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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

Sequential versus random access

If we have daily stock data, we could have them saved in different patterns. One way is to save them as stock ID, date, high, low, opening price, closing price, and trading volume. We could sort our stock ID and save them one after another. We have two ways to write a Python program to access IBM data: sequential access and random access. For sequential access, we read one line and check its stock ID to see if it matches our ticker. If not, we go to the next line, until we find our data. Such a sequential search is not efficient, especially when our dataset is huge, such as several gigabits. It is a good idea to generate an index file, such as IBM, 1,000, 2,000. Based on this information, we know that IBM's data is located from line 1,000 to line 2000. Thus, to retrieve IBM's data, we could jump to line 1,000 immediately without having to go through the first 999 lines. This is called random access.

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