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)

MACD-Bracket-Order strategy – fetching a backtesting report profit and loss table

After submitting a backtesting job on the AlgoBulls platform, the AlgoBulls backtesting engine starts executing the strategy. During its execution, along with the logs, the AlgoBulls backtesting engine also generates a P&L table in real time. This table holds information on every trade that's been punched in by the strategy. It also contains details on the mappings between entry and exit orders, the trade P&L, and the cumulative P&L, sorted chronologically, with the latest order first.

This table gives us insights into the strategy's overall performance with the help of individual and cumulative P&L numbers. The entry-exit order mapping also helps validate the strategy's behavior.

In this recipe, you will fetch the P&L table report for your strategy. This report is available as soon as the first trade is punched in by your strategy after you submit a backtesting...