Introduction

The book "Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques" written by Robert L. Kissell provides an in-depth discussion of the core concepts and practical methods of algorithmic trading. As a sequel to "The Science of Algorithmic Trading and Portfolio Management", this book adds new chapters on algorithmic trading, advanced trading analysis, regression analysis, optimization and advanced statistical methods.

Designed to provide readers with insights into trading strategies and model building in an ever-changing financial environment, this book covers pre- and post-trade analysis, clearing cost and risk analysis, and compliance and regulatory reporting requirements. The book highlights new investment techniques, including assistance with best execution processes, model validation, quality assurance testing, limit order modeling, and intelligent order routing analysis. In addition, this book details the use of advanced modeling techniques such as machine learning, predictive analytics, and neural networks in trading and finance, and provides a library of transaction cost analysis (TCA) programming tools that can be accessed through a variety of software applications and programming languages. This book provides quantitative analysts, algorithmic traders, financial engineers and data scientists with a comprehensive theoretical foundation and rich practical cases. It is a valuable resource for understanding and applying advanced statistics, optimization and machine learning techniques to algorithmic trading.

Main features

• Solid theoretical foundation
• Rich practical cases
• Clear operating instructions
• Suitable for practical applications

Suitable for the crowd

• Quantitative Analyst
• Algorithmic Trader
• Financial Engineer
• Data Scientist