Backtrader Community

j45p41

@j45p41 Hi guys, with the additional info provided can you help me understand why my code is not processing trades? - thanks.

j45p41

My apologies - i thought there was a problem due to discontinues dates.

here is my code in which I have disabled my analysers as they end up giving me a keyerror. When disabled I get a graph that does not execute trades.

import datetime
import time
import os.path
import sys
import backtrader as bt
import backtrader.feeds as btfeed
import ccxt
import csv
import io
import pandas as pd
from collections import OrderedDict
from multiprocessing import Pool, cpu_count
import math
import os



# DECLARE MODE FOR PROGRAM - OPTOMISATION OR STRATEGY
opt_mode = False
DEBUG = False

# LOG OUTPUT TO FILE
class Logger(object):
    def __init__(self, filename="Default.log"):
        self.terminal = sys.stdout
        self.log = open(filename, "a")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)

    def flush(self):
        pass

# CSV INPUT FILE FORMAT CONFIGURATION
class dataFeed(btfeed.GenericCSVData):
    params = (
        ('dtformat', '%Y%m%d'),
        ('datetime', 0),
        ('open', 1),
        ('high', 2),
        ('low', 3),
        ('close', 4),
        ('volume', 5),
        ('openinterest', 6)
    )
class OrderObserver(bt.observer.Observer):
    lines = ('created', 'expired',)

    plotinfo = dict(plot=True, subplot=True, plotlinelabels=True)

    plotlines = dict(
        created=dict(marker='*', markersize=8.0, color='lime', fillstyle='full'),
        expired=dict(marker='s', markersize=8.0, color='red', fillstyle='full')
    )

    def next(self):
        for order in self._owner._orderspending:
            if order.data is not self.data:
                continue

            if not order.isbuy():
                continue

            # Only interested in "buy" orders, because the sell orders
            # in the strategy are Market orders and will be immediately
            # executed

            if order.status in [bt.Order.Accepted, bt.Order.Submitted]:
                self.lines.created[0] = order.created.price

            elif order.status in [bt.Order.Expired]:
                self.lines.expired[0] = order.created.price

# MAIN STRATEGY DEFINITION - DEFINE VALUES HERE FOR NON-OPTOMISATION MODE
class firstStrategy(bt.Strategy):
    params = (
        ("period", 28),
        ("rsi_low", 30),
        ("rsi_high", 70),
    )

    #TRADE LOGGING FUNCTION
    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        if not opt_mode:
            dt = dt or self.datas[0].datetime.datetime(0)
            print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        self.startcash = self.broker.getvalue()
        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=self.params.period, safediv=True)


    #TRADE LOGGING FUNCTION
    def notify_trade(self, trade):
        if not trade.isclosed and not opt_mode:
            return

        self.log('TRADE INFO, PRICE  %.2f, GROSS %.2f, NET %.2f' %
                 (trade.price, trade.pnl, trade.pnlcomm))

    def next(self):
        if not self.position:
            if self.rsi < self.params.rsi_low:
                self.buy(size=10)
        else:
            if self.rsi > self.params.rsi_high:
                self.sell(size=10)
        if opt_mode and DEBUG:
            print('period: {}, rsi low: {}, rsi high {}'.format(self.params.period,self.params.rsi_low,self.params.rsi_high))

# if opt_mode == False:
#     def printTradeAnalysis(analyzer):
#         '''
#         Function to print the Technical Analysis results in a nice format.
#         '''
#         #Get the results we are interested in
#         total_open = analyzer.total.open
#         total_closed = analyzer.total.closed
#         total_won = analyzer.won.total
#         total_lost = analyzer.lost.total
#         win_streak = analyzer.streak.won.longest
#         lose_streak = analyzer.streak.lost.longest
#         pnl_net = round(analyzer.pnl.net.total,2)
#         strike_rate = round((total_won / total_closed) * 100,2)
#         #Designate the rows
#         h1 = ['Total Open', 'Total Closed', 'Total Won', 'Total Lost']
#         h2 = ['Strike Rate','Win Streak', 'Losing Streak', 'PnL Net']
#         r1 = [total_open, total_closed,total_won,total_lost]
#         r2 = [strike_rate, win_streak, lose_streak, pnl_net]
#         #Check which set of headers is the longest.
#         if len(h1) > len(h2):
#             header_length = len(h1)
#         else:
#             header_length = len(h2)
#         #Print the rows
#         print_list = [h1,r1,h2,r2]
#         row_format ="{:<15}" * (header_length + 1)
#         print("Trade Analysis Results:")
#         for row in print_list:
#             print(row_format.format('',*row))
#
#     def printSQN(analyzer):
#         sqn = round(analyzer.sqn,2)
#         print('SQN: {}'.format(sqn))

# INPUT CONDITIONS TO FEED INTO CEREBRO IS ADDED HERE
if __name__ == '__main__':
    sys.stdout = Logger("strat_evaluator.log")


    #periods = pd.DataFrame(columns=['FROM','TO'],index=[1,2,3,4,5,6,7,8,9,10,11,12])
    periods = pd.DataFrame(columns=['FROM','TO'],index=[1])
    periods.loc[1] = ('1990-1-01','2017-12-31')
    #periods.loc[2] = ('2017-02-01','2017-03-01')
    #periods.loc[3] = ('2017-03-01','2017-04-01')
    #periods.loc[4] = ('2017-04-01','2017-05-01')
    #periods.loc[5] = ('2017-05-01','2017-06-01')
    #periods.loc[6] = ('2017-06-01','2017-07-01')
    #periods.loc[7] = ('2017-07-01','2017-08-01')
    #periods.loc[8] = ('2017-08-01','2017-09-01')
    #periods.loc[9] = ('2017-09-01','2017-10-01')
    #periods.loc[10] = ('2017-10-01','2017-11-01')
    #periods.loc[11] = ('2017-11-01','2017-12-01')
    #periods.loc[12] = ('2017-12-01','2017-12-31')

    for index, row in periods.iterrows():


        # Variable for our starting cash
        startcash = 10000
        # Create an instance of cerebro
        cerebro = bt.Cerebro(optreturn=False)

        # Timing the whole operation
        time_at_start = time.time()

        if opt_mode:
            # ADD STRATEGY OPTIMISATION
            #cerebro.optstrategy(firstStrategy, period=range(11, 20), rsi_low=range(10, 50), rsi_high=range(51, 85))
            cerebro.optstrategy(firstStrategy, period=range(13, 14), rsi_low=range(20, 21), rsi_high=range(40, 41))
            cerebro.addanalyzer(bt.analyzers.SharpeRatio)
        else:
            #ADD STRATEGY
            cerebro.addstrategy(firstStrategy)

        #format dates for datafeed object
        fy,fm,fd = periods['FROM'][index].split('-')
        ty,tm,td = periods['TO'][index].split('-')


#READ DATA FROM CSV FILE

        filename = 'tickerData/F_BO.txt'
        modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
        datapath = os.path.join(modpath,str(filename))
        data = dataFeed(dataname=datapath, timeframe=bt.TimeFrame.Days,
                        fromdate=datetime.datetime(int(fy),int(fm),int(fd)),
                        todate=datetime.datetime(int(ty),int(tm),int(td)),)

        # Add the data to Cerebro
        cerebro.adddata(data)

        # Set our desired cash start
        cerebro.broker.setcash(startcash)

        if not opt_mode:
            # Add the analyzers we are interested in
            cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
            cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")


        #WRITER TEST
        #cerebro.addwriter(bt.WriterFile, csv=True, rounding=2)

        # RUN STRATEGY THROUGH CEREBRO USING INPUT DATA
        # Timing the operation
        time_at_end = time.time()
        time_elapsed = round(time_at_end - time_at_start,2)
        print('Time elapsed: {} seconds'.format(time_elapsed))
        print ('Running Cerebro')
        if not opt_mode: opt_runs = cerebro.run(tradehistory=False)
        else: opt_runs = cerebro.run(tradehistory=False, runonce = False, Exactbars=True)

        firstStrat = opt_runs[0]

        if opt_mode:
            # CREATE A LIST VARIABLE THAT CONTAINS RESULTS
            final_results_list = []
            for run in opt_runs:
                for strategy in run:
                    value = round(strategy.broker.get_value(), 2)
                    PnL = round(value - startcash, 2)
                    period = strategy.params.period
                    rsi_low = strategy.params.rsi_low
                    rsi_high = strategy.params.rsi_high
                    final_results_list.append([period, rsi_low, rsi_high, PnL])

            # Sort Results List
            by_period = sorted(final_results_list, key=lambda x: x[0])
            by_PnL = sorted(final_results_list, key=lambda x: x[3], reverse=True)

            # PRINT RESULTS IN OPTIMISATION AND FILTER TOP 3
            result_number = 0
            print('Results: Ordered by Profit:')
            for result in by_PnL:
                if result_number < 3:
                    print('Asset: {} Start: {}, End: {}, Period: {}, rsi_low: {}, rsi_high: {}, PnL: {}'.format(filename, periods['FROM'][index], periods['TO'][index], result[0], result[1], result[2], result[3]))
                    result_number = result_number + 1

        # Timing the operation
        time_at_end = time.time()
        time_elapsed = round(time_at_end - time_at_start,2)


        print('Time elapsed: {} seconds'.format(time_elapsed))
        if opt_mode == False:
            # print the analyzers
            # printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())
            # printSQN(firstStrat.analyzers.sqn.get_analysis())

            #Get final portfolio Value
            portvalue = cerebro.broker.getvalue()

            #Print out the final result
            print('Final Portfolio Value: ${}'.format(portvalue))
            cerebro.plot(style='candlestick')

        #cerebro.plot(style='candlestick')

for completeness here is the data that I am using

DATE, OPEN, HIGH, LOW, CLOSE, VOL, OI, P, R, RINFO
19900102,11520.0000,11520.0000,11316.0000,11322.0000,19779,38563,0,0,0.0000
19900103,11322.0000,11460.0000,11298.0000,11442.0000,16270,39377,0,0,0.0000
19900104,11472.0000,11520.0000,11340.0000,11346.0000,12743,39220,0,0,0.0000
19900105,11298.0000,11466.0000,11286.0000,11418.0000,11808,39932,0,0,0.0000
19900108,11448.0000,11808.0000,11442.0000,11784.0000,20844,40091,0,0,0.0000
19900109,11730.0000,11820.0000,11682.0000,11778.0000,19457,39502,0,0,0.0000
19900110,11760.0000,11778.0000,11598.0000,11724.0000,16266,39703,0,0,0.0000
19900111,11712.0000,11790.0000,11706.0000,11754.0000,13699,39704,0,0,0.0000
19900112,11826.0000,11844.0000,11634.0000,11640.0000,12677,38444,0,0,0.0000
19900115,11598.0000,11688.0000,11580.0000,11652.0000,6067,38307,0,0,0.0000
19900116,11610.0000,11640.0000,11490.0000,11496.0000,13691,37511,0,0,0.0000
19900117,11484.0000,11616.0000,11484.0000,11502.0000,12378,38223,0,0,0.0000
19900118,11508.0000,11580.0000,11496.0000,11538.0000,8570,37584,0,0,0.0000
19900119,11562.0000,11568.0000,11400.0000,11406.0000,14732,36865,0,0,0.0000
19900122,11400.0000,11448.0000,11316.0000,11358.0000,15163,36837,0,0,0.0000
19900123,11382.0000,11508.0000,11376.0000,11466.0000,13141,36918,0,0,0.0000
19900124,11472.0000,11514.0000,11436.0000,11448.0000,10550,36551,0,0,0.0000
19900125,11472.0000,11478.0000,11340.0000,11340.0000,14579,35655,0,0,0.0000
19900126,11340.0000,11430.0000,11328.0000,11418.0000,7547,34389,0,0,0.0000
19900129,11430.0000,11544.0000,11370.0000,11538.0000,11285,34198,0,0,0.0000
19900130,11544.0000,11574.0000,11442.0000,11460.0000,12167,33318,0,0,0.0000
19900131,11460.0000,11544.0000,11430.0000,11502.0000,10708,32328,0,0,0.0000
19900201,11520.0000,11700.0000,11502.0000,11532.0000,17957,32443,0,0,0.0000
19900202,11544.0000,11580.0000,11508.0000,11568.0000,14151,31419,0,0,0.0000
19900205,11568.0000,11634.0000,11460.0000,11478.0000,14241,30695,0,0,0.0000

j45p41

Hi,

I am trying to read in futures data in the following fomat:

DATE, OPEN, HIGH, LOW, CLOSE, VOL, OI, P, R, RINFO
19900531,104687.5000,104850.0000,104387.5000,104525.0000,18592,32108,0,0,0.0000
19900601,104750.0000,105512.5000,104612.5000,104762.5000,26697,27205,0,0,0.0000
19900604,104250.0000,104562.5000,103937.5000,104437.5000,15315,25228,0,0,0.0000
19900605,104575.0000,105250.0000,104425.0000,105137.5000,18054,25452,0,0,0.0000
19900606,105187.5000,105437.5000,105112.5000,105300.0000,10104,24872,0,0,0.0000
19900607,105250.0000,105450.0000,105137.5000,105425.0000,9931,24237,199009,199009,0.0000
19900608,103562.5000,103562.5000,103125.0000,103437.5000,12791,9134,0,0,-1600.0000

I intend to trim the data so that it does not include the last three columns.

However when I run using this data, due to it not being continuous days cerebro does not process trades correctly - the graphs is plotted but the trades do not execute.

I suspect there is a setting somewhere which will allow this data to be handled.

I have read: https://www.backtrader.com/blog/posts/2016-08-31-rolling-over-futures/rolling-futures-over.html

but can't figure out how to handle this data - any suggestions pls?

j45p41

@backtrader Thank you for your answer and I am sure a library will be coming together soon. I will certainly contribute as I get further into my backtrader journey :)

j45p41

Hi,

I would like to build an algorithm that tests the performance of a library of strategies. Can anybody point me to a resource where I can find strategies that I could easily port to BT? is there a BT library somewhere? Could I just use a source like quantopian?

j45p41

@backtrader thank you for your explanation and for taking the time to reply, I could have been more clear with my post. I didn’t realise that there were many ways of implementing RSI :). In that case is there a way of specifying how backtrder calculates the moving average?

I do think this is a great project and have invested a lot of time in it and benefitted a lot from the support on this forum. I am just trying to port my work on NT and got stuck here.

j45p41

Hi All,

I started to compare back test results in backtrader with ninjatrader and found that the RSI was different across the two platforms. I then used excel to calculate RSI and found that the ninjatrader results were much closer. Any thoughts?

Here is how I have implemented RSI:

        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=self.params.period)

    params = (
        ("period", 14),
        ("rsi_low", 30),
        ("rsi_high", 70),
    )



Relative Strength Index																				
																				
Date	 Close	Change	Gain	Loss 	Avg Gain	Avg Loss	RS	14-Day RSI		Backtrader										
20170110 0000	10.47441656									nan										
20170110 0015	10.41867814	-0.06		0.06						 nan										
20170110 0030	10.45	0.03	0.03							 nan										
20170110 0045	10.475	0.03	0.03							 nan										
20170110 0100	10.42873472	-0.05		0.05						 nan										
20170110 0115	10.48	0.05	0.05							 nan										
20170110 0130	10.41867859	-0.06		0.06						 nan										
20170110 0145	10.44251791	0.02	0.02							 nan										
20170110 0200	10.43046593	-0.01		0.01						 nan										
20170110 0215	10.42365979	-0.01		0.01						 nan										
20170110 0230	10.4397672	0.02	0.02							 nan										
20170110 0245	10.45	0.01	0.01							 nan										
20170110 0300	10.39934476	-0.05		0.05						 nan										
20170110 0315	10.48598604	0.09	0.09							 nan										
20170110 0330	10.4225	-0.06		0.06	0.02	0.02	0.82	45.20		45.18518519										
20170110 0345	10.45	0.03	0.03		0.02	0.02	0.92	48.04		53.0214425										
20170110 0400	10.451	0.00	0.00		0.02	0.02	0.93	48.15		50.10351967										
20170110 0415	10.45	0.00		0.00	0.02	0.02	0.92	48.05		47.27668845										
20170110 0430	10.45				0.01	0.02	0.92	48.05		52.54237288										
20170110 0445	10.49999991	0.05	0.05		0.02	0.01	1.17	53.88		52.42718447										
20170110 0500	10.45000018	-0.05		0.05	0.02	0.02	0.93	48.07		53.86533666										
20170110 0515	10.45000005	0.00		0.00	0.01	0.02	0.93	48.07		50.92838196										
20170110 0530	10.45000001	0.00		0.00	0.01	0.01	0.93	48.07		52.74725275										
20170110 0545	10.45000001				0.01	0.01	0.93	48.07		53.63128492										
20170110 0600	10.47363907	0.02	0.02		0.01	0.01	1.06	51.40		54.64480874										
20170110 0615	10.45	-0.02		0.02	0.01	0.01	0.93	48.08		50										
20170110 0630	10.38078549	-0.07		0.07	0.01	0.02	0.66	39.94		47.73869347										
20170110 0645	10.37896211	0.00		0.00	0.01	0.02	0.66	39.74		32.90734824										
20170110 0700	10.39299999	0.01	0.01		0.01	0.02	0.73	42.04		44.48669202										
20170110 0715	10.33099753	-0.06		0.06	0.01	0.02	0.55	35.59		29.96632997										
20170110 0730	10.39999996	0.07	0.07		0.01	0.02	0.84	45.60		43.01369863										
20170110 0745	10.32100859	-0.08		0.08	0.01	0.02	0.62	38.27		35.44018059										
20170110 0800	10.25	-0.07		0.07	0.01	0.03	0.50	33.11		30.54474708										
20170110 0815	10.31106544	0.06	0.06		0.02	0.02	0.68	40.53		32										
20170110 0830	10.29851782	-0.01		0.01	0.01	0.02	0.65	39.56		34.49691992										
20170110 0845	10.38999754	0.09	0.09		0.02	0.02	0.97	49.13		44.80968858										
20170110 0900	10.26039929	-0.13		0.13	0.02	0.03	0.65	39.57		36.5819209										
20170110 0915	10.28092716	0.02	0.02		0.02	0.03	0.71	41.51		38.40877915										
20170110 0930	10.41500297	0.13	0.13		0.03	0.02	1.10	52.29		46.48390942										
20170110 0945	10.41446338	0.00		0.00	0.03	0.02	1.09	52.25		47.79411765										
20170110 1000	10.3	-0.11		0.11	0.02	0.03	0.79	44.19		45.29616725										
20170110 1015	10.29151733	-0.01		0.01	0.02	0.03	0.77	43.65		44.98269896										
20170110 1030	10.26753076	-0.02		0.02	0.02	0.03	0.73	42.10		42.87343216										
20170110 1045	10.26753076				0.02	0.03	0.73	42.10		46.13496933										
20170110 1100	10.36798778	0.10	0.10		0.02	0.02	1.03	50.65		48.10874704										
20170110 1115	10.39981794	0.03	0.03		0.03	0.02	1.13	53.02		54.9436796										
20170110 1130	10.37865704	-0.02		0.02	0.02	0.02	1.05	51.26		58.61148198										
20170110 1145	10.37866488	0.00	0.00		0.02	0.02	1.05	51.26		54.94186047										
20170110 1200	10.48375725	0.11	0.11		0.03	0.02	1.44	59.09		61.84379001										
20170110 1215	10.60447758	0.12	0.12		0.03	0.02	1.93	65.87		63.20987654										
20170110 1230	10.62000044	0.02	0.02		0.03	0.02	2.00	66.64		75.86206897										
20170110 1245	10.62522697	0.01	0.01		0.03	0.02	2.02	66.91		75.29411765										
20170110 1300	10.54	-0.09		0.09	0.03	0.02	1.42	58.60		59.90491284										
20170110 1315	10.57	0.03	0.03		0.03	0.02	1.53	60.46		61.81818182										
20170110 1330	10.4873114	-0.08		0.08	0.03	0.02	1.14	53.34		64.86486486										
20170110 1345	10.48082444	-0.01		0.01	0.02	0.02	1.12	52.82		65.07177033										
20170110 1400	10.56144109	0.08	0.08		0.03	0.02	1.40	58.31		71.44948755										
20170110 1415	10.54820276	-0.01		0.01	0.03	0.02	1.33	57.14		70.11494253										
20170110 1430	10.55881489	0.01	0.01		0.03	0.02	1.37	57.87		65.73311367										
20170110 1445	10.48833087	-0.07		0.07	0.02	0.02	1.06	51.56		56.81114551										
20170110 1500	10.49210894	0.00	0.00		0.02	0.02	1.08	51.86		58.98251192										
20170110 1515	10.5696268	0.08	0.08		0.03	0.02	1.36	57.71		63.50777935										
20170110 1530	10.57971704	0.01	0.01		0.03	0.02	1.40	58.42		57.84313725										
20170110 1545	10.6	0.02	0.02		0.02	0.02	1.49	59.87		49.609375										
20170110 1600	10.5367546	-0.06		0.06	0.02	0.02	1.15	53.58		42.57602862										
20170110 1615	10.49412539	-0.04		0.04	0.02	0.02	0.99	49.78		39.02847571										
20170110 1630	10.49412539				0.02	0.02	0.99	49.78		45.5078125										
20170110 1645	10.54204356	0.05	0.05		0.02	0.02	1.18	54.03		47.35849057										
20170110 1700	10.59999996	0.06	0.06		0.02	0.02	1.41	58.59		61.18811881										
20170110 1715	10.54297	-0.06		0.06	0.02	0.02	1.13	53.01		55.57553957										
20170110 1730	10.53042645	-0.01		0.01	0.02	0.02	1.08	51.85		46.83026585										
20170110 1745	10.53177183	0.00	0.00		0.02	0.02	1.08	51.97		48.32635983										
20170110 1800	10.53177183				0.02	0.02	1.08	51.97		47.10920771										
20170110 1815	10.53177183				0.02	0.02	1.08	51.97		55.55555556										
20170110 1830	10.50000001	-0.03		0.03	0.02	0.02	0.94	48.35		50.94339623										
20170110 1845	10.501	0.00	0.00		0.01	0.02	0.94	48.47		40.05763689										
20170110 1900	10.4841257	-0.02		0.02	0.01	0.02	0.87	46.48		36.44067797										
20170110 1915	10.69000001	0.21	0.21		0.03	0.01	1.88	65.24		58.33333333										
20170110 1930	10.69207163	0.00	0.00		0.03	0.01	1.89	65.37		66.17954071										
20170110 1945	10.7	0.01	0.01		0.02	0.01	1.93	65.90		73.1981982										
20170110 2000	10.68555171	-0.01		0.01	0.02	0.01	1.78	63.97		70.96069869										
20170110 2015	10.61000001	-0.08		0.08	0.02	0.02	1.22	54.92		56.99588477										
20170110 2030	10.62635517	0.02	0.02		0.02	0.02	1.29	56.36		52.92792793										
20170110 2045	10.64551937	0.02	0.02		0.02	0.01	1.38	58.05		62.65356265										
20170110 2100	10.59000002	-0.06		0.06	0.02	0.02	1.07	51.80		56.66666667										
20170110 2115	10.59007388	0.00	0.00		0.02	0.02	1.07	51.80		56.47321429										
20170110 2130	10.68261187	0.09	0.09		0.02	0.02	1.51	60.11		63.95563771										
20170110 2145	10.57531175	-0.11		0.11	0.02	0.02	0.98	49.46		53.31278891										
20170110 2200	10.65	0.07	0.07		0.03	0.02	1.24	55.39		60.83815029										
20170110 2215	10.64999997	0.00		0.00	0.02	0.02	1.24	55.39		60.78147612										
20170110 2230	10.59781898	-0.05		0.05	0.02	0.02	1.02	50.58		57.85123967										
20170110 2245	10.59501682	0.00		0.00	0.02	0.02	1.01	50.33		40.91778203										
20170110 2300	10.64996841	0.05	0.05		0.02	0.02	1.23	55.07		46.35416667										
20170110 2315	10.58174336	-0.07		0.07	0.02	0.02	0.95	48.84		40.72327044										
20170110 2330	10.63890145	0.06	0.06		0.02	0.02	1.15	53.58		46.53902798										
20170110 2345	10.55001523	-0.09		0.09	0.02	0.03	0.87	46.38		45.66473988										
20170111 0000	10.55001523				0.02	0.02	0.87	46.38		44.37869822										
20170111 0015	10.55000001	0.00		0.00	0.02	0.02	0.87	46.38		42.68292683										
20170111 0030	10.6	0.05	0.05		0.02	0.02	1.04	51.00		50.76923077										
20170111 0045	10.64384486	0.04	0.04		0.02	0.02	1.21	54.69		53.89048991										
20170111 0100	10.59467128	-0.05		0.05	0.02	0.02	1.01	50.13		43.23076923										
20170111 0115	10.6	0.01	0.01		0.02	0.02	1.02	50.61		52.28519196										
20170111 0130	10.65	0.05	0.05		0.02	0.02	1.22	54.99		50										
20170111 0145	10.60000005	-0.05		0.05	0.02	0.02	1.01	50.20		45.62937063										
20170111 0200	10.60000005				0.02	0.02	1.01	50.20		50.19230769										
20170111 0215	10.60000005				0.02	0.02	1.01	50.20		50.48355899										
20170111 0230	10.57900005	-0.02		0.02	0.02	0.02	0.92	48.00		42.65010352										
20170111 0245	10.57900005				0.02	0.02	0.92	48.00		49.63855422										
20170111 0300	10.63829879	0.06	0.06		0.02	0.02	1.20	54.52		49.88009592										
20170111 0315	10.58000001	-0.06		0.06	0.02	0.02	0.93	48.13		53.88601036										
20170111 0330	10.58000001				0.02	0.02	0.93	48.13		53.88601036										
20170111 0345	10.62	0.04	0.04		0.02	0.02	1.11	52.56		58.21596244										
20170111 0400	10.62				0.02	0.01	1.11	52.56		52.65957447										
20170111 0415	10.59	-0.03		0.03	0.02	0.02	0.96	48.93		42.54143646										
20170111 0430	10.58000026	-0.01		0.01	0.01	0.02	0.91	47.74		47.67801858										
20170111 0445	10.63999999	0.06	0.06		0.02	0.01	1.21	54.81		55.29100529										
20170111 0500	10.68247875	0.04	0.04		0.02	0.01	1.44	59.03		54.32432432										
20170111 0515	10.66961308	-0.01		0.01	0.02	0.01	1.34	57.28		60.54216867										
20170111 0530	10.65	-0.02		0.02	0.02	0.01	1.20	54.63		57.10227273										
20170111 0545	10.64	-0.01		0.01	0.02	0.01	1.14	53.28		55.52486188										
20170111 0600	10.66999998	0.03	0.03		0.02	0.01	1.31	56.74		62.26415094										
20170111 0615	10.66999998				0.02	0.01	1.31	56.74		62.26415094										
20170111 0630	10.58623347	-0.08		0.08	0.01	0.02	0.84	45.75		43.43434343										
20170111 0645	10.58579206	0.00		0.00	0.01	0.02	0.84	45.70		50.88757396										
20170111 0700	10.64999864	0.06	0.06		0.02	0.01	1.16	53.67		58.70646766										
20170111 0715	10.63	-0.02		0.02	0.02	0.01	1.05	51.15		51.30890052										
20170111 0730	10.58579207	-0.04		0.04	0.01	0.02	0.85	46.01		46.00938967										
20170111 0745	10.58579209	0.00	0.00		0.01	0.02	0.85	46.01		49.49494949										
20170111 0800	10.59082391	0.01	0.01		0.01	0.01	0.88	46.72		51.40664962										
20170111 0815	10.64218629	0.05	0.05		0.02	0.01	1.15	53.42		50.2617801										
20170111 0830	10.5910003	-0.05		0.05	0.01	0.02	0.89	47.07		38.36317136										
20170111 0845	10.59100059	0.00	0.00		0.01	0.02	0.89	47.07		39.57783641										
20170111 0900	10.50065316	-0.09		0.09	0.01	0.02	0.61	37.85		33.40757238										
20170111 0915	10.26335002	-0.24		0.24	0.01	0.04	0.32	24.36		22.15657312										
20170111 0930	10.19	-0.07		0.07	0.01	0.04	0.28	21.78		16.66666667										
20170111 0945	10.18307636	-0.01		0.01	0.01	0.04	0.27	21.55		16.50618982										
20170111 1000	10.09613043	-0.09		0.09	0.01	0.04	0.23	18.83		16.43835616										
20170111 1015	10.16097308	0.06	0.06		0.01	0.04	0.36	26.29		23.27044025										
20170111 1030	10.09000042	-0.07		0.07	0.01	0.04	0.31	23.72		15.0872818										
20170111 1045	10.22018135	0.13	0.13		0.02	0.04	0.56	36.07		27.52192982										
20170111 1100	10.25708866	0.04	0.04		0.02	0.03	0.64	39.08		31.82320442										
20170111 1115	9.9	-0.36		0.36	0.02	0.06	0.36	26.22		22.82091918										
20170111 1130	10.10184103	0.20	0.20		0.03	0.05	0.63	38.53		33.24194654										
20170111 1145	10.0860799	-0.02		0.02	0.03	0.05	0.61	38.00		30.47752809

j45p41

@backtrader Hello again. So now I have been able to resolve this issue I running my optimisation in a for loop so that it repeats itself using a for loop:

if __name__ == '__main__':

    sys.stdout = Logger("firststrategy.log")


    periods = pd.DataFrame(columns=['FROM','TO'],index=[1,2,3,4,5,6,7,8,9,10,11,12])
    periods.loc[1] = ('2017-01-01','2017-02-01')
    periods.loc[2] = ('2017-02-01','2017-03-01')
    periods.loc[3] = ('2017-03-01','2017-04-01')
    periods.loc[4] = ('2017-04-01','2017-05-01')
    periods.loc[5] = ('2017-05-01','2017-06-01')
    periods.loc[6] = ('2017-06-01','2017-07-01')
    periods.loc[7] = ('2017-07-01','2017-08-01')
    periods.loc[8] = ('2017-08-01','2017-09-01')
    periods.loc[9] = ('2017-09-01','2017-10-01')
    periods.loc[10] = ('2017-10-01','2017-11-01')
    periods.loc[11] = ('2017-11-01','2017-12-01')
    periods.loc[12] = ('2017-12-01','2017-12-31')

    for index, row in periods.iterrows():


        # Variable for our starting cash
        startcash = 10000
        # Create an instance of cerebro
        cerebro = bt.Cerebro(optreturn=False)

        # ADD STRATEGY OPTIMISATION
        cerebro.optstrategy(firstStrategy, period=range(11, 20), rsi_low=range(10, 50), rsi_high=range(51, 85))

It runs great for the first date range but then the system runs out of memory on the second. The issue is that it is not flushing out the memory from the previous run. Is there a way to get cerbero to reset itself for second (and subsequent) iterations?

j45p41

okay got it!

used:

    self.rsi = bt.indicators.RSI_SMA(self.data.close, period=self.params.period, safediv=True)

sourced from:

https://community.backtrader.com/topic/98/float-division-by-zero

amazing what you can find when you search around :)

j45p41

I guess I could use:

try:
XXXX
except ZeroDivisionError:
print '0' #or whatever

just not sure how to implement. Ideally I would like the optomisation to continue with next usable values.

j45p41

@j45p41

Posts made by j45p41