def main(argv):
l_event_strategies = []
l_event_strategies.append(es.UnderThreshold('sp5002012', 'actual_close', 9))
#l_event_strategies.append(es.RelativeStrength('sp5002008', 'close', -5, 'SPY', 2))
#l_event_strategies.append(es.DailyChange('sp5002008', 'close', -10, '%'))
#l_event_strategies.append(es.RelativeStrengthWithVolume('sp5002008', 'close', -5, 'SPY', 2))
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2009, 12, 31)
print 'AnalysisEngine : Events to Analyze:'
i = 0
for e in l_event_strategies:
i += 1
print 'AnalysisEngine : %00d - %s' % (i, e.getName())
print
print 'AnalysisEngine : Profiling Events'
ep.profile_events(dt_start, dt_end, l_event_strategies)
print
print 'AnalysisEngine : Simulating Orders'
ms.simulate_orders(50000, l_event_strategies)
print
print 'AnalysisEngine : Analyzing Results'
pa.analize_results('$SPX', l_event_strategies, dt_start, dt_end)
def studyEvents(self):
logger.debug("Finding events")
df_events = self.findEventsAndGenerateOrders()
logger.debug("Creating study")
ep.eventprofiler(df_events,
self.df_price,
i_lookback=20,
i_lookforward=20,
s_filename=self.profilerOutFile,
b_market_neutral=True,
b_errorbars=True,
s_market_sym='SPY')
def profile_threshold_actual_close_events(ls_symbols, d_data,
actual_close_threshold, filename):
df_events = find_price_drop_events(ls_symbols, d_data,
actual_close_threshold)
ep.eventprofiler(df_events,
d_data,
i_lookback=20,
i_lookforward=20,
s_filename=filename,
b_market_neutral=True,
b_errorbars=True,
s_market_sym='SPY')
def main(argv):
# parameters
s_list_name = "sp5002012"
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2009, 12, 31)
s_bench_sym = "SPY"
i_initial_cash = 100000
print "TechInv : Bollinger Event Study"
# read market data
dataobj = da.DataAccess("Yahoo")
ls_symbols = dataobj.get_symbols_from_list(s_list_name)
ls_symbols.append(s_bench_sym)
d_data = get_symbol_data(dataobj, ls_symbols, dt_start, dt_end)
# create the object that implements the strategy
event_strategy = es.BollingerRelWeakness(s_list_name, d_data, i_initial_cash, 20)
df_events, i_event_counter = event_strategy.find_events(ls_symbols, "close", 2.0, s_bench_sym, 1.3)
print "EventProfiler : For the %1.1f %s event with the components of %s, we find %d events. Date Range = (%s) to (%s)." % (
event_strategy.f_size,
event_strategy.s_feature,
event_strategy.s_list_name,
i_event_counter,
dt_start,
dt_end,
)
print "TechInv : Profiling Events"
ep.profile_event(event_strategy, ls_symbols, dt_start, dt_end, df_events, 100, 5)
print ""
ms.simulate_market(event_strategy)
event_strategy.s_bench_sym = "$SPX"
print ""
pa.analyze_event(event_strategy, dt_start, dt_end)
import MyEvents2 as ev
import datetime as dt
import EventProfiler as ep
import numpy as np
#symbols = ['BFRE','ATCS','RSERF','GDNEF','LAST','ATTUF','JBFCF','CYVA','SPF','XPO','EHECF','TEMO','AOLS','CSNT','REMI','GLRP','AIFLY','BEE','DJRT','CHSTF','AICAF']
symbols = np.loadtxt('symbol-set2-wohyphen.txt', dtype='S10', comments='#')
#ARVIND: use ABX for testing
#symbols = ['ABX']
#symbols = symbols[0:20]
#ARVIND: changed the start and end day to include data during the non-recession period
startday = dt.datetime(2008, 1, 1)
#startday = dt.datetime(2004,1,1)
endday = dt.datetime(2008, 12, 31)
eventMatrix = ev.findEvents(symbols, startday, endday, verbose=True)
#print eventMatrix
#print 'eventMatrix call ended'
#print len(eventMatrix)
eventProfiler = ep.EventProfiler(eventMatrix,
startday,
endday,
lookback_days=20,
lookforward_days=20,
verbose=True)
#ARVIND: i need plotMarketNeutral to be False
eventProfiler.study(filename="MyEventStudy2.pdf",\
plotErrorBars=True,\
plotMarketNeutral=False,\
plotEvents=False,\
marketSymbol='SPY')
