quant-trading/CLAUDE.md

# CLAUDE.md

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

## Project Overview

quant-trading: a quantitative backtesting framework and live forward-testing trader for equity strategies across US (S&P 500) and China A-shares (CSI 300), plus a web UI to browse backtests and live books. Uses Yahoo Finance data with persistent local CSV storage.

The repository has two halves that share one data directory and one `.env`:

- **Python engine** (repo root, `uv`): strategies, backtests, `trader.py`, research scripts.
- **Web UI and API** (`web/`, Node + React + Vite): the API spawns `uv run python bridge.py {list,backtest,live}` at the repo root and reads `data/precomputed/backtest_results.json`, written by `precompute_backtest.py`. Both backtest paths run through `backtest_runner.py`.

`bridge.py`'s JSON output and `web/src/shared/strategyTypes.ts` are one contract: change both in the same commit.

## 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 monitor                                            # Monitor ALL markets & strategies (for tmux)
uv run python trader.py monitor --market us                                # Monitor US only
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
uv run python trader.py log --market us --strategy sim_recovery_mom_top10 --start 2026-03-01  # View daily log
uv run python trader.py simulate --integer-shares --fixed-fee 2.0 ...    # Integer shares mode
uv run python trader.py compare --market us --book selected_2026_09      # A named live book

# Precompute the strategy page's backtests (writes data/precomputed/)
uv run python precompute_backtest.py

# Factor research harness (PIT universe, next-open fills, phase averaging; data in data/research/)
uv run python -m research.lab.fetch_us                 # 20y OHLCV for every S&P 500 ever-member
uv run python -m research.lab.fetch_cn_yahoo           # CSI 300/500 ever-members (membership from baostock snapshot)
PYTHONPATH=. uv run python -m research.lab.factor_scan --market us       # or cn_hs300
PYTHONPATH=. uv run python -m research.lab.stepwise --market us --objective maximin
PYTHONPATH=. uv run python -m research.lab.evaluate --market us          # candidates vs champions
uv run python -m research.lab.fetch_us_fundamentals   # SEC EDGAR point-in-time facts + Yahoo splits (US)
PYTHONPATH=. uv run python -m research.lab.rotation --market us          # walk-forward factor weighting schemes
PYTHONPATH=. uv run python -m research.lab.persistence                   # does last year's best combo persist?
PYTHONPATH=. uv run --with numba python -m research.lab.us_sweep --part overlays   # construction / overlay sweeps
PYTHONPATH=. uv run --with numba python -m research.lab.us_final         # 3-sleeve US portfolio check

# Setup
uv sync                                  # Install/sync dependencies
cp .env.example .env                     # Then fill IPADS_API_KEY and QUANT_MARKET_PROXY
(cd web && npm ci)

# Tests
uv run python -m unittest discover tests
(cd web && npm test && npm run build)

# Web UI in development (API :8787, Vite :5173)
(cd web && npm run dev)
```

## Architecture

**Universe provider** (`universe.py`): Dynamically fetches index constituents (S&P 500 from Wikipedia; CSI 300 from China Securities Index's published constituent file, falling back to the latest `data/csi300_history.json` snapshot, then Wikipedia, whose table lagged the index by ~60 names in 2026) 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.

**Point-in-time membership** (`universe_history.py`): S&P 500 intervals rebuilt from Wikipedia's "Historical components of the S&P 500" change log into `data/sp500_history.json`; CSI 300 monthly snapshots in `data/csi300_history.json` (seeded from baostock 2008–2026, appended whenever the current list changes). `mask_prices_pit(prices, market)` masks non-member dates.

**Data manager** (`data_manager.py`): Persistent price storage in `data/{market}.csv` (close) and `data/{market}_{open,high,low,volume}.csv`. First run downloads 10 years of history; every run re-downloads the last 30 days. New tickers from index rebalances are backfilled automatically; tickers missing from a partial batch are retried alone before being marked unavailable (retried weekly). `update(market, tickers, with_open=True)` returns (close, open); `update(..., fields=[...])` returns `{field: panel}`.

**Backtest engine** (`main.py`): Orchestrates data loading, strategy execution, and visualization. `backtest()` has two modes. Close mode (no `open_data`, used by `main.py` and old research scripts) is vectorized and trades at the close the signal saw. Live mode (`open_data` given, used by the web UI through `backtest_runner.py`) keeps the book like `trader.py`: same signal timing (`strategy_positions`), holdings that drift, the $50/0.1% order floor, `trader.RESIZE_BAND`, fees on the orders placed (`simulate_live_book`).

**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). Orders: whole A-share lots are allocated nearest-lot within cash (`_allocate_lots`); a held name is resized only when ≥ `RESIZE_BAND` (10%) off target. Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`, 32 factor-combo strategies (`fc_{signal}_{freq}` — see `strategies/factor_combo.py`), the robust-momentum bar strategies `overnight_recovery_top10`, `lowrisk_momentum_top15`, `lowbeta_sharpe_mom_top20`, `lowrisk_top20`, and the US sleeve portfolio `us_three_sleeve`.

**Live books** (`trader.LIVE_BOOKS`): a book is a cohort of strategies, each with its own account, started on one date with one capital and a flat fee per order. The original book (every live strategy, `MONITOR_CAPITAL`, `MARKET_FEES`) keeps state in `data/trader_*.json`; a named book keeps it in `data/books/<book>/`. The monitor runs every book; `auto`/`status`/`log`/`compare`/`replay` and `bridge.py live` take `--book`, and `bridge.py books` lists them for the UI's book selector. `selected_2026_09` (29 strategies, US $10,000 / CN ¥100,000, $2 / ¥10 per order) starts on 2026-09-25 (US) and 2026-09-28 (CN).

**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. A `BarStrategy` declares `fields` (close/open/high/low/volume) and receives `{field: DataFrame}`; live it uses completed bars only (row d from bars through d-1, traded at the close of d), while close-price strategies read the session's open as their latest price.

**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.
- `robust_momentum.py` — Bar strategies from the 2026-09 PIT research (report: https://claude.ai/artifact/Mo9nrzs5SWj5FfyshERyWS): US `overnight_recovery` (overnight-return momentum + distance from 126d low), CN `lowrisk_momentum` (amplitude-cut momentum + low beta + price-volume corr + momentum/vol) and close-only `lowbeta_sharpe_mom`. Top-N with rank hysteresis, rebalance every 10 days, weights drift between rebalances. `low_risk` (US `lowrisk_top20`: idiosyncratic vol + low beta, top 20, monthly) is the defensive sleeve of the 2026-09-29 US research (report: https://claude.ai/artifact/VGhZvKkS9UBYZkLeqGjcNC).
- `sleeve_portfolio.py` — Several strategies in one account with fixed capital shares. `us_three_sleeve` = 1/3 each of `overnight_recovery_top10`, `lowrisk_top20`, `trend_rider_v7_vt24` (a mixed stock+ETF strategy); sleeve shares drift with their returns and reset at a month start once any is >25% off. 2011-2026 research: Sharpe 1.21 vs 0.88-1.03 for the sleeves alone. Research finding behind it: choosing factors by their recent record (rotation, factor momentum, last year's winner) did not beat a fixed composite; construction knobs (N, weighting, dynamic N) barely matter.
- `factor_combo.py` — Configurable factor-combination strategies with daily/weekly/biweekly/monthly rebalancing. US champions: `rec_mfilt+deep_upvol`, `ma200+mom7m+rec126`, `rec_mfilt+ma200`, `mom7m+rec126`. CN champions: `up_cap+quality_mom`, `down_resil+qual_mom`, `rec63+mom_gap`, `up_cap+mom_gap` — these were mined on today's CSI 300 members; on PIT members they earn ~3-7% a year (2016-2026). 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.

## 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, plus `data/precomputed/` (precomputed backtests), `data/web/` (watchlist, per-stock cache, backtest result cache) and `data/research/` (research panels, ~2 GB). Not checked into git (only `data/.gitkeep`, `data/sp500_history.json` and `data/csi300_history.json` are tracked).
- Python 3.12+, managed with `uv`.
- Do not show matplotlib figures when running backtests; use `--no-plot`.

## Server Deployment

Three `systemd --user` services, defined in `deploy/systemd/`, run from `/home/gahow/projects/quant-trading`:

| Service | Script | Role |
|---|---|---|
| `quant-trading-monitor` | `deploy/start-monitor.sh` | `trader.py monitor`: morning/evening phases for US + CN, all live strategies |
| `quant-trading-api` | `deploy/start-web-api.sh` | Web API on `127.0.0.1:8787` |
| `quant-trading-vite` | `deploy/start-web-vite.sh` | Vite on `127.0.0.1:5174`, behind nginx on `:5443` |

```bash
cp deploy/systemd/*.service ~/.config/systemd/user/
systemctl --user daemon-reload
systemctl --user enable --now quant-trading-monitor quant-trading-api quant-trading-vite
journalctl --user -u quant-trading-monitor -f
```

The monitor catches up on startup, so restarting it between phases is safe. If it starts mid-day (after open, before close), it falls back to `auto` mode for the evening phase.

Compare live books after a while:

```bash
uv run python trader.py compare              # US strategies
uv run python trader.py compare --market cn  # CN strategies
```

### Alternative: 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 /home/gahow/projects/quant-trading && 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 /home/gahow/projects/quant-trading && uv run python trader.py auto --market cn --strategy inverse_vol >> data/cron.log 2>&1
```