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)

The total volume traded for the day of a financial instrument

The total volume traded for a financial instrument is the sum total of all quantities that were traded (bought and sold, but counted once) in the day. For example, if trader A buys 10 quantities of stock X from trader B, while trader C sells 20 quantities of the same stock X to trader D, the total volume traded for X would be 10 + 20 = 30. It won't be 10 + 10 + 20 + 20 = 60 because the contribution of the trade to the total volume is considered only once. This data is dynamic in nature and may increase at any moment during the live trading hours.

Getting ready

Make sure the broker_connection and instrument1 objects are available in your Python namespace. Refer to the Technical requirements section of this chapter to set up broker_connection. Refer to the Attributes of a financial instruments recipe of this chapter to set up instrument1.

How to do it…

Fetch and print the total traded volume for the day of an instrument...