I spent 4 months asking GPT dumb stock market questions. Now the GPT powered bot I built is up ~300%.

Building a Profitable Quantitative Stock Trading Bot with GPT Integration: A 4-Month Journey to a 300% Return

By [Your Name], [Your Title/Position]

In recent months, I embarked on an experimental journey to develop an automated stock trading system powered by artificial intelligence, specifically leveraging large language models like GPT. Starting with a modest investment of $400 in June 2023, my efforts culminated in a trading pipeline that has achieved approximately a 300% return, with win rates ranging between 70% and 80%. This article shares the development process, technical architecture, and insights gleaned along the way.

The Starting Point: Asking the AI Dumb Questions

Initially, my goal was straightforward—see if an AI could manage stock trades autonomously. I handed ChatGPT full control of my small portfolio, excited to see what it could do with a simple starting capital. The first trade was a resounding success—doubling the initial investment in a single move. Buoyed by this, I decided to iterate further.

However, I quickly encountered a critical limitation: ChatGPT cannot access real-time market data. It’s an invaluable language model but lacks the ability to pull live quotes, prices, or news feeds independently. This discovery prompted a series of exploratory questions:
– “How do I obtain S&P 500 ticker symbols?”
– “What APIs can I use for live market data?”
– “How do I parse news headlines and incorporate sentiment?”

Over the course of four months, I persistently asked increasingly complex, sometimes silly, questions—ranging from fundamental API integrations to debugging code snippets. As I engaged with GPT, alongside other models like Claude and Grok, they began to generate code snippets, scripts, and workflows designed to bridge the data gap.

Building the System: From Concept to Automated Pipeline

The core challenge was to develop a pipeline capable of identifying profitable credit spreads using live data, news sentiment, and statistical models. The process was iterative: code would break, I’d debug, and together we’d refine the approach. Here’s a high-level overview of the pipeline stages:

Data Collection & Preparation

• Asset Selection: Extract a list of S&P 500 stocks with tight bid-ask spreads (<2%) and adequate liquidity.
• Option Data: Fetch options with 15-45 days to expiration (DTE), focusing on strikes with implied volatility (IV) between