Adopting a disruptive technology that will revolutionize how everyone invests for generations is most exciting now. This book discusses machine learning (ML) techniques that have been effectively used to handle enormous pools of money. This book addresses practical issues that practitioners experience on a daily basis and presents mathematical explanations of scientifically sound solutions, supported by code and examples. Readers become into active participants who can test the suggested fixes in a customized environment. Overall, the book is a fantastic resource and reference guide for deepening our grasp of machine learning and the quantitative battlegrounds of global financial markets.

Overview of book:

When enormous amounts of data are introduced into the system, machine learning has a tendency to be more accurate when deriving insights and making predictions. When it comes to daily transactions, invoices, payments, vendors, and clients, for instance, the financial services sector frequently encounters vast volumes of data that are ideal for machine learning.
Machine learning is now being used in operations by a large number of top fintech and financial services organizations, which has improved workflow, decreased risk, and improved portfolio optimization.
Your financial services company can benefit from artificial intelligence and machine learning by boosting key operations like fraud detection and claims processing and providing more interesting customer experiences through tailored, personalized offerings. The needs of financial institutions of all sizes, from the biggest corporations to the most cutting-edge start-ups, are handled by AI and ML capabilities. so that you can quicken the implementation of these revolutionary technologies. Companies in the banking and insurance sectors have access to millions of consumer records, which can be used to train machine learning algorithms and streamline the underwriting procedure. Machine learning algorithms can quickly decide on underwriting and credit scoring, saving businesses time and money that would otherwise be spent on hiring human decision-makers. Data scientists are able to train algorithms to compare millions of consumer records, search for unusual exceptions, and determine if a consumer is eligible for a loan or insurance.

Topic you are going to cover in this book:

Chapter 1 is Financial Machine Learning as Distinct subject in which we discuss the main reason for why Financial Machine learning projects usually fails. Next is about Data analysis which tell us about Financial data structures as well as essential types of financial data and how to deal with multi products series

Next is about Labeling and sample weights explain with numerical examples. After this we move to Fractionally Differentiated Features. We discuss its different method as well as how to implement this.

Then it is part 2 in which tell us about Ensemble Method then we move to next which is about cross-validation in Finance in which we discuss the goal of cross validation. After this we learn about the importance of Features. We discuss the features importance with substation Effects.

Chapter 9 is about hyper-parameter Tuning with cross validation. Then we will go to part 3  which starts from bet sizing which is about bet sizing with predicted probabilities. After this we discuss The Danger of Backtesting and its reason as well as solution.

Next we discuss backtesting through cross-validation with walk forward method. After this we move to Backtesting on Synthetic Data. In the last we discuss backtesting  statistics its types, its implementation as well as classification.

Next chapter is  understandings strategy Risk in which we discuss symmetric and asymmetric payouts. Then next is about machine learning Assets Allocation explained with numerical examples.

After this move to the part 4 of the book which start form structural Breaks, Entropy Features and Microstructural Features with first, second and third generational sequential Trade Models.

The last and fifth part of book starts from multiprocessing and vectorization with vectorization examples and multiprocessing engines. At the end we discuss some high performance computational Intelligence and Forecasting Technologies.

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