Help on Optimization code and Looping Opt.

[FrankieC]

Hello everybody,

I'm trying to set up a code to run in order optimizations (changing two parameters on a single indicator.

I'm trying to set up the code to have the optimization running for different timeframes during the day (I'm using 1min data frame on Nasdaq Futures).

This is my BacktraderOPT .py file.

import backtrader.indicators as btind

import matplotlib
import os
import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])
import pytz
import numpy as np
from pandas import DataFrame

## SuperTrend Indicator Import ##
from SupertrendBacktrader import *


# Create a Strategy
class TestStrategy(bt.Strategy):
    params = (('period', 18), ('multiplier', 2.8))

# The first data in the list self.datas[0] is the default data for trading operations and to keep all strategy elements synchronized (it’s the system clock)
    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        dt1 = self.data0.datetime.time(0)
        print('%s,%s, %s' % (dt.isoformat(), dt1.isoformat(), txt))

    def __init__(self):
        init_time = time.time()
        self.startcash = self.broker.getvalue()
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close
        self.Superbands = SuperTrendBand(period=self.params.period, multiplier=self.params.multiplier)
        self.Supertrend = SuperTrend(period=self.params.period, multiplier=self.params.multiplier)
        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None


        # Indicators for the plotting show
        #bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
        #bt.indicators.WeightedMovingAverage(self.datas[0], period=25,subplot=True)

        #bt.indicators.ATR(self.datas[0], plot=False)
        self.buyp_sellm = bt.ind.CrossOver(self.dataclose,self.Supertrend)

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Size: %.2f, OpenPrice: %.2f, Closeprice: %.2f, Comm %.2f' %
                    (order.executed.size,
                     order.executed.price,
                     order.executed.value,
                     order.executed.comm))

                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
                self.opsize = order.executed.size
            else:  # Sell
                self.log(
                    'SELL EXECUTED, Size: %.2f, OpenPrice: %.2f, Closeprice: %.2f, Comm %.2f' %
                         (order.executed.size,
                          order.executed.price,
                          order.executed.value,
                          order.executed.comm))


        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        # Write down: no pending order
        self.order = None

    def notify_trade(self, trade):

        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):

        if self.data.datetime.time() < Session_begin:
            self.close()# don't operate until x
            return  #
        if self.data.datetime.time() > Session_end:
            self.close()# don't operate after y
            return  #
        # Simply log the closing price of the series from the reference
        #self.log('Close, %.2f' % self.dataclose[0])

        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return

        # Check if we are in the market
        #if not self.position:
                #CB0=self.dataclose[0] > self.Supertrend[0]
                #CB1=self.dataclose[-1] < self.Supertrend [-1]
                    # Not yet ... we MIGHT BUY if ...

        if self.buyp_sellm==1:

            # BUY, BUY, BUY!!! (with all possible default parameters)
            self.close()
            self.log('BUY CREATE, %.2f' % self.dataclose[0])

            # Keep track of the created order to avoid a 2nd order
            self.order = self.buy()


        elif self.buyp_sellm==-1:

            # SELL, SELL, SELL!!! (with all possible default parameters)
            self.close()
            self.log('SELL CREATE, %.2f' % self.dataclose[0])

                # Keep track of the created order to avoid a 2nd order
            self.order = self.sell()

    def stop(self):
        pnl = round(self.broker.getvalue() - self.startcash, 3)
        print('SuTR Period: {} SuTR Mult: {}Final PnL: {}'.format(
            self.params.period,self.params.multiplier, pnl))

if __name__ == '__main__':
    # Variable for our starting cash
    startcash = 100000

    # Create a cerebro entity
    cerebro = bt.Cerebro(optreturn=False)

    # Add a strategy
    cerebro.optstrategy(TestStrategy, period=range(10, 41, 1), multiplier=np.linspace(1.0,4.0,31))


    data = btfeeds.GenericCSVData(
        dataname='CME_MINI_NQ1!Date.csv',

        fromdate=fromdate,
        todate=todate,
        sessionstart=datetime.time(14, 00),
        sessionend=datetime.time(19, 00),

        dtformat=('%Y-%m-%d %H:%M:%S'),
        datetime=1,
        high=3,
        low=4,
        open=2,
        close=5,
        volume=8,
        openinterest=-1,

        timeframe=bt.TimeFrame.Minutes,
        compression=1
    )

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

    # Set our desired cash start
    cerebro.broker.setcash(startcash)
    # Set the commission
    cerebro.broker.setcommission(commission=0.00014, mult=20)
    # Run over everything
    opt_runs = cerebro.run()

    # Generate results list
    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
            multiplier= round(strategy.params.multiplier,2)
            final_results_list.append([period,multiplier, 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[2], reverse=False)

    # Print results

    print('Results: Ordered by Profit:')
    for result in by_PnL:
        print('SuTR Period: {}, SuTR Mult: {},Final PnL: {}'.format(result[0], result[1], result[2]))
    print("Opt Start on ",fromdate, "and ends on",todate)


#print(fromdate,todate, Session_begin,Session_end)
print("Total script time is --- %s minutes ---" % ((time.time() - start_time)/60))

My code recalls SuperTrend custom indicator from another file and gives as an output a pivot data frame as .csv with period as rows and multiplier as columns with PnL as values.

ResultsListdf = DataFrame(final_results_list)
print(ResultsListdf)
ResultsListdf=ResultsListdf.pivot(index=0,columns=1,values=2)

fromdate_Time=fromdate.strftime("%H:%M-")
fromdate_Date=fromdate.strftime("%d:%m-")
fromdate_Year=fromdate.strftime("%Y-")
fromdate_DateTime = fromdate.strftime("%d-%m-%Y<%H:%M")
todate_Time=todate.strftime("%H:%M<")
todate_Date=todate.strftime("%d-%m<")
todate_Year=todate.strftime("%Y")
todate_DateTime = todate.strftime("%d-%m-%Y>%H:%M")

outFileName=("SXTR-"+fromdate_Time+todate_Time+fromdate_Date+todate_Date+fromdate_Year+todate_Year+'.csv')
ResultsListdf.to_csv(r'/PycharmProjects/pythonProject3/'+outFileName,sep=',')

So far my code runs smoothly and each optimization takes roughly 28min for 18 days of 1 min data with a filter to open positions only between 14:00 and 19:00. The strategy open a long/short position when signal is given by closing previous (if any) and opening a new one to follow the trend.

What I'm trying to do is to set different session times and run in loop optimization to get different .csv files. This way I can see for different time of the day which set of parameters performs better. This by just clicking Run and leave the computer doing calculations (while the fans start to sound like a jet engine).

#%%

import matplotlib
import os
import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])
import pytz
import numpy as np
from pandas import DataFrame

#%%.  ERROR
#DataImportVar
fromdate = datetime.datetime(2021, 3, 3, 14, 00)
todate = datetime.datetime(2021, 3, 3, 19, 00)
#SessionTimeFilter
Session_begin = datetime.time(14, 0)
Session_end = datetime.time(19, 0)

exec(open('BacktraderOPT.py').read())

#Divide two parts with cell (Not Working)

#%%
    #DataImportVar
fromdate = datetime.datetime(2021, 3, 3, 15, 00)
todate = datetime.datetime(2021, 3, 3, 19, 00)
    #SessionTimeFilter
Session_begin = datetime.time(16, 0)
Session_end = datetime.time(19, 0)

exec(open('BacktraderOPT.py').read())

To get faster result I've set to optimize only one day.
Whenever I run the second cell I get the following error.
Tried to run this in Jupyter but it really takes forever.

AttributeError: Can't get attribute 'TestStrategy' on <module 'main' (built-in)>

Any suggestion on how to solve this and save time by running this only one time?

Have posted whole code for suggestions on how to improve it. I'm not really good at coding and I reckon that this code has plenty of room for improvement.

Thanks in advance

F.C