if int(optSwitch) == 0:
        opt = n
    elif int(optSwitch) == 1:
        #from scipy.optimize import brute
        #if iOpt == 1: #Optimize returns
        from scipy.optimize import brute
        # The following fix has been introduced because brute is not accounting for the value n_max when optimizing
        n_max_new = n_max + n_incr
        opt = brute(returns_in_Fxn, ((n_min, n_max_new, n_incr),), finish=None)


        #opt = brute(returns_in_Fxn, ((n_min, n_max, n_incr),), finish=None)
        #opt = brute(returns_in_Fxn, ((n_min, n_max, n_incr),), full_output=True, finish=None)
        # The following fix has been introduced because brute is not accounting for the value n_max when optimizing
        #n_max_new = n_max + n_incr
        #opt = brute(returns_in_Fxn, ((n_min, n_max, n_incr),), full_output=True, finish=None)
        import xlwt



        wb = xlwt.Workbook()
        ws = wb.add_sheet('A Opt Sheet')
        ws.write(0,0,'optSwitch')
        ws.write(0,1, optSwitch)
        ws.write(1,0,'n_min')
        ws.write(1,1, n_min)
        ws.write(2,0,'n_max')
        ws.write(2,1, n_max)
        ws.write(3,0,'n_incr')
        ws.write(3,1, n_incr)



        #ws.write(4,0,'opt')
        #row = 5
        #for col, data in enumerate(opt):
        #    ws.write(row, col, data)
        #ws.write(4,1, opt)
        #n_min = df_param.ix['n']['Min']
        #n_max = df_param.ix['n']['Max']
        #n_incr = df_param.ix['n']['increment']
        wb.save('/var/www/html/finpoc/public/xlsx/exampleOpt_'+fn_append+'.xls')
        #elif iOpt == 2: #Optimize PnL
        #    opt = brute





    sys.stderr.write("======================= DEBUG ===================")
    sys.stdout = sys.stderr
    pprint(MA_array)


    return_vals = {"in": returns_in_Fxn_1(opt, True), "all": returns_all_Fxn((opt), True), "out": returns_out_Fxn((opt), True), "back_test": 1, "optimization_n": opt}


    return return_vals