ALGORITHMIC TRADING WITH PYTHON: Build Financial Systems and Risk Tools for Smart Market Analysis

Unlock the power of algorithmic trading with Algorithmic Trading with Python: Build Financial Systems and Risk Tools for Smart Market Analysis. This comprehensive guide, authored by Cecilia Riches, is your definitive resource for mastering the art and science of automated trading using Python. Whether you're a beginner looking to automate your first trading strategy or an experienced trader seeking advanced techniques, this book offers a step-by-step roadmap to building robust, data-driven trading systems.
Spanning foundational concepts to cutting-edge trends, the book is structured into five parts: Foundations of Algorithmic Trading, Building Trading Strategies, Risk Management and Optimization, Execution and Deployment, and Advanced Topics and Future Trends. Readers will explore critical topics such as:

• Core Concepts: Understand algorithmic trading, financial markets, and Python’s pivotal role, with practical guidance on setting up your Python environment using libraries like Pandas, NumPy, and yfinance.
• Strategy Development: Design and backtest trading strategies, from moving average crossovers to machine learning-driven models, with real-world case studies like trend-following forex and mean-reversion ETF strategies.
• Risk Management: Master risk frameworks, including Value-at-Risk (VaR), stress testing, and portfolio optimization, with tools like PyPortfolioOpt to balance returns and risk.
• Live Trading: Learn to execute trades via broker APIs (e.g., Alpaca, Interactive Brokers) and deploy systems on cloud platforms like AWS, ensuring scalability and security.
• Advanced Techniques: Dive into high-frequency trading, sentiment analysis with NLP, and alternative data integration, with Python implementations for predictive models and DeFi strategies.
• Future Trends: Stay ahead with insights on AI, quantum computing, and decentralized finance, preparing you for the evolving trading landscape.

With over 300 pages of actionable content, including code examples, case studies, and a comprehensive appendix of Python libraries, datasets, and a glossary, this book equips you with the tools to succeed. Each chapter blends theory with hands-on Python code, supported by a GitHub repository for practical experimentation. Retail traders, hobbyists, and aspiring quants will find the clear explanations and structured approach invaluable.