Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
Thumbnail 1

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

4.4/5
Product ID: 217678875
Secure Transaction

Product is unavailable

Oops! The product you're looking for is currently unavailable. Explore similar products for a perfect fit!

Description

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Reviews

4.4

All from verified purchases

S**S

Fantastic read

Initially - I thought it would be a book that would touch the basics only. I was very pleasantly surprised. Fantastic read and highly recommended high quality content

A**.

All you need

Amazing book, detailed and has everything I needed and more

E**K

A superb educator and reference book

As one US reviewer stated quite rightly "For anyone that wants this book you should understand that this book is what you make of it.", there is NO hand-holding in this book, you are expected to get on with it and read-around as required/needed, it may be reasonably introductory but it isn't dumbed-down (thankfully).Especially appreciated the first section of the book on electronic trading basics and economic/portfolio fundamentals. Go deeper and such an amazing repertoire of models (and ideas) this book will keep you occupied for many months.It covers a huge amount and used in conjunction with the internet for reference you'll find you can gain significant momentum to move forward beyond your expectations.I knocked off a star as much of the environment (uses a HUGE number of libraries) simply causes conflicts of library versions, and it takes quite a bit of time to sort out these conflicts (even with Conda, most of the issues surround early Pandas dependency with Zipline that breaks other software). Additionally much of the code simply does not work (data files ceasing to exist on the net without extensive searching though you will usually find after searching)... you may need to hack the code to work in many cases, it does assume in my opinion the following:Basic PythonReasonably proficient Pandas (uses quite a bit in some interesting ways)Ability to read-around a lot of the discussed signals/indicators and other economic stuff (is interesting though!)The ability to get up to speed (without guidance) on third-party librariesAbility to configure multiple Conda environments and sort out library issues (stack overflow etc.)Familiarity with basic RegressionUnderstanding of Gradient BoostingUnderstanding of basic FFNN, Convolutional, NLP, Time-Series, GAN"s.Some Linear Algebra, Calculus and Stats useful (not essential though but will deepen your understanding).Advice: Separate Conda environment (Python 3.6) for Zipline, and a 3.8 for everything else.Still highly recommended in my opinion, but certainly not for the faint of heart and you will need a little experience in resolving stuff like package conflicts, the amount of valuable information in this book however is HUGE, it really seems in a class of its own in a pragmatic sense.Note:Thankfully zipline (from the defunct Quantopian) is not extensively used, mostly in the early chapters as it is garbage (imho), one of the most poorly designed quant trading packages out there, now abandoned by its former owner. It simply is not good software by a long stretch and I suspect very strongly will be dropped in the next book edition, it is kludge upon kludge upon kludge and its only real use was Quantopian integration. That the new owner Robinhood also seems to not care much about it speaks volumes. This may change but I highly doubt it. However it is useful to know the basics as these can help when learning other libraries.

J**S

A comprehensive introduction

I received a review copy from the publisher. This book is a great guide for the quant-trading enthusiast. If you are new to trading and ML, and are looking to get your hands dirty, I would warmly recommend this introduction.The book combines an introduction to quantitative trading with an introduction to machine learning. It introduces a range of free datasources and open source tools to help the reader get started with building their own models. On the ML side, it provides a tour of the popular ML algorithms from OLS to GANs. It often demonstrates the use of the introduced techniques by implementing a strategy based on them, which is a really nice idea in my view. The reader will emerge with a good understanding of how to build and test a strategy utilizing ML.As a disclaimer, this is not a comprehensive "How to start a hedgefund" guide, as it leaves out many important aspects of running a fund (it is a bit shallow on risk management, leaves out topics like liquidity & cash management, and does not describe how to set up live trading, deal with compliance, etc.)

A**R

Good book but terrible cut

S**R

Great book, nice examples.

The book overall is very didactic, my only recommendation would be to use a more simple set up as many of the recommended tools and libraries are outdated, not the author’s fault but renders impossible to follow some of the examples. A more simple set of standard libraries, perhaps could be more stable and allow to follow better the presented examples.

D**O

Great book with additional e-learning material

Great! Nothing to complain about at this point!

J**G

The most thorough and the most practical book on Machine Learning for trading

I love all Stefan's books, which are all well written and logically organized - very easy to follow. They not only provide detailed information on the theories behind, but also provide many practical examples and even Jupyter Notebooks that can be used in real life situations. They also covered almost all areas of Machine Learning in trading. They are just like THE bibles of Machine Learning in trading to me. Highly recommended!Got both "Hands-On Machine Learning for Algorithmic Trading" and "Machine Learning for Algorithmic Trading", if you want to master Machine Learning in trading!

D**N

Good but many typos

The contents of the book is good and I'd recommend it.However, there are many typos, especially in the formulas - better double check if it is right before you want to use it.Also, some of the operations in python code are a bit cumbersome. For example,prices.bb_high.sub(prices.close).div(prices.bb_high).apply(np.log1p)can much easier be written as prices.eval('log(1+(bb_high - close)/bb_high)')

M**

plazo de entrega fatal

El libro muy bien, pero el plazo de entrega muy mal. Hice la compra porque me aseguraron que llegaba un día y llegó 3 días más tarde a pesar de ser una compra Prime.

Common Questions

Trustpilot

TrustScore 4.5 | 7,300+ reviews

Vikram D.

The MOLLE sheath is of exceptional quality. Very happy with my purchase.

2 weeks ago

Meera L.

Smooth transaction and product arrived in perfect condition.

3 weeks ago

Shop Global, Save with Desertcart
Value for Money
Competitive prices on a vast range of products
Shop Globally
Serving millions of shoppers across more than 100 countries
Enhanced Protection
Trusted payment options loved by worldwide shoppers
Customer Assurance
Trusted payment options loved by worldwide shoppers.
Desertcart App
Shop on the go, anytime, anywhere.

Trustpilot

TrustScore 4.5 | 7,300+ reviews

Anita G.

Good experience, but the tracking updates could be better.

2 months ago

Ali H.

Fast shipping and excellent packaging. The Leatherman tool feels very premium and sturdy.

1 day ago

Machine Learning For Algorithmic Trading Predictive Models To Extract Signals | Desertcart Colombia