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Add 32 factor-combo strategies with configurable rebalancing frequency

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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>
This commit is contained in:
Gahow Wang
2026-04-08 10:41:34 +08:00
parent a66b039d2d
commit ae25f2f6b5
13 changed files with 3402 additions and 1 deletions
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CLAUDE.md
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@@ -44,7 +44,7 @@ No test suite or linter is configured.
**Backtest engine** (`main.py`): Orchestrates data loading, strategy execution, and visualization. The `backtest()` function is vectorized — it takes a strategy and price DataFrame, applies transaction costs (proportional + optional fixed per-trade fee) via turnover, and returns an equity curve. Supports two execution modes: `close` (classic) and `open-close` (signal on open prices, execute at close).
**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`.
**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`, plus 32 factor-combo strategies (`fc_{signal}_{freq}` — see `strategies/factor_combo.py`).
**Strategy protocol** (`strategies/base.py`): All strategies inherit from `Strategy` ABC and implement `generate_signals(data) → DataFrame` where the returned DataFrame contains portfolio weights (rows = dates, columns = assets, values sum to ~1.0 per row). Each strategy is responsible for applying its own 1-day lag via `.shift(1)` to avoid lookahead bias — the backtest engine does not shift.
@@ -59,6 +59,7 @@ No test suite or linter is configured.
- `momentum_quality.py` — Momentum + return consistency + low drawdown
- `adaptive_momentum.py` — Momentum weighted by inverse volatility
- `recovery_momentum.py` — Recovery (price/63d low) + 12-1mo momentum composite. Best US performer.
- `factor_combo.py` — Configurable factor-combination strategies with daily/weekly/biweekly/monthly rebalancing. US champions: `rec_mfilt+deep_upvol` (50.7% CAGR monthly), `ma200+mom7m+rec126`, `rec_mfilt+ma200`, `mom7m+rec126`. CN champions: `up_cap+quality_mom` (26.1% CAGR monthly), `down_resil+qual_mom`, `rec63+mom_gap`, `up_cap+mom_gap`. All registered in trader.py as `fc_{signal}_{freq}` (e.g., `fc_rec_mfilt_deep_upvol_monthly`). 32 new strategies total.
**Metrics** (`metrics.py`): Standalone functions for portfolio analytics (Sharpe, Sortino, Calmar, max drawdown, etc.). `summary()` prints a formatted report and returns a dict.