AI News, About the Book

About the Book

Have you ever wondered how to build a cryptocurrency trading bot?

Along the way, you will learn how to trade cryptocurrency in real-time, using cutting edge big data technology and machine learning.

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 this book offers a great overview of cryptocurrency, blockchain, big data technologies, and how they all fit together.

A big data approach to analyzing automating cryptocurrency trading

Have you ever wondered how to build a cryptocurrency trading bot?

Along the way, you will learn how to trade cryptocurrency in real-time, using cutting edge big data technology and machine learning.

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 this book offers a great overview of cryptocurrency, blockchain, big data technologies, and how they all fit together.

He currently consults for big data brokerages while building businesses in the reputation management and lead generation spaces.

What this book Can Do For You

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.

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.

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.

J.P.Morgan's massive guide to machine learning and big data jobs in finance

If they don't they'll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

In future, they say machines will become increasingly prevalent over the medium term too: "Machines have the ability to quickly analyze news feeds and tweets, process earnings statements, scrape websites, and trade on these instantaneously."

In the long term, however, humans will retain an advantage: "Machines will likely not do well in assessing regime changes (market turning points) and forecasts which involve interpreting more complicated human responses such as those of politicians and central bankers, understanding client positioning, or anticipating crowding,"

If you want to survive as a human investor, this is where you will need to make your niche, Before machine learning strategies can be implemented, data scientists and quantitative researchers need to acquire and analyze the data with the aim of deriving tradable signals and insights.

They can include anything from data generated by individuals (social media posts, product reviews, search trends, etc.), to data generated by business processes (company exhaust data, commercial transaction, credit card data, etc.) and data generated by sensors (satellite image data, foot and car traffic, ship locations, etc.).

The purpose of deep learning is to use multi-layered neural networks to analyze a trend, while reinforcement learning encourages algorithms to explore and find the most profitable trading strategies.

Morgan says deep learning is particularly well suited to the pre-processing of unstructured big data sets (for instance, it can be used to count cars in satellite images, or to identify sentiment in a press release.).

Existing buy side and sell side quants with backgrounds in computer science, statistics, maths, financial engineering, econometrics and natural sciences should therefore be able to reinvent themselves.

"It is much easier for a quant researcher to change the format/size of a dataset, and employ better statistical and Machine Learning tools, than for an IT expert, silicon valley entrepreneur, or academic to learn how to design a viable trading strategy,"

The report says that too many recruiters and hiring managers are incapable of distinguishing between an ability to talk broadly about artificial intelligence and an ability to actually design a tradeable strategy At the same time, compliance teams will need to be able to vet machine learning models and to ensure that data is properly anonymized and doesn't contain private information.

Part 1: A n00bs Guide To Deep Cryptocurrency Trading

People have traditionally relied on a central authority when it comes to transacting money (or value) around a society.

If now, I took a photo of this car and saved it to my computer, I would end up with a digital representation of this car as a bunch of pixel colours as a file.

Of course it’s entirely possible I was so generous and selfless that I sent this picture of a car out to thousands of people as a spam email before giving it to you.

Members in the network with large amounts of compute capability take all of the past 10 minutes transactions and compete to validate them, and the winner receives a reward of cryptocurrency (generally.) This public ledger is open source, which means there are no nasty surprises coded deep into the software (read: we can trust it more).

The openness, decentralised and cryptographic nature of the blockchain makes it very hard to hack, unlike monolithic banking software which is regularly making headlines as hackers make off with millions of stolen currency.

when I make an exchange it is clear who now unambiguously owns it and as a bonus, we don’t need any third party verification (such as my corrupt self) to ensure I didn’t send extra copies for myself!

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