🤖 Multi-Agent Trading System

An advanced AI-powered trading framework that leverages multiple specialized agents to analyze markets and generate trading signals.

🎯 Overview

This system employs a multi-agent architecture where specialized AI agents collaborate to make informed trading decisions. Each agent represents a distinct investment philosophy or analytical approach, working together to achieve robust market analysis.

⚠️ Educational Purpose Only: This project is designed for learning and research. It simulates trading decisions without executing real trades.

🏗️ Architecture

Agent Hierarchy

┌─────────────────────────────────────────────────────┐
│           Portfolio Manager (Orchestrator)          │
│  • Aggregates all agent signals                     │
│  • Makes final trading decisions                    │
│  • Manages position sizing                          │
└───────────────────┬─────────────────────────────────┘
                    │
        ┌───────────┴───────────┐
        │                       │
┌───────▼────────┐    ┌────────▼─────────┐
│ Strategy Agents│    │  Analysis Agents │
│                │    │                  │
│ • Value        │    │  • Fundamentals  │
│ • Growth       │    │  • Technicals    │
│ • Momentum     │    │  • Sentiment     │
│ • Contrarian   │    │  • Risk Manager  │
└────────────────┘    └──────────────────┘

Specialized Agents

Investment Philosophy Agents:

• Value Agent: Identifies undervalued assets with strong fundamentals
• Growth Agent: Focuses on high-potential disruptive companies
• Momentum Agent: Captures trending market movements
• Contrarian Agent: Finds opportunities in market inefficiencies

Analysis Agents:

• Fundamentals Agent: Analyzes financial statements and metrics
• Technicals Agent: Processes price action and technical indicators
• Sentiment Agent: Evaluates market sentiment from news and social data
• Risk Manager: Calculates risk metrics and sets position limits

Orchestration:

• Portfolio Manager: Synthesizes all signals and makes final decisions

🚀 Key Features

✨ Multi-Agent Collaboration

• Diverse Perspectives: Each agent brings unique analytical strengths
• Consensus Mechanism: Weighted voting system for trade signals
• Conflict Resolution: Risk manager provides final safety checks

📊 Comprehensive Analysis

• Fundamental Analysis: P/E ratios, revenue growth, profit margins
• Technical Analysis: Moving averages, RSI, MACD, volume patterns
• Sentiment Analysis: News sentiment, social media trends
• Risk Assessment: Volatility metrics, drawdown analysis, correlation

🎯 Intelligent Decision Making

• Signal Aggregation: Combines multiple data sources
• Position Sizing: Dynamic allocation based on conviction levels
• Risk Management: Stop-loss, take-profit, and position limits
• Portfolio Optimization: Diversification and rebalancing

🔧 Extensible Framework

• Modular Design: Easy to add new agents or strategies
• Pluggable Data Sources: Support for multiple market data APIs
• Backtesting Engine: Historical performance simulation
• Logging & Monitoring: Comprehensive trade and performance tracking

💡 How It Works

1. Data Collection

Each agent retrieves relevant market data from configured sources:

• Price data (OHLCV)
• Financial statements
• News articles
• Market sentiment indicators

2. Agent Analysis

Agents independently analyze the data using their specialized methodology:

# Example: Value Agent analyzing a stock
value_signal = value_agent.analyze(ticker="AAPL")
# Returns: {
#   "action": "BUY",
#   "conviction": 0.75,
#   "reasoning": "Trading below intrinsic value with strong moat"
# }

3. Signal Aggregation

The Portfolio Manager collects all agent signals:

signals = {
    "value_agent": {"action": "BUY", "conviction": 0.75},
    "growth_agent": {"action": "HOLD", "conviction": 0.45},
    "momentum_agent": {"action": "BUY", "conviction": 0.82},
    "technical_agent": {"action": "BUY", "conviction": 0.68},
}

4. Decision & Execution

Based on weighted consensus and risk constraints:

# Weighted decision
final_decision = portfolio_manager.decide(signals)
# Output: "BUY 50 shares AAPL" (if consensus > threshold)

📁 Project Structure

Multi-Agent-Trading-System/
│
├── agents/
│   ├── base_agent.py           # Abstract base class for all agents
│   ├── value_agent.py          # Value investing strategy
│   ├── growth_agent.py         # Growth investing strategy
│   ├── momentum_agent.py       # Momentum trading strategy
│   ├── fundamentals_agent.py   # Financial analysis
│   ├── technicals_agent.py     # Technical analysis
│   ├── sentiment_agent.py      # Market sentiment analysis
│   ├── risk_manager.py         # Risk assessment
│   └── portfolio_manager.py    # Orchestration & final decisions
│
├── data/
│   ├── data_fetcher.py         # Market data retrieval
│   ├── preprocessor.py         # Data cleaning & transformation
│   └── cache.py                # Data caching utilities
│
├── backtesting/
│   ├── engine.py               # Backtesting framework
│   ├── metrics.py              # Performance calculations
│   └── visualizer.py           # Results visualization
│
├── utils/
│   ├── config.py               # Configuration management
│   ├── logger.py               # Logging utilities
│   └── helpers.py              # Helper functions
│
├── main.py                     # Entry point
├── requirements.txt            # Python dependencies
├── .env.example                # Environment variables template
└── README.md                   # This file

🛠️ Installation

Prerequisites

• Python 3.11 or higher
• pip package manager

Setup

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/Multi-Agent-Trading-System.git
cd Multi-Agent-Trading-System

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment

cp .env.example .env
# Edit .env with your API keys (if using real data sources)

🚀 Usage

Basic Example

from agents import PortfolioManager
from agents import ValueAgent, GrowthAgent, TechnicalAgent
from data import DataFetcher

# Initialize agents
value = ValueAgent()
growth = GrowthAgent()
technical = TechnicalAgent()

# Create portfolio manager
portfolio = PortfolioManager(
    agents=[value, growth, technical],
    capital=100000,
    risk_limit=0.02  # 2% max risk per trade
)

# Analyze a stock
ticker = "AAPL"
decision = portfolio.analyze_and_decide(ticker)

print(f"Decision: {decision['action']}")
print(f"Position Size: {decision['shares']} shares")
print(f"Reasoning: {decision['reasoning']}")

Running Backtests

from backtesting import BacktestEngine

engine = BacktestEngine(
    portfolio_manager=portfolio,
    start_date="2020-01-01",
    end_date="2023-12-31",
    initial_capital=100000
)

results = engine.run(tickers=["AAPL", "MSFT", "GOOGL"])
results.plot()  # Visualize performance

📊 Performance Metrics

The system tracks comprehensive performance metrics:

• Returns: Total return, annualized return, monthly returns
• Risk: Volatility, Sharpe ratio, maximum drawdown
• Win Rate: Percentage of profitable trades
• Trade Statistics: Average win/loss, profit factor
• Agent Contribution: Individual agent performance analysis

🔑 Key Technologies

• Python 3.11+: Core programming language
• Pandas: Data manipulation and analysis
• NumPy: Numerical computing
• Scikit-learn: Machine learning utilities
• Matplotlib/Plotly: Data visualization
• Requests: API data fetching

🎓 Educational Value

This project demonstrates:

1. Software Architecture: Multi-agent systems, design patterns
2. Financial Analysis: Fundamental, technical, and sentiment analysis
3. Risk Management: Position sizing, portfolio optimization
4. Data Engineering: API integration, data processing, caching
5. Machine Learning: Signal processing, pattern recognition

⚠️ Disclaimer

This software is for educational purposes only.

• Does not constitute financial advice
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• The authors assume no liability for financial losses
• Always consult with licensed financial advisors

🤝 Contributing

Contributions are welcome! Areas for improvement:

• Additional trading agents (e.g., mean reversion, pairs trading)
• Enhanced risk management algorithms
• Integration with more data sources
• Advanced backtesting features
• Machine learning model improvements

📄 License

MIT License - see LICENSE file for details

📬 Contact

Calvin Owoade
Portfolio: [your-portfolio-site.com]
LinkedIn: [linkedin.com/in/your-profile]
GitHub: @callowo-stack

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