Telegram Signal Collector

Collect new trading signals from Telegram channels you’re subscribed to. Uses Pyrogram to receive channel messages, LangChain + OpenAI to classify/parse, and SQLAlchemy to persist to SQLite.

• No history scraping; only new messages as they arrive.
• LLM classification + structured parsing (Pydantic model).
• Minimal valid signal: symbol, side.
• JSON arrays for take_profits / stop_loss.
• API endpoints (FastAPI) + lightweight React viewer.

1. Fill .env (see .env.example) with API_ID and API_HASH.
2. Run:

   python -m app.login

   Follow the interactive login. A session file is created at $.pyrogram/signals.session (by default).

Use Python 3.13.

python -m venv .venv
source .venv/bin/activate
poetry install  # or: pip install -r requirements.txt if you keep one
uvicorn app.api.server:app --reload

• On startup, the app initializes the DB, builds the LLM client, starts the Telegram listener, and immediately begins processing new channel messages inline.

cd web
npm i
npm run dev

The dev server proxies /api to http://localhost:8000.

See .env.example. Key values:

• Telegram: API_ID, API_HASH, TELEGRAM_SESSION_DIR, TELEGRAM_SESSION_NAME
• LLM: OPENAI_API_KEY, OPENAI_MODEL (default: gpt-4o-mini)
• DB: DB_URL (default: sqlite+aiosqlite:///./signals.db)

• Default SQLite DB: signals.db
• Tables: channels, signals
• Uniqueness: (channel_id, message_id) prevents duplicates
• UTC times

• GET /api/health
• GET /api/channels
• GET /api/symbols
• GET /api/channels/{channel_id}/signals?limit&offset
• GET /api/symbols/{symbol}/signals?limit&offset
• GET /api/stats/channels
• GET /api/stats/symbols

• A small regex gate quickly filters obvious noise; LLM still makes final decision + structured parse.
• Handle message edits by wiring Pyrogram’s edit handler and updating by (channel_id, message_id).
• Swap ChatOpenAI for another BaseLanguageModel if needed.