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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
Languages
Tools
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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

Choosing appropriate precision

The default precision for Python has 16 decimal places as shown in the following example. This is good enough for most finance-related problems or research:

>>>7/3
2.3333333333333335

We could use the round() function to change the precision as follows:

>>>payment1=3/7
>>>payment1
0.42857142857142855
>>>payment2=round(y,5)
>>>payment2
0.42857

Assume that the units for both payment1 and payment2 are in millions. The difference could be huge after we apply the round() function with just two decimal places! If we use one dollar as our unit, the exact payment is $428,571. However, if we use millions instead and apply two decimal places, we end up with 430,000, which is shown in the following example. The difference is $1,429:

>>>payment1*10**6
428571.4285714285
>>>payment2=round(payment1,2)
>>>payment2
0.43
>>>payment2*10**6
430000.0
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