@backtrader Hi Admin,
Appreciate the prompt response. What do you mean by low variability of data?
Thanks!
For code/output blocks: Use ``` (aka backtick or grave accent) in a single line before and after the block. See: http://commonmark.org/help/
J
Posts made by jabbarabdullah
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RE: ZeroDivisionError not on RSIposted in General Discussion
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RE: ZeroDivisionError not on RSIposted in General Discussion
Okay, I have narrowed down the problem to:
self.inds[d]['ADX'] = bt.indicators.AverageDirectionalMovementIndex(d, period=9, plot=False) self.inds[d]['PDI'] = bt.indicators.PlusDirectionalIndicator(d, period=9, plot=False) self.inds[d]['MDI'] = bt.indicators.MinusDirectionalIndicator(d, period=9, plot=False)I believe due to the internal calculation of the indicators, zero division error is triggered.
I read as many postings as possible in this forum but I can't find a solution to this problem.
safedivandsafezeroparameters only available to RSI and stochastic hence, I cant solve this ADX zero division error.Kindly appreciate your guidance to overcome this issue especially on live trading.
Thanks!
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ZeroDivisionError not on RSIposted in General Discussion
Hi all,
I have codes below that works fine in backtesting but cannot execute in live trade using TWS. it returned the zero error as follows:
File "C:\Users\User\Anaconda3\lib\site-packages\backtrader\linebuffer.py", line 744, in next self[0] = self.operation(self.a[0], self.b[0]) ZeroDivisionError: float division by zeroHowever, i have set the
safediv=Trueon the RSI in it still doesn't work. This strategy is the combination of RSI, ADX and Bollinger Band.appreciate if you can take a look at my code and let me know what I missed.
from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt import datetime as dt import pytz datalist = ['XXXX', 'YYYY'] #Update data names csh=100000 #Change this accordingly!!! port_start_cash=100000 #Change this accordingly!!! SL=0.025 #stop loss pct TP=0.01 #take profit pct prop=1/len(datalist) #proportion of portfolio batch = 5 #rounding entrytime = dt.time(9,30) # start trade time exittime = dt.time(15,55) # ending trade time stakesize=10 lwbnd=50 #RSI Lower bound upbnd=55 #RSI Upper bound commis=1 #commission TPpct=TP*100 #TP in pctge SLpct=SL*100 #SL in pctge class MainSt(bt.Strategy): #Strategy set up data_live = False #start with data not live def notify_data(self, data, status, *args, **kwargs): #notify when data turn to live print('*' * 5, '%s DATA NOTIF: %s' % (data._name,data._getstatusname(status)), *args) if status == data.LIVE: self.data_live = True def log(self, txt, dt=None, vlm=None): #self log of data dt = dt or self.datas[0].datetime.datetime(0) vlm = vlm or self.data.volume[0] print('%s) %s, %s' % (len(self), dt.isoformat(), txt)) def __init__(self): #pre defined indicators self.order = {} self.buyprice = {} self.buycomm = {} self.bar_executed = {} self.inds = dict() self.o = dict() self.lendata = dict() for i, d in enumerate(self.datas): #iterate the data name self.order[d] = None self.buyprice[d] = None self.buycomm[d] = None self.bar_executed[d] = None self.inds[d] = dict() self.inds[d]['sma'] = bt.indicators.SimpleMovingAverage(d.close, period=30) #sma self.inds[d]['rsi'] = bt.indicators.RelativeStrengthIndex(d.close, period=9, safediv=True, upperband=upbnd,lowerband=lwbnd, plotname= 'rsi:'+d._name) #rsi self.inds[d]['ADX'] = bt.indicators.AverageDirectionalMovementIndex(d, period=9, plot=False) self.inds[d]['PDI'] = bt.indicators.PlusDirectionalIndicator(d, period=9, plot=False) self.inds[d]['MDI'] = bt.indicators.MinusDirectionalIndicator(d, period=9, plot=False) self.inds[d]['SD'] = bt.indicators.StandardDeviation(d, period=30, safepow=True) self.inds[d]['TopBB'] = self.inds[d]['sma']+2.5*self.inds[d]['SD'] self.inds[d]['BotBB'] = self.inds[d]['sma']-2.5*self.inds[d]['SD'] def notify_order(self, order): #notify when order happen if order.status in [order.Submitted, order.Accepted]: return if order.status in [order.Completed]: if order.isbuy(): #when buy order created in next self.log('%s: BUY EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm self.buy_executed_price = order.executed.price self.order = None self.bar_executed = len(self) else: #when sell order created in next # Sell self.log('%s: SELL EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.sell_executed_price = order.executed.price self.last_size = order.executed.size self.order = None self.bar_executed = len(self) self.bar_executed = len(self) self.last_executed_price = order.executed.price elif order.status in [order.Canceled, order.Margin, order.Rejected]: return ''' self.log('%s Order Canceled/Margin/Rejected' % (order.data._name)) ''' # Write down: no pending order[order.data._name] self.order = None def notify_trade(self, trade): #notify trade info if not trade.isclosed: return self.log('%s: OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.data._name, trade.pnl, trade.pnlcomm)) def next_open(self): #define the buy criterias here for i, d in enumerate(self.datas): pv = self.broker.getvalue() #get broker value cashd = self.broker.getcash() #get broker cash port_gaind = pv - port_start_cash #calculate total portfolio gain target = csh * prop # Ideal total value of the position price = d.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / batch) # How many batches is this trade? shares = batches * batch if not self.data_live: # make sure live then only trade!!! return if not self.getposition(d).size: #make sure there is no position to start buy if (port_gaind <= -csh*SL): #check if portfolio value less then portfolio stop loss, don't buy return if (port_gaind >= csh*TP): #check if portfolio value less then portfolio stop loss, don't buy return if (d.datetime.time(0) >= exittime): # Don't buy when already end of day return if ((self.inds[d]['rsi'][0] < lwbnd)and(1<(self.inds[d]['MDI'][0]/self.inds[d]['PDI'][0])<1.3)and(30<self.inds[d]['ADX'][0]<40)and(d.datetime.time(0) >= entrytime)): #buy if RSI lower than lower bound and entry time is as defined print('%s pv: %.2f, cash:%.2f, rsi: %.2f, ADX: %.2f, PDI: %.2f, MDI: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0], self.inds[d]['ADX'][0], self.inds[d]['PDI'][0], self.inds[d]['MDI'][0])) self.log('%s: BUY CREATE, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], shares)) self.order = self.buy(data=d, size = shares, coo=True, coc=False) #buy data d shares using maximum allocated size def next(self): #define the sell criterias here for i, d in enumerate(self.datas): pv = self.broker.getvalue() #get broker value cashd = self.broker.getcash() #get broker cash port_gain = pv - port_start_cash #calculate total portfolio gain target = csh * prop # Ideal total value of the position price = d.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / batch) # How many batches is this trade? shares = batches * batch ''' self.log('%s, Close: %.2f, pv: %.2f, cash: %.2f, rsi: %.2f, ADX: %.2f, PDI/MDI: %.2f, MDI/PDI: %.2f' % (d._name, d.close[0], pv, cashd, self.inds[d]['rsi'][0], self.inds[d]['ADX'][0], self.inds[d]['PDI'][0]/self.inds[d]['MDI'][0], self.inds[d]['MDI'][0]/self.inds[d]['PDI'][0])) ''' if not self.data_live: # make sure live then only trade!!! return if self.getposition(d).size: #make sure there is position to start sell if (d.close[0] >= (self.getposition(d).price*(1+.005))): #if curent close price >= the SU price, sell position print('%s pv: %.2f, cash:%.2f, rsi: %.2f, ADX: %.2f, PDI: %.2f, MDI: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0], self.inds[d]['ADX'][0], self.inds[d]['PDI'][0], self.inds[d]['MDI'][0])) self.log('%s: SELL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, 0.5, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size), coo=False, coc=True) self.order = self.buy(data=d, size = shares, coo=True, coc=False) else: if ((d.close[0]<=self.inds[d]['BotBB'][0])): print('%s pv: %.2f, cash:%.2f, rsi: %.2f, ADX: %.2f, PDI: %.2f, MDI: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0], self.inds[d]['ADX'][0], self.inds[d]['PDI'][0], self.inds[d]['MDI'][0])) self.log('%s: SELL CREATE (Price<BotBB), %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size = abs(self.getposition(d).size), coo=False, coc=True) else: if ((d.close[0]>=self.inds[d]['TopBB'][0])): print('%s pv: %.2f, cash:%.2f, rsi: %.2f, ADX: %.2f, PDI: %.2f, MDI: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0], self.inds[d]['ADX'][0], self.inds[d]['PDI'][0], self.inds[d]['MDI'][0])) self.log('%s: SELL CREATE (Price>TopBB), %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size = abs(self.getposition(d).size), coo=False, coc=True) else: if (d.datetime.time(0) >= exittime): #exit trade when end of day arrive print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0])) self.log('%s: EOD STOP, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size), coo=False, coc=True) else: if (port_gain >= csh*TP): #exit trade when portfolio gain >= take profit print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0])) self.log('%s: TAKE PROFIT VAL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, TPpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size), coo=False, coc=True) else: if (port_gain <= -csh*SL): #exit trade for stop loss print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pv, cashd, self.inds[d]['rsi'][0])) self.log('%s: STOPLOSS VAL CREATE (<%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, SLpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size), coo=False, coc=True) def run(args=None): #run the engine cerebro = bt.Cerebro(cheat_on_open=True) #set COO to true to buy on same len store = bt.stores.IBStore(host='127.0.0.1', port=7496, reconnect=5) #live port is 7496 and paper port is 7497 cerebro.broker = store.getbroker() #connect to broker #must use this to send trade to TWS for i in datalist: #iterate the data in datalist data = store.getdata(dataname=i, timeframe=bt.TimeFrame.Ticks, tz = pytz.timezone('US/Eastern')) cerebro.resampledata(data, timeframe=bt.TimeFrame.Seconds, compression=15) #resample all data to live trade cerebro.addstrategy(MainSt) #add strategy here cerebro.run() #run everything # Plot the result # Print out the starting conditions if __name__ == '__main__': #execute run here run()Thank you in advance!
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RE: Assigning independent value for multi assetsposted in General Discussion
But that will simply divide the total portfolio amount with number of data feeds right? I was thinking of something like
self.broker.get_value(data=d)for each asset because my take profit is based on the increase in value of data d
(ie.self.getposition(d).size*d.close[0] >= self.broker.get_cash(d)*(1+take_profit)). However, we cannot simply assigndata=dto theget_value, so I am quite lost now/ Really appreciate your help please.Thank you.
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Assigning independent value for multi assetsposted in General Discussion
Hi all,
I have a multiple data feed strategy and I want to assign the starting cash and value for each and every data in the strategy to be independent in order for my Stop loss/Take Profit strategy to work (Something like
data_d_cash = portfolio_cash * (1/len(datalist)).I need each data to have its own independent proportion for the stop loss/ take profit to work for each
din the strategy below.from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt import datetime as dt import pytz import math cn1='XXX' cn2='YYY' datalist = [cn1,cn2] csh=100000 stdt=dt.datetime(2019,07,2,9,30) enddt=dt.datetime(2019,07,2,16,00) SL=0.01 TP=2*SL SU=0.005 SUpct=SU*100 prop=1/len(datalist) #proportion of portfolio batch=10 #rounding entrytime = dt.time(9,45) exittime = dt.time(15,55) stakesize=10 lwbnd=40 upbnd=90 commis=0.05 TPpct=TP*100 SLpct=SL*100 def rounddown(x): return int(math.floor(x / 1)) * 1 class IBCommission(bt.CommInfoBase): """A :class:`IBCommision` charges the way interactive brokers does. """ params = (('stocklike', True), ('commtype', bt.CommInfoBase.COMM_FIXED),) def _getcommission(self, size, price, pseudoexec): return self.p.commission class PropSizer(bt.Sizer): """A position sizer that will buy as many stocks as necessary for a certain proportion of the portfolio to be committed to the position, while allowing stocks to be bought in batches (say, 100)""" params = {"prop": prop, "batch": batch} def _getsizing(self, comminfo, cash, data, isbuy): """Returns the proper sizing""" for i, d in enumerate(self.datas): if isbuy: # Buying target = csh * self.params.prop # Ideal total value of the position price = self.data.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / self.params.batch) # How many batches is this trade? shares = batches * self.params.batch # The actual number of shares bought if shares * price > cash: return 0 # Not enough money for this trade else: return shares else: # Selling return self.broker.getposition(d).size # Clear the position class MainSt(bt.Strategy): data_live = False def notify_data(self, data, status, *args, **kwargs): print('*' * 5, 'DATA NOTIF:', data._getstatusname(status), *args) if status == data.LIVE: self.data_live = True def log(self, txt, dt=None, vlm=None): dt = dt or self.datas[0].datetime.datetime(0) vlm = vlm or self.data.volume[0] print('%s) %s, %s' % (len(self), dt.isoformat(), txt)) def __init__(self): self.order = {} self.buyprice = {} self.buycomm = {} self.bar_executed = {} self.inds = dict() self.o = dict() self.lendata = dict() for i, d in enumerate(self.datas): self.order[d] = None self.buyprice[d] = None self.buycomm[d] = None self.bar_executed[d] = None self.inds[d] = dict() self.inds[d]['sma'] = bt.indicators.SimpleMovingAverage(d.close, period=54) self.inds[d]['rsi'] = bt.indicators.RelativeStrengthIndex(d.close, period=14,safediv=True, upperband=upbnd,lowerband=lwbnd) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: return if order.status in [order.Completed]: if order.isbuy(): self.log('%s: BUY EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm self.last_executed_price = order.executed.price self.order = None self.bar_executed = len(self) else: # Sell self.log('%s: SELL EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.last_executed_price = order.executed.price self.order = None self.bar_executed = len(self) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('%s Order Canceled/Margin/Rejected' % (order.data._name)) # Write down: no pending order[order.data._name] 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): for i, d in enumerate(self.datas): pv = self.broker.get_value(None) pvd = self.broker.get_value()*prop cshd = self.broker.get_cash()*prop self.stakes = abs(rounddown(pv/d.close[0])) target = csh * prop # Ideal total value of the position price = d.close[0] shares_ideal = pvd / price # How many shares are needed to get target batches = int(shares_ideal / batch) # How many batches is this trade? shares = batches * batch if not self.getposition(d).size: if (pv >= csh*(1+TP)): return if (pv <= csh*(1-SL)): return if (d.datetime.time(-1) >= exittime): return if ((self.inds[d]['rsi'][0] <= lwbnd)and(d.datetime.time(0) >= entrytime)): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: BUY CREATE, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], shares)) self.order = self.buy(data=d, size=shares) else: if (self.inds[d]['rsi'][0] >= upbnd): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: SELL CREATE (RSI>Upbnd), %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size = abs(self.getposition(d).size)) else: if (d.close[0] >= ((self.getposition(d).price*(1+SU)))): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: SELL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, SUpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size = abs(self.getposition(d).size)) else: if (d.close[0] <= ((self.getposition(d).price*(1-SU)))): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: SELL CREATE (<%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, SUpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size = abs(self.getposition(d).size)) ''' else: if (pvd >= target*(1+TP)): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: TAKE PROFIT VAL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, TPpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size)) else: if (pvd <= target*(1-SL)): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: STOPLOSS VAL CREATE (<%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, SLpct, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size)) ''' else: if (d.datetime.time(0) >= exittime): print('%s pv: %.2f, cash:%.2f, rsi: %.2f'% (d._name, pvd, cshd, self.inds[d]['rsi'][0])) self.log('%s: EOD STOP, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.getposition(d).size)) self.order = self.sell(data=d, size=abs(self.getposition(d).size)) def runstrat(): # Get a pandas dataframe cerebro = bt.Cerebro() ibstore = bt.stores.IBStore(port=7497, host='127.0.0.1', clientId=12345) #create our data list is_first = True #Loop through the list adding to cerebro. for i in datalist: data = ibstore.getdata(dataname=i,fromdate=stdt, historical =True, useRTH=True, tz = pytz.timezone('US/Eastern'), todate=enddt, timeframe=bt.TimeFrame.Seconds, compression=15) ''' if i in datalist: if is_first: data_main_plot = data is_first = False else: data.plotinfo.plotmaster = data_main_plot else: data.plotinfo.plot = False ''' cerebro.adddata(data) cerebro.broker.setcash(csh) comminfo = IBCommission(commission=commis) cerebro.broker.addcommissioninfo(comminfo) cerebro.addwriter(bt.WriterFile, csv=True, rounding=2, out="C:\\Users\\User\\Desktop\\Backtest Library\\TestResults.csv") start_value = cerebro.broker.getvalue() cerebro.addstrategy(MainSt) # Run over everything cerebro.run() # Plot the result cerebro.plot(volume=False) # Print out the starting conditions print(' ') print('--','Summary','--') print('Start capital: %.2f' % start_value) # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) print('Final Portfolio Cash: %.2f' % cerebro.broker.getcash()) print('Final PnL Value: %.2f' % (cerebro.broker.getvalue()-start_value)) if __name__ == '__main__': runstrat()Appreciate your help. Thanks!
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RE: Multi Assets Multi Order Executions Problemposted in General Discussion
@backtrader said in Multi Assets Multi Order Executions Problem:
@jabbarabdullah said in Multi Assets Multi Order Executions Problem:
Appreciate if you could explain further on what do you mean by 'NO'.
You say the orders are getting executed twice and the answer is: NO
@ab_trader said in Multi Assets Multi Order Executions Problem:
You have two data feeds, you cycle thru them twice and print same info twice. Check if data name is same as order data name, then print.
On the other side you don't need to go thru all data feeds. Just print order info.I quoted the code in
notify_orderwhere you do exactly what @ab_trader is telling you. You print the execution twice (which is not the same as two executions), because you loop through the datas innotify_orderI see, got it. Removed the loop in
notify_orderand it worked. Thanks @backtrader and @ab_trader !Just one more thing. when I tried executing the code with different date which the take profit and stop loss in my code should work, the numbers went crazy and it did not really stop the trade as expected. It worked great for a single asset data feed code but when I change it to multi assets data feeds, it went haywire. What went wrong? The single data feed code for comparison as follows:
from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt import datetime as dt import pytz import math cn1='UUUU' cn2='VVVV' csh=100000 stdt=dt.datetime(2019,06,28,9,30) enddt=dt.datetime(2019,06,28,16,00) SL=0.01 TP=2*SL SU=0.005 SUpct=SU*100 prop=1 #proportion of portfolio batch=10 #rounding entrytime = dt.time(9,45) exittime = dt.time(15,55) stakesize=10 lwbnd=40 upbnd=85 commis=0.05 TPpct=TP*100 SLpct=SL*100 def rounddown(x): return int(math.floor(x / 1)) * 1 class IBCommission(bt.CommInfoBase): """A :class:`IBCommision` charges the way interactive brokers does. """ params = (('stocklike', True), ('commtype', bt.CommInfoBase.COMM_FIXED),) def _getcommission(self, size, price, pseudoexec): return self.p.commission class PropSizer(bt.Sizer): """A position sizer that will buy as many stocks as necessary for a certain proportion of the portfolio to be committed to the position, while allowing stocks to be bought in batches (say, 100)""" params = {"prop": prop, "batch": batch} def _getsizing(self, comminfo, cash, data, isbuy): """Returns the proper sizing""" if isbuy: # Buying target = csh * self.params.prop # Ideal total value of the position price = data.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / self.params.batch) # How many batches is this trade? shares = batches * self.params.batch # The actual number of shares bought if shares * price > cash: return 0 # Not enough money for this trade else: return shares else: # Selling return self.broker.getposition(data).size # Clear the position class TestStrategy(bt.Strategy): data_live = False def notify_data(self, data, status, *args, **kwargs): print('*' * 5, 'DATA NOTIF:', data._getstatusname(status), *args) if status == data.LIVE: self.data_live = True def log(self, txt, dt=None, vlm=None): dt = dt or self.datas[0].datetime.datetime(0) vlm = vlm or self.data.volume[0] print('%s, %s, %s, Volume, %s' % (len(self), dt.isoformat(), txt, vlm)) def __init__(self): self.dataclose = self.datas[0].close self.order = None self.buyprice = None self.buycomm = None self.sma0 = bt.indicators.SimpleMovingAverage(self.datas[0], period=20) self.sma = bt.indicators.SimpleMovingAverage(self.datas[0], period=54) self.rsi = bt.indicators.RelativeStrengthIndex(period=14,safediv=True, upperband=upbnd,lowerband=lwbnd) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: return self.last_executed_price = order.executed.price if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm self.last_executed_price = order.executed.price else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.last_executed_price = order.executed.price self.bar_executed = len(self) 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): pv = self.broker.get_value() self.stakes = abs(rounddown(pv/self.dataclose[0])) target = csh * prop # Ideal total value of the position price = self.data.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / batch) # How many batches is this trade? shares = batches * batch if self.order: return if not self.position: if (pv >= csh*(1+TP)): return if (pv <= csh*(1-SL)): return if (self.data.datetime.time(0) >= exittime): return if ((self.rsi[0] <= lwbnd)and(pv > csh*(1-SL))and(self.data.datetime.time(0) >= entrytime)): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('BUY CREATE, %.2f, VLM BOUGHT: %.2f' % (self.dataclose[0], shares)) self.order = self.buy() else: if (self.dataclose[0] >= (self.last_executed_price*(1+SU))): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('SELL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (SUpct, self.dataclose[0], self.position.size)) self.order = self.sell(exectype=bt.Order.Stop) else: if (self.rsi[0] >= upbnd): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('SELL CREATE (RSI>Upbnd), %.2f, VLM BOUGHT: %.2f' % (self.dataclose[0], self.position.size)) self.order = self.close() else: if (pv >= csh*(1+TP)): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('TAKE PROFIT VAL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (TPpct, self.dataclose[0], self.position.size)) self.order = self.sell(exectype=bt.Order.StopLimit, price=self.dataclose[0]) else: if (pv <= csh*(1-SL)): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('STOPLOSS VAL CREATE (<%.2fpct), %.2f, VLM BOUGHT: %.2f' % (SLpct, self.dataclose[0], self.position.size)) self.order = self.sell(exectype=bt.Order.StopLimit, price=self.dataclose[0]) return else: if (self.data.datetime.time(0) >= exittime): print('rsi:', self.rsi[0]) print('pv: ', pv) self.log('EOD STOP, %.2f, VLM BOUGHT: %.2f' % (self.dataclose[0], self.position.size)) self.order = self.close(exectype=bt.Order.Stop) def runstrat(): # Get a pandas dataframe cerebro = bt.Cerebro() ibstore = bt.stores.IBStore(port=7497, host='127.0.0.1', clientId=12345) data0 = ibstore.getdata(dataname=cn1,fromdate=stdt, historical =True, useRTH=True, tz = pytz.timezone('US/Eastern'), todate=enddt, timeframe=bt.TimeFrame.Seconds, compression=15) cerebro.adddata(data0, name=cn1) data1 = ibstore.getdata(dataname=cn2,fromdate=stdt, historical =True, useRTH=True, tz = pytz.timezone('US/Eastern'), todate=enddt, timeframe=bt.TimeFrame.Seconds, compression=15) cerebro.adddata(data1, name=cn2) data1.plotinfo.plotmaster = data0 cerebro.broker.setcash(csh) comminfo = IBCommission(commission=commis) cerebro.broker.addcommissioninfo(comminfo) cerebro.addwriter(bt.WriterFile, csv=True, rounding=2, out="C:\\Users\\User\\Desktop\\Backtest Library\\TestResults.csv") start_value = cerebro.broker.getvalue() cerebro.addstrategy(TestStrategy) cerebro.addsizer(PropSizer) # Run over everything cerebro.run() # Plot the result cerebro.plot(volume=False) # Print out the starting conditions print(' ') print('--',cn1,'--') print('Start capital: %.2f' % start_value) # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) print('Final PnL Value: %.2f' % (cerebro.broker.getvalue()-start_value)) if __name__ == '__main__': runstrat()Appreciate your comment on this. Thanks!
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RE: Multi Assets Multi Order Executions Problemposted in General Discussion
Hi @backtrader ,
@backtrader said in Multi Assets Multi Order Executions Problem:
@jabbarabdullah said in Multi Assets Multi Order Executions Problem:
it somehow executed the order for each asset twice
NO
Appreciate if you could explain further on what do you mean by 'NO'.
@jabbarabdullah said in Multi Assets Multi Order Executions Problem:
def notify_order(self, order): for i, d in enumerate(self.datas):@jabbarabdullah said in Multi Assets Multi Order Executions Problem:
I'm trying to simply execute the multi assets
The magician inside me can say:
multi == 2Apologies, but I don't really get that. So how do I remedy the situation then?
Thanks.
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Multi Assets Multi Order Executions Problemposted in General Discussion
Hi,
I'm trying to simply execute the multi assets order based on the same strategy. However, when I run my strategy, it somehow executed the order for each asset twice and I can't figure out what is the problem. Appreciate if you could take a look at my code below. Thanks!
from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt import datetime as dt import pytz import math cn1='XXXX' cn2='YYYY' csh=100000 stdt=dt.datetime(2019,06,28,9,30) enddt=dt.datetime(2019,06,28,16,00) SL=0.01 TP=2*SL SU=0.005 SUpct=SU*100 prop=1/4 #proportion of portfolio batch=1 #rounding entrytime = dt.time(9,45) exittime = dt.time(15,30) stakesize=10 lwbnd=40 upbnd=75 commis=0.05 TPpct=TP*100 SLpct=SL*100 def rounddown(x): return int(math.floor(x / 1)) * 1 class IBCommission(bt.CommInfoBase): """A :class:`IBCommision` charges the way interactive brokers does. """ params = (('stocklike', True), ('commtype', bt.CommInfoBase.COMM_FIXED),) def _getcommission(self, size, price, pseudoexec): return self.p.commission class PropSizer(bt.Sizer): """A position sizer that will buy as many stocks as necessary for a certain proportion of the portfolio to be committed to the position, while allowing stocks to be bought in batches (say, 100)""" params = {"prop": prop, "batch": batch} def _getsizing(self, comminfo, cash, data, isbuy): """Returns the proper sizing""" for i, d in enumerate(self.datas): if isbuy: # Buying target = csh * self.params.prop # Ideal total value of the position price = self.data.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / self.params.batch) # How many batches is this trade? shares = batches * self.params.batch # The actual number of shares bought if shares * price > cash: return 0 # Not enough money for this trade else: return shares else: # Selling return self.broker.getposition(d).size # Clear the position class MainSt(bt.Strategy): data_live = False def notify_data(self, data, status, *args, **kwargs): print('*' * 5, 'DATA NOTIF:', data._getstatusname(status), *args) if status == data.LIVE: self.data_live = True def log(self, txt, dt=None, vlm=None): dt = dt or self.datas[0].datetime.datetime(0) vlm = vlm or self.data.volume[0] print('%s) %s, %s' % (len(self), dt.isoformat(), txt)) def __init__(self): self.order = {} self.buyprice = {} self.buycomm = {} self.bar_executed = {} self.inds = dict() self.o = dict() self.lendata = dict() for i, d in enumerate(self.datas): self.order[d] = None self.buyprice[d] = None self.buycomm[d] = None self.bar_executed[d] = None self.inds[d] = dict() self.inds[d]['sma'] = bt.indicators.SimpleMovingAverage(d.close, period=54) self.inds[d]['rsi'] = bt.indicators.RelativeStrengthIndex(d.close, period=14,safediv=True, upperband=upbnd,lowerband=lwbnd) def notify_order(self, order): for i, d in enumerate(self.datas): if order.status in [order.Submitted, order.Accepted]: return self.last_executed_price = order.executed.price if order.status in [order.Completed]: if order.isbuy(): self.log('%s: BUY EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.buyprice[d] = order.executed.price self.buycomm[d] = order.executed.comm self.last_executed_price = order.executed.price self.order[d] = None self.bar_executed[d] = len(self) else: # Sell self.log('%s: SELL EXECUTED, Price: %.2f, Cost: %.2f, Size: %.2f, Comm %.2f' % (order.data._name, order.executed.price, order.executed.value, order.executed.size, order.executed.comm)) self.last_executed_price = order.executed.price self.order[d] = None self.bar_executed[d] = len(self) self.bar_executed[d] = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('%s Order Canceled/Margin/Rejected' % (order.executed._name)) # Write down: no pending order[order.data._name] self.order[d] = 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): for i, d in enumerate(self.datas): pv = self.broker.get_value() * prop self.stakes = abs(rounddown(pv/d.close[0])) target = csh * prop # Ideal total value of the position price = d.close[0] shares_ideal = target / price # How many shares are needed to get target batches = int(shares_ideal / batch) # How many batches is this trade? shares = batches * batch if not self.getposition(d).size and not self.order[d]: if (pv >= target*(1+TP)): return if (pv <= target*(1-SL)): return if (self.data.datetime.time(0) >= exittime): return if ((self.inds[d]['rsi'][0] <= lwbnd)and(pv > target*(1-SL))and(self.data.datetime.time(0) >= entrytime)): print('%s pv: %.2f, rsi: %.2f'% (d._name, pv, self.inds[d]['rsi'][0])) self.log('%s: BUY CREATE, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], shares)) self.order[d] = self.buy(data=d, size=shares) else: if (self.inds[d]['rsi'][0] >= upbnd): print('%s pv: %.2f, rsi: %.2f'% (d._name, pv, self.inds[d]['rsi'][0])) self.log('%s: SELL CREATE (RSI>Upbnd), %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.position.size)) self.order[d] = self.close(data=d, size=abs(self.position.size)) else: if (pv >= target*(1+TP)): print('%s pv: %.2f, rsi: %.2f'% (d._name, pv, self.inds[d]['rsi'][0])) self.log('%s: TAKE PROFIT VAL CREATE (>%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, TPpct, d.close[0], self.position.size)) self.order[d] = self.sell(data=d, size=abs(self.position.size)) else: if (pv <= target*(1-SL)): print('%s pv: %.2f, rsi: %.2f'% (d._name, pv, self.inds[d]['rsi'][0])) self.log('%s: STOPLOSS VAL CREATE (<%.2fpct), %.2f, VLM BOUGHT: %.2f' % (d._name, SLpct, d.close[0], self.position.size)) self.order[d] = self.sell(data=d, size=abs(self.position.size)) return else: if (self.data.datetime.time(0) >= exittime): print('%s pv: %.2f, rsi: %.2f'% (d._name, pv, self.inds[d]['rsi'][0])) self.log('%s: EOD STOP, %.2f, VLM BOUGHT: %.2f' % (d._name, d.close[0], self.position.size)) self.order[d] = self.close(data=d, exectype=bt.Order.Stop, size=abs(self.position.size)) def runstrat(): # Get a pandas dataframe cerebro = bt.Cerebro() ibstore = bt.stores.IBStore(port=7497, host='127.0.0.1', clientId=12345) #create our data list datalist = [(cn1,'XXXX'),(cn2,'YYYY')] is_first = True #Loop through the list adding to cerebro. for i in datalist: data = ibstore.getdata(dataname=i[0],fromdate=stdt, historical =True, useRTH=True, tz = pytz.timezone('US/Eastern'), todate=enddt, timeframe=bt.TimeFrame.Seconds, compression=15) if i in datalist: if is_first: data_main_plot = data is_first = False else: data.plotinfo.plotmaster = data_main_plot else: data.plotinfo.plot = False cerebro.adddata(data, name=i[1]) cerebro.broker.setcash(csh) comminfo = IBCommission(commission=commis) cerebro.broker.addcommissioninfo(comminfo) cerebro.addwriter(bt.WriterFile, csv=True, rounding=2, out="C:\\Users\\User\\Desktop\\Backtest Library\\TestResults.csv") start_value = cerebro.broker.getvalue() cerebro.addstrategy(MainSt) cerebro.addsizer(PropSizer) # Run over everything cerebro.run() # Plot the result cerebro.plot(volume=False) # Print out the starting conditions print(' ') print('--','Summary','--') print('Start capital: %.2f' % start_value) # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) print('Final PnL Value: %.2f' % (cerebro.broker.getvalue()-start_value)) if __name__ == '__main__': runstrat()the problematic results:
Server Version: 76 TWS Time at connection:20190704 20:50:32 SGT XXXX pv: 25000.00, rsi: 39.81 65) 2019-06-28T09:46:00, XXXX: BUY CREATE, 53.66, VLM BOUGHT: 465.00 66) 2019-06-28T09:46:15, XXXX: BUY EXECUTED, Price: 53.66, Cost: 24951.90, Size: 465.00, Comm 0.05 66) 2019-06-28T09:46:15, XXXX: BUY EXECUTED, Price: 53.66, Cost: 24951.90, Size: 465.00, Comm 0.05 YYYY pv: 24998.83, rsi: 33.98 108) 2019-06-28T09:56:45, YYYY: BUY CREATE, 31.86, VLM BOUGHT: 784.00 109) 2019-06-28T09:57:00, YYYY: BUY EXECUTED, Price: 31.84, Cost: 24962.56, Size: 784.00, Comm 0.05 109) 2019-06-28T09:57:00, YYYY: BUY EXECUTED, Price: 31.84, Cost: 24962.56, Size: 784.00, Comm 0.05 YYYY pv: 25028.35, rsi: 75.65 251) 2019-06-28T10:32:30, YYYY: SELL CREATE (RSI>Upbnd), 32.05, VLM BOUGHT: 465.00 252) 2019-06-28T10:32:45, YYYY: SELL EXECUTED, Price: 32.05, Cost: 14805.60, Size: -465.00, Comm 0.05 252) 2019-06-28T10:32:45, YYYY: SELL EXECUTED, Price: 32.05, Cost: 14805.60, Size: -465.00, Comm 0.05 XXXX pv: 25025.31, rsi: 77.52 290) 2019-06-28T10:42:15, XXXX: SELL CREATE (RSI>Upbnd), 53.62, VLM BOUGHT: 465.00 291) 2019-06-28T10:42:30, XXXX: SELL EXECUTED, Price: 53.62, Cost: 24951.90, Size: -465.00, Comm 0.05 291) 2019-06-28T10:42:30, XXXX: SELL EXECUTED, Price: 53.62, Cost: 24951.90, Size: -465.00, Comm 0.05 291) 2019-06-28T10:42:30, OPERATION PROFIT, GROSS -18.60, NET -18.70P/S: I intentionally not using the bracket order on this and try to find a workaround for it.
Thanks again.