An end-to-end autonomous system that discovers alpha in academic papers, GitHub repositories, and trading forums, then rigorously backtests strategies using a professional event-driven engine.
Follow these steps to get running in 5 minutes.
- Docker Desktop (or Engine) installed and running.
- Conda (Miniconda or Anaconda) installed.
- Google AI Studio API Keys (Free tier works).
git clone <your-repo-url>
cd research_backtesting_agents-Project
# Create .env from template
cp .env.example .env
# Edit .env and paste your Google API Keys
nano .env Runs PostgreSQL (with pgvector) and SearXNG (Meta-Search Engine).
docker compose up -d# Create Environment
conda create -n pydantic_ai_env python=3.12 -y
conda activate pydantic_ai_env
# Install Dependencies
pip install -r requirements.txt
# Initialize Database
python -c "from research_backtesting_agentsV2.src.common.database import init_db; import asyncio; asyncio.run(init_db())"cd research_backtesting_dashboard
npm install
npm run build
npm start- Access at:
http://localhost:3000
Run the agents directly if you don't want to use the dashboard controls.
Mode A: Deep Research (Scout & Sniper) Auto-discovers strategies 24/7.
# Make sure you are in the root directory
conda activate pydantic_ai_env
python research_backtesting_agentsV2/research_main.pyMode B: Backtesting Pipeline Picks up discovered strategies and tests them.
python research_backtesting_agentsV2/backtest_main.py.
├── .env # Global Configuration (Secrets, Ports)
├── docker-compose.yml # Infrastructure (DB, SearXNG)
├── research_backtesting_agentsV2/ # 🧠 Python Agents
│ ├── research_main.py # Research Loop
│ ├── backtest_main.py # Backtest Loop
│ └── src/ # Core Logic
└── research_backtesting_dashboard/ # 📊 Next.js UI
- Database Connection Failed? Check if Docker is running (
docker ps). - API Quota Errors? Add more keys to
RESEARCH_KEYSin.env. - Connection Issues? Run the diagnostic tool:
python check_connection.py
MIT License. See LICENSE for details.