Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques 
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 Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. 
 
 
 
 



        

            
            

                
Publisher
                
Academic Press
            
            
            

                
Dimensions
                
19.1 x 3.66 x 23.5 cm
            
            
            

                
Language
                
English
            
            
            

                
Print length
                
612 pages
            
            
            

                
Edition
                
2nd
            
            
            

                
Book Type
                
Paperback
            
            
            

                
ISBN-10
                
0128156309
            
            
            

                
ISBN-13
                
978-0128156308
            
            
            

                
Publication date
                
18 Sept. 2020
            
            
        
         




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