finance.py

"""
Some examples playing around with yahoo finance data
"""

from datetime import datetime
from pandas.compat import zip

import matplotlib.finance as fin
import numpy as np
from pylab import show


from pandas import Index, DataFrame
from pandas.core.datetools import BMonthEnd
from pandas import ols

startDate = datetime(2008, 1, 1)
endDate = datetime(2009, 9, 1)


def getQuotes(symbol, start, end):
    quotes = fin.quotes_historical_yahoo(symbol, start, end)
    dates, open, close, high, low, volume = zip(*quotes)

    data = {
        'open': open,
        'close': close,
        'high': high,
        'low': low,
        'volume': volume
    }

    dates = Index([datetime.fromordinal(int(d)) for d in dates])
    return DataFrame(data, index=dates)

msft = getQuotes('MSFT', startDate, endDate)
aapl = getQuotes('AAPL', startDate, endDate)
goog = getQuotes('GOOG', startDate, endDate)
ibm = getQuotes('IBM', startDate, endDate)

px = DataFrame({'MSFT': msft['close'],
                'IBM': ibm['close'],
                'GOOG': goog['close'],
                'AAPL': aapl['close']})
returns = px / px.shift(1) - 1

# Select dates

subIndex = ibm.index[(ibm['close'] > 95) & (ibm['close'] < 100)]
msftOnSameDates = msft.reindex(subIndex)

# Insert columns

msft['hi-lo spread'] = msft['high'] - msft['low']
ibm['hi-lo spread'] = ibm['high'] - ibm['low']

# Aggregate monthly


def toMonthly(frame, how):
    offset = BMonthEnd()

    return frame.groupby(offset.rollforward).aggregate(how)

msftMonthly = toMonthly(msft, np.mean)
ibmMonthly = toMonthly(ibm, np.mean)

# Statistics

stdev = DataFrame({
    'MSFT': msft.std(),
    'IBM': ibm.std()
})

# Arithmetic

ratios = ibm / msft

# Works with different indices

ratio = ibm / ibmMonthly
monthlyRatio = ratio.reindex(ibmMonthly.index)

# Ratio relative to past month average

filledRatio = ibm / ibmMonthly.reindex(ibm.index, method='pad')