No clue if this thing actually works… but yo, it’s WORKIN’!

Innovative AI-Driven Trading System: Streamlining Stock Selection and Risk Management

In the ever-evolving world of stock trading, leveraging advanced technology can provide a significant edge. Recently, I embarked on developing an AI-powered trading assistant designed to identify promising options trades across various sectors. Although I’m still testing its full capabilities, the results so far are promising—source code and detailed tutorials are publicly available for educational purposes.

Here’s an overview of how this system functions:

1. Sector & Stock Selection
   The process begins by compiling a curated list of stocks with a focus on artificial intelligence-related industries, such as electric vehicle manufacturers, semiconductor companies, and data analytics firms. This targeted approach helps focus on high-growth potential sectors.

2. Accessing Real-Time Market Data
   Next, the system taps into live market feeds via platforms like TastyTrade to retrieve essential data: current quotes, implied volatility, and the “Greeks”—market metrics that quantify risk and potential price movements.

3. Trade Opportunity Identification
   Using custom scripting, the system scans for two popular options strategies:

4. Bull Put Spreads: bets that a stock’s price remains stable or rises.

5. Bear Call Spreads: bets that a stock does not surge unexpectedly.

6. Price Fairness & Risk Assessment
   To avoid reckless guesses, the system employs the Black-Scholes model to evaluate the fair pricing of options and computes the Probability of Profit (POP). This quantitative measure helps prioritize trades with higher likelihoods of success and attractive payout ratios.

7. Trade Ranking & Selection
   All potential trades are ranked against key metrics—profitability chances and reward-to-risk profile. The top three candidates are then presented in a clear, organized table for quick decision-making.

8. Final Validation with AI & Market Contexts
   Before executing trades, the system utilizes AI tools like GPT or GROK to analyze upcoming market events, such as earnings reports or Federal Reserve decisions. This step ensures the selected trades align with the broader market environment and the overall portfolio risk profile.

9. Execution & Feedback
   With a simple command, the system promptly generates a list of optimal trades for the day, eliminating hours of analysis and manual data crunching.

The goal is to streamline the trading process while maintaining rigorous risk management. I welcome feedback and insights—particularly on the modeling aspects. The entire project is open-source on GitHub, with comprehensive tutorials available on YouTube to facilitate learning and adaptation.

This approach demonstrates how modern AI and automation can