Yes,

I've tried the following with some success. It seems that I can control the plot size, but not plot font size. I've varied "font.size" from "20" all the way to "100" and they just don't seem to change.

import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 30, 20  # that's default image size for this interactive session
pylab.rcParams['font.family'] = 'sans-serif'
pylab.rcParams['font.sans-serif'] = ['Bitstream Vera Sans']
pylab.rcParams['font.serif'] = ['Bitstream Vera Sans']
pylab.rcParams["font.size"] = "100"

@backtrader
Again, order_target_percent does not work properly. It is always calculating position target value with the wrong basis using the previous close, regardless if you look-ahead or using the new cheat_on_open.

If you look at broker.py line 365 def _get_value(), its very clear that the portfolio values is only calculated at close. order_target_percent thenh calculates account/position value with the current close (see line 1301, strategy.py, target *= self.broker.getvalue()).

What should've happened, for cheat_on_open, is that broker.get_value() should return the value of the account at the current open for calculating sizing but instead it uses previous close for sizing.

It is not obvious if you trade only a single security at 10% sizing but errors will show up all over the place if you trade more than one security with more than 95% of the portfolio invested.

The intellectually lazy way to bypass this bug is to never allocate over 90-95% of the whole portfolio to prevent this problem from showing up. The downside of salvaging backtesting this way is that it is difficult to verify backtrader output verified against Excel or simple vectorized backtests.

I am trying to calculate the covariance matrix of a multiple asset portfolio at every period with N lookback period. My current implementation uses a counter to wait out the lookback period and then runs all calculations in the def next() method.

 def next(self):
        if (self.counter < self.p.rperiod):
            self.counter += 1
        else:
            # stack the closes together into a numpy array for ease of calculation
            closes = np.stack(([d.close.get(0,self.p.rperiod).tolist() for d in self.datas]))
            rets = np.diff(np.log(closes))
            # covariance matrix. will be shoved somewhere else for records
            cov = np.cov(rets)

While it 'works', what is the more backtrader way of doing the same thing?

For example, there's the PctChange indicator for calculating percentage change that is defined at __init__.

def __init__(self):
    rets = [bt.ind.PctChange(d, period=self.p.rperiod) for d in self.datas]

But how do I access the value of N periods of indicators, run calculations, and then construct cross-asset indicators? And would it be possible to have, say, an indicator that contains multidimensional matrices?

Much thanks!

@backtrader
? I am already placing trade w/ price=self.data.open[0] and cheat_on_open. Backtrader will reject those orders internally due to reasons outlined above.

On another thought, how do we place orders on margin? Adding those margin cash will be another way to solve this issue. Does backtrader keep track of gross exposure vs net exposure/unlevered? I can tolerate trading on a few points of margin to get fill in both live/simulation but need a wy to track gross/net exposure.

@backtrader
Again, order_target_percent does not work properly. It is always calculating position target value with the wrong basis using the previous close, regardless if you look-ahead or using the new cheat_on_open.

If you look at broker.py line 365 def _get_value(), its very clear that the portfolio values is only calculated at close. order_target_percent thenh calculates account/position value with the current close (see line 1301, strategy.py, target *= self.broker.getvalue()).

What should've happened, for cheat_on_open, is that broker.get_value() should return the value of the account at the current open for calculating sizing but instead it uses previous close for sizing.

It is not obvious if you trade only a single security at 10% sizing but errors will show up all over the place if you trade more than one security with more than 95% of the portfolio invested.

The intellectually lazy way to bypass this bug is to never allocate over 90-95% of the whole portfolio to prevent this problem from showing up. The downside of salvaging backtesting this way is that it is difficult to verify backtrader output verified against Excel or simple vectorized backtests.

@backtrader

Got it. Back to spaghetti code try except lookahead.

In your mind, what would be the best way to structure the code for live trading for 100% invested allocation purposes, aside from leaving 1-1.5% cash to make sure everyhing fills?

@backtrader

You are still using the close price for the calculation of the stake, which may be significantly different of the open. If you don't specifically specify the opening price it will not be used for the calculation. The calculation of the percent doesn't know you are cheating and are able to act before the broker

So in other words, cheat_on_open doesn't really cheat on open for open_target_percent?

Here's the log if I print out the orders when they got canceled/margin/rejected.

On 2006-07-18, it bought 100 shares of SPY at $99.04, for $9904, with $96 cash left.

On 2007-07-26, it sold 100 shares of SPY at $122.45, account balance should be $12,245 + prior cash of $96, and broker.getcash() reports $12,341. So selling w/ order_target_percent had no problem to sell at open with cheat_on_open.

But then the problem came. It turns around and buys 171 shares of AGG for $72.45 for $12,388.95, which is greater than $12,341 account value and got rejected. Thus the rejection. Which means, order_target_percent with cheat_on_open does not work for allocation trades.

Two bugs here:

1. Since the notify_order logged buying AGG before selling SPY, It could be that if we used order_target_percent sizer, the trade ordering will be mixed up and backtrader ends up placing buy before sell.

2. On the other hand, the order_target_percent sizer also did not size AGG properly if we look at the account values alone. It seemed to be using prior close of SPY ($123.61) to calculate account value. Previous close is $123.61 x 100 shares + $91 cash = $12452 account value > $12388.95, which can buy at max 171 share of AGG at $72.45. But in reality, backtrader order_target_percent sizer with cheat_on_open needs to be and should only be calculating account size using Open only. Never previous close.

2006-07-18, VIX: 17.74, VIX_VXV:1.03 SPY BUY EXECUTED, 100 shares at $99.04
2007-07-26, VIX: 20.74, VIX_VXV:1.09 AGG Order Canceled/Margin/Rejected, 171 shares
12341.0
123.61
Ref: 13711
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 171
Price: 72.47
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732883.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-07-26, VIX: 20.74, VIX_VXV:1.09 SPY SELL EXECUTED, -100 shares at 122.45
2007-09-18, VIX: 20.35, VIX_VXV:0.99 SPY BUY EXECUTED, 99 shares at $121.34
2007-10-22, VIX: 21.64, VIX_VXV:1.01 SPY SELL EXECUTED, -99 shares at 121.94
2007-10-22, VIX: 21.64, VIX_VXV:1.01 AGG Order Canceled/Margin/Rejected, 166 shares
12400.4
Ref: 13714
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 166
Price: 74.8
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732971.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-10-23, VIX: 20.41, VIX_VXV:0.97 SPY BUY EXECUTED, 99 shares at $124.07
2007-11-01, VIX: 23.21, VIX_VXV:1.03 AGG Order Canceled/Margin/Rejected, 168 shares
12548.9
Ref: 13717
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 168
Price: 74.92
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732981.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-11-01, VIX: 23.21, VIX_VXV:1.03 SPY SELL EXECUTED, -99 shares at 125.57
2007-11-02, VIX: 23.01, VIX_VXV:0.99 SPY BUY EXECUTED, 101 shares at $124.13
2007-11-05, VIX: 24.31, VIX_VXV:1.01 AGG Order Canceled/Margin/Rejected, 167 shares
12392.35
Ref: 13720
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 167
Price: 74.93
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732985.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-11-05, VIX: 24.31, VIX_VXV:1.01 SPY SELL EXECUTED, -101 shares at 122.58
2007-11-06, VIX: 21.39, VIX_VXV:0.96 SPY BUY EXECUTED, 99 shares at $123.58
2007-11-07, VIX: 26.49, VIX_VXV:1.05 AGG Order Canceled/Margin/Rejected, 166 shares
12358.69
Ref: 13723
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 166
Price: 74.85
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732987.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-11-07, VIX: 26.49, VIX_VXV:1.05 SPY SELL EXECUTED, -99 shares at 123.24
2007-11-13, VIX: 24.10, VIX_VXV:0.98 SPY BUY EXECUTED, 101 shares at $119.08
2007-11-14, VIX: 25.94, VIX_VXV:1.01 SPY SELL EXECUTED, -101 shares at 122.24

Here's the code that produces these two bugs:

import backtrader as bt
import backtrader.feeds as btfeeds
from datetime import datetime
import pandas_datareader.data as web
import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 20, 10  # that's default image size for this interactive session
pylab.rcParams["font.size"] = "20"

class vix_ratio(bt.Strategy):

    def log(self, txt):
        print("%s, VIX: %.2f, VIX_VXV:%.2f %s" % (self.vix.datetime.date(0).isoformat(), self.vix.close[0], self.vix_ratio, txt))       
              
    def __init__(self):
        self.spy = self.data0
        self.agg = self.data1
        self.vix = self.data2
        self.vxv = self.data3
        self.vix_ratio = 0
        self.flag = True
    
    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 enougth cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(order.data._name + ' BUY EXECUTED, %.0f shares at $%.2f' % (order.size, order.executed.price))
            elif order.issell():
                self.log(order.data._name + ' SELL EXECUTED, %.0f shares at %.2f' % (order.size, order.executed.price))

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log(order.data._name + ' Order Canceled/Margin/Rejected, %.0f shares' % (order.size))#, order.price))
            print(self.broker.getcash())
            print(self.spy[-1])
            print(order)
        # Write down: no pending order
        self.order = None
        
    def next_open(self): # Using cheat-on-open
        self.vix_ratio = self.vix.close[0]/self.vxv.close[0]
        
        if (self.vix_ratio <= 1 or self.vix.close[0] <= 20) and self.flag:
            self.flag = False
            self.order = self.order_target_percent(data=self.agg, target=0)
            self.order = self.order_target_percent(data=self.spy, target=1)
        elif (self.vix_ratio > 1 and self.vix.close[0] > 20) and not self.flag:
            self.flag = True
            self.order = self.order_target_percent(data=self.spy, target=0)
            self.order = self.order_target_percent(data=self.agg, target=1)
            
def run_strat():
    start = datetime(2006,7,17)
    end = datetime(2017,4,21)   
    
    spy = btfeeds.YahooFinanceData(dataname="SPY", fromdate=start, todate=end)
    agg = btfeeds.YahooFinanceData(dataname="AGG", fromdate=start, todate=end)
    vix = btfeeds.YahooFinanceData(dataname="^VIX", fromdate=start, todate=end)
    vxv = btfeeds.YahooFinanceData(dataname="^VXV", fromdate=start, todate=end)
    #vix = bt.feeds.PandasData(dataname=web.DataReader("^VIX", 'yahoo', start, end), name='VIX')
    #vxv = bt.feeds.PandasData(dataname=web.DataReader("^VXV", 'yahoo', start, end), name='VXV')
    
    cerebro = bt.Cerebro(cheat_on_open=True)
    
    cerebro.adddata(spy, name="SPY")
    cerebro.adddata(agg, name="AGG")
    cerebro.adddata(vix, name="VIX")
    cerebro.adddata(vxv, name="VXV")
    
    cerebro.addstrategy(vix_ratio)
    cerebro.broker.setcash(10000.0)
    #cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
    
    results = cerebro.run()
        
    cerebro.plot()
    """strat = results[0]
    pyfoliozer = strat.analyzers.getbyname('pyfolio')
    returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()

    return returns, positions, transactions
    """
if __name__ == '__main__':
    #r, p, t = run_strat()
    run_strat()

@backtrader
Okay. I changed the code to using the new cheat_on_open param and loaded the data using backtrader internal btfeeds. With the new cheat_on_open combined with order_target_percent, the model shouldn't be out of cash. And w/ import backtrader.feeds as btfeeds, it should take care of data alignment problem. I've also made sure I place order_target_percent(target = 0) ahead of order_target_percent(target = 1) so I never buy before I sell.

With these above feature combined, I shouldn't be getting get order rejected/margin/etc errors. But I am still getting those, big time, throwing the whole backtest result off.

But I am still getting a whole lot of errors on trade log errors. Here are the first several lines:

2006-07-18, VIX: 17.74, VIX_VXV:1.03 SPY BUY EXECUTED, 100 shares at $99.04
2007-07-26, VIX: 20.74, VIX_VXV:1.09 AGG Order Canceled/Margin/Rejected, 171 shares
2007-07-26, VIX: 20.74, VIX_VXV:1.09 SPY SELL EXECUTED, -100 shares at 122.45
2007-09-18, VIX: 20.35, VIX_VXV:0.99 SPY BUY EXECUTED, 99 shares at $121.34
2007-10-22, VIX: 21.64, VIX_VXV:1.01 SPY SELL EXECUTED, -99 shares at 121.94
2007-10-22, VIX: 21.64, VIX_VXV:1.01 AGG Order Canceled/Margin/Rejected, 166 shares
2007-10-23, VIX: 20.41, VIX_VXV:0.97 SPY BUY EXECUTED, 99 shares at $124.07
2007-11-01, VIX: 23.21, VIX_VXV:1.03 AGG Order Canceled/Margin/Rejected, 168 shares
2007-11-01, VIX: 23.21, VIX_VXV:1.03 SPY SELL EXECUTED, -99 shares at 125.57
2007-11-02, VIX: 23.01, VIX_VXV:0.99 SPY BUY EXECUTED, 101 shares at $124.13
2007-11-05, VIX: 24.31, VIX_VXV:1.01 AGG Order Canceled/Margin/Rejected, 167 shares
2007-11-05, VIX: 24.31, VIX_VXV:1.01 SPY SELL EXECUTED, -101 shares at 122.58
2007-11-06, VIX: 21.39, VIX_VXV:0.96 SPY BUY EXECUTED, 99 shares at $123.58
2007-11-07, VIX: 26.49, VIX_VXV:1.05 AGG Order Canceled/Margin/Rejected, 166 shares

The full code is as follows, run on Jupyter Notebook.

import backtrader as bt
import backtrader.feeds as btfeeds
from datetime import datetime
import pandas_datareader.data as web
import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 20, 10  # that's default image size for this interactive session
pylab.rcParams["font.size"] = "20"

class vix_ratio(bt.Strategy):

    def log(self, txt):
        print("%s, VIX: %.2f, VIX_VXV:%.2f %s" % (self.vix.datetime.date(0).isoformat(), self.vix.close[0], self.vix_ratio, txt))       
              
    def __init__(self):
        self.spy = self.data0
        self.agg = self.data1
        self.vix = self.data2
        self.vxv = self.data3
        self.vix_ratio = 0
        self.flag = True
    
    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 enougth cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(order.data._name + ' BUY EXECUTED, %.0f shares at $%.2f' % (order.size, order.executed.price))
            elif order.issell():
                self.log(order.data._name + ' SELL EXECUTED, %.0f shares at %.2f' % (order.size, order.executed.price))

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log(order.data._name + ' Order Canceled/Margin/Rejected, %.0f shares' % (order.size))
        # Write down: no pending order
        self.order = None
        
    def next_open(self): # Using cheat-on-open
        self.vix_ratio = self.vix.close[0]/self.vxv.close[0]
        
        if (self.vix_ratio <= 1 or self.vix.close[0] <= 20) and self.flag:
            self.flag = False
            self.order = self.order_target_percent(data=self.agg, target=0)
            self.order = self.order_target_percent(data=self.spy, target=1)
        elif (self.vix_ratio > 1 and self.vix.close[0] > 20) and not self.flag:
            self.flag = True
            self.order = self.order_target_percent(data=self.spy, target=0)
            self.order = self.order_target_percent(data=self.agg, target=1)
            
def run_strat():
    start = datetime(2006,7,17)
    end = datetime(2017,4,21)   
    
    spy = btfeeds.YahooFinanceData(dataname="SPY", fromdate=start, todate=end)
    agg = btfeeds.YahooFinanceData(dataname="AGG", fromdate=start, todate=end)
    vix = btfeeds.YahooFinanceData(dataname="^VIX", fromdate=start, todate=end)
    vxv = btfeeds.YahooFinanceData(dataname="^VXV", fromdate=start, todate=end)
    
    cerebro = bt.Cerebro(cheat_on_open=True)
    
    cerebro.adddata(spy, name="SPY")
    cerebro.adddata(agg, name="AGG")
    cerebro.adddata(vix, name="VIX")
    cerebro.adddata(vxv, name="VXV")
    
    cerebro.addstrategy(vix_ratio)
    cerebro.broker.setcash(10000.0)
    
    results = cerebro.run()        
    cerebro.plot()

if __name__ == '__main__':
    run_strat()

@backtrader Very nice! One step closer to the quality of R's Quantstrat package. Will test it soon

Hi All,

Trying to port a simple volatility based strategy from Excel to Backtrader and I ran into some very unexpected behaviors regarding order creation/execution. Specifically, sometimes the orders are created twice in a given day with the first one executed and the second order cancelled/rejected?

Here's the log output:

2006-07-18, VIX: 17.74, VIX_VXV:1.03 SPY Order Canceled/Margin/Rejected, 96 shares
Ref: 17069
OrdType: 0
OrdType: Buy
Status: 7
Status: Margin
Size: 96
Price: 98.75
Price Limit: None
TrailAmount: None
TrailPercent: None
ExecType: 0
ExecType: Market
CommInfo: None
End of Session: 732509.9999999999
Info: AutoOrderedDict()
Broker: None
Alive: False
2007-05-17, VIX: 13.51, VIX_VXV:0.95 SPY SELL EXECUTED, -1 shares at 122.89
2007-05-17, VIX: 13.51, VIX_VXV:0.95 SPY SELL EXECUTED, -1 shares at 122.89

Here's the code

class vix_ratio(bt.Strategy):

    def log(self, txt):
        print("%s, VIX: %.2f, VIX_VXV:%.2f %s" % (self.vix.datetime.date(0).isoformat(), self.vix.close[0], self.vix_ratio, txt))       
              
    def __init__(self):
        self.vix_ratio = 0
        
        self.spy = self.data0
        self.agg = self.data1
        self.vix = self.data2
        self.vxv = self.data3
        return None
    
    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 enougth cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(order.data._name + ' BUY EXECUTED, %.0f shares at $%.2f' % (order.size, order.executed.price))
            elif order.issell():
                self.log(order.data._name + ' SELL EXECUTED, %.0f shares at %.2f' % (order.size, order.executed.price))

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log(order.data._name + ' Order Canceled/Margin/Rejected, %.0f shares' % (order.size))#, order.price))
            print(order)
        # Write down: no pending order
        self.order = None
        
    def next(self):
        #print(self.sma1[0], self.sma2[0])
        self.vix_ratio = self.vix.close[0]/self.vxv.close[0]
        
        if self.vix_ratio > 1 and self.vix.close[0] > 20:
            self.order = self.order_target_percent(data=self.spy, target=0)
            self.order = self.order_target_percent(data=self.agg, target=0.99)
        else: 
            self.order = self.order_target_percent(data=self.agg, target=0)
            self.order = self.order_target_percent(data=self.spy, target=0.99)
                
def run_strat():
    start = datetime(2006,7,17)
    end = datetime(2017,4,21)   
    
    spy = btfeeds.YahooFinanceData(dataname="SPY", fromdate=start, todate=end)
    agg = btfeeds.YahooFinanceData(dataname="AGG", fromdate=start, todate=end)
    vix = bt.feeds.PandasData(dataname=web.DataReader("^VIX", 'yahoo', start, end), name='VIX')
    vxv = bt.feeds.PandasData(dataname=web.DataReader("^VXV", 'yahoo', start, end), name='VXV')
    
    cerebro = bt.Cerebro(runonce=False)
    
    cerebro.adddata(spy, name="SPY")
    cerebro.adddata(agg, name="AGG")
    cerebro.adddata(vix, name="VIX")
    cerebro.adddata(vxv, name="VXV")
    
    cerebro.addstrategy(vix_ratio)
    cerebro.broker.setcash(10000.0)
    cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
    
    results = cerebro.run()

@ab_trader @backtrader Thanks! I will try that.