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Mastering Python for Finance

You're reading from   Mastering Python for Finance Implement advanced state-of-the-art financial statistical applications using Python

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789346466
Length 426 pages
Edition 2nd Edition
Languages
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Author (1):
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James Ma Weiming James Ma Weiming
Author Profile Icon James Ma Weiming
James Ma Weiming
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Overview of Financial Analysis with Python FREE CHAPTER 3. Section 2: Financial Concepts
4. The Importance of Linearity in Finance 5. Nonlinearity in Finance 6. Numerical Methods for Pricing Options 7. Modeling Interest Rates and Derivatives 8. Statistical Analysis of Time Series Data 9. Section 3: A Hands-On Approach
10. Interactive Financial Analytics with the VIX 11. Building an Algorithmic Trading Platform 12. Implementing a Backtesting System 13. Machine Learning for Finance 14. Deep Learning for Finance 15. Other Books You May Enjoy

A deep learning price prediction model with TensorFlow

In this section, we will learn how to use TensorFlow as a deep learning framework in building a price prediction model. Five years of pricing data, from 2013 to 2017, will be used for training our deep learning model. We will attempt to predict the prices of Apple (AAPL) in the following year of 2018.

Feature engineering our model

The daily adjusted closing prices of our data make up the target variables. The independent variables defining the features of our model are made up of these technical indicators:

  • Relative strength index (RSI)
  • Williams %R (WR)
  • Awesome oscillator (AO)
  • Volume-weighted average price (VWAP)
  • Average daily trading volume (ADTV)
  • 5-day moving average...
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