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Machine Learning in Finance
Use Machine Learning Techniques for Day Trading and Value Trading in the Stock Market
Bob Mather
Livre broché | Anglais
27,45 €
+ 54 points
Description
Are you a machine learning enthusiast looking for a practical day to day application? Or are you just trying to incorporate machine learning software in your trading decisions?
This book is your answer.
While machine learning and finance have generally been seen as separate entities, this book looks at several applications of machine learning in the financial world. Whether it is predicting the best time to buy a stock in a day trading scenario, or to determine the long term value of a stock; financial ratios and common sense have always been used as reliable indicators.
But how do these compare about advanced machine learning algorithms like clustering and regression? When would be the best time to use these?
While machine learning and finance have generally been seen as separate entities, this book looks at several applications of machine learning in the financial world. Whether it is predicting the best time to buy a stock in a day trading scenario, or to determine the long term value of a stock; financial ratios and common sense have always been used as reliable indicators.
But how do these compare about advanced machine learning algorithms like clustering and regression? When would be the best time to use these?
What's Included In This Book:
- What is Financial Machine Learning
- Developing a Trading Strategy for Stocks
- Machine Learning to Determine Current Value of Stocks
- Optimal Time to Buy Stocks
- Machine Learning Algorithm to Predict When to Sell a Stock
- Determine Value of a Penny Stock
- Trading Automation Software
- Conclusion
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 90
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9781922300058
- Date de parution :
- 15-07-19
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 148 mm x 210 mm
- Poids :
- 117 g
