Matplotlib和Numpy Math

# Modules
import datetime
import numpy as np
import matplotlib.finance as finance
import matplotlib.mlab as mlab
import matplotlib.pyplot as plot

# Define quote
startdate = datetime.date(2010,10,1)
today = enddate = datetime.date.today()
ticker = 'uso'

# Catch CSV
fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)

# From CSV to REACARRAY
r = mlab.csv2rec(fh); fh.close()
# Order by Desc
r.sort()


### Methods Begin
def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential'

    """
    x = np.asarray(x)
    if type=='simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()


    a =  np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a
### Methods End


prices = r.adj_close
dates = r.date
ma20 = moving_average(prices, 20, type='simple')
ma50 = moving_average(prices, 50, type='simple')

# Get when ma20 crosses ma50
equal = np.round(ma20,1)==np.round(ma50,1)
dates_cross  = (dates[equal])
prices_cross = (prices[equal])

# Get when ma20 > ma50
ma20_greater_than_ma50 = np.round(ma20,1) > np.round(ma50,1)
dates_ma20_greater_than_ma50  = (dates[ma20_greater_than_ma50])
prices_ma20_greater_than_ma50 = (prices[ma20_greater_than_ma50])

print dates_ma20_greater_than_ma50
print prices_ma20_greater_than_ma50

store the price of the "price_cross"
see if one day after the "ma20_greater_than_ma50" statment is true, if true store the price as "price of the one day after"
now do "next price_cross" - "price of the one day after"  (price2 - price1) for all occurences