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

Statistical arbitrage

As we saw in the previous section, arbitrage is based on the idea of mispricing: a situation in which an asset is priced incorrectly. But to say whether something is priced incorrectly or correctly, we need a reference that is known to be priced correctly, don’t we?

In classical arbitrage, such a reference is the asset price itself, and we take advantage of mispricing across different trading venues trading the same asset. Statistical arbitrage (stat arb) uses the concept of fair value to determine whether the asset is mispriced. In simple terms, with classical arbitrage, we compare the price of the asset versus another price of the asset that exists at the same moment in time. With stat arb, we compare the price of the asset to a theoretical fair value to which we expect the price to revert in the future.

In a certain sense, stat arb is a modification or extension of the concept of mean reversion. Indeed, a successful mean reversion strategy is based...