def findEvents(symbols, startday,endday,weeks, marketSymbol,verbose=False):
# week calc
week=dt.timedelta(weeks=weeks)
time = startday.time()
lookbackday=dt.datetime.combine(startday,time)-week
# week cal
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
closefield = "close"
lookbackTimestamps = du.getNSEdays(lookbackday,startday,timeofday)
analyzeTimestamps=du.getNSEdays(startday,endday,timeofday)
timestamps=lookbackTimestamps+analyzeTimestamps
np_eventmat=pandas.DataFrame(index=timestamps, columns=symbols)
dataobj = da.DataAccess('NSEData')
close = dataobj.get_data(timestamps, symbols, closefield)
close = (close.fillna(method='ffill')).fillna(method='backfill')
## close.to_csv('res.csv',sep=',')
for i in range(0, len(analyzeTimestamps)):
print 'Iteration : ',i+1
currentDates = [close.index[j] for j in range(i,len(lookbackTimestamps)+1+i)]
subDataframe=close.ix[currentDates]
eventDate= currentDates[len(currentDates)-1]
maxValue=subDataframe.idxmax()
minValue=subDataframe.idxmin()
for stockname, date in maxValue.T.iteritems():
value=(close[stockname][eventDate]/close[stockname][date])-1
if(value<=0.00 and value>-0.02):
np_eventmat[stockname][eventDate]='52wk+++,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
elif(value<-0.02 and value>-0.05):
np_eventmat[stockname][eventDate]='52wk++,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
elif(value<-0.05 and value>-0.07):
np_eventmat[stockname][eventDate]='52wk+,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
for stockname, date in minValue.T.iteritems():
value=(close[stockname][eventDate]/close[stockname][date])-1
if(value>=0.00 and value<0.02):
np_eventmat[stockname][eventDate]='52wk---,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
elif(value>0.02 and value<0.05):
np_eventmat[stockname][eventDate]='52wk--,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
elif(value>0.05 and value<0.07):
np_eventmat[stockname][eventDate]='52wk-,'+str(close[stockname][eventDate])+','+str(close[stockname][date])+','+str(date.date())+','+str(value*100)+','+str(currentDates[0].date())+' - '+str(eventDate.date())
return np_eventmat
def __init__(self,eventMatrix,startday,endday,\
lookback_days = 20, lookforward_days =20,\
verbose=False):
""" Event Profiler class construtor
Parameters : evenMatrix
: startday
: endday
(optional) : lookback_days ( default = 20)
(optional) : lookforward_days( default = 20)
eventMatrix is a pandas DataMatrix
eventMatrix must have 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.
= status bit(positively confirms the event occurence)
"""
self.eventMatrix = eventMatrix
self.startday = startday
self.endday = endday
self.symbols = eventMatrix.columns
self.lookback_days = lookback_days
self.lookforward_days = lookforward_days
self.total_days = lookback_days + lookforward_days + 1
# original code
# self.dataobj = da.DataAccess('Yahoo')
# original code
# modified code to access NSE data
self.dataobj = da.DataAccess('NSEData')
# modified code
self.timeofday = dt.timedelta(hours=16)
# original code
# self.timestamps = du.getNYSEdays(startday,endday,self.timeofday)
# original code
# modified code
self.timestamps = du.getNSEdays(startday,endday,self.timeofday)
# modified code
self.verbose = verbose
if verbose:
print __name__ + " reading historical data"
self.close = self.dataobj.get_data(self.timestamps,\
self.symbols, "close", verbose=self.verbose)
self.close = (self.close.fillna()).fillna(method='backfill')
def findEvents(symbols, startday,endday, marketSymbol,verbose=False):
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
timestamps = du.getNSEdays(startday,endday,timeofday)
endOfQuarter=getQuarterEndDates(timestamps)
dataobj = da.DataAccess('NSEData')
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)
# 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 : Analysis Quarter End prices of stocks
for symbol in symbols:
for i in endOfQuarter:
np_eventmat[symbol][i] = 1.0 #overwriting by the bit, marking the event
return np_eventmat
def findEvents(data, startday,endday, marketSymbol,verbose=False):
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
timestamps = du.getNSEdays(startday,endday,timeofday)
dataobj = da.DataAccess('NSEData')
if verbose:
print __name__ + " reading data"
# Reading the Data
symbols=getSymbols(data, marketSymbol)
eventDetails=getDates(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)
# 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 : Analysing Stock Prices before and after the occurence of an event.
# Stocks are analysed on specific dates as per data provided in csv files.
for stock in eventDetails:
stockName=stock[0]
for i in range(1,len(stock)):
np_eventmat[stockName][stock[i]] = 1.0 #overwriting by the bit, marking the event
return np_eventmat
def findEvents(symbols, startday,endday, marketSymbol,verbose=False):
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
timestamps = du.getNSEdays(startday,endday,timeofday)
dataobj = da.DataAccess('NSEData')
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 : Analysis of events occuring when stock price falls to a certain value
for symbol in symbols:
for i in range(1,len(mktneutDM[symbol])):
if SPYValues[i]<-0.01 and mktneutDM[symbol][i] < -0.02 : # 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
import qstkutil.DataAccess as da
import datetime as dt
import matplotlib.pyplot as plt
from pylab import *
import pandas
print pandas.__version__
#
# Prepare to read the data
#
symbols = ["NSE","ABB.NS","ACC.NS","TCS.NS"]
startday = dt.datetime(2008,1,1)
endday = dt.datetime(2011,12,31)
timeofday=dt.timedelta(hours=16)
timestamps = du.getNSEdays(startday,endday,timeofday)
#dataobj = da.DataAccess('Yahoo')
dataobj = da.DataAccess('NSEData')
voldata = dataobj.get_data(timestamps, symbols, "volume",verbose=True)
close = dataobj.get_data(timestamps, symbols, "close",verbose=True)
actualclose = dataobj.get_data(timestamps, symbols, "actual_close",verbose=True)
#
# Plot the adjusted close data
#
plt.clf()
newtimestamps = close.index
pricedat = close.values # pull the 2D ndarray out of the pandas object
plt.plot(newtimestamps,pricedat)
def findEvents(self, symbols, columnIndexes, verbose=False):
eventDetails=self.eventDetailsData
# Reading the Data for the list of Symbols.
timeofday=dt.timedelta(hours=16)
timestamps = du.getNSEdays(self.startday,self.endday,timeofday)
stockDates=self.getDates(eventDetails, timestamps, columnIndexes)
dataobj = da.DataAccess('NSEData')
if verbose:
print __name__ + " reading data"
# Reading the Data
close = dataobj.get_data(timestamps, symbols, self.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)
NSEValues=close[self.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[self.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 : Analysing Stock Prices before and after the occurence of an event.
# Stocks are analysed on specific dates as per data provided in csv files accessed from Google Docs.
if(self.analyzeLookbackDays):
for stock in stockDates:
stockName=stock[0]
for i in range(1,len(stock)):
if(self.analyzeStock(stockName, stock[i], mktneutDM)):
np_eventmat[stockName][stock[i]] = 1.0 #overwriting by the bit, marking the event
else:
if((self.marketChangeMin!='NA' and self.marketChangeMax!='NA') or (self.stockChangeMin!='NA' and self.stockChangeMax!='NA')):
np_eventmat=self.getRangeChangeMatrix(stockDates, NSEValues, mktneutDM, np_eventmat)
elif(self.marketValueChange!='NA' or self.stockValueChange!='NA'):
np_eventmat=self.getValueChangeMatrix(stockDates, NSEValues, mktneutDM, np_eventmat)
else:
for stock in stockDates:
stockName=stock[0]
for i in range(1,len(stock)):
np_eventmat[stockName][stock[i]] = 1.0 #overwriting by the bit, marking the event
return np_eventmat
