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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

Building a trend-following forex trading platform


Suppose from the forex tick data collected, we resample the time series at regular intervals. The average of prices over a reasonably short time period and long time period are calculated. The beta of the price series is taken as the ratio of the short-term average prices to the long-term average prices. In price series, where there is no trend, the ratio is 1—short-term prices are equal to the long-term prices. When prices are on an uptrend, the short-term prices are higher than the long-term average price levels and the beta is more than 1. Conversely, when prices are on a downtrend, the beta is less than 1.

In this section, we will discuss the implementation of a trend-following trading system to buy a position when prices are in an uptrend and sell when prices are going downtrend.

Setting up the main program

Let's create a class named ForexSystem that inherits the oandapy.Streamer class with the following required variables in the constructor...

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