General Discussion

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  Writing data at to [0] element of line
  • 1324535

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  Here's a little example that seems to replicate what I struggle with (taken from your introduction post). I don't think values get written to [-1] anymore as I assumed initially after getting an unexpected result. This is the next step where I attempt to overwrite the close value: def next(self): # Simply log the closing price of the series from the reference self.log('OLD Close, %.2f' % self.dataclose[0]) self.data_0[0]=10 self.log('NEW Close, %.2f' % self.dataclose[0]) if self.position.size == 0: if self.dataclose[0] <= self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] <= self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) self.buy() Output: Starting Portfolio Value: 100.00 1995-02-01, OLD Close, 2.09 1995-02-01, NEW Close, 10.00 1995-02-02, OLD Close, 2.12 1995-02-02, NEW Close, 10.00 1995-02-03, OLD Close, 2.17 1995-02-03, NEW Close, 10.00 1995-02-03, BUY CREATE, 10.00 1995-02-06, OLD Close, 2.19 1995-02-06, NEW Close, 10.00 1995-02-07, OLD Close, 2.19 1995-02-07, NEW Close, 10.00 1995-02-08, OLD Close, 2.17 1995-02-08, NEW Close, 10.00 1995-02-09, OLD Close, 2.15 1995-02-09, NEW Close, 10.00 Final Portfolio Value: 99.98 Now given the code initiates only 1 buy order @ 10 and the close is 10 for all days, we would expect fin value = 100. This value changes if I extend the time series. It seems I am missing something about the data handling here. Full code below (file path would need to be amended): from future import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) Import the backtrader platform import backtrader as bt Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close def next(self): # Simply log the closing price of the series from the reference self.log('OLD Close, %.2f' % self.dataclose[0]) self.data_0[0]=10 self.log('NEW Close, %.2f' % self.dataclose[0]) if self.position.size == 0: if self.dataclose[0] <= self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] <= self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) self.buy() if name == 'main': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, 'PythonTrading\\DataStorage\\orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(1995, 2, 1), # Do not pass values before this date todate=datetime.datetime(1995, 2, 10), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
• N

  How to access the "params" tuple?
  • niklay

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  @niklay said in How to access the "params" tuple?: I started with the Quickstart tutorials and found some confusing code (see attached). The "params" tuple is created with two separate tuples inside. However, I can access these inside tuples by typing params.maperiod . What's exactly the confusing part? You declare params (which you can also declare as a dict) and you can access them. It's a declarative style. As pointed out by @Laurent-Michelizza , there is a shorhand notation where params is shortened to p @niklay said in How to access the "params" tuple?: However, if I rename params to another name I cannot access them anymore. Of course. How are you expecting the platform to know which params have been defined if you give the declaration a different name?
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  Bug : There is no margin rate limit when short selling
  leverage margin • • ToReality

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  Where to get OHLCV data on crypto currencies?
  • Søren Pallesen

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  i didn't like whatever basically any of the other libraries had going on, so i wrote my own (for gdax) with the below output: messing around with websocket live data cached and aggregated @ 10s slices (with buy & sell vol). both of these are currently dumping to mongo, but i'm sure they can also be redirected live to pandas or something in a stream capacity.
• C

  Anyone use backtrader to do live trading on Bitcoin exchange?
  pairs trading crypto • • CptanPanic

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  You guys are doing a fantastic job and I am just about to dive right in; However, I am stuck with a very simple RSI-Algo implementation using the ccxt branch: import backtrader as bt from datetime import datetime class firstCryptoStrategy(bt.Strategy): def __init__(self): self.rsi = bt.indicators.RSI_SMA(self.data.close, period=14) def next(self): if not self.position: if self.rsi < 30: self.buy(size=100) else: if self.rsi > 70: self.sell(size=100) #Variable for starting cash startcash = 10000 #Create instance of cerebro cerebro = bt.Cerebro() #Add strategy cerebro.addstrategy(firstCryptoStrategy) #Get data symbol = 'BTC/USDT' data = bt.feeds.CCXT(exchange='bittrex', symbol=symbol, timeframe=bt.TimeFrame.Minutes, fromdate = datetime(2018,1,1), todate = datetime(2018,4,1) ) # Add the data to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(startcash) # Run over everything cerebro.run() # Get final portfolio Value portvalue = cerebro.broker.getvalue() pnl = portvalue - startcash # Print out the final result print('Final Portfolio Value: ${}'.format(portvalue)) print('P/L: ${}'.format(pnl)) #Finally plot the end results cerebro.plot(style='candlestick') I was expecting some P/L output and a plot. Neither happens, what am I missing? I was running the code in jupyter... Thanks you!
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  Feeding Multiple Data Feeds in Indicators
  • KT

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  @backtrader Here is the link to the new post: link text
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  Backtest vs live bars "off-by-1" discrepancy?
  • nooby_mcnoob

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  @dasch Looks like bars match backtest/live trading now. It's odd to get a bar labeled "10:56 AM" at 10:55, but it's understandable and reproducible which is more important than guessing :) Thanks for the update!
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  Trade markers misaligned with price due to gaps in data?
  • aaggmm

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  Thanks, hope there is way to fix it in the future.
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  How to force plotyticks on main chart (not subplot)
  • tomasrollo

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  Change subplot configuration when calling plot()
  • scoupafi

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  Do as you see fit. That's not expected to be manipulated by the user, who has no direct access to the subplots.
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  Hedging support
  • btalgo

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  This hedging support offered by FX Brokers and CFD is really a bad idea and I am glad that backtrader is handling orders the way it is doing it at the moment. Which is the way to do it. I am not sure what benefits do you get from hedging. If you have a loss, you can always exit it and re-enter, do need to supposedly hedge it by entering the opposite direction. The result is the same.
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  How to get data out of cerebro.run()
  • Dinesh Dileep Gaurav

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  Adding a code sample to explain what I need. In the below code sample, I just print out close values of two stocks in my strategy in each iteration of next(). What do I need to do if I want to push it to a say a pandas dataframe (with the index as dates). from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) import pandas as pd import numpy as np import datetime import matplotlib # Import the backtrader platform import backtrader as bt import backtrader.feeds as btfeeds def MakeArbitraryData(): # make random 'open,high,close,low' data A = np.random.randint(0,10,size=(5,4)) #create 5 X 4 matrix of random integers Apd = pd.DataFrame(data=A,columns=['Open','High','Close','Low']) DateRange = [datetime.date(2018,3,i) for i in range(1,6)] Apd['Date']=pd.Series(DateRange,dtype='datetime64[ns]') Apd = Apd.set_index('Date') return Apd #add a strategy to just print close value class TestStrategy(bt.Strategy): def log(self, txt, dt=None): """logging function for this strategy this is essentially the print function for the strategy""" dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(),txt)) def __init__(self): self.Stock1close = self.datas[0].close self.Stock2close = self.datas[1].close # To keep track of pending orders self.order = None def next(self): self.log('Stock 1 Close, %.2f' % self.Stock1close[0]) self.log('Stock 2 Close, %.2f' % self.Stock2close[0]) if self.order: return if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() cerebro.addstrategy(TestStrategy) Stock1Data = MakeArbitraryData() #GetStockData(Symbol[0],fromDate,toDate) Stock2Data = MakeArbitraryData() #GetStockData(Symbol[1],fromDate,toDate)# data1 = bt.feeds.PandasData(dataname=Stock1Data, # datetime='Date', nocase=True, ) data2 = bt.feeds.PandasData(dataname=Stock2Data, # datetime='Date', nocase=True, ) # Add the Data Feed to Cerebro cerebro.adddata(data1) cerebro.adddata(data2) # Set our desired cash start cerebro.broker.setcash(1000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) # cerebro.plot()
• N

  Live data does not match historical data for ibtest.py #359
  • nooby_mcnoob

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  @dasch @backtrader Thanks for your help. The PEBKAC is resolved :-) I guess it is obvious to you, but perhaps a default mode of "backtest bars should match live bars" would follow the principle of least surprise? In any case, here are some live resampled bars, followed by the corresponding historical resampled bars with a timeframe of 5 minutes with rightedge=False. This shows that live bars match historical bars. Now the question I have to answer myself is: "why did righedge=False not work in my system when I tried it live?" Live Data0, 0196, 736793.5625, 2018-04-09T09:30:00.000000, 1.27725, 1.27794, 1.27709, 1.27773, -1.0, 0, 1.277366 Data0, 0197, 736793.5659722222, 2018-04-09T09:35:00.000000, 1.27773, 1.27882, 1.27745, 1.27874, -1.0, 0, 1.277632 Data0, 0198, 736793.5694444445, 2018-04-09T09:40:00.000000, 1.27874, 1.27876, 1.27756, 1.27769, -1.0, 0, 1.27775 Data0, 0199, 736793.5729166666, 2018-04-09T09:45:00.000000, 1.27769, 1.27791, 1.27729, 1.27751, -1.0, 0, 1.277784 Data0, 0200, 736793.5763888889, 2018-04-09T09:50:00.000000, 1.27751, 1.27751, 1.2765, 1.27675, -1.0, 0, 1.277684 Data0, 0201, 736793.5798611111, 2018-04-09T09:55:00.000000, 1.27675, 1.27726, 1.27634, 1.27681, -1.0, 0, 1.2775 Data0, 0202, 736793.5833333334, 2018-04-09T10:00:00.000000, 1.27681, 1.27716, 1.27641, 1.27712, -1.0, 0, 1.277176 Data0, 0203, 736793.5868055555, 2018-04-09T10:05:00.000000, 1.27712, 1.27748, 1.27683, 1.27736, -1.0, 0, 1.27711 Data0, 0204, 736793.5902777778, 2018-04-09T10:10:00.000000, 1.27736, 1.27757, 1.27725, 1.27736, -1.0, 0, 1.2770800000000002 Data0, 0205, 736793.59375, 2018-04-09T10:15:00.000000, 1.27736, 1.2774, 1.27652, 1.27652, -1.0, 0, 1.277034 Historical Data0, 0196, 736793.5625, 2018-04-09T09:30:00.000000, 1.27725, 1.27794, 1.27709, 1.27773, -1.0, 0, 1.277366 Data0, 0197, 736793.5659722222, 2018-04-09T09:35:00.000000, 1.27773, 1.27882, 1.27745, 1.27874, -1.0, 0, 1.277632 Data0, 0198, 736793.5694444445, 2018-04-09T09:40:00.000000, 1.27874, 1.27876, 1.27756, 1.27769, -1.0, 0, 1.27775 Data0, 0199, 736793.5729166666, 2018-04-09T09:45:00.000000, 1.27769, 1.27791, 1.27729, 1.27751, -1.0, 0, 1.277784 Data0, 0200, 736793.5763888889, 2018-04-09T09:50:00.000000, 1.27751, 1.27751, 1.2765, 1.27675, -1.0, 0, 1.277684 Data0, 0201, 736793.5798611111, 2018-04-09T09:55:00.000000, 1.27675, 1.27726, 1.27634, 1.27681, -1.0, 0, 1.2775 Data0, 0202, 736793.5833333334, 2018-04-09T10:00:00.000000, 1.27681, 1.27716, 1.27641, 1.27712, -1.0, 0, 1.277176 Data0, 0203, 736793.5868055555, 2018-04-09T10:05:00.000000, 1.27712, 1.27748, 1.27683, 1.27736, -1.0, 0, 1.27711 Data0, 0204, 736793.5902777778, 2018-04-09T10:10:00.000000, 1.27736, 1.27757, 1.27725, 1.27736, -1.0, 0, 1.2770800000000002 Data0, 0205, 736793.59375, 2018-04-09T10:15:00.000000, 1.27736, 1.2774, 1.27652, 1.27652, -1.0, 0, 1.277034
• N

  Does your backtesting match your live trades?
  • nooby_mcnoob

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  How to get historical data properly?
  • eggachecat

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  @backtrader Thanks a lot!
• 

  Trading with absolute amount of money allocated for each trade
  sizer • • thanhuwe8

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  You can probably use target value orders - Docs - Target orders
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  Running Optiomisation fails on Google Cloud and AWS
  • j45p41

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  It's not cerebro what you want to flush because you are creating a new instance each time, it is python itself and depends on the rest of things you do in your code (which have obviously been redacted)
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  What does Cerebro stand for?
  • PiSquared

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  @backtrader OK thanks!
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  How to send orders to Binance
  • PiSquared

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• 

  Walk Forward Analysis Demonstration
  • Curtis Miller

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  This is really cool. It would be really neat to get Walk forward optimization and genetic solver (using optunity) combined into one supported by backtrader. thinking more about the interface, maybe it makes sense to introduce a new method in cerebro to setup genetic optimization (since it only needs lower/upper boundaries and only works for doubles, maybe something like:) cerebro.genoptaddstrategy(Strategy, sma1=[3,15], sma2=[5,25]) and then run could be modified to use optinuity (optionally using pmap argument to use multiprocessing). To support WFO, perhaps a new method could be added as well called walkforward(...). I'll probably try to code this up as and when I get some time and send in a pull request if I can make it happen and if its ok with you?