price - 1.0
|
|
|
|
if is_stopped:
|
|
if gain < restore_level:
|
|
is_stopped = False
|
|
else:
|
|
w.iloc[i] = 0.0
|
|
if park_col:
|
|
w.at[w.index[i], park_col] = scale.iloc[i]
|
|
else:
|
|
if gain >= self.pt_threshold:
|
|
is_stopped = True
|
|
w.iloc[i] = 0.0
|
|
if park_col:
|
|
w.at[w.index[i], park_col] = scale.iloc[i]
|
|
|
|
return w
|
|
|
|
|
|
__all__ = ["TrendRiderV7"]
|