Book Image

Python Algorithmic Trading Cookbook

By : Pushpak Dagade
Book Image

Python Algorithmic Trading Cookbook

By: Pushpak Dagade

Overview of this book

If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice.
Table of Contents (16 chapters)
Computing Candlesticks and Historical Data

The historical data of a financial instrument is data about all the past prices at which a financial instrument was brought or sold. An algorithmic trading strategy is always vpot_candlestickirtually executed on historical data to evaluate its past performance before it's deployed with real money. This process is called backtesting. Historical data is quintessential for backtesting (covered in detail in Chapter 8, Backtesting Strategies). Also, historical data is needed for computing technical indicators (covered in detail in Chapter 5, Computing and Plotting Technical Indicators), which help in making buy-or-sell decisions in real-time. Candlestick patterns are widely used tools for stock analysis. Various types of candlestick patterns are commonly used by analysts. This chapter provides recipes that show you how to fetch historical...