f_symreturn_today = (f_symprice_today / f_symprice_yest) - 1
f_marketreturn_today = (f_marketprice_today / f_marketprice_yest) - 1
# Event is found if the symbol is down more then 3% while the
# market is up more then 2%
if f_symprice_today <5 and f_symprice_yest >= 5:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
return df_events
if __name__ == '__main__':
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2009, 12, 31)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))
dataobj = da.DataAccess('Yahoo')
ls_symbols = dataobj.get_symbols_from_list('sp5002012')
ls_symbols.append('SPY')
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
df_events = find_events(ls_symbols, d_data)
print "Creating Study"
ep.eventprofiler(df_events, d_data, i_lookback=20, i_lookforward=20,
s_filename='MyEventStudy2008_2012_2_Comp.pdf', b_market_neutral=True, b_errorbars=True,
s_market_sym='SPY')
#################################################
################ 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')
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2009, 12, 31)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))
dataobj = da.DataAccess('Yahoo')
ls_symbols1 = dataobj.get_symbols_from_list('sp5002008')
ls_symbols2 = dataobj.get_symbols_from_list('sp5002012')
ls_symbols1.append('SPY')
ls_symbols2.append('SPY')
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data1 = dataobj.get_data(ldt_timestamps, ls_symbols1, ls_keys)
ldf_data2= dataobj.get_data(ldt_timestamps, ls_symbols2, ls_keys)
c = dict(zip(ls_keys, ldf_data1))
d_data1 = dict(zip(ls_keys, ldf_data1))
c2 = dict(zip(ls_keys, ldf_data2))
d_data2 = dict(zip(ls_keys, ldf_data2))
df_events1 = find_events(ls_symbols1, d_data1)
df_events2 = find_events(ls_symbols2, d_data2)
print "Creating Study"
ep.eventprofiler(df_events1, d_data1, i_lookback=20, i_lookforward=20,
s_filename='MyEventStudy1.pdf', b_market_neutral=True, b_errorbars=True,
s_market_sym='SPY')
ep.eventprofiler(df_events2, d_data2, i_lookback=20, i_lookforward=20,
s_filename='MyEventStudy2.pdf', b_market_neutral=True, b_errorbars=True,
s_market_sym='SPY')
