https://github.com/The-Swarm-Corporation/ForexTreeSwarm
A sophisticated forex market analysis system using a swarm of specialized AI agents organized in a forest structure to provide comprehensive market insights and trading recommendations.
https://github.com/The-Swarm-Corporation/ForexTreeSwarm
agents ai finance forex ml multi-agent quantitative-trading swarms
Last synced: 3 months ago
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A sophisticated forex market analysis system using a swarm of specialized AI agents organized in a forest structure to provide comprehensive market insights and trading recommendations.
- Host: GitHub
- URL: https://github.com/The-Swarm-Corporation/ForexTreeSwarm
- Owner: The-Swarm-Corporation
- License: mit
- Created: 2024-12-13T22:20:13.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T05:51:09.000Z (9 months ago)
- Last Synced: 2025-07-02T22:37:32.462Z (3 months ago)
- Topics: agents, ai, finance, forex, ml, multi-agent, quantitative-trading, swarms
- Language: Python
- Homepage: https://swarms.xyz/
- Size: 26.4 KB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-swarms-list - ForexTreeSwarm
README
# Forex Forest System
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://github.com/The-Swarm-Corporation/Legal-Swarm-Template)
[](https://github.com/kyegomez/swarms)
A sophisticated forex market analysis system using a swarm of specialized AI agents organized in a forest structure to provide comprehensive market insights and trading recommendations.
## Overview
The Forex Forest System combines real-time market data collection with distributed AI analysis through a multi-layered tree structure of specialized agents. Each agent focuses on specific aspects of market analysis, working together to generate holistic trading recommendations.
## System Architecture
### Data Collection Layer
```mermaid
flowchart TD
A[ForexDataFeed] --> B[ECB Rates]
A --> C[Forex Factory]
A --> D[Trading Economics]
A --> E[DailyFX]
B --> F[Exchange Rates]
C --> G[Economic Calendar]
D --> H[Economic Indicators]
E --> I[Market News]
F --> J[Market Data Aggregator]
G --> J
H --> J
I --> J
J --> K[Forest Swarm Input]
```
The system collects data from multiple reliable sources:
- European Central Bank (ECB): Real-time exchange rates
- Forex Factory: Economic calendar events
- Trading Economics: Economic indicators and forecasts
- DailyFX: Market news and analysis
### Forest Swarm Structure
```mermaid
flowchart TD
subgraph "Forest Swarm"
A[Strategy Coordination Tree] --> B[Technical Analysis Tree]
A --> C[Fundamental Analysis Tree]
A --> D[Sentiment Analysis Tree]
subgraph "Technical Tree"
B --> TA1[Price Action Analyst]
B --> TA2[Cross Rate Analyst]
B --> TA3[Volatility Analyst]
end
subgraph "Fundamental Tree"
C --> FA1[Economic Data Analyst]
C --> FA2[News Impact Analyst]
C --> FA3[Central Bank Analyst]
end
subgraph "Sentiment Tree"
D --> SA1[News Sentiment Analyst]
D --> SA2[Risk Sentiment Analyst]
D --> SA3[Market Positioning Analyst]
end
end
```
### Analysis Flow
```mermaid
sequenceDiagram
participant DF as DataFeed
participant TS as Technical Swarm
participant FS as Fundamental Swarm
participant SS as Sentiment Swarm
participant SC as Strategy Coordinator
DF->>TS: Market Data
DF->>FS: Economic Data
DF->>SS: News & Sentiment Data
par Technical Analysis
TS->>TS: Analyze Patterns
and Fundamental Analysis
FS->>FS: Analyze Economics
and Sentiment Analysis
SS->>SS: Analyze Sentiment
end
TS->>SC: Technical Signals
FS->>SC: Fundamental Assessment
SS->>SC: Sentiment Indicators
SC->>SC: Synthesize Analysis
SC->>+SC: Generate Recommendations
```
## Features
### Modular Agent Structure
- Technical Analysis Tree
- Price action analysis
- Cross-rate correlations
- Volatility assessment
- Fundamental Analysis Tree
- Economic data evaluation
- News impact analysis
- Central bank policy tracking
- Sentiment Analysis Tree
- News sentiment analysis
- Risk sentiment monitoring
- Market positioning assessment
- Strategy Coordination Tree
- Signal synthesis
- Risk management
- Position sizing
### Real-time Data Processing
- Asynchronous data collection
- Multiple data source integration
- Automated data validation
- Error handling and logging
### Intelligent Analysis
- Multi-perspective market analysis
- Cross-validation of signals
- Risk-aware recommendations
- Continuous market monitoring
## Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/forex-forest-system.git
# Install dependencies
pip install -r requirements.txt
```
Required dependencies:
- Python 3.8+
- aiohttp
- beautifulsoup4
- loguru
- swarms
## Usage
```python
from forex_forest import ForexForestSystem
async def main():
# Initialize the system
system = ForexForestSystem()
# Start market monitoring
await system.monitor_markets(interval_seconds=300)
if __name__ == "__main__":
asyncio.run(main())
```
## Configuration
The system can be configured through environment variables:
```bash
FOREX_FOREST_LOG_LEVEL=INFO
FOREX_FOREST_INTERVAL=300 # Analysis interval in seconds
FOREX_FOREST_PAIRS=EUR/USD,GBP/USD,USD/JPY # Comma-separated currency pairs
```
## Output Format
The system generates structured analysis output:
```json
{
"timestamp": "2024-12-13T10:00:00Z",
"recommendations": [
{
"pair": "EUR/USD",
"action": "buy",
"confidence": 8,
"entry_points": [1.0850, 1.0830],
"stop_loss": 1.0800,
"take_profit": 1.0900,
"rationale": "Strong technical setup with fundamental support"
}
]
}
```
## Logging
The system uses structured logging with rotation:
- Log file: `forex_forest.log`
- Rotation: 500 MB
- Log level: Configurable through environment variables
## Error Handling
The system implements comprehensive error handling:
- Graceful degradation on data source failures
- Automatic retry mechanisms
- Detailed error logging
- Circuit breakers for external APIs
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.