import os, sys, datetime, csv, time
import matplotlib.numerix as nx
from matplotlib.dates import date2num, datestr2num
from matplotlib.mlab import load
from pylab import figure, show, draw
import dateutil.parser
datadir = os.path.join('..', 'data')
tickerfile = os.path.join(datadir, 'nasdaq100.dat')
tickers = [line.strip() for line in file(tickerfile)]
months = dict(Jan=1, Feb=2, Mar=3, Apr=4, May=5, Jun=6, Jul=7, Aug=8, Sep=9,
Oct=10, Nov=11, Dec=12)
def to_datenum2(s):
d,m,y = s.split('-')
return date2num(datetime.date(int(y), months[m], int(d)))
class DailyData:
def __init__(self, ticker):
# load assumes floats unless you provide a converter
# dictionary keyed by column number
self.ticker = ticker
tickerfile = os.path.join(datadir, '%s.csv'%ticker)
# datestr2num is slow but it's easy
(self.date, self.open, self.high, self.low,
self.close, self.volume, self.adjclose) = load(
tickerfile, delimiter=',',
converters={0:to_datenum2},
#converters={0:datestr2num},
skiprows=1, unpack=True)
if 1: # pay the load only once in interactive mode
tickerd = dict()
for ticker in tickers:
tickerd[ticker] = DailyData(ticker)
class ScatterPoint:
"""
Create an interface to scatter plot point where individual points
can be selected and introspected. We can represent four
dimensions in a scatter: x, y, size and color. The object will
provide a method for each of those, and a method called"plotdata"
which will provide a detail plot of the underlying data
Derived classes mush define the following attributes
x: x axis data value as a scale'
y: y axis data value as a scalar'
size: the size the scatter marker in points^2'
color: the color of the marker; can be a colormappable scalar'
"""
def plotraw(self, fig):
'plot the raw time data in the Figure instance'
pass
class DailyPoint(ScatterPoint):
def __init__(self, dailydata):
self.dailydata = dailydata
close = dailydata.close
# daily returns
self.dailyreturn = (close[1:]-close[:-1])/close[:-1]
# total returns
self.totalreturn = (close[-1] - close[0])/close[0]
# mean of daily returns
self.mudaily = nx.mlab.mean(self.dailyreturn)
# std of daily returns
self.sigmadaily = nx.mlab.std(self.dailyreturn)
# average daily volume of shares traded
self.totalvolume = nx.mlab.mean(dailydata.volume)
# the lag1 autocorrelation of daily returns
self.lag1corr = nx.mlab.corrcoef(self.dailyreturn[1:], self.dailyreturn[:-1])[0,1]
# Define the attributes needed for the scatter point interface
self.x = self.lag1corr
self.y = self.sigmadaily
self.size = 10*nx.log(self.totalvolume)
self.color = self.totalreturn
def plotraw(self, fig):
dd = self.dailydata
fig.clf()
ax1 = fig.add_subplot(211)
ax1.plot_date(dd.date, dd.close, '-')
ax2 = fig.add_subplot(212, sharex=ax1) # sharex
ax2.bar(dd.date, dd.volume)
ax2.xaxis_date()
# hide tick labels on 211
# share x-axes
# improve tick formatting
# toolbar tick formatting
# xlabels, ylabels and titles
# volume formatting
# make the upper axes xticks invisible
for label in ax1.xaxis.get_ticklabels():
label.set_visible(False)
# rotate the lower axes ticks
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
# use nicer tick locating and formatting
from matplotlib.dates import DateFormatter, MonthLocator
from matplotlib.ticker import FuncFormatter
tickloc = MonthLocator()
tickfmt = DateFormatter('%b %Y')
ax2.xaxis.set_major_formatter(tickfmt)
ax2.xaxis.set_major_locator(tickloc)
# fix the y axis volume formatter
def millions(x, pos=None):
return '%d'%int(x/1e6)
ax2.yaxis.set_major_formatter(FuncFormatter(millions))
# fix the toolbar formatting
ax1.fmt_xdata = DateFormatter('%Y-%m-%d')
ax2.fmt_xdata = DateFormatter('%Y-%m-%d')
if 1:
points = [DailyPoint(dailydata) for dailydata in tickerd.values()]
data = [(p.x, p.y, p.size, p.color) for p in points]
x, y, size, color = zip(*data)
if 0:
fig = figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, size, color, alpha=0.75)
if 1:
fig = figure(2)
p = points[0]
p.plotraw(fig)
draw()
show()
