Welcome to the Era of "Quantamental" AI Trading.
You have mastered Python syntax, data structures, and web architecture in the previous volumes. Now, it is time to apply that technical prowess to the most complex and unforgiving dataset in existence: The Financial Markets.
Volume 7: Python for Finance & AI Trading is not a "get rich quick" scheme. It is a comprehensive engineering manual for developers and aspiring quants who want to build professional-grade trading infrastructure. This book bridges the gap between classical technical analysis and state-of-the-art Large Language Models (LLMs), teaching you how to build systems that trade on both Math (price action) and News (sentiment).
What You Will Build:
- The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data with yfinance and CCXT, and visualize markets with mplfinance.
- The AI Financial Analyst: Deploy FinBERT to decode news sentiment in milliseconds. Build RAG pipelines to "chat" with PDF 10-K filings using LangChain, and transcribe earnings calls using OpenAI Whisper.
- The Strategy Core: Abandon slow loops for Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) to derive the Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.
- Algorithmic Execution: Learn the mathematics of survival with the Kelly Criterion. Connect securely to exchanges, manage Rate Limits, and execute orders with precision.
- The Capstone Project: Architect a Hybrid 'News + Math' Trading Bot that executes trades only when technical indicators align with AI-driven market sentiment.
Who This Book Is For:
Written for Python developers, data scientists, and active traders who want to move beyond manual analysis. If you are ready to stop guessing and start engineering your edge, this volume is your blueprint.
You can read it as a standalone.
All source code on GitHub.
Stop watching the market. Start coding it.
Check also the other books in this series
