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107 lines
3.4 KiB
107 lines
3.4 KiB
# Basic Agent Example
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This example demonstrates how to create and configure a sophisticated AI agent using the Swarms framework. In this tutorial, we'll build a Quantitative Trading Agent that can analyze financial markets and provide investment insights. The agent is powered by GPT models and can be customized for various financial analysis tasks.
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## Prerequisites
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- Python 3.7+
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- OpenAI API key
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- Swarms library
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## Tutorial Steps
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1. First, install the latest version of Swarms:
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```bash
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pip3 install -U swarms
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```
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2. Set up your environment variables in a `.env` file:
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```plaintext
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OPENAI_API_KEY="your-api-key-here"
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WORKSPACE_DIR="agent_workspace"
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```
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3. Create a new Python file and customize your agent with the following parameters:
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- `agent_name`: A unique identifier for your agent
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- `agent_description`: A detailed description of your agent's capabilities
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- `system_prompt`: The core instructions that define your agent's behavior
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- `model_name`: The GPT model to use
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- Additional configuration options for temperature and output format
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4. Run the example code below:
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## Code
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```python
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import time
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from swarms import Agent
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# Initialize the agent
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agent = Agent(
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agent_name="Quantitative-Trading-Agent",
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agent_description="Advanced quantitative trading and algorithmic analysis agent",
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system_prompt="""You are an expert quantitative trading agent with deep expertise in:
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- Algorithmic trading strategies and implementation
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- Statistical arbitrage and market making
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- Risk management and portfolio optimization
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- High-frequency trading systems
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- Market microstructure analysis
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- Quantitative research methodologies
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- Financial mathematics and stochastic processes
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- Machine learning applications in trading
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Your core responsibilities include:
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1. Developing and backtesting trading strategies
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2. Analyzing market data and identifying alpha opportunities
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3. Implementing risk management frameworks
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4. Optimizing portfolio allocations
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5. Conducting quantitative research
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6. Monitoring market microstructure
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7. Evaluating trading system performance
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You maintain strict adherence to:
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- Mathematical rigor in all analyses
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- Statistical significance in strategy development
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- Risk-adjusted return optimization
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- Market impact minimization
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- Regulatory compliance
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- Transaction cost analysis
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- Performance attribution
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You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
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max_loops=1,
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model_name="gpt-4o-mini",
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dynamic_temperature_enabled=True,
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output_type="json",
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safety_prompt_on=True,
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)
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out = agent.run("What are the best top 3 etfs for gold coverage?")
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time.sleep(10)
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print(out)
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```
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## Example Output
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The agent will return a JSON response containing recommendations for gold ETFs based on the query.
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## Customization
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You can modify the system prompt and agent parameters to create specialized agents for different use cases:
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| Use Case | Description |
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| Market Analysis | Analyze market trends, patterns, and indicators to identify trading opportunities |
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| Portfolio Management | Optimize asset allocation and rebalancing strategies |
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| Risk Assessment | Evaluate and mitigate potential risks in trading strategies |
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| Trading Strategy Development | Design and implement algorithmic trading strategies | |