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 – simple moving average

SMA is a lagging trend indicator. It is used to smooth the price data by eliminating noise and thus identifying trends.

SMA is the simplest form of a moving average. Each output value is the average of the previous n values of the historical data. You can define the value of n, which is also called the time period. In SMA, each value in the time period carries the same weight, and values outside the time period are not included. This makes it less responsive to recent changes compared to previous changes in the data, and is thus useful for smoothing out the prices' data. A consecutive rise in SMA indicates a clear bullish trend, while a consecutive fall indicates a bearish trend. Thus, it is a trend indicator. Also, since it indicates the trend after it has started, it is a lagging indicator.

SMA is widely used in technical analysis. It is also used for calculating other technical indicators, either in combination with itself or other...