AI Agent Directed Hedge Fund Tutorial

Overview

• Goal: Build an AI agent directed hedge fund with under 100 lines of code.
• Functions: Handles market analysis, sentiment tracking, macroeconomic insights, strategy development, and risk assessment.
• Tools: Uses the Perplexity Sonar API for real-time data.
• Outcome: Create a data-driven investment strategy for any stock.

AI Framework

• Modular Framework: Different agents for different investment decision-making aspects.
  • Market Data Analyst: Retrieves financial metrics.
  • Sentiment Analyst: Evaluates news and social media trends.
  • Macroeconomic Analyst: Assesses economic factors like interest rates and GDP trends.
  • Quantitative Strategist: Synthesizes insights into a trading strategy.
  • Risk Manager: Evaluates potential risks before finalizing investment decisions.

AI Stack

• Lang Chain: Framework for building agentic workflows.
  • Efficient structure for multiple AI agents.
• Sonar API: Provides real-time search-enabled insights.
  • Offers up-to-date market data, sentiment, and macroeconomic factors.

Important Notes

• Disclaimer: Not financial or investment advice.
• Critical Thinking: Necessary when reviewing AI results due to potential error rates.

Setup Instructions

1. API Setup:
   • Visit so.perplexity.ai and start building.
   • Obtain API key and purchase credits.
   • Generate API key after payment.
2. Python Environment:
   • Create virtual or Conda environment (Python 3.9).
   • Install necessary libraries using pip: Lang chain, Lang chain community, IPython kernel.
3. Code Environment:
   • Use a code editor supporting Jupyter notebooks.
   • Import necessary libraries and modules.

AI Model Initialization

• Set Model Temperature: 0.5 for balance between creativity and consistency.
• Initialize AI Model: Use Sonar API with the API key.

Pipeline Setup

1. Market Data Analyst: Uses LLM chain.
   • Fetches key financial metrics.
   • Stores response for further processing.
2. Sentiment and Macroeconomic Analysts:
   • Conducts real-time searches for sentiment and economic conditions.
3. Quantitative Strategist:
   • Processes combined outputs of previous agents.
   • Generates structured investment strategy.
4. Risk Manager:
   • Evaluates potential risks in strategy.
   • Ensures risk exposure is considered.

Workflow Orchestration

• Sequential Chain:
  • Connects agents in order: Data collectors, strategist, then risk manager.
  • Input: Ticker symbol.
  • Output: Structured results after each agent.

Execution

• Run AI Hedge Fund Function:
  • Takes a ticker symbol and prints results.
  • Uses Sequential Chain to execute the pipeline.
  • Outputs are reasoned step-by-step.

Final Thoughts

• Agent's Utility: Depends on model trust and real-time search.
• Accuracy: Sonar model is effective but needs testing.
• Engagement: Encouraged to try the model and subscribe for more tutorials.