text: '''
  (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 March, 5, 2012
  
  @author: Sourabh Bajaj
  @contact: sourabhbajaj90@gmail.com
  @summary: Event Profiler Tutorial
  '''
  
  
  import pandas 
  from qstkutil import DataAccess as da
  import numpy as np
  import math
  import copy
  import qstkutil.qsdateutil as du
  import datetime as dt
  import qstkutil.DataAccess as da
  import qstkutil.tsutil as tsu
  import qstkstudy.EventProfiler as ep
  
  """
  Accepts a list of symbols along with start and end date
  Returns the Event Matrix which is a pandas Datamatrix
  Event matrix has the following structure :
      |IBM |GOOG|XOM |MSFT| GS | JP |
  (d1)|nan |nan | 1  |nan |nan | 1  |
  (d2)|nan | 1  |nan |nan |nan |nan |
  (d3)| 1  |nan | 1  |nan | 1  |nan |
  (d4)|nan |  1 |nan | 1  |nan |nan |
  ...................................
  ...................................
  Also, d1 = start date
  nan = no information about any event.
  1 = status bit(positively confirms the event occurence)
  """
  
  # Get the data from the data store
  storename = "Yahoo" # get data from our daily prices source
  # Available field names: open, close, high, low, close, actual_close, volume
  closefield = "close"
  volumefield = "volume"
  window = 10
  
  def findEvents(symbols, startday,endday, marketSymbol,verbose=False):
  
  	# Reading the Data for the list of Symbols.	
  	timeofday=dt.timedelta(hours=16)
  	timestamps = du.getNYSEdays(startday,endday,timeofday)
  	dataobj = da.DataAccess('Yahoo')
  	if verbose:
              print __name__ + " reading data"
  	# Reading the Data
  	close = dataobj.get_data(timestamps, symbols, closefield)
  	
  	# Completing the Data - Removing the NaN values from the Matrix
  	close = (close.fillna(method='ffill')).fillna(method='backfill')
  
  	
  	# Calculating Daily Returns for the Market
  	tsu.returnize0(close.values)
  	SPYValues=close[marketSymbol]
  
  	# Calculating the Returns of the Stock Relative to the Market 
  	# So if a Stock went up 5% and the Market rised 3%. The the return relative to market is 2% 
  	mktneutDM = close - close[marketSymbol]
  	np_eventmat = copy.deepcopy(mktneutDM)
  	for sym in symbols:
  		for time in timestamps:
  			np_eventmat[sym][time]=np.NAN
  
  	if verbose:
              print __name__ + " finding events"
  
  	# Generating the Event Matrix
  	# Event described is : Market falls more than 3% plus the stock falls 5% more than the Market
  	# Suppose : The market fell 3%, then the stock should fall more than 8% to mark the event.
  	# And if the market falls 5%, then the stock should fall more than 10% to mark the event.
  
  	for symbol in symbols:
  		
  	    for i in range(1,len(mktneutDM[symbol])):
  	        if SPYValues[i]<-0.03 and mktneutDM[symbol][i] < -0.05 : # When market fall is more than 3% and also the stock compared to market is also fell by more than 5%.
               		np_eventmat[symbol][i] = 1.0  #overwriting by the bit, marking the event
  			
  	return np_eventmat
  
  
  #################################################
  ################ MAIN CODE ######################
  #################################################
  
  
  symbols = np.loadtxt('SP500port.csv',dtype='S10',comments='#', skiprows=1)
  # You might get a message about some files being missing, don't worry about it.
  
  #symbols =['BFRE','ATCS','RSERF','GDNEF','LAST','ATTUF','JBFCF','CYVA','SPF','XPO','EHECF','TEMO','AOLS','CSNT','REMI','GLRP','AIFLY','BEE','DJRT','CHSTF','AICAF']
  startday = dt.datetime(2008,1,1)
  endday = dt.datetime(2010,12,31)
  eventMatrix = findEvents(symbols,startday,endday,marketSymbol='SPY',verbose=True)
  
  eventProfiler = ep.EventProfiler(eventMatrix,startday,endday,lookback_days=20,lookforward_days=20,verbose=True)
  
  eventProfiler.study(filename="MyEventStudy.pdf",plotErrorBars=True,plotMarketNeutral=True,plotEvents=False,marketSymbol='SPY')
  
  
  