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

Optimization – the blessing and the curse of algo trading

Do you remember how the performance of a simple overnight strategy that we created earlier in this chapter radically changed when we replaced a tight stop of 5 pips with a wider stop of 50 pips?

But this fact raises another important question: why 5 and 50 pips? Why not 6 and 45? Or 10 and 76?

Any quantitative strategy depends on the values of its parameters, and the procedure of finding the best combination of parameters that delivers the best results of the backtesting is called optimization.

Optimization is a massive topic. I’d even say it’s overwhelmingly vast and complex. At first glance, it looks straightforward: let’s find the best combination of parameter values and then run the strategy live with these very values. However, the problem is that we always test and optimize our strategies using past data. And I hope you already understood and remember well that markets are anything...