AI-Powered Stock Trading Simulation Platform

A modular, agentic trading simulator where AI agents mimic real-world investor strategies and execute trades based on live market data, research tools, and persistent memory.

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🚀 Features

• Agent-Based Trading: Simulates traders like Warren Buffett, Ray Dalio, George Soros, and Cathie Wood.
• Real-Time & EOD Market Data: Integrates with the Polygon API for real or delayed stock prices.
• Research Agent: Nested LLM researcher with Brave search, web fetch, and memory.
• Autonomous Execution: Agents make decisions, execute trades, send push updates, and self-log.
• Live Dashboard: View trader performance via a real-time Gradio UI.
• SQLite Persistence: Accounts, transactions, logs, and market data stored persistently.
• Modular Microservices: MCP-based server tools for accounts, markets, research, and push notifications.

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🏁 Getting Started

1. Clone the repository and install the requirements.
2. Launch the MCP servers configured in mcp_params.py.
3. Create a .env file with your API keys. In addition to the Polygon and Pushover values, include keys for any LLM providers you plan to use: OPENROUTER_API_KEY, DEEPSEEK_API_KEY, GOOGLE_API_KEY, GROK_API_KEY, ALPHA_VANTAGE_API_KEY, SMITHERY_API_KEY.
4. Start the dashboard with python app.py.

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🧱 Architecture Overview

• trading_floor.py — Main loop, launches and schedules traders
• traders.py — Trader class definition, model setup, and decision logic
• accounts.py — Account logic (buy, sell, PnL, strategy)
• market.py — Share price engine using Polygon API
• push_server.py — Push notifications using Pushover
• alpha_client.py — Connects to Smithery (optional external research)
• templates.py — Prompt instructions for traders and researchers
• database.py — SQLite layer for accounts, logs, and market history
• app.py — Gradio dashboard with charts and logs

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🔧 Tool Server Configuration

Server	Script	Purpose
Market	market_server.py or mcp_polygon	Share prices
Accounts	accounts_server.py	Trading actions
Push	push_server.py	Notifications
Research	brave-search, fetch, memory	News and memory tools

Launch configuration is controlled via mcp_params.py

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🧠 How It Works

1. Scheduler loop starts and checks market status.
2. Each trader agent is launched with their own persona and model.
3. The agent receives account + strategy context, performs research, and executes trades.
4. Tools are invoked via subprocess (MCP protocol): buy_shares, lookup_share_price, etc.
5. Logs and transactions are saved and visualized in real-time.
6. Push notifications summarize each trade session.

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🖥️ Dashboard

To run the Gradio UI:

python app.py

• View live holdings, transactions, PnL
• Logs update every 0.5 seconds
• Charts update every 2 minutes

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⚙️ Environment Variables

Set in .env:

POLYGON_API_KEY=...
POLYGON_PLAN=paid / realtime / free
PUSHOVER_USER=...
PUSHOVER_TOKEN=...
BRAVE_API_KEY=...
OPENROUTER_API_KEY=...
DEEPSEEK_API_KEY=...
GOOGLE_API_KEY=...
GROK_API_KEY=...
ALPHA_VANTAGE_API_KEY=...
SMITHERY_API_KEY=...
USE_MANY_MODELS=true / false
RUN_EVERY_N_MINUTES=30
RUN_EVEN_WHEN_MARKET_IS_CLOSED=true / false

LLM provider keys and ALPHA_VANTAGE_API_KEY are optional but required if you plan to use those models or the Alpha Vantage tools.

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📦 Dependencies

• gradio
• polygon (SDK)
• pydantic
• openai, asyncio, uv
• mcp (Model Context Protocol)
• sqlite3

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🧪 Development Notes

• Reset trader state: python reset.py
• Extend with new personas in reset.py
• Add new tools using FastMCP
• Customize prompts in templates.py

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🧵 Credits

Project created under the guidance of Ed Donner as part of an advanced exploration of agent-based systems and LLM-powered tool use.

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📬 Contact

Happy to collaborate or share more! Let’s build smarter agents. 🧠

#AI #OpenAI #Polygon #StockTrading #AgenticAI