AI-Driven Stock Market Analysis & Trading

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prerequisites
Intermediate Python • basic data handling
skills learned
Time-series signal engineering • Risk-aware back-testing • API integration and live data handling
Ronnie Rahman
1 week · 2-4 hours per week · INTERMEDIATE
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Look inside

Too much real-time stock data has overwhelmed QuantumTrade Analytics legacy systems and valuable trades are slipping through the cracks. In this liveProject, your mission is to build an AI-enhanced trading strategy engine that responds in milliseconds. This platform uses intelligent algorithms to process vast datasets, generate real-time signals, and optimize trading strategies. You'll work with Python libraries like pandas, NumPy, and Matplotlib to analyze trends, model trading strategies with dynamic programming, and integrate live data using the yfinance API.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ronnie Rahman
Ronnie Rahman is a Senior Software Developer and an AI engineer with ilem Group in Casablanca in collaboration with Swisstiming in Switzerland where he delivers algorithm-driven software for digital-transformation projects. He earned a BSc (Hons) in Computer Science in AI from the University of Hertfordshire, concentrating on neural networks, robotics, quantum computing, and AI algorithms. A frequent speaker at regional Python and data-science meet-ups, he contributes to open-source performance tools and mentors developers on writing efficient and explainable code.

prerequisites

This liveProject is aimed at Python programmers interested in turning theoretical algorithms into a portfolio of practical applications. To begin you will need to be familiar with:


TOOLS
  • Intermediate Python
  • Intermediate pandas
  • Basic NumPy
  • Basic yfinance API

TECHNIQUES
  • Algorithm design
  • Data analysis
  • Financial market fundamentals

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