Stock trading is one of the most challenging problems statisticians are trying to solve. There are multiple technical indicators, such as trend direction, momentum or lack of momentum in the market, volatility for profit potential, and volume measures to monitor the popularity in the market, to name a few. These indicators can be used to create strategy to high-probability trading opportunities. Days/weeks/months can be spent discovering the relationships between technical indicators. An efficient and less time-consuming tool, such as a decision tree, can be used. The main advantage of a decision tree is that it is a powerful and easily interpretable algorithm, which gives a good head start.
- Tech Categories
- Best Sellers
- New Releases
- Books
- Videos
- Audiobooks
Tech Categories Popular Audiobooks
- Articles
- Newsletters
- Free Learning
You're reading from Practical Machine Learning Cookbook
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Read more about Atul Tripathi
Unlock this book and the full library FREE for 7 days
Author (1)
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Read more about Atul Tripathi