%hide
#%auto
import urllib
class Day:
def __init__(self, date, open, high, low, close, volume):
self.date = date
self.open=float(open); self.high=float(high); self.low=float(low); self.close=float(close)
self.volume=int(volume)
def __repr__(self):
return '%10s %4.2f %4.2f %4.2f %4.2f %10d'%(self.date, self.open, self.high,
self.low, self.close, self.volume)
class Stock:
def __init__(self, symbol):
self.symbol = symbol.upper()
def __repr__(self):
return "%s (%s)"%(self.symbol, self.yahoo()['price'])
def yahoo(self):
url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (self.symbol, 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7')
values = urllib.urlopen(url).read().strip().strip('"').split(',')
data = {}
data['price'] = values[0]
data['change'] = values[1]
data['volume'] = values[2]
data['avg_daily_volume'] = values[3]
data['stock_exchange'] = values[4]
data['market_cap'] = values[5]
data['book_value'] = values[6]
data['ebitda'] = values[7]
data['dividend_per_share'] = values[8]
data['dividend_yield'] = values[9]
data['earnings_per_share'] = values[10]
data['52_week_high'] = values[11]
data['52_week_low'] = values[12]
data['50day_moving_avg'] = values[13]
data['200day_moving_avg'] = values[14]
data['price_earnings_ratio'] = values[15]
data['price_earnings_growth_ratio'] = values[16]
data['price_sales_ratio'] = values[17]
data['price_book_ratio'] = values[18]
data['short_ratio'] = values[19]
return data
def historical(self):
try:
return self.__historical
except AttributeError:
pass
symbol = self.symbol
def get_data(exchange):
name = get_remote_file('http://finance.google.com/finance/historical?q=%s:%s&output=csv'%(exchange, symbol.upper()),
verbose=False)
return open(name).read()
R = get_data('NASDAQ')
if "Bad Request" in R:
R = get_data("NYSE")
R = R.splitlines()
headings = R[0].split(',')
self.__historical = []
try:
for x in reversed(R[1:]):
date, opn, high, low, close, volume = x.split(',')
self.__historical.append(Day(date, opn,high,low,close,volume))
except ValueError:
pass
self.__historical = Sequence(self.__historical,cr=True,universe=lambda x:x)
return self.__historical
def plot_average(self, spline_samples=10):
d = self.historical()
if len(d) == 0:
return text('no historical data at Google Finance about %s'%self.symbol, (0,3))
avg = list(enumerate([(z.high+z.low)/2 for z in d]))
P = line(avg) + points(avg, rgbcolor='black', pointsize=4) + \
text(self.symbol, (len(d)*1.05, d[-1].low), horizontal_alignment='right', rgbcolor='black')
if spline_samples > 0:
k = 250//spline_samples
spl = spline([avg[i*k] for i in range(len(d)//k)] + [avg[-1]])
P += plot(spl, (0,len(d)+30), color=(0.7,0.7,0.7))
P.xmax(260)
return P
def plot_diff(self):
d = self.historical()
if len(d) == 0:
return text('no historical data at Google Finance about %s'%self.symbol, (0,3))
diff = []
for i in range(1, len(d)):
z1 = d[i]; z0 = d[i-1]
diff.append((i, (z1.high+z1.low)/2 - (z0.high + z0.low)/2))
P = line(diff,thickness=0.5) + points(diff, rgbcolor='black', pointsize=4) + \
text(self.symbol, (len(d)*1.05, 0), horizontal_alignment='right', rgbcolor='black')
P.xmax(260)
return P
symbols = ['bsc', 'vmw', 'sbux', 'aapl', 'amzn', 'goog', 'wfmi', 'msft', 'yhoo', 'ebay', 'java', 'rht', ]; symbols.sort()
stocks = dict([(s,Stock(s)) for s in symbols])
@interact
def data(symbol = symbols, other_symbol='', spline_samples=(8,[0..15])):
if other_symbol != '':
symbol = other_symbol
S = Stock(symbol)
html('<h1 align=center><font color="darkred">%s</font></h1>'%S)
S.plot_average(spline_samples).save('avg.png', figsize=[10,2])
S.plot_diff().save('diff.png', figsize=[10,2])
Y = S.yahoo()
k = Y.keys(); k.sort()
html('Price during last 52 weeks:<br>Grey line is a spline through %s points (do not take seriously!):<br> <img src="cell://avg.png">'%spline_samples)
html('Difference from previous day:<br> <img src="cell://diff.png">')
html('<table align=center>' + '\n'.join('<tr><td>%s</td><td>%s</td></tr>'%(k[i], Y[k[i]]) for i in range(len(k))) + '</table>')
