Gahow Wang
83495bcd23
The three services become quant-trading-monitor, quant-trading-api and quant-trading-vite, run from /home/gahow/projects/quant-trading. Their units were previously untracked files in ~/.config/systemd/user. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
69 lines
3.0 KiB
Markdown
69 lines
3.0 KiB
Markdown
# quant-trading web
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Web UI and API for the quant-trading repository: strategy backtests and live books (via `../bridge.py`), plus a personal watchlist agent.
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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。
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## Run
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```bash
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npm install
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npm run dev
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```
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Frontend: http://127.0.0.1:5173
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Backend: http://127.0.0.1:8787
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LAN access:
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```bash
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HOST=0.0.0.0 CLIENT_PORT=5174 PORT=8787 npm run dev
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```
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Open `http://gahow-pc.ipads-lab.se.sjtu.edu.cn:5174` from another device on the same network.
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## IPADS API
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配置文件：
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```env
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IPADS_API_KEY=your_api_key
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```
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固定服务端配置：
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```text
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base_url = "http://tianx.ipads-lab.se.sjtu.edu.cn:8319/v1"
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wire_api = "responses"
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env_key = "IPADS_API_KEY"
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```
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The backend calls:
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```text
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POST http://tianx.ipads-lab.se.sjtu.edu.cn:8319/v1/responses
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Authorization: Bearer ${IPADS_API_KEY}
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```
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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.
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## Features
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- Add/remove watchlist symbols such as `600519`, `000001.SZ`, `00700.HK`, `AAPL`, `MSFT.US`.
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- Normalize symbols into A股、港股、美股 markets.
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- Generate a daily report with earnings timing, peer stocks, valuation discount/premium, related news, and buy-watch/hold/avoid recommendations.
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- Refreshes the cached daily report every 24 hours while the backend process is running.
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- 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.
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- Cache related market context such as sector, liquidity, volatility, drawdown, relative strength, capital flow, sentiment, and risk flags.
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- 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.
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- Add tags to every watchlist stock and filter the watchlist by tag.
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- 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.
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- K-line cache updates incrementally: once 5 years of daily bars exist locally, refreshes request only the latest missing window and merge by date.
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## Local cache
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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.
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Production runs as systemd services; see "Server Deployment" in the root `CLAUDE.md`.
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