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Quantitative trading strategies using Pythontechnical analysis, statistical testing, and machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative trading strategies using Pythonby Peng Liu.
Reminder of title:	technical analysis, statistical testing, and machine learning /
Author:	Liu, Peng.
Published:	Berkeley, CA :Apress :2023.
Description:	xi, 337 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electronic trading of securities.
Online resource:	https://doi.org/10.1007/978-1-4842-9675-2
ISBN:	9781484296752$q(electronic bk.)

Quantitative trading strategies using Pythontechnical analysis, statistical testing, and machine learning /
Liu, Peng.

Quantitative trading strategies using Pythontechnical analysis, statistical testing, and machine learning /[electronic resource] :by Peng Liu. - Berkeley, CA :Apress :2023. - xi, 337 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1: Introduction to Quantitative Trading -- Chapter 2: Understanding the Electronic Market -- Chapter 3: Understanding Risk and Return -- Chapter 4: Forward and Futures Contracts -- Chapter 5: Trend Following Strategy -- Chapter 6: Momentum Trading Strategy -- Chapter 7: Backtesting A Trading Strategy -- Chapter 8: Statistical Arbitrage with Hypothesis Testing -- Chapter 9: Optimizing Trading Strategies with Bayesian Optimization -- Chapter 10: Optimizing Trading Strategies with Machine Learning.

Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python. Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part II introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part III covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you'll come away from it with a firm understanding of core trading strategies and how to use Python to implement them. You will: Master the fundamental concepts of quantitative trading Use Python and its popular libraries to build trading models and strategies from scratch Perform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using Python Utilize common trading strategies such as trend-following, momentum trading, and pairs trading Evaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtesting.

ISBN: 9781484296752$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-9675-2doiSubjects--Topical Terms:

203852
Electronic trading of securities.

LC Class. No.: QA76.73.P98 / L58 2023

Dewey Class. No.: 332.632283

Quantitative trading strategies using Pythontechnical analysis, statistical testing, and machine learning /
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505 0 $a Chapter 1: Introduction to Quantitative Trading -- Chapter 2: Understanding the Electronic Market -- Chapter 3: Understanding Risk and Return -- Chapter 4: Forward and Futures Contracts -- Chapter 5: Trend Following Strategy -- Chapter 6: Momentum Trading Strategy -- Chapter 7: Backtesting A Trading Strategy -- Chapter 8: Statistical Arbitrage with Hypothesis Testing -- Chapter 9: Optimizing Trading Strategies with Bayesian Optimization -- Chapter 10: Optimizing Trading Strategies with Machine Learning.
520 $a Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python. Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part II introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part III covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you'll come away from it with a firm understanding of core trading strategies and how to use Python to implement them. You will: Master the fundamental concepts of quantitative trading Use Python and its popular libraries to build trading models and strategies from scratch Perform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using Python Utilize common trading strategies such as trend-following, momentum trading, and pairs trading Evaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtesting.
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