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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
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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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Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python 2. Using Python as an Ordinary Calculator FREE CHAPTER 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

Candlesticks representation of IBM's daily price

We could use candlesticks to represent the daily opening, high, low, and closing prices. The vertical line represents high and low prices, while a rectangular bar represents open-close span. When the close price is higher than the opening price, we have a black bar. Otherwise, we would have a red bar. The following program will show exactly this:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.dates import DateFormatter, WeekdayLocator, HourLocator, DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo, candlestick,\
     plot_day_summary, candlestick2
date1 = ( 2013, 10, 20)
date2 = ( 2013, 11, 10 )
ticker='IBM'
mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays
alldays    = DayLocator()               # minor ticks on the days
weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12
dayFormatter = DateFormatter('%d')      # e.g., 12
quotes = quotes_historical_yahoo...
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