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

Measuring performance – alpha and beta revisited

In Chapter 9, Trading Strategies and Their Core Elements, we touched on two important concepts that are mainly used to analyze performance: alpha and beta. Back then, we looked at them from a slightly different angle: we were in search of opportunities to systematically make profits in the market and considered all these metrics only from that standpoint. However, don’t forget that they were originally suggested for evaluating the performance of an investment – if put in simple words, to judge whether the investment outperforms or beats the market or not.

Note

I will intentionally simplify the concepts of both alpha and beta and avoid exact mathematical formulae for their calculation. Using them requires good command of the theory of probabilities, and I know from my past experience that it’s the very domain of mathematics that causes a lot of confusion to many readers. So forgive me, math purists, but...