https://github.com/shaadclt/multi-agent-financial-analysis
This project utilizes a multi-agent system powered by crewAI to monitor, analyze, and strategize in the financial markets, specifically focusing on stock trading. Each agent in the system specializes in a unique aspect of trading, working together to provide comprehensive insights and actionable strategies.
https://github.com/shaadclt/multi-agent-financial-analysis
crewai financial-analysis
Last synced: 6 months ago
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This project utilizes a multi-agent system powered by crewAI to monitor, analyze, and strategize in the financial markets, specifically focusing on stock trading. Each agent in the system specializes in a unique aspect of trading, working together to provide comprehensive insights and actionable strategies.
- Host: GitHub
- URL: https://github.com/shaadclt/multi-agent-financial-analysis
- Owner: shaadclt
- License: mit
- Created: 2024-08-09T16:18:59.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-12T06:00:34.000Z (about 1 year ago)
- Last Synced: 2025-04-10T01:10:49.505Z (6 months ago)
- Topics: crewai, financial-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Financial Analysis using Multi-agent system
This project utilizes a multi-agent system powered by crewAI to monitor, analyze, and strategize in the financial markets, specifically focusing on stock trading. Each agent in the system specializes in a unique aspect of trading, working together to provide comprehensive insights and actionable strategies.
## Overview
The Multi-agent Financial Analysis system is designed to assist in the following tasks:
1. **Data Analysis**: Continuously monitor and analyze market data to identify trends and predict market movements.
2. **Trading Strategy Development**: Create and refine trading strategies based on insights from market analysis and user-defined risk tolerance.
3. **Trade Execution Planning**: Suggest optimal trade execution strategies by evaluating the timing, price, and logistics of potential trades.
4. **Risk Management**: Evaluate and provide insights on the risks associated with potential trading activities.
## System Architecture
### Agents
#### Data Analyst Agent:
- **Role**: Monitor and analyze market data in real-time to identify trends and predict market movements.
- **Goal**: Provide crucial insights for informed trading decisions.
#### Trading Strategy Developer Agent:
- **Role**: Develop and test various trading strategies based on insights from the Data Analyst Agent.
- **Goal**: Devise profitable and risk-averse trading strategies.
#### Trade Advisor Agent:
- **Role**: Suggest optimal trade execution strategies based on approved trading strategies.
- **Goal**: Ensure efficient and timely trade execution.
#### Risk Advisor Agent:
- **Role**: Evaluate and provide insights on the risks associated with potential trading activities.
- **Goal**: Align trading activities with the firm’s risk tolerance.
### Tasks
#### Data Analysis Task:
- **Description**: Continuously monitor and analyze market data for a selected stock.
- **Output**: Insights and alerts about significant market opportunities or threats.
#### Strategy Development Task:
- **Description**: Develop and refine trading strategies based on insights from the Data Analyst and user-defined risk tolerance.
- **Output**: A set of potential trading strategies that align with the user's risk tolerance.
#### Execution Planning Task:
- **Description**: Analyze approved trading strategies to determine the best execution methods for the selected stock.
- **Output**: Detailed execution plans suggesting how and when to execute trades.
#### Risk Assessment Task:
- **Description**: Evaluate the risks associated with the proposed trading strategies and execution plans.
- **Output**: A comprehensive risk analysis report detailing potential risks and mitigation recommendations.
## License
This project is licensed under the [MIT License](LICENSE.txt).