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)

Converting a DataFrame into other formats

This recipe demonstrates the conversion of DataFrame objects into other formats, such as .csv files, json objects, and pickle objects. Conversion into a .csv file makes it easier to further work on the data using a spreadsheet application. The json format is useful for transmitting DataFrame objects over web APIs. The pickle format is useful for transmitting DataFrame objects created in one Python session to another Python session over sockets without having to recreate them.

Getting ready

Make sure the object df is available in your Python namespace. Refer to Creating a pandas.DataFrame object recipe of this chapter to set up this object.

How to do it…

Execute the following steps for this recipe:

  1. Convert and save df as a CSV file:
>>> df.to_csv('dataframe.csv', index=False)
  1. Convert df to a JSON string:
>>> df.to_json()

We get the following output: