TIANHE
137 lines
4.1 KiB
Markdown
137 lines
4.1 KiB
Markdown
# QuantDinger Python API (backend)
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Flask-based local-first backend for QuantDinger: market data, indicators, AI analysis, backtesting, and a strategy runtime (with an optional pending-order worker).
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This repository is intentionally simple: **no external database is required by default**. Data is stored in a local SQLite file (`quantdinger.db`) created/updated automatically on startup.
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## What you get
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- **Multi-market data layer**: factory-based providers (crypto / US stocks / CN&HK stocks / futures, etc.)
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- **Indicators + backtesting**: persisted runs/history in SQLite
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- **AI multi-agent analysis**: optional web search + OpenRouter LLM integration
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- **Strategy runtime**: thread-based executor, with optional auto-restore on startup
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- **Pending orders worker (optional)**: polls queued orders and dispatches signals (webhook/notifications)
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- **Local auth (single-user)**: `/login` with env-configured admin credentials (JWT)
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## Project layout
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```text
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backend_api_python/
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├─ app/
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│ ├─ __init__.py # Flask app factory + startup hooks
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│ ├─ config/ # Settings (env-driven)
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│ ├─ data_sources/ # Data sources + factory
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│ ├─ routes/ # REST endpoints
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│ ├─ services/ # Analysis, agents, strategies, search, ...
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│ └─ utils/ # SQLite helpers, config loader, logging, HTTP utils
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├─ env.example # Copy to .env for local config
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├─ requirements.txt
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├─ run.py # Entrypoint (loads .env, applies proxy env, starts Flask)
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├─ gunicorn_config.py # Optional production config
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└─ README.md
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```
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## Quick start (local development)
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### Prerequisites
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- Python 3.10+ recommended
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### 1) Install dependencies
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```bash
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cd backend_api_python
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pip install -r requirements.txt
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```
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### 2) Create your local `.env`
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Windows (CMD):
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```bash
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copy env.example .env
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```
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Windows (PowerShell):
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```bash
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Copy-Item env.example .env
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```
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Then edit `.env` and set at least:
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- `SECRET_KEY`
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- `ADMIN_USER`
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- `ADMIN_PASSWORD`
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Optional but common:
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- `OPENROUTER_API_KEY` (for AI analysis)
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- `FINNHUB_API_KEY` / `SEARCH_GOOGLE_*` / `SEARCH_BING_API_KEY` (for richer data/search)
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- `PROXY_PORT` or `PROXY_URL` (if your network blocks some providers)
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### 3) Start the API server
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```bash
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python run.py
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```
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Default address: `http://localhost:5000`
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## Database (SQLite)
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- Default file: `backend_api_python/data/quantdinger.db` (override via `SQLITE_DATABASE_FILE`)
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- Tables are created/updated automatically on startup (see `app/utils/db.py`)
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- `qd_addon_config` exists for backward compatibility, but **this backend reads secrets from `.env` / OS env**, not from the database (see `app/utils/config_loader.py`)
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## AI memory augmentation (local-only)
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This backend includes a lightweight, privacy-first **memory-augmented multi-agent** system:
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- Memory DBs (per role): `backend_api_python/data/memory/*_memory.db`
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- Reflection DB (optional auto-verify loop): `backend_api_python/data/memory/reflection_records.db`
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- API hooks:
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- `POST /api/analysis/multi` (main entry)
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- `POST /api/analysis/reflect` (manual learn from post-trade outcomes)
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- Controls: see `.env` / `env.example`:
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- `ENABLE_AGENT_MEMORY`, `AGENT_MEMORY_*`
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- `ENABLE_REFLECTION_WORKER`, `REFLECTION_WORKER_INTERVAL_SEC`
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## Frontend integration (Vue dev server)
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The Vue dev server proxies `/api/*` to this backend by default:
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- Frontend: `http://localhost:8000`
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- Backend: `http://localhost:5000`
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Proxy config: `quantdinger_vue/vue.config.js`
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## Useful endpoints
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```text
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GET /health
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POST /login
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GET /info
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GET /api/indicator/kline
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POST /api/analysis/multi
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```
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## Production (optional)
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Gunicorn example:
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```bash
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gunicorn -c gunicorn_config.py "run:app"
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```
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## Troubleshooting
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- If outbound data/search requests fail, configure `PROXY_PORT` (or `PROXY_URL`) in `.env`.
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- If you don’t want strategies to auto-restore on startup, set `DISABLE_RESTORE_RUNNING_STRATEGIES=true`.
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- If you don’t want the pending-order worker, set `ENABLE_PENDING_ORDER_WORKER=false`.
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## License
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Apache License 2.0. See repository root `LICENSE`.
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