Gahow Wang
5dbabc3e08
A live book is a cohort of strategies, each with its own account, started on one date with one capital and a flat fee per order (trader.LIVE_BOOKS). The original book keeps its state in data/; a named book in data/books/<book>/. The monitor runs every book; auto/status/log/compare/ replay take --book. A fresh account opens on the book's first session. selected_2026_09: US $10,000 and $1 per order from 2026-09-25, CN ¥50,000 and ¥5 per order from 2026-09-28 (the first CN session after the choice). Strategies beat their benchmark in the Apr-Sep 2026 point-in-time replay and passed a 10-year PIT backtest (US Sharpe >= 0.6, MaxDD > -50%; CN Sharpe >= 0.2, CAGR > 0), no daily variants, at most two frequencies per factor signal, plus the three 2026-09 research strategies. bridge.py live --book and bridge.py books feed a book selector on the live page (/api/strategies/live?book=, /api/strategies/live/books). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
quant-trading web
Web UI and API for the quant-trading repository: strategy backtests and live books (via ../bridge.py), plus a personal watchlist agent.
统一个人股票监控 MVP，支持 A股、港股、美股自选股，按日生成监控报告。数据接口固定使用 IPADS Responses API，密钥从仓库根目录 .env 的 IPADS_API_KEY 读取。
Run
npm install
npm run dev
Frontend: http://127.0.0.1:5173
Backend: http://127.0.0.1:8787
LAN access:
HOST=0.0.0.0 CLIENT_PORT=5174 PORT=8787 npm run dev
Open http://gahow-pc.ipads-lab.se.sjtu.edu.cn:5174 from another device on the same network.
IPADS API
配置文件：
IPADS_API_KEY=your_api_key
固定服务端配置：
base_url = "http://tianx.ipads-lab.se.sjtu.edu.cn:8319/v1"
wire_api = "responses"
env_key = "IPADS_API_KEY"
The backend calls:
POST http://tianx.ipads-lab.se.sjtu.edu.cn:8319/v1/responses
Authorization: Bearer ${IPADS_API_KEY}
The response output text must be JSON matching the stock snapshot schema used by the agent. If IPADS_API_KEY is blank, the app uses deterministic demo data so the dashboard remains usable.
Features
- Add/remove watchlist symbols such as
600519,000001.SZ,00700.HK,AAPL,MSFT.US. - Normalize symbols into A股、港股、美股 markets.
- Generate a daily report with earnings timing, peer stocks, valuation discount/premium, related news, and buy-watch/hold/avoid recommendations.
- Refreshes the cached daily report every 24 hours while the backend process is running.
- View each watchlist stock with locally cached D/W/M/Y K-line chart data, zoom controls, MA5/MA20/MA60, RSI14, MACD, volume, and cached financial statements. US K-line and quote/research fields use Nasdaq as the fixed data source and target at least 5 years of verified daily OHLCV history when available.
- Cache related market context such as sector, liquidity, volatility, drawdown, relative strength, capital flow, sentiment, and risk flags.
- Run an agent-backed quantitative analysis across technical, valuation, growth, quality, liquidity, sentiment, and risk factors to classify each stock as buy-watch/hold/avoid.
- Add tags to every watchlist stock and filter the watchlist by tag.
- In configured provider mode, prompts require verified data and do not permit approximate or fabricated values; missing data is surfaced instead of being filled with synthetic values.
- K-line cache updates incrementally: once 5 years of daily bars exist locally, refreshes request only the latest missing window and merge by date.
Local cache
Web state lives in the repo-level data/web/ (see src/server/paths.ts): watchlist.json, per-stock snapshot/K-line/financial data under cache/, and backtest results under strategy_cache/. The strategy page reads data/precomputed/backtest_results.json, written by precompute_backtest.py at the repo root.
Production runs as systemd services; see "Server Deployment" in the root CLAUDE.md.