quant/CLAUDE.md

# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

Quantitative backtesting framework for comparing equity trading strategies across multiple markets. Supports US (S&P 500) and China A-shares (CSI 300). Uses Yahoo Finance data with persistent local CSV storage. Includes a daily trading simulator with automated cron support.

## Commands

```bash
# Backtesting
uv run python main.py                    # Run US backtest (default)
uv run python main.py --market cn        # Run China A-share backtest
uv run python main.py --capital 50000    # Custom starting capital
uv run python main.py --top-n 20         # Override stock selection count
uv run python main.py --years 5          # Backtest last N years only
uv run python main.py --no-plot          # Skip matplotlib charts
uv run python main.py --fixed-fee 2.0    # Add $2 per-trade fixed fee
uv run python main.py --execution open-close  # Signal on open, execute at close

# Daily trading simulator
uv run python trader.py auto --market us --strategy recovery_mom_top10   # Single daily run (for cron)
uv run python trader.py morning --market us --strategy recovery_mom_top10  # Morning: generate orders
uv run python trader.py evening --market us --strategy recovery_mom_top10  # Evening: record execution
uv run python trader.py status  --market us --strategy recovery_mom_top10  # Portfolio status
uv run python trader.py simulate --market us --strategy recovery_mom_top10 --start 2026-01-01 --end 2026-04-01  # Historical replay

# Setup
uv sync                                  # Install/sync dependencies
```

No test suite or linter is configured.

## Architecture

**Universe provider** (`universe.py`): Dynamically fetches index constituents (S&P 500 from Wikipedia, CSI 300 from Wikipedia) and converts to Yahoo Finance ticker format. Constituent lists are cached daily in `data/universe_*.json`. New markets are added by registering in `UNIVERSES` dict.

**Data manager** (`data_manager.py`): Persistent price storage in `data/{market}.csv` (close) and `data/{market}_open.csv` (open). First run downloads 10 years of history; subsequent runs append only new trading days (fast bulk fetch for gaps ≤7 days). New tickers from index rebalances are backfilled automatically. Tickers with >50% missing data are dropped. Pass `with_open=True` to also maintain open price files.

**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`.

**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.

**Strategies**:
- `buy_and_hold.py` — Equal-weight static allocation
- `momentum.py` — Cross-sectional momentum (12-1 month factor), selects top_n winners
- `inverse_vol.py` — Inverse-volatility (risk parity) weighting
- `multi_factor.py` — Combines momentum + value factors with benchmark MA timing filter
- `mean_reversion.py` — Short-term mean reversion
- `trend_following.py` — MA crossover + momentum trend filter
- `dual_momentum.py` — Absolute + relative momentum
- `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.

**Metrics** (`metrics.py`): Standalone functions for portfolio analytics (Sharpe, Sortino, Calmar, max drawdown, etc.). `summary()` prints a formatted report and returns a dict.

## Key Conventions

- Strategies output **weights**, not buy/sell signals. Weights are normalized so each row sums to 1 (or 0 during warm-up).
- Warm-up periods: each strategy zeros out its first N rows where indicators are undefined.
- `data/` contains persistent price CSVs, universe JSON caches, and trader state files. Not checked into git (only `data/.gitkeep` is tracked).
- Python 3.12+, managed with `uv`.
- Do not show matplotlib figures when running backtests; use `--no-plot`.

## Server Deployment (Cron)

Run daily after market close. The `auto` command is idempotent — safe to re-run:

```bash
# US market: 5pm ET weekdays
0 17 * * 1-5  cd /path/to/quant && uv run python trader.py auto --market us --strategy recovery_mom_top10 >> data/cron.log 2>&1

# CN market: 4pm CST weekdays
0 16 * * 1-5  cd /path/to/quant && uv run python trader.py auto --market cn --strategy inverse_vol >> data/cron.log 2>&1
```