def strat_backtest1(strat, start, end, num, diff, startval):
"""
@summary: Back tests a strategy defined in a python script that takes
in a start
and end date along with a starting value a set number of times.
@param strat: filename of python script strategy
@param start: starting date in a datetime object
@param end: ending date in a datetime object
@param num: number of tests to perform
@param diff: offset in days of the tests
@param startval: starting value of fund during back tests
@return fundsmatrix: Datamatrix of fund values returned from each test
@rtype datamatrix
"""
fundsmatrix = []
startdates = du.getNextNNYSEdays(start, num * diff, dt.timedelta(hours=16))
enddates = du.getNextNNYSEdays(end, num * diff, dt.timedelta(hours=16))
for start, end in zip(startdates, enddates):
cmd = "python %s %s %s temp_alloc.pkl" % (
strat,
start.strftime("%m-%d-%Y"),
end.strftime("%m-%d-%Y")
)
os.system(cmd)
return alloc_backtest('temp_alloc.pkl', startval)
return fundsmatrix
def strat_backtest1(strat, start, end, num, diff, startval):
"""
@summary: Back tests a strategy defined in a python script that takes
in a start
and end date along with a starting value a set number of times.
@param strat: filename of python script strategy
@param start: starting date in a datetime object
@param end: ending date in a datetime object
@param num: number of tests to perform
@param diff: offset in days of the tests
@param startval: starting value of fund during back tests
@return fundsmatrix: Datamatrix of fund values returned from each test
@rtype datamatrix
"""
fundsmatrix = []
startdates = du.getNextNNYSEdays(start, num * diff, dt.timedelta(hours=16))
enddates = du.getNextNNYSEdays(end, num * diff, dt.timedelta(hours=16))
for i in range(0, num):
os.system( 'python ' + strat + ' ' + startdates[i].strftime("%m-%d-%Y")\
+ ' ' + enddates[i].strftime("%m-%d-%Y") + \
' temp_alloc.pkl')
return alloc_backtest('temp_alloc.pkl', startval)
return fundsmatrix
tot_invest = float(sys.argv[1])
dataobj = da.DataAccess('Yahoo')
'''
symbols = dataobj.get_symbols_from_list("sp5002012")
starter = dt.datetime(2011,1,1)
ender = dt.datetime(2011,12,31)
timeday=dt.timedelta(hours=16)
timestamps = du.getNYSEdays(starter,ender,timeday)
close = dataobj.get_data(timestamps, symbols, "close")
'''
simulation = dict()
for count in csvdata:
timestamp = du.getNextNNYSEdays( dt.datetime(count['year'],count['month'],count['day']),1,dt.timedelta(hours=16) )[0]
if(firstts == None):
firstts = timestamp
if (secondts != None) and (secondts != timestamp) :
temp = timestamp - dt.timedelta(days = 1)
if(firstts <= temp):
totaltime = du.getNYSEdays(firstts,temp, dt.timedelta(hours=16))
sym = [k for k,v in simulation.iteritems()]
data = dataobj.get_data(totaltime, sym, "close")
totaltime = data.index
data = data.values
# Values Extracted from QSTK and then Modified as per the orders on the matching days from timestamp
for ix in range(len(totaltime)):
val = tot_invest
for ix in range(len(sym)):
