Discover the inner workings of today's forex market, the essential risks in forex algo trading, and how to mitigate them
Key Features
- Build trading applications with research and without advanced Python programming skills
- Dive into professional fx trading while enhancing your trading apps to be more accurate
- Develop simple yet efficient backtesting applications to help keep your expectations realistic
Book Description
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.
What you will learn
- Explore the forex market organization and operations
- Understand the sources of alpha and the concept of algo trading
- Get a grasp on typical risks and ways to mitigate them
- Understand fundamental and technical analysis
- Connect to data sources and check the integrity of market data
- Use API and FIX protocol to send orders
- Translate trading ideas into code
- Run reliable backtesting emulating real-world market conditions
Who this book is for
This book is for financial traders and python developers who are interested in forex trading. Academic researchers looking to focus on practical applications will find this book useful. This book can also help established fx market professionals who want to take the first steps in algo trading. Familiarity with Python and object-oriented programming within the scope of an online course or self-study is a must. Knowledge of network protocols and interfaces is a plus but not a prerequisite, as is specific knowledge about markets and trading.
Table of Contents
- Developing Trading Strategies – Why They Are Different
- Using Python for Trading Strategies
- FX Market Overview from a Developer's Standpoint
- Trading Application – What's Inside?
- Retrieving and Handling Market Data with Python
- Basics of Fundamental Analysis and Its Possible Use in FX Trading
- Technical Analysis and Its Implementation in Python
- Data Visualization in FX Trading with Python
- Trading Strategies and Their Core Elements
- Types of Orders and Their Simulation in Python
- Backtesting and Theoretical Performance
- Sample Strategy – Trend-Following
- To Trade or Not to Trade – Performance Analysis
- Where to Go Now?
Alexey Krishtop is a quantitative trader and researcher with 20 years of experience in developing automated trading solutions. He is currently the head of trading and research at Edgesense Technologies and CTO at ForexVox Ltd. He develops market models and trading algorithms for FX, commodities, and crypto. He was one of the first traders who started using Python as the ultimate environment for quantitative trading and suggested a few approaches to developing trading apps that, today, have become standard among many quant traders. He has worked as a director of education with the Algorithmic Traders Association, where he developed an exhaustive course in systematic and algo trading, which covers the worlds of both quantitative models and discretionary approaches.
