research: individual stock swing, new frameworks, literature alpha, DCA
Four research directions beyond V7+VT36: 1. single_stock_swing: 20 famous stocks (Mag 7 + others), per-stock optimized swing trading. High-vol growth stocks (AMD Sharpe 1.55, TSLA 1.54) work best, but overfitting risk is extreme — universal params only TSLA is viable. Not competitive with V7. 2. v7_literature_alpha: 9 academic directions (VIX overlay, Kelly sizing, multi-MA, cross-asset, momentum acceleration, VIX mean- reversion, vol-adaptive PT, combined). V3's regime engine already implicitly captures most literature signals. MA130 marginally better than MA150 (+0.02 Sharpe, within noise). 3. new_frameworks_eval: volatility trading (SVXY risk-off) and calendar effects (turn-of-month). SVXY and V7 regime structurally conflict — SVXY crashes exactly when V7 goes risk-off. Turn-of-month has decent Sharpe (1.30) but only 28% annual. Nothing beats V7. 4. smart_dca_eval: fixed/VIX-scaled/MA-deviation/value-averaging/RSI DCA into SPY/QQQ/TQQQ/UPRO + V7 hybrids. Smart DCA barely beats fixed DCA. Any DCA hybrid dilutes V7's alpha. DCA only useful for new monthly contributions that can't lump-sum into V7. Conclusion: V7+VT36 remains SOTA across all tested frameworks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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research/v7_literature_alpha.py
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research/v7_literature_alpha.py
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"""Literature-informed alpha research: can we beat V7+VT36?
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Grounded in specific academic/industry research:
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1. VIX regime overlay — Simon & Campasano (2014): VIX level as exogenous fear signal
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2. Kelly-optimal sizing — Kelly (1956), Thorp (2006): return-aware position sizing
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3. Multi-timeframe voting — Faber (2007): multiple MAs reduce false signals
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4. Cross-asset confirmation — Asness et al. (2013): correlated asset agreement
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5. Momentum acceleration — Moskowitz et al. (2012): 2nd derivative of trend
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6. VIX mean-reversion entry — Whaley (2009): buy panic, sell complacency
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7. Carry-enhanced risk-off — Koijen et al. (2018): hold yield during defensive periods
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8. Regime-dependent PT — Optimal stopping theory: vol-drag-aware thresholds
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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import data_manager
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import metrics
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from strategies.base import Strategy
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from strategies.permanent import TrendRiderV3
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from strategies.trend_rider_v7 import TrendRiderV7
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from main import backtest
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YEARS = 10
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CAPITAL = 100_000
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TX_COST = 0.001
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FIXED_FEE = 2.0
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class V7Enhanced(Strategy):
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"""V7 with pluggable regime enhancer and sizing model."""
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def __init__(
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self,
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regime_enhancer=None,
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sizing_model="vol_target",
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pt_model="fixed",
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target_vol=0.36, min_lev=0.75, max_lev=1.0,
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pt_threshold=0.30, pt_band=0.10, pt_park="SHY",
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ma_long=150, **v3_kw,
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):
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self.regime_enhancer = regime_enhancer
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self.sizing_model = sizing_model
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self.pt_model = pt_model
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self.target_vol = target_vol
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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="SPY", risk_on=("TQQQ", "UPRO"), risk_off=("GLD", "DBC"),
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ma_long=ma_long, **v3_kw,
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)
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def generate_signals(self, data):
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w = self.v3.generate_signals(data)
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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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# Regime enhancement: override V3's decision in specific conditions
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if self.regime_enhancer:
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w = self.regime_enhancer(w, data)
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# Sizing
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daily_ret = data.pct_change(fill_method=None).fillna(0.0)
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common = w.columns.intersection(daily_ret.columns)
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port_rets = (w[common] * daily_ret[common]).sum(axis=1)
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if self.sizing_model == "kelly":
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# Kelly: scale = E[r] / Var[r], clipped
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roll_mean = port_rets.rolling(60, min_periods=21).mean() * 252
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roll_var = port_rets.rolling(60, min_periods=21).var() * 252
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kelly_f = (roll_mean / roll_var.clip(lower=0.01)).clip(-1, 2)
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scale = kelly_f.clip(lower=self.min_lev, upper=self.max_lev)
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scale = scale.shift(1).fillna(1.0)
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else:
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realized_vol = port_rets.rolling(60, min_periods=21).std() * np.sqrt(252)
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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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scale = scale.shift(1).fillna(1.0)
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w = w.mul(scale, axis=0)
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# Profit-take
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if self.pt_threshold <= 0:
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return w
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risk_on_set = set(self.v3.risk_on)
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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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ep, cs, stopped = None, None, False
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rl = self.pt_threshold - self.pt_band
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if self.pt_model == "vol_adaptive":
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# PT threshold inversely proportional to vol drag
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# Vol drag ≈ leverage² × σ² / 2; for 3x: 9σ²/2
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# Optimal PT ≈ base / (1 + k * σ²)
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realized_vol_arr = port_rets.rolling(60, min_periods=21).std().to_numpy() * np.sqrt(252)
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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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cs, ep, stopped = None, None, False
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continue
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if sym != cs:
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cs = sym
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ep = float(data[sym].iloc[i-1]) if i > 0 and sym in data.columns else None
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stopped = False
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continue
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if sym not in risk_on_set:
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continue
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if ep is None or ep <= 0 or sym not in data.columns:
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continue
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y = float(data[sym].iloc[i-1]) if i > 0 else float(data[sym].iloc[i])
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g = y / ep - 1.0
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if self.pt_model == "vol_adaptive":
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rv = realized_vol_arr[i] if i < len(realized_vol_arr) and not np.isnan(realized_vol_arr[i]) else 0.25
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# Higher vol → lower threshold (take profits faster)
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t = self.pt_threshold * (0.25 / max(rv, 0.10))
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t = np.clip(t, 0.15, 0.50)
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r = t * (1 - self.pt_band / self.pt_threshold)
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else:
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t = self.pt_threshold
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r = rl
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if stopped:
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if g < r: stopped = False
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else:
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w.iloc[i] = 0.0
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if park_col: w.at[w.index[i], park_col] = scale.iloc[i]
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elif g >= t:
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stopped = True
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w.iloc[i] = 0.0
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if park_col: w.at[w.index[i], park_col] = scale.iloc[i]
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return w
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# =========================================================================
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# Regime enhancers
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# =========================================================================
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def vix_overlay(vix_high=25, vix_low=15):
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"""Force risk-off when VIX > threshold. Simon & Campasano (2014)."""
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def enhancer(w, data):
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if "^VIX" not in data.columns:
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return w
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vix = data["^VIX"].shift(1).fillna(20)
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risk_on_cols = [c for c in ["TQQQ", "UPRO"] if c in w.columns]
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risk_off_cols = [c for c in ["GLD", "DBC"] if c in w.columns]
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park = "SHY" if "SHY" in w.columns else ""
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for i in range(len(w)):
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v = vix.iloc[i]
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if np.isnan(v): continue
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ron_w = sum(float(w.iat[i, w.columns.get_loc(c)]) for c in risk_on_cols)
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if ron_w > 0.01 and v > vix_high:
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for c in risk_on_cols:
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w.iat[i, w.columns.get_loc(c)] = 0.0
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if risk_off_cols:
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w.iat[i, w.columns.get_loc(risk_off_cols[0])] = ron_w
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return w
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return enhancer
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def multi_timeframe(windows=(50, 150, 200), min_agree=2):
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"""Multi-MA voting. Faber (2007). Need majority of MAs bullish."""
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def enhancer(w, data):
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if "SPY" not in data.columns:
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return w
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spy = data["SPY"]
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votes = pd.DataFrame(index=data.index)
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for win in windows:
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ma = spy.rolling(win).mean()
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votes[f"ma{win}"] = (spy > ma).astype(int)
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total_votes = votes.sum(axis=1).shift(2) # PIT: shift 2 to match V3
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risk_on_cols = [c for c in ["TQQQ", "UPRO"] if c in w.columns]
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risk_off_cols = [c for c in ["GLD", "DBC"] if c in w.columns]
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for i in range(len(w)):
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ron_w = sum(float(w.iat[i, w.columns.get_loc(c)]) for c in risk_on_cols)
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if ron_w > 0.01 and total_votes.iloc[i] < min_agree:
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for c in risk_on_cols:
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w.iat[i, w.columns.get_loc(c)] = 0.0
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if risk_off_cols:
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w.iat[i, w.columns.get_loc(risk_off_cols[0])] = ron_w
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return w
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return enhancer
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def cross_asset_confirm():
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"""Require both SPY and QQQ trends to agree. Asness et al. (2013)."""
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def enhancer(w, data):
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if "SPY" not in data.columns or "QQQ" not in data.columns:
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return w
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spy_bull = (data["SPY"] > data["SPY"].rolling(150).mean()).shift(2).fillna(False)
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qqq_bull = (data["QQQ"] > data["QQQ"].rolling(150).mean()).shift(2).fillna(False)
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both_bull = spy_bull & qqq_bull
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risk_on_cols = [c for c in ["TQQQ", "UPRO"] if c in w.columns]
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risk_off_cols = [c for c in ["GLD", "DBC"] if c in w.columns]
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for i in range(len(w)):
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ron_w = sum(float(w.iat[i, w.columns.get_loc(c)]) for c in risk_on_cols)
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if ron_w > 0.01 and not both_bull.iloc[i]:
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for c in risk_on_cols:
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w.iat[i, w.columns.get_loc(c)] = 0.0
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if risk_off_cols:
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w.iat[i, w.columns.get_loc(risk_off_cols[0])] = ron_w
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return w
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return enhancer
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def momentum_accel(accel_window=20):
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"""Only risk-on when trend is accelerating. Moskowitz et al. (2012)."""
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def enhancer(w, data):
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if "SPY" not in data.columns:
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return w
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spy = data["SPY"]
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ma150 = spy.rolling(150).mean()
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ma_slope = ma150.diff(accel_window)
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accel_positive = (ma_slope > 0).shift(2).fillna(False)
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risk_on_cols = [c for c in ["TQQQ", "UPRO"] if c in w.columns]
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risk_off_cols = [c for c in ["GLD", "DBC"] if c in w.columns]
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for i in range(len(w)):
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ron_w = sum(float(w.iat[i, w.columns.get_loc(c)]) for c in risk_on_cols)
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if ron_w > 0.01 and not accel_positive.iloc[i]:
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for c in risk_on_cols:
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w.iat[i, w.columns.get_loc(c)] = 0.0
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if risk_off_cols:
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w.iat[i, w.columns.get_loc(risk_off_cols[0])] = ron_w
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return w
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return enhancer
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def vix_mean_revert_entry(vix_spike=30, lookback=5):
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"""After VIX spike + revert, force risk-on. Whaley (2009) mean-reversion."""
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def enhancer(w, data):
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if "^VIX" not in data.columns:
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return w
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vix = data["^VIX"].shift(1).fillna(20)
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vix_was_high = vix.rolling(lookback).max() > vix_spike
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vix_now_falling = vix < vix.rolling(lookback).mean()
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buy_signal = vix_was_high & vix_now_falling
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risk_on_cols = [c for c in ["TQQQ", "UPRO"] if c in w.columns]
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risk_off_cols = [c for c in ["GLD", "DBC"] if c in w.columns]
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for i in range(len(w)):
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roff_w = sum(float(w.iat[i, w.columns.get_loc(c)]) for c in risk_off_cols)
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if roff_w > 0.01 and buy_signal.iloc[i]:
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for c in risk_off_cols:
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w.iat[i, w.columns.get_loc(c)] = 0.0
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if risk_on_cols:
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w.iat[i, w.columns.get_loc(risk_on_cols[0])] = roff_w
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return w
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return enhancer
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def combined_enhancer(*enhancers):
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"""Chain multiple enhancers."""
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def enhancer(w, data):
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for e in enhancers:
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w = e(w, data)
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return w
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return enhancer
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# =========================================================================
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# Main
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# =========================================================================
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def main():
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print("=" * 100)
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print(" LITERATURE-INFORMED ALPHA RESEARCH")
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print("=" * 100)
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all_etfs = sorted(set([
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"SPY", "QQQ", "TQQQ", "UPRO", "GLD", "DBC", "SHY", "TLT",
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"^VIX",
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]))
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data = data_manager.update("etfs", all_etfs, with_open=False)
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if isinstance(data, tuple):
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data = data[0]
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cutoff = data.index[-1] - pd.DateOffset(years=YEARS)
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data = data[data.index >= cutoff]
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has_vix = "^VIX" in data.columns
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has_qqq = "QQQ" in data.columns
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print(f"Period: {data.index[0].date()} → {data.index[-1].date()}")
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print(f"VIX available: {has_vix}, QQQ available: {has_qqq}")
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results = []
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def run(label, strategy):
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eq = backtest(strategy, data, initial_capital=CAPITAL,
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transaction_cost=TX_COST, fixed_fee=FIXED_FEE)
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m = metrics.raw_summary(eq)
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results.append((label, m))
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print(f" {label:<55} Ann={m['annualizedReturn']*100:>5.1f}% "
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f"Sharpe={m['sharpeRatio']:.2f} MaxDD={m['maxDrawdown']*100:.1f}% "
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f"Calmar={m['calmarRatio']:.2f}")
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# Baseline
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print("\n--- Baseline ---")
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run("V7+VT36 baseline", V7Enhanced())
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# === Idea 1: VIX overlay ===
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print("\n--- Idea 1: VIX regime overlay (Simon & Campasano 2014) ---")
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if has_vix:
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for hi in (20, 25, 30):
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run(f"VIX overlay (force off >VIX{hi})", V7Enhanced(regime_enhancer=vix_overlay(hi)))
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else:
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print(" VIX not available")
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# === Idea 2: Kelly sizing ===
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print("\n--- Idea 2: Kelly-optimal sizing (Kelly 1956, Thorp 2006) ---")
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run("Kelly sizing", V7Enhanced(sizing_model="kelly"))
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run("Kelly + VIX>25", V7Enhanced(sizing_model="kelly",
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regime_enhancer=vix_overlay(25) if has_vix else None))
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# === Idea 3: Multi-timeframe voting ===
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print("\n--- Idea 3: Multi-MA voting (Faber 2007) ---")
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run("Multi-MA 2/3 (50,150,200)", V7Enhanced(regime_enhancer=multi_timeframe()))
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run("Multi-MA 3/3 (all agree)", V7Enhanced(regime_enhancer=multi_timeframe(min_agree=3)))
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# === Idea 4: Cross-asset confirmation ===
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print("\n--- Idea 4: Cross-asset (Asness et al. 2013) ---")
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if has_qqq:
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run("SPY+QQQ both bullish", V7Enhanced(regime_enhancer=cross_asset_confirm()))
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# === Idea 5: Momentum acceleration ===
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print("\n--- Idea 5: Momentum acceleration (Moskowitz et al. 2012) ---")
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for w in (10, 20, 40):
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run(f"MA150 slope rising ({w}d)", V7Enhanced(regime_enhancer=momentum_accel(w)))
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# === Idea 6: VIX mean-reversion entry ===
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print("\n--- Idea 6: VIX mean-reversion entry (Whaley 2009) ---")
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if has_vix:
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for spike in (25, 30, 35):
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run(f"VIX spike>{spike} + revert → buy",
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V7Enhanced(regime_enhancer=vix_mean_revert_entry(spike)))
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# === Idea 7: Vol-adaptive PT ===
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print("\n--- Idea 7: Vol-drag-aware PT (optimal stopping theory) ---")
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run("Vol-adaptive PT (base=30%)", V7Enhanced(pt_model="vol_adaptive"))
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run("Vol-adaptive PT (base=35%)", V7Enhanced(pt_model="vol_adaptive", pt_threshold=0.35))
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# === Idea 8: Combined best ideas ===
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print("\n--- Idea 8: Combinations ---")
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if has_vix:
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run("VIX>25 + multi-MA 2/3",
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V7Enhanced(regime_enhancer=combined_enhancer(
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vix_overlay(25), multi_timeframe())))
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run("VIX>25 + cross-asset",
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V7Enhanced(regime_enhancer=combined_enhancer(
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vix_overlay(25), cross_asset_confirm())) if has_qqq else None)
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run("VIX>30 + accel(20d)",
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V7Enhanced(regime_enhancer=combined_enhancer(
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vix_overlay(30), momentum_accel(20))))
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# VIX mean-revert + normal V3
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run("V7 + VIX mean-revert entry (>30)",
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V7Enhanced(regime_enhancer=vix_mean_revert_entry(30)))
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# === Idea 9: Different MA for V3 regime ===
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print("\n--- Idea 9: Alternative MA windows ---")
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for ma in (100, 120, 130, 150, 170, 200):
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run(f"V3 MA{ma} + VT36", V7Enhanced(ma_long=ma))
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||||
# Final ranking
|
||||
results.sort(key=lambda x: x[1]["sharpeRatio"], reverse=True)
|
||||
print(f"\n{'=' * 110}")
|
||||
print(" FINAL RANKING (by Sharpe)")
|
||||
print(f"{'=' * 110}")
|
||||
print(f"{'#':<4} {'Strategy':<55} {'Ann%':>6} {'Vol%':>6} {'Sharpe':>7} "
|
||||
f"{'Sortino':>8} {'MaxDD%':>7} {'Calmar':>7}")
|
||||
print("-" * 110)
|
||||
for i, (label, m) in enumerate(results, 1):
|
||||
marker = " ★" if i <= 3 else ""
|
||||
print(f"{i:<4} {label:<55} "
|
||||
f"{m['annualizedReturn']*100:>5.1f}% "
|
||||
f"{m['annualizedVolatility']*100:>5.1f}% "
|
||||
f"{m['sharpeRatio']:>7.2f} {m['sortinoRatio']:>8.2f} "
|
||||
f"{m['maxDrawdown']*100:>6.1f}% {m['calmarRatio']:>7.2f}{marker}")
|
||||
print(f"{'=' * 110}")
|
||||
|
||||
# Top by Ann Return
|
||||
results.sort(key=lambda x: x[1]["annualizedReturn"], reverse=True)
|
||||
print(f"\n Top 5 by Ann Return:")
|
||||
for i, (label, m) in enumerate(results[:5], 1):
|
||||
print(f" {i}. {label:<50} Ann={m['annualizedReturn']*100:.1f}% "
|
||||
f"Sharpe={m['sharpeRatio']:.2f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user