@run-out said in How to extract historical account value from backtrader or its results:

Dictionary

dictionary = results[0].analyzers.getbyname("cash_market").get_analysis()
df = pd.DataFrame(dictionary).T
df.columns = ["Date", "Cash", "Value"]

It works, thank you so much.

I guess the system double_unders can't be reached from outside of the object

@jin-lin Did you ever figure this out? I have the same problem, I'm wondering if something changed with IBTWS that breaks the IbPy package or something like that.

I have a trading bot using Oanda that I'm trying to get working. Oanda for the pair that I'm considering using 50x leverage, and I want to reduce this to 5. The easiest way that I've though of is to reduce the cash available to the system to the actual trading results achievable on oanda will be simulated by backtrader. Essentially I want to remove cash from the broker and add it to the fund, making a backup account that is used to adjust the size of the actual account I'm using for trading calculations.

As the first step to implementing this, I use the following code:

    def nextstart(self):
        des_leverage = 5
        act_leverage = 50
        starting_cash = self.broker.get_cash()
        use_cash = starting_cash * des_leverage / act_leverage
        adjustment = use_cash - starting_cash
        self.broker.add_cash(adjustment)
        current_cash = self.broker.get_cash()
        print('starting cash:$ {}' . format(starting_cash))
        print('desired cash due to leverage:$ {}' . format(use_cash))
        print('adjustment: $ {}' . format(adjustment))
        print('current cash: $ {}' . format(current_cash))

The results are below. I haven't implemented the adding the subtracted money to the fund, but it doesn't look like my add_cash is working. Any ideas?

starting cash:$ 1000.0
desired cash due to leverage:$ 100.0
adjustment: $ -900.0
current cash: $ 1000.0

Thanks.

Am I correct in assuming that backtrader doesn't exit trades when a margin call would have happened? I have a system I'm testing in forex which can be highly leveraged but I want to use a lesser amount. I've set the leverage to 4 (from 50 that is available at many forex outlets) but I'm not able to see that any trades have been exited for a margin exceedance if the market goes against the trade and the account value falls below the required amount for leverage.

It appears that new trades are rejected due to margin if there is insufficient money in the trading account, but I don't see where existing trades are exited due to margin.

I haven't seen this explicitly stated in the documentation yet but I might have missed it.

Thanks.

@run-out said in How to extract historical account value from backtrader or its results:

Dictionary

dictionary = results[0].analyzers.getbyname("cash_market").get_analysis()
df = pd.DataFrame(dictionary).T
df.columns = ["Date", "Cash", "Value"]

It works, thank you so much.

I guess the system double_unders can't be reached from outside of the object

I know this should be fairly easy to do but I'm stumped. I want to get the account value by bar from a cerebro instance so I can do analysis on a spreadsheet.

For some reason I can't seem to figure out how to make this happen. I have this observer:

class AcctValue(bt.Observer):
    alias = ('Value',)
    lines = ('value',)
 
    plotinfo = {"plot": True, "subplot": True}
 
    def next(self):
        self.lines.value[0] = self._owner.broker.getvalue()    # Get today's account value (cash + stocks)
 

which I call with this code block:

# Create an instance of cerebro
cerebro = bt.Cerebro()
# Input a starting broker account value
cerebro.broker.setcash(startcash)


# Add our strategy
cerebro.addstrategy(Boll)


 
comminfo = forexSpreadCommisionScheme(spread = spread, leverage = leverage)
comminfo.show_spread_leverage()
cerebro.broker.addcommissioninfo(comminfo)
 
# Add the data to Cerebro
cerebro.adddata(data)
 
# Add a sizer

cerebro.addsizer(ForexSizer,risk_per_trade=.0025,ATR_mult=6)

#cerebro.addobserver(OrderObserver) 
cerebro.addobserver(AcctValue)
cerebro.addobservermulti(bt.observers.BuySell, markersize=4.0)    # Plots up/down arrows


# Add the analyzers we are interested in
#cerebro.addanalyzer(bt.analyzers.GrossLeverage, _name="gl")
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")
cerebro.addanalyzer(bt.analyzers.Returns, _name="ret")
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='mysharpe')
cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='ann')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='dd')   
cerebro.addanalyzer(bt.analyzers.Transactions, _name = 'transactions')
#cerebro.addanalyzer(AcctStats)

# Run over everything
strategies = cerebro.run()

firstStrat = strategies[0]
result=strategies[0]

 
#Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash
 
#Print out the final result
print('Final Portfolio Value: ${}'.format(round(portvalue,2)))
print('P/L: ${}'.format(round(pnl,2)))
printSharpe(firstStrat.analyzers.mysharpe.get_analysis())
printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())
printSQN(firstStrat.analyzers.sqn.get_analysis())
printAnnualReturns(result.analyzers.ann.get_analysis())
printReturns(result.analyzers.ret.get_analysis())
printDD(result.analyzers.dd.get_analysis())

using

b= cerebro.Observer.AccountValue()
b

doesn't work.
When I look at the vars(result) I see:
<main.AcctValue at 0x7f931b97a610>
but I don't know how to access this.

Can anyone help me?

The following is my attempt to make a custom position sizer based on ATR and the multiplier for the given future. I'm sure this is relatively easy but I seem to have hit a wall.

I seem to have a position size of 0 for every trade.
What am I getting wrong?

Thanks.

class comm_sizer(bt.Sizer):
    def __init__(self, atr):
        #self.atr_parameter= atr_parameter
        atr= self.atr
        risk_per_trade=5000
        atr_risk_per_trade=5
        
        sizing = math.floor((risk_per_trade/(mult*atr_risk_per_trade*atr)))
        
   # position = self.broker.getposition(data)
    
    def _getsizing(self,comminfo,cash,data,isbuy):
        
        return sizing

I have a system where I enter a long position and simultaneously enter a protective stop. I have a separate exit that sometimes occurs. When this occurs, I'd like to be able to cancel the protective stop. I currently use more than one instrument, (currently looking at a basket of 5 stocks), so I'd like to either find a way to determine which orders are still open by symbol or cancel all open orders by symbol.
After looking through the documentation I believe this should be possible but I can't seem to get it to work.
Does anyone have any ideas?

Thanks.