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)
Fetching Financial Data

Having financial data handy is essential for carrying out algorithmic trading. Financial data can be both static and dynamic in nature. Static financial data is data that doesn't change during trading hours. Static data consists of lists of financial instruments, the attributes of financial instruments, the circuit limits of financial instruments, and the recorded close price of the last trading day. Dynamic financial data is data that may change continuously during trading hours. Dynamic data consists of market depth, the last traded prices, the time and quantity of financial instruments, and the recorded high and low prices of the day. This chapter includes recipes on fetching various types of financial data.

The following is a list of the recipes in this chapter:

  • Fetching the list of financial instruments
  • Attributes of a financial instrument
  • Expiry...