Hands-On AI Trading with Python, QuantConnect, and AWS
eBook - PDF

Hands-On AI Trading with Python, QuantConnect, and AWS

  1. 410 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Hands-On AI Trading with Python, QuantConnect, and AWS

About this book

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance

Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.

Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.

The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:

  • Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.
  • Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.
  • Predict market volatility regimes and allocate funds accordingly.
  • Predict daily returns of tech stocks using classifiers.
  • Forecast Forex pairs' future prices using Support Vector Machines and wavelets.
  • Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.
  • Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.
  • Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.
  • Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.
  • AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.

Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.

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Yes, you can access Hands-On AI Trading with Python, QuantConnect, and AWS by Jiri Pik,Ernest P. Chan,Jared Broad,Philip Sun,Vivek Singh in PDF and/or ePUB format, as well as other popular books in Business & Trading. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2025
Print ISBN
9781394268436
eBook ISBN
9781394267675
Edition
1
Subtopic
Trading

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. Biographies
  6. Preface: QuantConnect
  7. Introduction
  8. Part I: Foundations of Capital Markets and Quantitative Trading
  9. Part II: Foundations of AI and ML in Algorithmic Trading
  10. Part III: Advanced Applications of AI in Trading and Risk Management
  11. References
  12. Subject Index
  13. Code Index
  14. EULA