research: extensive V7 optimization and V8 (TMF) evaluation
Research scripts exploring paths beyond V7+VT36: - regime_stock_picker_eval: V3 regime + S&P 500 stock picking - v7_parameter_sweep: VT range (20-48%) + adaptive PT variants - v7_synthetic_leverage_eval: synthetic 2x/3x leveraged individual stocks - v7_breakthrough_eval/fixed: ensemble, cross-market, alt regime engines - v7_three_ideas_eval: TMF risk-off, PT entry reset, fast exit - v7_trade_audit: full 10y trade log and alpha attribution - sota_ranking: comprehensive cross-strategy ranking Key findings: - VT36 is optimal risk-return tradeoff (+7% vs VT28, Sharpe ~flat) - PT30 is structural optimum for 3x ETFs (all adaptive variants worse) - V8 (TMF risk-off) debunked: +5% was 1-day lookahead bias artifact - V3 regime engine irreplaceable (all simplified alternatives fail) - PT mechanism is dominant alpha source (+15.6pp ann, +0.58 Sharpe) V8 strategy file kept for reference (not registered). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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research/v7_breakthrough_fixed.py
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282
research/v7_breakthrough_fixed.py
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"""Fixed re-run for Directions B and C based on review feedback.
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Direction B fix: recalibrate V3 thresholds per-sector (scale by vol ratio).
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Direction C fix: monkey-patch V3._desired_regime inside real V7, preserving
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the full state machine (confirm_days, cooloff, stop_loss, dd_stop).
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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 main import backtest
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from strategies.trend_rider_v7 import TrendRiderV7
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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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def load_etf_data():
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all_etfs = sorted(set([
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"SPY", "TQQQ", "UPRO", "GLD", "DBC", "SHY",
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"SOXL", "SMH", "TECL", "XLK", "TNA", "IWM", "FAS", "XLF",
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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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return data[data.index >= cutoff]
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def run(label, strategy, panel):
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eq = backtest(strategy, panel, 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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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"Sortino={m['sortinoRatio']:.2f} Calmar={m['calmarRatio']:.2f}")
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return label, eq, m
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# =========================================================================
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# DIRECTION B FIX: per-sector calibrated thresholds
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# =========================================================================
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def direction_b_fixed(etf_data):
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print("\n" + "=" * 100)
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print(" DIRECTION B FIXED: Sector V7 with recalibrated thresholds")
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print("=" * 100)
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results = []
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core = [t for t in ["SPY", "TQQQ", "UPRO", "GLD", "DBC", "SHY"] if t in etf_data.columns]
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# Baseline
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r = run("V7+VT36 baseline (SPY→TQQQ/UPRO)", TrendRiderV7(target_vol=0.36, min_lev=0.75), etf_data[core])
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results.append(r)
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eq_v7 = r[1]
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# Estimate vol ratios for threshold scaling
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rets = etf_data.pct_change(fill_method=None).dropna()
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spy_vol = rets["SPY"].std() * np.sqrt(252) if "SPY" in rets.columns else 0.18
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print(f"\n SPY realized vol: {spy_vol:.1%}")
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sector_configs = [
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("SMH", ("SOXL",), "Semiconductor"),
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("XLK", ("TECL",), "Technology"),
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("IWM", ("TNA",), "Russell 2000"),
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("XLF", ("FAS",), "Financials"),
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]
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sector_eqs = {}
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for signal, risk_on, name in sector_configs:
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if signal not in etf_data.columns or risk_on[0] not in etf_data.columns:
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print(f" SKIP {name}: missing data")
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continue
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sig_vol = rets[signal].std() * np.sqrt(252) if signal in rets.columns else spy_vol
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vol_ratio = sig_vol / spy_vol
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print(f" {signal} vol: {sig_vol:.1%}, ratio to SPY: {vol_ratio:.2f}")
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needed = [signal] + list(risk_on) + ["GLD", "DBC", "SHY"]
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panel = etf_data[[t for t in needed if t in etf_data.columns]]
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# Uncalibrated (original V3 thresholds)
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v7_raw = TrendRiderV7(
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signal=signal, risk_on=risk_on, risk_off=("GLD", "DBC"),
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target_vol=0.36, min_lev=0.75,
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)
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r = run(f" {name} UNCALIBRATED", v7_raw, panel)
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results.append(r)
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# Calibrated: scale vol/dd/peak thresholds by vol ratio
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v7_cal = TrendRiderV7(
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signal=signal, risk_on=risk_on, risk_off=("GLD", "DBC"),
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target_vol=0.36, min_lev=0.75,
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# V3 thresholds scaled by sector vol ratio
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vol_enter=0.14 * vol_ratio,
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vol_exit=0.20 * vol_ratio,
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dd_stop=0.05 * vol_ratio,
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peak_enter=0.02 * vol_ratio,
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peak_exit=0.05 * vol_ratio,
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)
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r = run(f" {name} CALIBRATED (×{vol_ratio:.1f})", v7_cal, panel)
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results.append(r)
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sector_eqs[name] = r[1]
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# Ensembles with calibrated sectors
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if sector_eqs:
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print()
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for name, sec_eq in sector_eqs.items():
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for v7_pct in (0.5, 0.7):
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idx = eq_v7.index.intersection(sec_eq.index)
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v7_a = eq_v7.reindex(idx).ffill()
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sec_a = sec_eq.reindex(idx).ffill()
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ens = (v7_a / v7_a.iloc[0]) * v7_pct + (sec_a / sec_a.iloc[0]) * (1 - v7_pct)
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ens = ens * CAPITAL
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m = metrics.raw_summary(ens)
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label = f" {int(v7_pct*100)}% SPY-V7 + {int((1-v7_pct)*100)}% {name[:8]}-V7 (cal)"
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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"Sortino={m['sortinoRatio']:.2f} Calmar={m['calmarRatio']:.2f}")
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results.append((label, ens, m))
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return results
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# =========================================================================
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# DIRECTION C FIX: inject alt regime into REAL V3 state machine
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# =========================================================================
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def direction_c_fixed(etf_data):
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print("\n" + "=" * 100)
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print(" DIRECTION C FIXED: Alt regimes inside real V3 state machine")
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print("=" * 100)
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core = [t for t in ["SPY", "TQQQ", "UPRO", "GLD", "DBC", "SHY"] if t in etf_data.columns]
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results = []
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# Baseline
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r = run("V7+VT36 (V3 full regime, baseline)", TrendRiderV7(target_vol=0.36, min_lev=0.75), etf_data[core])
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results.append(r)
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# Alt regimes: monkey-patch V3._desired_regime, preserving full FSM
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def make_alt_v7(regime_fn, label):
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v7 = TrendRiderV7(target_vol=0.36, min_lev=0.75)
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v7.v3._desired_regime = regime_fn
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return v7
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# --- Simple MA variants ---
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for window in (100, 150, 200, 250):
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def regime_ma(closes, current, w=window):
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if len(closes) < w:
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return "risk_off"
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return "risk_on" if closes[-1] > np.mean(closes[-w:]) else "risk_off"
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r = run(f"Simple MA{window}", make_alt_v7(regime_ma, f"MA{window}"), etf_data[core])
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results.append(r)
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# --- Dual MA crossover ---
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for short, long in ((50, 200), (50, 150), (20, 100)):
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def regime_dual(closes, current, s=short, l=long):
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if len(closes) < l:
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return "risk_off"
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return "risk_on" if np.mean(closes[-s:]) > np.mean(closes[-l:]) else "risk_off"
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r = run(f"Dual MA {short}/{long}", make_alt_v7(regime_dual, ""), etf_data[core])
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results.append(r)
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# --- ROC variants ---
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for window in (42, 63, 126):
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def regime_roc(closes, current, w=window):
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if len(closes) < w + 1 or closes[-w-1] <= 0:
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return "risk_off"
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return "risk_on" if closes[-1] / closes[-w-1] > 1.0 else "risk_off"
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r = run(f"ROC {window}d", make_alt_v7(regime_roc, ""), etf_data[core])
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results.append(r)
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# --- MA + vol filter (simplified V3) ---
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for ma_w, vol_cap in ((150, 0.20), (150, 0.25), (200, 0.22)):
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def regime_mavol(closes, current, mw=ma_w, vc=vol_cap):
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if len(closes) < max(mw, 21):
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return "risk_off"
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above = closes[-1] > np.mean(closes[-mw:])
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if not above:
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return "risk_off"
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rets = np.diff(closes[-21:]) / np.maximum(closes[-21:-1], 1e-12)
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vol = float(np.std(rets, ddof=1) * np.sqrt(252))
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return "risk_on" if vol < vc else "risk_off"
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r = run(f"MA{ma_w} + Vol<{int(vol_cap*100)}%", make_alt_v7(regime_mavol, ""), etf_data[core])
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results.append(r)
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# --- Composite (MA + ROC + vol) ---
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for thresh in (2, 3):
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def regime_comp(closes, current, t=thresh):
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if len(closes) < 200:
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return "risk_off"
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score = 0
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if closes[-1] > np.mean(closes[-150:]):
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score += 1
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if closes[-64] > 0 and closes[-1] / closes[-64] > 1.0:
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score += 1
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rets = np.diff(closes[-21:]) / np.maximum(closes[-21:-1], 1e-12)
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if np.std(rets, ddof=1) * np.sqrt(252) < 0.22:
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score += 1
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return "risk_on" if score >= t else "risk_off"
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r = run(f"Composite {thresh}/3", make_alt_v7(regime_comp, ""), etf_data[core])
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results.append(r)
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# --- MA + slope (MA must be rising) ---
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for slope_w in (10, 20):
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def regime_slope(closes, current, sw=slope_w):
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if len(closes) < 150 + sw:
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return "risk_off"
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ma_now = np.mean(closes[-150:])
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ma_prev = np.mean(closes[-150-sw:-sw])
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return "risk_on" if (closes[-1] > ma_now and ma_now > ma_prev) else "risk_off"
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r = run(f"MA150 + Rising({slope_w}d)", make_alt_v7(regime_slope, ""), etf_data[core])
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results.append(r)
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# --- Adaptive MA (fast in low vol, slow in high vol) ---
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for pivot in (0.15, 0.18, 0.22):
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def regime_adapt(closes, current, p=pivot):
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if len(closes) < 200:
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return "risk_off"
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rets = np.diff(closes[-61:]) / np.maximum(closes[-61:-1], 1e-12)
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vol = np.std(rets, ddof=1) * np.sqrt(252)
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w = 200 if vol > p else 100
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return "risk_on" if closes[-1] > np.mean(closes[-w:]) else "risk_off"
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r = run(f"Adaptive MA (pivot={int(pivot*100)}%)", make_alt_v7(regime_adapt, ""), etf_data[core])
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results.append(r)
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# Sort and display
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results.sort(key=lambda x: x[2]["sharpeRatio"], reverse=True)
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print(f"\n--- Direction C FIXED Results (sorted by Sharpe) ---")
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for i, (label, _, m) in enumerate(results, 1):
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marker = " ★" if i <= 3 else ""
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print(f" {i:<3} {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}{marker}")
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return results
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def main():
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print("=" * 100)
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print(" V7 BREAKTHROUGH EVAL — FIXED RE-RUN (per review feedback)")
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print("=" * 100)
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etf_data = load_etf_data()
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print(f"Period: {etf_data.index[0].date()} → {etf_data.index[-1].date()}")
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print(f"ETFs: {sorted(etf_data.columns.tolist())}")
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results_b = direction_b_fixed(etf_data)
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results_c = direction_c_fixed(etf_data)
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# Cross-direction top 10
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all_r = [(f"[B] {l}", eq, m) for l, eq, m in results_b] + \
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[(f"[C] {l}", eq, m) for l, eq, m in results_c]
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all_r.sort(key=lambda x: x[2]["sharpeRatio"], reverse=True)
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print(f"\n{'=' * 100}")
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print(" FINAL: Top 10 by Sharpe")
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print(f"{'=' * 100}")
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for i, (label, _, m) in enumerate(all_r[:10], 1):
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print(f" {i:<3} {label:<60} 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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all_r.sort(key=lambda x: x[2]["annualizedReturn"], reverse=True)
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print(f"\n FINAL: Top 10 by Ann. Return")
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print(f" {'-' * 95}")
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for i, (label, _, m) in enumerate(all_r[:10], 1):
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print(f" {i:<3} {label:<60} 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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if __name__ == "__main__":
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main()
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