Mismatch between Analysers and Observers

jwsmith

Hi there,

In my code I make use of the standard Observers and Analysers. Below is some simplified code:

        cerebro.addobserver(bt.observers.Broker)
        cerebro.addobserver(bt.observers.Trades)
...

        # Add a strategy,data,sizers etc.

        # Add the analyzers we are interested in
        cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="TradeAnalyzer")

        # Run over everything
        results = cerebro.run()
        strategy_results = results[0]

        TA=strategy_results.analyzers.TradeAnalyzer.get_analysis()

        # PNL
        print(round(TA.pnl.net.total,2))
        #Strike rate
        print(round((TA.won.total/TA.total.closed)*100,2))
...

This will print a PNL that is correct, and a Strike rate that isn't correct.
For example, in the attached sanity check plot, there isn't a single unprofitable trade, which is also in agreement with the output in the strategy (I can attach if needed).
However, according to the Trades observer, strike rate from TA is reported as 62.5%. (won=5/lost=3).
So then this makes me question my Sharpe and SQN analysers as well.

Any help is appreciated!
Josh

Here's the full output of TA if that helps.

AutoOrderedDict([('total', AutoOrderedDict([('total', 8), ('open', 0), ('closed', 8)])), ('streak', AutoOrderedDict([('won', AutoOrderedDict([('current', 1), ('longest', 4)])), ('lost', AutoOrderedDict([('current', 0), ('longest', 2)]))])), ('pnl', AutoOrderedDict([('gross', AutoOrderedDict([('total', 480.4899999999998), ('average', 60.06124999999997)])), ('net', AutoOrderedDict([('total', 320.5035299999998), ('average', 40.06294124999997)]))])), ('won', AutoOrderedDict([('total', 5), ('pnl', AutoOrderedDict([('total', 340.37375999999966), ('average', 68.07475199999993), ('max', 140.4145000000004)]))])), ('lost', AutoOrderedDict([('total', 3), ('pnl', AutoOrderedDict([('total', -19.870229999999886), ('average', -6.623409999999962), ('max', -13.243230000000224)]))])), ('long', AutoOrderedDict([('total', 8), ('pnl', AutoOrderedDict([('total', 320.5035299999998), ('average', 40.06294124999997), ('won', AutoOrderedDict([('total', 340.37375999999966), ('average', 68.07475199999993), ('max', 140.4145000000004)])), ('lost', AutoOrderedDict([('total', -19.870229999999886), ('average', -6.623409999999962), ('max', -13.243230000000224)]))])), ('won', 5), ('lost', 3)])), ('short', AutoOrderedDict([('total', 0), ('pnl', AutoOrderedDict([('total', 0.0), ('average', 0.0), ('won', AutoOrderedDict([('total', 0.0), ('average', 0.0), ('max', 0.0)])), ('lost', AutoOrderedDict([('total', 0.0), ('average', 0.0), ('max', 0.0)]))])), ('won', 0), ('lost', 0)])), ('len', AutoOrderedDict([('total', 23), ('average', 2.875), ('max', 6), ('min', 1), ('won', AutoOrderedDict([('total', 20), ('average', 4.0), ('max', 6), ('min', 1)])), ('lost', AutoOrderedDict([('total', 3), ('average', 1.0), ('max', 1), ('min', 1)])), ('long', AutoOrderedDict([('total', 23), ('average', 2.875), ('max', 6), ('min', 1), ('won', AutoOrderedDict([('total', 20), ('average', 4.0), ('max', 6), ('min', 1)])), ('lost', AutoOrderedDict([('total', 3), ('average', 1.0), ('max', 1), ('min', 1)]))])), ('short', AutoOrderedDict([('total', 0), ('average', 0.0), ('max', 0), ('min', 9223372036854775807), ('won', AutoOrderedDict([('total', 0), ('average', 0.0), ('max', 0), ('min', 9223372036854775807)])), ('lost', AutoOrderedDict([('total', 0), ('average', 0.0), ('max', 0), ('min', 9223372036854775807)]))]))]))])
 2019-04-24 12:48:13 INFO [backtest.py:119] | Final Portfolio Value: 10320.50

jwsmith

I should mention that I have self.set_coc(True) in the Strategy, which explains why the first trade is profitable: Buys at 3rd bar close, and sells at 4th close.
I.e., shift red and green arrows 1 to the left.

backtrader

One of the major problems:

A user reports something he/she believes to be a problem (it may or may not be)
The user posts half-cooked code which cannot of course be used for anything
No sample data is provided to reproduce the problem
No logging information is provided
A chart is the proof of all sins

Let's use the chart then ...

0_1556138826884_a867a6d4-de38-49a4-b7eb-870d1750823d-image.png

That should say everything, because unless David Copperfield, this mere mortal needs something reliable and reproducible to work with.

jwsmith

Fair point.
Full minimal example below.
It seems the issue(?) is with commission. The TA Analyser seems to take it into account, the Observers (specifically Trades) does not.
You can test this by enabling and disabling commission. With commission=0%, the Observer and Analyser StrikeRate match, with commision!=0%, they do not.

So then the question: Is there an easy way to get Observers (or is it just the Trades one where this happens?) to take into account commission?

import backtrader as bt
from datetime import datetime


class firstStrategy(bt.Strategy):


    def __init__(self):
        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=20)
        self.broker.set_coc(True)

    def next(self):
        if not self.position:
            # Some bogus values to get some trades
            if self.rsi[0] < 30:
                self.buy(size=5)
        else:
            if self.rsi[0] > 20:
                self.close()


#Variable for our starting cash
startcash = 10000

cerebro = bt.Cerebro()
cerebro.addstrategy(firstStrategy)
# File is one of the txt files in datas dir, just with most of entries removed for simplicity.
data = bt.feeds.YahooFinanceCSVData(dataname="yhoo.txt")
cerebro.adddata(data)

# Set our desired cash start
cerebro.broker.setcash(startcash)
cerebro.broker.setcommission(commission=0.01)

# Add the analyzers we are interested in
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="TradeAnalyzer")
# Run over everything
results = cerebro.run()
strategy_results = results[0]

TA=strategy_results.analyzers.TradeAnalyzer.get_analysis()
print(TA)
final_portfolio_values=dict()
final_portfolio_values["StrikeRate"]=round((TA.won.total/TA.total.closed)*100,2)
#Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))
print("StrikeRate = ",final_portfolio_values["StrikeRate"])
#... And plot
cerebro.plot(style='candlestick')

Cheers,
Josh

backtrader

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