a big data approach to analyzing & automating cryptocurrency trading
Learn how to use Bitcoin data to train an algorithm to execute crypto trades in real-time. Gain insight into blockchain and big data architectures. Walk away with the know how to build a quantitative trading pipeline on your own.
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IntroductionHave you ever wondered how to build a cryptocurrency trading bot? Have you wondered how you can apply machine learning to the financial markets? Have you wondered how you can get out of Excel and into the world of big data? You have come to the right place! Welcome to Real-Time Crypto. In this book, we will build on your basic Python and data skills and turn you into a bona fide big data engineer. Along the way, you will learn how to trade cryptocurrency in real-time, using cutting edge big data technology and machine learning.
What is Real-Time Crypto
Let's start with a basic definition of what we mean by Real-Time Crypto and what we will build in this book:A big data pipeline which receives streaming data from a cryptocurrency exchange, processes the data, applies a predictive algorithm, executes a trade, and stores the results for future usage.
To accomplish this we are going to have to:
- Process large volumes of data
- Combine multiple data sources
- Apply a machine learning model
In this book you will gain exposure to the fundamentals of data processing. These include:
- normalizing data fields: putting like with like
- information extraction: finding critical information buried within the data
- data enrichment: adding relevant information to improve the quality of the data.
We will need to combine multiple data sources to accurately inform our trading decisions. This is a common task for a data scientist or data engineer so we'll get hands-on by combining historic pricing data and real-time data from an exchange.
We are going to train a machine learning model on a historic, static data set. In our real-time pipeline we will learn how to apply this model to data on the fly.
Cryptocurrency trading pipelines don’t have to be complicated, and they shouldn’t be. Many times, the simplest solution is the best solution. The complexity of your pipeline's architecture should depend on the volume of your data and sophistication of your strategy. Remember though, simple can be powerful.
Goals of the Book
By the time you finish this book you'll have a foundational understanding of cryptocurrency analysis and automation. You'll have a solid understanding of machine learning-based cryptocurrency trading pipelines-how they work and how to build one-and you’ll have a complete pipeline running on your computer! You will feel confident in your ability to take what you've learned and apply it to the creation of production-level pipelines for processing whatever data may come your way.
Real-World Use Case
Rather than work with a toy dataset that doesn’t look anything like the stuff you typically deal with, you’ll be implementing a real-world use case with real world data: predicting how Bitcoin prices will change in order to time trades with your very own trading bot.
First, we will process historical data and train a predictive machine learning model. We’ll be using the past few years of minute-by-minute Bitcoin prices. Our time series forecasting model will predict price swings in Bitcoin so that we know when to trade.
Next, we will incorporate this algorithm into a streaming data pipeline. This pipeline will take real-time data from Gemini, a major cryptocurrency exchange, and use our algorithm to determine whether to execute trades. Our pipeline's architecture will include websockets, Kafka, Spark, and Elasticsearch.
A big data approach to analyzing & automating cryptocurrency trading
Have you ever wondered how to build a cryptocurrency trading bot? Have you wondered how to apply machine learning to the financial markets? Have you ever wondered how to get out of Excel and into the world of big data?
You have come to the right place! Welcome to Real-Time Crypto. In this book we will build on your basic Python and data skills and turn you into a bona fide big data engineer. Along the way you will learn how to trade cryptocurrency in real-time, using cutting edge big data technology and machine learning.
Is this book for you?
If you’re interested in the intersection of cryptocurrency and automation this book is for you. If you want to learn about cutting edge big data architectures this book is for you. If you’d like to learn about machine learning and time series forecasting, well, guess what? This book is for you!
Ideally, you have a basic understanding of the Python programming language so that you can follow along and build your own trading bot using the book’s code examples. If you don’t know how to write code, not to worry. This book offers a great overview of cryptocurrency, blockchain, big data technologies, and how they all fit together.
What You’ll Get
- Learn to build a real-time cryptocurrency trading bot
- Use machine learning algorithms on Bitcoin data to predict future prices
- Gain a practical understanding of Apache Spark, Kafka, and Elasticsearch
- Work with comprehensive code examples to build a cryptocurrency machine learning pipeline from start to finish
- Get everything you need to quickly and easily visualize your bot’s performance using Elasticsearch and Kibana
Get ready to deploy your own quantitative trading strategies. Understand key components of big data architecture. Remove the mystery from blockchain.
Brandon is a technologist with experience deploying big data applications in both the public and private sectors.
He has used Python to wrangle massive datasets and to build data pipelines with tools like Spark, Kafka, and Elasticsearch. Brandon is passionate about using Python for natural language processing and finding meaning in huge quantities of unstructured data. He is also interested in cryptocurrencies and has used Python to analyze the entire Bitcoin blockchain, revealing inefficient practices among the major players in the space.
He currently spends his time on a big data startup which is using geospatial data to build a product in the cybersecurity and physical security space.