Basic Agent Example

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.

Prerequisites

• Python 3.7+

• OpenAI API key

• Swarms library

Tutorial Steps

1. First, install the latest version of Swarms:

pip3 install -U swarms

1. Set up your environment variables in a .env file:

OPENAI_API_KEY="your-api-key-here"
WORKSPACE_DIR="agent_workspace"

1. Create a new Python file and customize your agent with the following parameters:

2. agent_name: A unique identifier for your agent

3. agent_description: A detailed description of your agent's capabilities

4. system_prompt: The core instructions that define your agent's behavior

5. model_name: The GPT model to use

6. Additional configuration options for temperature and output format

7. Run the example code below:

Code

import time
from swarms import Agent

# Initialize the agent
agent = Agent(
    agent_name="Quantitative-Trading-Agent",
    agent_description="Advanced quantitative trading and algorithmic analysis agent",
    system_prompt="""You are an expert quantitative trading agent with deep expertise in:
    - Algorithmic trading strategies and implementation
    - Statistical arbitrage and market making
    - Risk management and portfolio optimization
    - High-frequency trading systems
    - Market microstructure analysis
    - Quantitative research methodologies
    - Financial mathematics and stochastic processes
    - Machine learning applications in trading

    Your core responsibilities include:
    1. Developing and backtesting trading strategies
    2. Analyzing market data and identifying alpha opportunities
    3. Implementing risk management frameworks
    4. Optimizing portfolio allocations
    5. Conducting quantitative research
    6. Monitoring market microstructure
    7. Evaluating trading system performance

    You maintain strict adherence to:
    - Mathematical rigor in all analyses
    - Statistical significance in strategy development
    - Risk-adjusted return optimization
    - Market impact minimization
    - Regulatory compliance
    - Transaction cost analysis
    - Performance attribution

    You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
    max_loops=1,
    model_name="gpt-4o-mini",
    dynamic_temperature_enabled=True,
    output_type="json",
    safety_prompt_on=True,
)

out = agent.run("What are the best top 3 etfs for gold coverage?")

time.sleep(10)
print(out)

Example Output

The agent will return a JSON response containing recommendations for gold ETFs based on the query.

Customization

You can modify the system prompt and agent parameters to create specialized agents for different use cases:

Use Case	Description
Market Analysis	Analyze market trends, patterns, and indicators to identify trading opportunities
Portfolio Management	Optimize asset allocation and rebalancing strategies
Risk Assessment	Evaluate and mitigate potential risks in trading strategies
Trading Strategy Development	Design and implement algorithmic trading strategies