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>
New FactorComboStrategy class (strategies/factor_combo.py) implements
8 champion factor signals (4 US, 4 CN) discovered through iterative
factor research, each at 4 rebalancing frequencies (daily/weekly/
biweekly/monthly). Registered in trader.py as fc_{signal}_{freq}.
Existing strategies and state files are untouched — safe to git pull
and restart monitor on server.
Also includes factor research scripts (factor_loop.py, factor_research.py,
etc.) used to discover and validate these factors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backtesting engine supporting 11 strategies across US (S&P 500) and CN (CSI 300)
markets with open-to-close execution, proportional + fixed per-trade fees.
Daily trader (trader.py) with auto/morning/evening/simulate/status commands
and cron-friendly `auto` mode for unattended daily runs on a server.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
