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)

Calculating the government taxes charged

For every order that's completed successfully, the government may charge a certain fee, which is a fraction of the price at which the instrument was bought or sold. While the amount may seem small, it is important to keep track of government taxes as they may end up eating a significant chunk of your profit at the end of the day.

The government charge depends on the location of the exchange, and varies from segment to segment. For the purpose of this recipe, we will consider government taxes at a rate of 0.1%.

How to do it…

We execute the following steps to complete this recipe:

  1. Calculate the government taxes that are charged per trade:
>>> entry_price = 1245
>>> brokerage = (0.1 * 1245)/100
>>> print(f'Government taxes charged per trade: {brokerage:.4f}')

We'll get the following output:

Government taxes charged per trade: 1.2450

  1. Calculate the total government taxes that are charged for 10 trades:
>>> total_brokerage = 10 * (0.1 * 1245) / 100
>>> print(f'Total Government taxes charged for 10 trades: \
{total_brokerage:.4f}')

We'll get the following output:

Total Government taxes charged for 10 trades: 12.4500

How it works…

In step 1, we start with the price at which a trade was bought or sold, entry_price. For this recipe, we have used 1245. Next, we calculate 0.1% of the price, which comes to 1.245. Then, we calculate the total brokerage for 10 such trades, which comes out as 10 * 1.245 = 12.245.

For every order, government taxes are charged twice. The first time is when the order has entered a position, while the second time is when it has exited the position. To get the exact details of the government taxes that are charged for your trades, please refer to the list of government taxes provided by your exchange.