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Getting Started with Forex Trading Using Python

Getting Started with Forex Trading Using Python

By : Alex Krishtop
4.3 (3)
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Getting Started with Forex Trading Using Python

Getting Started with Forex Trading Using Python

4.3 (3)
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)
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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 live market data

Before we move on, I strongly recommend you reread the Working with saved and live data – keep your app universal section in Chapter 5, Retrieving and Handling Market Data with Python, and the Sliding windows section in Chapter 7, Technical Analysis and Its Implementation in Python. We are going to use the same architecture to create live plots of market data.

Important reminder

Whatever data we receive from a live data source should go into a queue. This should be done in a separate thread. Then, data is read from the queue into a sliding window that controls the actual amount of data – for any processing or plotting.

When we worked with static historical data, we used very convenient methods that allowed us to read an entire dataset into memory in one line of code and then navigate through it. Of course, any convenience is always paid for, and in this case, the fee is running the risk of peeking ahead (see the Trading logic –...

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