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 – exponential moving average

EMA is a lagging trend indicator. It is used to smooth the price data by eliminating noise and thus identifying trends, with more weightage to recent values.

The EMA technical indicator calculation is cumulative and includes all the data with decreasing weights. Past values have a lower contribution to the average, while recent values have a greater contribution. The further away the value, the smaller the contribution. Thus, EMA is a moving average that is more responsive to recent changes in the data.

The EMA technical indicator is not like the SMA technical indicator, where each value in the time period carries equal weight and values outside of the time period are not included in the calculation.

EMA is widely used in technical analysis. It is also used for calculating other technical indicators, either in combination with itself or other indicators, with the same or different time periods.

A recursive formula for calculating EMA...