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

Live trading – where Python faces its limits

Thus said, trading applications written in pure Python are not suitable for any live trading activity that assumes the minimization of time from the moment market data is received to the moment an order is sent. Therefore, traditional arbitrage and many high-frequency trading activities (which sometimes suggest sending thousands of orders per second) are definitely not for Python.

Besides that, there is another risk even for slow trading strategies that derive from automated memory management. We already know that trading strategies rely on price time series and the amount of processed market data may be quite large. Although both native Python and third-party libraries such as pandas offer data structures that ensure data persistence, it may become problematic to update data on the fly, especially in trading environments with high throughputs.

There are different ways to speed up Python to some extent. There are static compilers...