content: '''
  (c) 2011, 2012 Georgia Tech Research Corporation
  This source code is released under the New BSD license.  Please see
  http://wiki.quantsoftware.org/index.php?title=QSTK_License
  for license details.
  
  Created on January, 24, 2013
  
  @author: Sourabh Bajaj
  @contact: sourabhbajaj@gatech.edu
  @summary: Example tutorial code.
  '''
  
  # QSTK Imports
  import QSTK.qstkutil.qsdateutil as du
  import QSTK.qstkutil.tsutil as tsu
  import QSTK.qstkutil.DataAccess as da
  
  # Third Party Imports
  import datetime as dt
  import matplotlib.pyplot as plt
  import pandas as pd
  import numpy as np
  
  
  def main():
      ''' Main Function'''
      # Reading the portfolio
      na_portfolio = np.loadtxt('tutorial3portfolio.csv', dtype='S5,f4',
                          delimiter=',', comments="#", skiprows=1)
      print na_portfolio
  
      # Sorting the portfolio by symbol name
      na_portfolio = sorted(na_portfolio, key=lambda x: x[0])
      print na_portfolio
  
      # Create two list for symbol names and allocation
      ls_port_syms = []
      lf_port_alloc = []
      for port in na_portfolio:
          ls_port_syms.append(port[0])
          lf_port_alloc.append(port[1])
  
      # Creating an object of the dataaccess class with Yahoo as the source.
      c_dataobj = da.DataAccess('Yahoo')
      ls_all_syms = c_dataobj.get_all_symbols()
      # Bad symbols are symbols present in portfolio but not in all syms
      ls_bad_syms = list(set(ls_port_syms) - set(ls_all_syms))
  
      if len(ls_bad_syms) != 0:
          print "Portfolio contains bad symbols : ", ls_bad_syms
  
      for s_sym in ls_bad_syms:
          i_index = ls_port_syms.index(s_sym)
          ls_port_syms.pop(i_index)
          lf_port_alloc.pop(i_index)
  
      # Reading the historical data.
      dt_end = dt.datetime(2011, 1, 1)
      dt_start = dt_end - dt.timedelta(days=1095)  # Three years
      # We need closing prices so the timestamp should be hours=16.
      dt_timeofday = dt.timedelta(hours=16)
  
      # Get a list of trading days between the start and the end.
      ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
  
      # Keys to be read from the data, it is good to read everything in one go.
      ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
  
      # Reading the data, now d_data is a dictionary with the keys above.
      # Timestamps and symbols are the ones that were specified before.
      ldf_data = c_dataobj.get_data(ldt_timestamps, ls_port_syms, ls_keys)
      d_data = dict(zip(ls_keys, ldf_data))
  
      # Copying close price into separate dataframe to find rets
      df_rets = d_data['close'].copy()
      # Filling the data.
      df_rets = df_rets.fillna(method='ffill')
      df_rets = df_rets.fillna(method='bfill')
      df_rets = df_rets.fillna(1.0)
  
      # Numpy matrix of filled data values
      na_rets = df_rets.values
      # returnize0 works on ndarray and not dataframes.
      tsu.returnize0(na_rets)
  
      # Estimate portfolio returns
      na_portrets = np.sum(na_rets * lf_port_alloc, axis=1)
      na_port_total = np.cumprod(na_portrets + 1)
      na_component_total = np.cumprod(na_rets + 1, axis=0)
  
      # Plotting the results
      plt.clf()
      fig = plt.figure()
      fig.add_subplot(111)
      plt.plot(ldt_timestamps, na_component_total, alpha=0.4)
      plt.plot(ldt_timestamps, na_port_total)
      ls_names = ls_port_syms
      ls_names.append('Portfolio')
      plt.legend(ls_names)
      plt.ylabel('Cumulative Returns')
      plt.xlabel('Date')
      fig.autofmt_xdate(rotation=45)
      plt.savefig('tutorial3.pdf', format='pdf')
  
  if __name__ == '__main__':
      main()
  