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Item #:
44731486
Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading
87% of respondents would recommend this to a friend
Item #:
44731486
AED 520
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Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading.
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What Stands Out
Data Science Integration
Merges data science techniques with finance, providing robust methodologies to detect regime changes efficiently, thereby enhancing decision-making in volatile markets.
Machine Learning Application
Utilizes advanced machine learning algorithms to analyze complex datasets, offering predictive insights that outperform traditional analytical methods in trading strategies.
Algorithmic Trading Focus
Focuses on algorithmic trading solutions that adapt in real-time to market fluctuations, ensuring traders can capitalize on emerging trends quickly and effectively.
Product Details
- Presents a new data-driven approach to financial data analysis called Directional Change
- Applies machine learning to financial market monitoring and algorithmic trading
- Explores topics such as data science, machine learning for regime change detection, regime characterisation, market monitoring, and algorithmic trading
- Includes a Foreword by Richard Olsen
- Useful for researchers in computational finance, machine learning, and data science
- Authored by Jun Chen, who received his PhD in computational finance, and Edward P K Tsang, an Emeritus Professor at the University of Essex
| Publisher | Chapman and Hall/CRC |
| Publication date | September 15, 2020 |
| Edition | 1st |
| Language | English |
| Print length | 164 pages |
| ISBN-10 | 0367536285 |
| ISBN-13 | 978-0367536282 |
| Item Weight | 1 pounds (450 grams) |
| Dimensions | 6.31 x 0.64 x 9.38 inches (16 x 1.6 x 23.8 cm) |
Who Should Buy?
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Data Scientists
Ideal for professionals looking to apply machine learning techniques within financial datasets for regime change detection.
-
Traders
Beneficial for traders seeking to enhance their algorithmic trading strategies using insights from regime change analytics.
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Finance Researchers
Great for researchers focused on academic studies related to computational finance and quantitative analysis methodologies.
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Beginners in Finance
Not suitable for novices without foundational knowledge of finance, data science, or computational techniques.
Product Description
Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading
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Financial Engineering Editorial Review
The book under review is one-of-a-kind in terms of the subject matter it deals with – the use of hidden Markov models in algorithmic trading. The book is written in a very readable and friendly style, and it is a sure hand-holder for those who are not comfortable with maths. However, some reviewers have shared their disappointment with the lack of depth in the book concerning certain aspects of trading, and that some concepts could be replaced by simpler trend indicators. Nonetheless, the book's discussion around hidden Markov models in algorithmic trading is relatively unique, which alone can make it a worthwhile read. As with any book in this field, the inclusion of the source code is essential for replicating the experiments, and some reviewers have thus requested access to the R code used in the book.
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Pros
- Unique topic not commonly covered in other trading books
- Easy to read and understand for those not comfortable with maths
Cons
- Lack of depth in some areas
Product Price History
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Features & Benefits
- New data-driven approach to financial data analysis
- Applies machine learning to financial market monitoring and algorithmic trading
- Discovers historical regime changes with Hidden Markov Model
- One can monitor the market to detect whether the market regime has changed
- Regime tracking information can design trading algorithms
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