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)

Trend indicators – parabolic stop and reverse

Parabolic stop and reverse (SAR) is a leading trend indicator.

The parabolic SAR computes a trailing stop loss for every data point. As the data points are stop-loss points, they are away from the prices when there is a trend and cross the price line during a trend reversal. The parabolic SAR takes two parameters as input: the acceleration factor and the maximum point.

The formula for computing the parabolic SAR is not straightforward and is hence not mentioned here. If you are interested in the underlying math, please refer to the official documentation of TA-Lib on parabolic SAR at http://www.tadoc.org/indicator/SAR.htm. Although it is a good idea to know the mathematics of how this works, this recipe does not require you to understand or remember the given formula. We use a third-party Python package, talib, which provides a ready function for calculating the parabolic SAR.

Getting started

Make sure your Python namespace has the...