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Trading Bot

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/

Repo Layout

  • 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

Current Architecture

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 vs AI

  • Core bot remains unchanged in principle: price ingestion, feature generation, OLS ranking, meta-learner, portfolio logic
  • AI trading bot is separate and now uses the trained quant model over HTTP
  • The AI path is batched and designed to call a remote CPU inference endpoint, not a local model

Secrets

Still used

  • 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

No longer used by the AI trading bot

  • MODAL_TOKEN_ID
  • MODAL_TOKEN_SECRET

Main Workflows

  • .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 remote trained-model endpoint
    • 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

AI-Only Smoke Test

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

Quant Platform

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

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.

Local Setup

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

For AI-only testing:

python run_ai_trading_smoke.py

Notes

  • The AI bot is remote-only and expects the trained model to be served externally.
  • The current deployment target is Lightning AI CPU.
  • 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.

Lightning Account

  • The funded Lightning deployment account for this repo is Rohan San
  • Username: rohansanthoshkumar1
  • Email: rohan.santhoshkumar1@gmail.com
  • 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