Building an Agentic Trading Solution: Lessons from the First Week

As a solution architect and founder of The Independent Quant, I've embarked on an exciting journey to develop an agentic trading solution. This solution aims to learn and adapt to market conditions, executing trades based on predefined strategies without human intervention. In this post, I'll share the insights and challenges I encountered during the first week of development.

Overview

The goal of this project is to create an algorithmic trading desk that can analyze market patterns, identify trading opportunities, and execute trades with a favorable risk-to-reward ratio. The solution is built using AWS services, with a focus on AWS Bedrock for decision-making and Amazon OpenSearch for semantic indexing.

3 Big Ideas

1. Architecture Design and Stack Organization

   • The first major challenge I faced was organizing the AWS Cloud Development Kit (CDK) stacks for this agentic solution. Unlike traditional stacks, this project required a new approach to stack organization. I learned that grouping services by function and update frequency is crucial. For example, data services like DynamoDB should be in their own stack, while frequently updated services should be separated to avoid conflicts.

2. Semantic Indexing with Amazon OpenSearch

   • Amazon OpenSearch plays a vital role in this architecture by providing semantic search capabilities. This allows the solution to understand and respond to prompts about market conditions, such as identifying "round top" or "round bottom" patterns. However, creating and managing indexes in OpenSearch proved to be more complex than expected. I discovered that while CDK can handle most infrastructure as code, certain tasks, like index creation, are more efficiently managed through the AWS console.

3. Cost Management and Budgeting

   • One of the unexpected challenges was managing the costs associated with running this solution. Initially, I estimated the monthly cost to be around $150, but the actual burn rate was much higher, peaking at over $300. This was primarily due to the serverless OpenSearch service, which ran continuously and incurred significant charges. I had to pivot to a regular OpenSearch setup to bring costs down to a more manageable $30-$50 per month.

Why It Matters

Understanding these challenges and solutions is crucial for anyone looking to build a similar agentic trading solution. Proper stack organization, effective use of semantic search, and meticulous cost management are key to successful deployment and maintenance of such a system.

How to Apply It

1. Organize Your Stacks Wisely

   • When designing your cloud architecture, consider grouping services by function and update frequency. This will make your solution more manageable and less prone to conflicts.

2. Leverage Semantic Search

   • Utilize services like Amazon OpenSearch to enhance your solution's ability to understand and respond to complex queries. Don't hesitate to use the AWS console for tasks that are cumbersome to handle programmatically.

3. Monitor and Manage Costs

   • Keep a close eye on your AWS spending. Use budget controls and cost explorers to identify and address unexpected expenses. Pivot to more cost-effective solutions when necessary.

Key Takeaways

• Stack Organization: Group services by function and update frequency for better management.
• Semantic Search: Use Amazon OpenSearch for enhanced query understanding and response.
• Cost Management: Monitor AWS costs closely and be prepared to pivot to more cost-effective solutions.

If you found these insights valuable and are interested in following along with my journey to build this algorithmic trading desk, consider signing up for our newsletter at The Independent Quant. You'll receive exclusive content and updates directly in your inbox. Until next time, keep it green!