#### 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.

# Calculating the brokerage charged

For every order completed successfully, the broker may charge a certain fee, which is usually a small 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 the brokerage as it may end up eating a significant chunk of your profit at the end of the day.

The brokerage that's charged varies from broker to broker and also from segment to segment. For the purpose of this recipe, we will consider a brokerage of 0.01%.

## How to do it…

We execute the following steps to complete this recipe:

1. Calculate the brokerage that's charged per trade:
`>>> entry_price = 1245>>> brokerage = (0.01 * 1245)/100>>> print(f'Brokerage charged per trade: {brokerage:.4f}')`

We'll get the following output:

`Brokerage charged per trade: 0.1245`
1. Calculate the total brokerage that's charged for 10 trades:
`>>> total_brokerage = 10 * (0.01 * 1245) / 100>>> print(f'Total Brokerage charged for 10 trades: \            {total_brokerage:.4f}')`

We'll get the following output:

`Total Brokerage charged for 10 trades: 1.2450`

## 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 0.01% of the price, which comes to 0.1245. Then, we calculate the total brokerage for 10 such trades, which comes out as 10 * 0.1245 = 1.245.

For every order, the brokerage is 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 brokerage that's been charged for your trades, please refer to the list of charges offered by your broker.