# Create .env file for your API keys
cp .env.example .env

# Set your API keys
OPENAI_API_KEY=your-openai-api-key
GROQ_API_KEY=your-groq-api-key
FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key

# src/agents/valuation.py

class ValuationAgent:
    def __init__(self, data):
        self.data = data

    def calculate_intrinsic_value(self):
        # Implement valuation logic here
        intrinsic_value = self.data['earnings'] * 15  # Simplified example
        return intrinsic_value

    def generate_signal(self):
        intrinsic_value = self.calculate_intrinsic_value()
        if self.data['current_price'] < intrinsic_value:
            return "BUY"
        else:
            return "SELL"

# src/backtester.py

def backtest_strategy(ticker, start_date, end_date):
    # Fetch historical data
    historical_data = fetch_historical_data(ticker, start_date, end_date)
    results = []

    for date, data in historical_data.items():
        signal = run_agents(data)
        results.append((date, signal))

    return results

# src/tools/api.py

def fetch_data(api_url):
    try:
        response = requests.get(api_url)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"API request failed: {e}")
        return None

import asyncio
import aiohttp

async def fetch_data_async(session, url):
    async with session.get(url) as response:
        return await response.json()

async def main(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_data_async(session, url) for url in urls]
        return await asyncio.gather(*tasks)

# Usage
urls = ["https://api.example.com/data1", "https://api.example.com/data2"]
data = asyncio.run(main(url

## Lessons from the Trenches

### Key Technical Lessons Learned

1. **Modular Design**: The project benefits from a modular architecture where each agent has a specific role (e.g., valuation, sentiment analysis, risk management). This separation of concerns makes it easier to maintain and extend the system. Future enhancements can be made by adding new agents or modifying existing ones without affecting the overall system.

2. **API Integration**: Successfully integrating multiple APIs (OpenAI, Groq, Financial Datasets) highlighted the importance of handling API keys securely and managing dependencies effectively. Using environment variables for sensitive information is a best practice that enhances security.

3. **Backtesting Framework**: Implementing a backtesting framework allowed for the evaluation of trading strategies against historical data. This is crucial for validating the effectiveness of the agents before deploying them in a live environment.

4. **Data Handling**: Managing financial data efficiently is key. The project demonstrated the need for robust data handling and preprocessing techniques to ensure that the agents receive clean and relevant data for analysis.

5. **Risk Management**: The inclusion of a risk manager agent underscored the importance of risk assessment in trading strategies. It is essential to quantify risk and set position limits to protect against significant losses.

### What Worked Well

1. **Agent Collaboration**: The interaction between different agents (e.g., combining insights from sentiment, fundamentals, and technical analysis) provided a comprehensive view of potential trading opportunities. This collaborative approach led to more informed decision-making.

2. **User-Friendly Interface**: The command-line interface (CLI) for running the hedge fund and backtester was intuitive. The ability to specify tickers, date ranges, and reasoning outputs made it easy for users to interact with the system.

3. **Educational Focus**: The project’s emphasis on educational purposes resonated well with users. Providing clear disclaimers and documentation helped set appropriate expectations and encouraged learning.

4. **Community Engagement**: The inclusion of a contributing guide and feature request section fostered community involvement. This can lead to a more vibrant ecosystem around the project, with users contributing improvements and new features.

### What You'd Do Differently

1. **Enhanced Documentation**: While the README is informative, additional documentation, such as a detailed user guide or tutorials, could help new users understand the system better. Including examples of how to interpret agent outputs would also be beneficial.

2. **Testing Framework**: Implementing a comprehensive testing framework (unit tests, integration tests) would improve code reliability and facilitate easier debugging. This is especially important in financial applications where errors can have significant consequences.

3. **Performance Optimization**: As the project scales, performance may become an issue, especially with multiple agents processing data simultaneously. Profiling the code and optimizing bottlenecks would be a priority in future iterations.

4. **User Interface**: While the CLI is functional, developing a graphical user interface (GUI) could enhance user experience, making it more accessible to those who may not be comfortable with command-line tools.

### Advice for Others

1. **Start Small**: If you're new to building complex systems, start with a small, focused project. Gradually add features and complexity as you become more comfortable with the technologies involved.

2. **Focus on Modularity**: Design your system with modularity in mind. This will make it easier to test, maintain, and extend your project over time.

3. **Prioritize Security**: Always prioritize security when handling sensitive information, such as API keys. Use environment variables and avoid hardcoding sensitive data in your codebase.

4. **Engage with the Community**: Don’t hesitate to seek feedback and engage with the community. Open-source projects thrive on collaboration, and community input can lead to valuable improvements.

5. **Document Everything**: Good documentation is crucial for any project. It not only helps others understand your work but also serves as a reference for you in the future.

## What's Next?

## Conclusion

As we reach the current milestone of our AI Hedge Fund project, we are excited to share that our proof of concept has successfully integrated multiple AI agents, each embodying distinct investment philosophies. From the value-driven insights of the Ben Graham Agent to the innovative strategies of the Cathie Wood Agent, our system is designed to simulate trading decisions and provide a comprehensive educational experience. The initial testing phase has yielded promising results, showcasing the potential of AI in analyzing market sentiment, fundamentals, and technical indicators.

Looking ahead, our development plans are ambitious. We aim to enhance the capabilities of our agents by incorporating more sophisticated machine learning algorithms and expanding our dataset to include a broader range of financial instruments. Additionally, we plan to implement a user-friendly interface that will allow contributors to interact with the system more intuitively. We invite you to join us on this journey by contributing your ideas, code, or feedback. Your involvement can help shape the future of this project and push the boundaries of what AI can achieve in the realm of finance.

As we reflect on this side project journey, we are reminded of the power of collaboration and innovation. This endeavor has not only deepened our understanding of AI and finance but has also fostered a vibrant community of learners and contributors. We encourage you to explore the project, experiment with the code, and share your insights. Together, we can unlock new possibilities and pave the way for the next generation of AI-driven investment strategies.

Thank you for being a part of the AI Hedge Fund project. Let’s continue to learn, innovate, and grow together!
## Project Development Analytics
### timeline gant

![Commit timelinegant](https://daily.borninsea.com/assets/ai-hedge-fund-timeline_chart.png)


### Commit Activity Heatmap
This heatmap shows the distribution of commits over the past year:

![Commit Heatmap]()

### Contributor Network
This network diagram shows how different contributors interact:

![Contributor Network](https://daily.borninsea.com/assets/ai-hedge-fund-contribution_network.png)

### Commit Activity Patterns
This chart shows when commits typically happen:

![Commit Activity](https://daily.borninsea.com/assets/ai-hedge-fund-commit_activity.png)

### Code Frequency
This chart shows the frequency of code changes over time:

![Code Frequency](https://daily.borninsea.com/assets/ai-hedge-fund-code_frequency.png)



* Repository URL: [https://github.com/wanghaisheng/ai-hedge-fund](https://github.com/wanghaisheng/ai-hedge-fund)
* Stars: **0**
* Forks: **0**