Book Image

Getting Started with Forex Trading Using Python

By : Alex Krishtop
Book Image

Getting Started with Forex Trading Using Python

By: Alex Krishtop

Overview of this book

Algorithm-based trading is a popular choice for Python programmers due to its apparent simplicity. However, very few traders get the results they want, partly because they aren’t able to capture the complexity of the factors that influence the market. Getting Started with Forex Trading Using Python helps you understand the market and build an application that reaps desirable results. The book is a comprehensive guide to everything that is market-related: data, orders, trading venues, and risk. From the programming side, you’ll learn the general architecture of trading applications, systemic risk management, de-facto industry standards such as FIX protocol, and practical examples of using simple Python codes. You’ll gain an understanding of how to connect to data sources and brokers, implement trading logic, and perform realistic tests. Throughout the book, you’ll be encouraged to further study the intricacies of algo trading with the help of code snippets. By the end of this book, you’ll have a deep understanding of the fx market from the perspective of a professional trader. You’ll learn to retrieve market data, clean it, filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live.
Table of Contents (21 chapters)
1
Part 1: Introduction to FX Trading Strategy Development
5
Part 2: General Architecture of a Trading Application and A Detailed Study of Its Components
11
Part 3: Orders, Trading Strategies, and Their Performance
15
Part 4: Strategies, Performance Analysis, and Vistas

Visualizing static market data with pandas

pandas is “a fast, powerful, flexible, and easy to use open source data analysis and manipulation tool, built on top of the Python programming language”, as declared on its official web page at https://pandas.pydata.org. It was originally developed exactly for the purpose of manipulating time series data, especially market prices.

Instead of native Python lists or NumPy arrays, pandas uses DataFrames as a core data object. You can think of a DataFrame as a table, where columns represent various named time series (or any other series) and rows contain actual data, with the first row always containing the names of the series. Pretty much the same as with the historical market data file that we’ve used so far? Yes, and this makes the learning curve with pandas really steep.

pandas offers methods to add, delete, and rearrange columns, create and modify indices, slice and create subsets, merge and reshape DataFrames,...