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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

More about the risks specific to algo trading

We have already considered the main risks in any trading: operational, systemic, and transactional. Let’s highlight another kind of risk that is specific to algo trading.

When you develop and backtest a strategy using compressed data, along with limit or stop orders, there is a risk that more than one of these orders will be simulated on the same bar. Typically, this happens when the order prices are too close to each other and the data resolution is not granular enough. For example, if you place a limit and a stop order at a distance of 5 pips from each other and run a backtest using daily data, then on most days, both orders should be executed during a single bar. This is what you want to avoid at all costs because the backtester has no idea about how the price has actually moved inside this single bar and therefore no one knows which of the two orders will have been triggered first and which next. So, it is extremely important...

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