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.
Table of contents
Chapter 1: The Ticker - Fetching Stock and Crypto Data with yfinance and CCXT
Chapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC Data
Chapter 3: Financial Visualization - Candlestick Charts and Volume Plots with mplfinance
Chapter 4: The Returns - Calculating Log Returns, Volatility, and CAGR
Chapter 5: Correlation and Heatmaps - Understanding Asset Relationships
Chapter 6: Financial NLP - Introduction to FinBERT and Sentiment Analysis
Chapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)
Chapter 8: News Intelligence - Summarizing Real-Time Market News with LangChain
Chapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio with OpenAI Whisper
Chapter 10: The AI Advisor - Building a Portfolio Recommender with GPT-4
Chapter 11: Technical Indicators - Moving Averages, RSI, and MACD Calculation
Chapter 12: The Philosophy of Backtesting - Look-ahead Bias and Overfitting
Chapter 13: Vectorized Backtesting - Fast Strategy Testing with Pandas
Chapter 14: Optimization - Finding the Best Parameters without Curve Fitting
Chapter 15: Portfolio Management - Modern Portfolio Theory and the Efficient Frontier
Chapter 16: Connecting to Exchanges - API Keys, Rate Limits, and Security
Chapter 17: Order Types - Market, Limit, Stop-Loss, and Trailing Stops
Chapter 18: Risk Management - Position Sizing and Kelly Criterion
Chapter 19: Sentiment-Based Trading - Executing Trades Based on AI News Analysis
Chapter 20: The Capstone - Building a 'News + Math' Crypto Trading Bot
If printed, this ebook would span over 400 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.
Check also the other books in this series
