Quantitative Trading(2nd Edition)
Authors:
Ernest P. Chan
In Stock: 2 Left
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Total Price:
$0
Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1119800064, 978-1119800064
Book publisher: Wiley
Book Price $0 : Ernest P. Chan's 'Quantitative Trading: How to Build Your Own Algorithmic Trading Business' offers an insightful exploration into the mechanics and methodologies crucial for creating a successful algorithmic trading strategy. The book breaks down complex quantitative trading strategies into simple concepts, making it accessible for both novices and experienced traders. Chan provides an in-depth analysis of market inefficiencies and quantitative finance, while explaining how to capitalize on statistical arbitrage. His approach highlights the use of systematic trading strategies and their real-world application, particularly focusing on backtesting, risk management, and programming languages like Python and MATLAB. Throughout the text, Chan integrates a practical 'solution manual'-like approach, offering an 'Answer Key' to the typical challenges traders might face, making it a favorable choice for individuals interested in applying quantitative analysis to create alpha. Although lacking a traditional 'table of content', the book comprehensively covers topics ranging from market microstructure to the nuances of trading psychology, delivered in a step-by-step fashion ideal for algorithmic strategy development and portfolio management. Chan's work has been well-received for its pragmatic advice and clear exposition, providing traders with a concrete foundation for developing profitable trading algorithms. The book also delves into risk-adjusted returns and portfolio optimization, underscoring the importance of rigorous testing and iteration of models crucial to avoiding trading pitfalls. Through example-driven methodologies, Chan empowers readers to better understand and refine their approach to quantitative finance. LSI Context: Concepts such as stochastic calculus, technical indicators, data mining in finance, machine learning in trading, and econometric models are pivotal in understanding the intricate structure of algorithmic trading systems. This cheap reference is an excellent addition to any student's academic toolkit.
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