Python Algorithmic Trading Cookbook

Introduction

The book «Python Algorithmic Trading Cookbook» (author — Pushpak Dagade, 2020, 562 pages) is a practical guide to creating automated trading systems in Python. Unlike academic textbooks, this is exactly a "cookbook," where each section is a ready-made recipe with code, explanations, and results. The book is aimed at developers who want to quickly move from theory to real trading robots without delving into excessive mathematics.

What the book is about

The publication covers the full development cycle of an algorithmic trader: from obtaining market data to deploying a strategy on a real account. Main topics:

• Collecting and storing historical and streaming data (Yahoo Finance, Alpha Vantage, Interactive Brokers);
• Technical analysis and generating trading signals (moving averages, RSI, MACD, candlestick patterns);
• Developing and testing strategies (backtesting considering commissions and slippage);
• Risk and portfolio management (position sizing, stop-losses, diversification);
• Automating order execution via broker APIs (IBKR, Alpaca, OANDA);
• Visualizing results and real-time monitoring.

Each recipe contains a problem statement, Python code (using pandas, numpy, matplotlib, backtrader, ib_insync, and other libraries), and a detailed breakdown of how it works.

Who this book is for

The book is intended for intermediate-level Python developers who are already familiar with the basics of the language (loops, functions, classes, working with libraries) and want to apply their skills in the financial sector. It will also be useful for:

• Traders looking to automate their strategies;
• Technical students studying financial markets;
• Data scientists interested in time series and forecasting;
• Freelancers creating custom trading robots.

For a full understanding of the material, it is advisable to have a basic understanding of financial instruments (stocks, options, futures) and exchange operations, but the author provides brief explanations along the way.

What the reader will learn

After studying the book, you will be able to:

• Download historical quotes from various sources and convert them into a convenient format;
• Implement popular technical analysis indicators (SMA, EMA, Bollinger Bands, Ichimoku) and create your own;
• Write and test trading strategies on historical data using the backtrader library;
• Account for commissions, slippage, and other market realities during backtesting;
• Connect to broker APIs (Interactive Brokers, Alpaca) and send orders automatically;
• Visualize trades, the equity curve, and performance metrics (Sharpe ratio, drawdown);
• Deploy a trading robot on a cloud server (AWS, DigitalOcean) for 24/7 operation.

Strengths

The main advantage of the book is its practical focus. Each recipe is accompanied by complete code that can be immediately run and modified. The author uses current library versions (at the time of the book's release) and provides links to documentation. Particularly noteworthy are:

• Clear structure: recipes are grouped by topic, from simple to complex;
• Availability of ready-made scripts for backtesting and trading that can be adapted to your needs;
• Explanation of not only the technical side but also the logic of decision-making (why an indicator gives a signal, what the risks are);
• A section on risk management — rare for recipe books, but critically important for real trading.

Drawbacks include the lack of in-depth machine learning analysis (the book focuses on classical algorithms) and the fact that some APIs (e.g., Alpha Vantage) may have changed since then. However, the basic principles remain