کتاب دانلود کتب لاتین علوم انسانیتجارت، اقتصاد، و بانکداری
تشخیص تغییر رژیم در امور مالی محاسباتی - علم داده، یادگیری ماشین و تجارت الگوریتمی
تشخیص تغییر رژیم در امور مالی محاسباتی - علم داده، یادگیری ماشین و تجارت الگوریتمی

Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading
نویسنده:
سال انتشار: 2021
تعداد صفحات: 165
زبان فایل: انگلیسی
فرمت فایل: pdf
حجم فایل: 15MB
رمز فایل: www.ketabdownload.com
قیمت: 250,000ريال

افزودن به سبد دانلود

Publisher: Chapman and Hall/CRC

ISBN-10: 0367536285

ISBN-13: 978-0367536282

ASIN: B08GJ8QM86

1st Edition

by Jun Chen (Author), Edward P K Tsang (Author)

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:

Data science: as an alternative to time series, price movements in a market can be summarised as directional changes

Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model

Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change

Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed

Algorithmic trading: regime tracking information can help us to design trading algorithms

It will be of great interest to researchers in computational finance, machine learning and data science.