Layered Memory Trader

This project implements a multi-agent trading bot that uses a layered memory system to make trading decisions. The agents, each with a unique personality and risk profile, debate their decisions to reach a consensus. The system is designed for backtesting on historical data.

Inspiration

This project is inspired by the paper "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance", which explores the use of cognitive architectures in developing autonomous trading agents. Our implementation of a multi-agent system with layered memory and a debate mechanism draws upon the concepts presented in this research.

Features

• Multi-Agent System: Utilizes a team of agents with different time-horizon specializations (short, mid, long-term).
• Layered Memory: Maintains short, mid, and long-term memory buffers for a comprehensive market view.
• Semantic Memory: Employs a FAISS-based semantic memory for searching qualitative data like news and reflections.
• News Integration: Fetches and integrates historical news data for enhanced decision-making.
• Debate Mechanism: Agents debate their findings to arrive at a collective trading decision.
• Reflection: Agents reflect on their decisions, and these reflections are stored for later analysis.
• Backtesting Framework: Built to test strategies on historical data, with a clear separation of training and testing periods.
• Performance Evaluation: Includes a script to evaluate the bot's performance based on quantitative and qualitative metrics.

How It Works

1. Data Ingestion: The DataManager loads historical price data and news data for specified tickers.
2. Memory Update: The MemoryManager updates the short, mid, and long-term memory buffers with the latest data.
3. News Processing: News data is stored in semantic memory for contextual analysis.
4. Agent Analysis: Each agent analyzes the memory snapshot and, in the case of the LongTermAgent, queries the SemanticMemory for relevant news context.
5. Voting & Debate: The agents cast their votes (BUY, SELL, HOLD) with a confidence score. The Debate class resolves these votes into a final decision.
6. Reflection: The outcome of the decision is simulated, and a reflection is generated and stored.
7. Evaluation: The evaluate.py script analyzes the stored reflections to gauge the performance of the trading strategy.

Results

The system's performance is evaluated through comprehensive backtesting and analysis. Below are the key results from the evaluation:

Decision Distribution

The following chart shows how the trading decisions are distributed across BUY, SELL, and HOLD actions:

Portfolio Performance

This chart compares the performance of our multi-agent trading system against a simple "Buy and Hold" benchmark strategy:

The evaluation generates detailed performance metrics including:

• Sharpe Ratio: Risk-adjusted return measurement
• Maximum Drawdown: Largest peak-to-trough decline
• Final Portfolio Value: Compared against buy-and-hold benchmark
• Trade Log: Complete record of all trading decisions and outcomes

For detailed performance metrics, see the generated documentation/results/summary_report.md after running the evaluation.

Getting Started

Prerequisites

• Python 3.8+
• The required packages listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/your-username/LayeredMemoryTrader.git
   cd LayeredMemoryTrader

2. Install the dependencies:

   pip install -r requirements.txt

News Data Setup (Optional)

The system can work with or without real news data. For enhanced decision-making, you can set up free news APIs:

Free News Data Sources

1. NewsAPI (Recommended)

   • Free Tier: 100 requests/day
   • Signup: https://newsapi.org/register
   • Features: Historical news search, multiple sources

2. Alpha Vantage

   • Free Tier: 500 requests/day
   • Signup: https://www.alphavantage.co/support/#api-key
   • Features: News sentiment analysis, financial news

Setup Instructions

1. Get API Keys:

   • Sign up for free accounts at the links above
   • Copy your API keys

2. Configure Environment Variables: Create a .env file in your project root:

   # News API Keys
   NEWSAPI_KEY=your_newsapi_key_here
   ALPHA_VANTAGE_KEY=your_alpha_vantage_key_here
   
   # Existing keys
   GEMINI_API_KEY=your_gemini_key_here

3. Download News Data:

   python scripts/download_news_data.py

Note: If you don't set up API keys, the system will create placeholder news data and function normally.

Usage

1. Download Historical Data: Run the following command to download historical data for stocks. This will create/update the historical_data.csv file.

   python scripts/download_historical_data.py

2. Download News Data (Optional): If you have API keys set up, download news data:

   python scripts/download_news_data.py

3. Run a Backtest: To run a backtest on the training or testing data, you can run the trader.py script. By default, it runs on the training data.

   python trader.py

   You can also modify trader.py to run on the test data.

4. Evaluate Performance: To evaluate the performance of a backtest run, execute the evaluate.py script:

   python evaluate.py

   This will run a backtest on the test data and then print a performance summary.

API Limits and Best Practices

• NewsAPI: 100 requests/day, 1 request/second
• Alpha Vantage: 500 requests/day, 5 requests/minute
• Start with small date ranges for testing
• Monitor your API usage
• Consider upgrading to paid tiers for more data

Troubleshooting

• "API key not found": Check your .env file exists and API key names are correct
• "No news found": Verify API keys are valid and ticker symbols are correct
• Rate limiting errors: Wait a few minutes before retrying or reduce the number of tickers