Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud) has made quant trading accessible to a larger audience. Each of the above steps involve lot of research and trial and error to get right. Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today's high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach -- his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field.

Machine learning is a buzzword often thrown about when discussing the future of finance and the world. You may have heard of neural networks solving problems in facial recognition, language processing, and even financial markets, yet without much explanation. It is easy to view this field as a black box, a magic machine that somehow produces solutions, but nobody knows why it works. It is true that machine learning techniques (neural networks in particular) pick up on obscure and hard to explain features, however there is more room for research, customization, and analysis than may first appear. Today we'll be discussing at a high level the various factors to be considered when researching investing through the lens of machine learning. The contents of this notebook and further discussions on this topic are heavily inspired by Marcos Lopez de Prado's book Advances in Financial Machine Learning.

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this article. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are ten critical mistakes underlying most of those failures.

I'll introduce the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz's CLA in particular: instability, concentration and under-performance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers.