In the fast-paced world of AI development, one developer’s side project is turning heads with its audacious goal: building an agent that can identify stock market plays yielding over 100% gains in short timeframes. Gavin, the creator behind the experiment, recently shared his journey of integrating AI agents into a Ruby on Rails application, demonstrating how modern tools can democratize financial analysis for developers.

The Quick-and-Dirty Setup

Gavin prioritized speed over perfection, repurposing an existing Rails app to avoid the drudgery of spinning up new infrastructure. At its core, the system connects to OpenAI for tool-calling capabilities and uses a vector database for semantic search—a setup initially built for a developer recruitment feature. 'I like to move quick when I have an idea,' Gavin notes, emphasizing the trade-offs between optimal architecture and rapid iteration.

The agent’s data pipeline starts with Alpha Vantage, an economic news API favored for its YCombinator ties and category-based filtering. Despite API limitations—only 75 requests per minute on the paid tier—Gavin implemented a cron job to pull the latest three articles hourly. This frugal approach minimizes OpenAI costs while capturing timely insights:

# Example cron setup for hourly news ingestion
Rails.env.production? ? 
  every :hour do
    rake 'news:ingest_recent_articles'
  end
end

How the Agent Works

Upon ingesting news, the agent follows a multi-step process:
1. Vector Search: Stocks and company descriptions are vectorized, allowing the agent to find relevant tickers based on article content. Gavin skipped Elasticsearch in favor of his existing vector DB, citing convenience.
2. Play Generation: Using OpenAI, the agent analyzes articles for market-moving events (e.g., earnings surprises or regulatory changes) and proposes 'aggressive plays'—high-risk, high-reward stock actions.
3. Human Oversight: Plays are presented as starting points for Gavin’s own research, with small-cap stocks often filtered out due to inconsistent results.

Implications and Early Wins

This experiment highlights both the promise and challenges of AI in finance. On one hand, Gavin reports several plays achieving triple-digit gains, proving agents can surface lucrative opportunities. On the other, data sourcing remains a hurdle—Alpha Vantage’s constraints forced compromises, and the agent’s reliance on news symbols (like $AAPL) limits its discovery scope. For developers, it underscores how off-the-shelf AI can accelerate niche applications, but success demands creative problem-solving around data and scalability.

Beyond stocks, Gavin’s approach is a blueprint for agent-driven workflows in other domains, from real-time monitoring to automated research. As he refines the model, the key takeaway is clear: in the AI era, agility often trumps perfection.

Source: Gavin's blog on agents for stock prediction