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 an instrument

Instruments, also known as financial instruments or securities, are assets that can be traded in an exchange. In an exchange, there can easily be tens of thousands of instruments. This recipe demonstrates how to fetch an instrument based on its exchange and trading symbol.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe in this chapter to set up this object.

How to do it…

Fetch an instrument for a specific trading symbol and exchange:

>>> broker_connection.get_instrument(segment='NSE', tradingsymbol='TATASTEEL')

You'll get the following output:

segment: NSE
exchange: NSE
tradingsymbol: TATASTEEL
broker_token: 895745
tick_size: 0.05
lot_size: 1
expiry:
strike_price: 0.0

How it works…

The broker_connection object provides a handy method, get_instrument, for fetching any financial instrument. It takes segment and tradingsymbol as attributes before returning an instrument. The return object is an instance of the Instrument class.