This repo now contains both parts of the system in one place:

1. The daily trading bot at the repo root
2. The train-once quant model platform under quant_platform/

• Root: live trading bot, daily orchestration, SQLite state, email/reporting
• quant_platform/: corpus building, one-time GPU training, frozen LoRA adapter workflow, backtesting/research platform

trading_bot/
├── main.py                      # Daily core bot + AI bot orchestration
├── llm_trader.py                # AI trading branch using the trained model
├── trained_model_client.py      # Remote HTTP client for trained-model inference
├── trained_model_service_runtime.py
├── lightning_trained_model_app.py
├── deploy_lightning_inference.py
├── backtesting/                 # Existing research stack in the bot repo
└── quant_platform/              # Merged train-once quant platform repo

• Core bot remains unchanged in principle: price ingestion, feature generation, OLS ranking, meta-learner, portfolio logic
• AI trading bot is separate and now prefers the trained quant model over HTTP when Lightning full-runtime is available
• If the full runtime is unavailable or unaffordable, the AI path falls back to a deterministic local distilled manager that reuses the AI memory journal

• TRAINED_MODEL_INFERENCE_URL: deployed inference URL for the AI trading bot
• TRAINED_MODEL_API_KEY: optional auth for the trained-model endpoint
• TWELVEDATA_API_KEYS, ALPHAVANTAGE_API_KEYS: optional price providers

• MODAL_TOKEN_ID
• MODAL_TOKEN_SECRET

• .github/workflows/daily_trading_bot.yml
  • Daily root bot workflow
  • Core + AI orchestration
• .github/workflows/ai_trading_smoke.yml
  • AI-only smoke test against the trained-model path
  • Does not run the core strategy
• .github/workflows/deploy_lightning_inference.yml
  • Deploys the trained-model inference service to Lightning AI
  • Leaves the core bot untouched

Manual:

python run_ai_trading_smoke.py

GitHub Actions:

• Actions -> AI Trading Smoke
• This tests only the AI trading branch and the trained-model endpoint

The full train-once quant platform has been merged into:

• quant_platform/

That subtree contains:

• corpus builders
• training scripts
• backtest engine
• inference/API scaffolding
• configs, docs, and tests from the original train-once repo

Start there if you want to inspect the model/training system rather than the daily bot.

pip install -r requirements.txt
python main.py daily_job

For AI-only testing:

python run_ai_trading_smoke.py

• The AI bot is not remote-only anymore:
  • preferred path: Lightning-hosted trained-model inference
  • fallback path: local distilled manager with shared AI memory
• The current preferred deployment target is Lightning AI CPU when project balance allows it.
• The Lightning inference app can either mount a ready adapter directory or download a tar.gz / .zip archive via TRAINED_MODEL_ADAPTER_ARCHIVE_URL.
• The core bot and AI bot remain logically separate even though they now live in one combined repo.

• The funded Lightning deployment account for this repo is Rohan San
• Username: rohansanthoshkumar1
• Email: [email protected]
• The repo should use the GitHub secrets LIGHTNING_USERNAME, LIGHTNING_API_KEY, and LIGHTNING_USER_ID for that account
• Do not switch deployment to the separate rohansanthoshkumar account unless explicitly told to do so