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One article of quantitative framework backtrader read analyzer

2022-06-13 07:15:00 【Zhuge said talk】

The Nobel Prize winner William sharp

brief introduction

Strategic performance evaluation is a very important part of quantitative trading , Investing requires more than just knowing the yield of the strategy , You also need to understand the risks of the strategy .backtrader Offer a variety of analyzer analyzer , Multiple performance indicators can be output , Used to analyze the effect of strategy backtesting .

Usage method

cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn')
cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.003, annualize=True, _name='SharpeRatio')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')

strats = cerebro.run()

#  The first strategy 
strat = strats[0]

print("--------------- AnnualReturn -----------------")
print(strat.analyzers.AnnualReturn.get_analysis())
print("--------------- SharpeRatio -----------------")
print(strat.analyzers.SharpeRatio.get_analysis())
print("--------------- DrawDown -----------------")
print(strat.analyzers.DrawDown.get_analysis())

AnnualReturn Represents the annualized yield analyzer , SharpeRatio Represents sharp ratio analyzer , DrawDown Representative backtest analyzer . The results of each analyzer are usually expressed as OrderedDict The form of a dictionary returns , As shown below , Can pass keys Take what you need values, Such as strat.analyzers.SharpeRatio.get_analysis()['sharperatio'] .

--------------- AnnualReturn -----------------
OrderedDict([(2010, 0.0055023006637382466), (2011, 0.0015355496623354892), (2012, 0.004218277850025043), (2013, 0.09220659214070048), (2014, 0.05213380413050861), (2015, 0.012115724348371826), (2016, -0.017901621371985033), (2017, 0.06763449420829182), (2018, 0.01391412496311406), (2019, 0.05472002239157425), (2020, 1.8864865026259587), (2021, 0.36892175167157526), (2022, -0.2711252801992251)])
--------------- SharpeRatio -----------------
OrderedDict([('sharperatio', 0.3360518820606975)])
--------------- DrawDown -----------------
AutoOrderedDict([('len', 145), ('drawdown', 35.66222565509548), ('moneydown', 21054.40185546875), ('max', AutoOrderedDict([('len', 648), ('drawdown', 40.770090001923634), ('moneydown', 24070.00244140625)]))])

You can see it , Analyzer The output of the analyzer is some numerical value , No drawings . With SharpeRatio For example , The sharp ratio is a numerical value after calculation 0.3360518820606975. actually ,Analyzers It doesn't contain lines object , It also means that analyzer Object is very memory friendly , It won't take up a lot of memory .

Go further ： One analyzer The analyzer analyzes the performance of a strategy rather than the performance of the whole system . adopt addanalyzer Function to add the parser class , stay cerebro.run Runtime , Each analyzer instance is linked to each policy , If the backtest contains 3 Strategies , Then the same parser will have 3 Instances are created , Each instance will be linked to a different policy
some Analyzer The parser object uses other analyzers Analyzer to complete the task , such as SharpeRatio Used TimeReturn The output of the analyzer to further calculate , But these dependent users are not visible

backtrader Self contained Analyzers analyzer

AnnualReturn： Annualized rate of return
Calmar： Kama ratio
DrawDown： retracement
TimeDrawDown： Rollback at specified time granularity
GrossLeverage： Total leverage
PositionsValue： Position value
PyFolio： Generate data compatible pyfolio
LogReturnsRolling： rolling log Yield
PeriodStats： Basic statistics for a given time period
Returns： Calculate the total by logarithm 、 Average 、 Reunite with 、 Annualized rate of return
SharpeRatio： Sharp ratio
SharpeRatio_A： Annualized Sharpe ratio
SQN： System mass number System Quality Number
TimeReturn： Yield in different periods
TradeAnalyzer： Transaction statistics , If you win 、 Failure times, etc
Transactions： The subject matter of each transaction 、 Price 、 Quantity, etc
VWR： variability weighted return, Volatility weighted yield

frequently-used Analyzer Analyzer has ：AnnualReturn, DrawDown, PyFolio, SharpeRatio, TimeReturn.

newly build Analyzer analyzer

If backtrader The built-in analyzer cannot meet the requirements , You can build it yourself Analyzer analyzer . With SharpeRatio For example , The reference codes are as follows . Sharp ratio is one of the most commonly used performance evaluation indicators , It reflects the excess return contributed by unit volatility , The bigger the better . The sharp ratio is equal to the excess return of the relative risk-free return of the strategy divided by the standard deviation of the return .

import backtrader as bt

from backtrader.utils.py3 import map
from backtrader.mathsupport import average, standarddev

class SharpeRatio(bt.Analyzer):
    params = (
           ('timeframe', bt.TimeFrame.Years), 
           ('riskfreerate', 0.01),
    )

    def __init__(self):
        super(SharpeRatio, self).__init__()
        self.anret = bt.analyzers.AnnualReturn()

    # analyzer Like strategy , It's all from the beginning 0 root bar Began to run 
    def start(self):
        pass

    def prenext(self):
        pass
    
    def nextstart(self):
        pass

    def next(self):
        pass

    #  Generally, the evaluation index of the whole strategy is calculated after the end of the strategy 
    def stop(self):
        retfree = [self.p.riskfreerate] * len(self.anret.rets)
        retavg = average(list(map(operator.sub, self.anret.rets, retfree)))
        retdev = standarddev(self.anret.rets)

        self.ratio = retavg / retdev

    # Analyzer Class specific methods , Return the class containing the analysis results dict object 
    def get_analysis(self):
        return dict(sharperatio=self.ratio)

    #  Support the same information printing function as policy 
    def notify_order(self, order):
        pass

    def notify_trade(self, trade):
        pass
    
    def notify_cashvalue(self, cash, value):
        pass
    
    def notify_fund(self, cash, value, fundvalue, shares):
        pass

As can be seen from the above code , Even though Analyzer Objects have no Lines Object and does not need to be in lines Last iteration , But they follow the same operating mode as the policy , Of course, there are get_analysis This unique method is used to return the analysis results .

stay backtrader Use in PyFolio

cerebro.addanalyzer(bt.analyzers.PyFolio)
strats = cerebro.run()
# retrieve the 1st strategy
strat0 = strats[0]
pyfolio = strats.analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()

import pyfolio as pf
pf.create_full_tear_sheet(returns)

pyfolio stay Jupyter Notebook It can also run , But in Jupyter It is the best one to run and support .

Follow the baseline benchmark contrast

import backtrader as bt

cerebro = bt.Cerebro()

spypd = bt.feeds.PandasData(dataname=spydata, name="spy")
cerebro.adddata(spypd)

cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Years, _name='TimeReturn')
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Years, data=spypd, _name='BenchTimeReturn')

strats = cerebro.run()

time_return = strat.analyzers.getbyname('TimeReturn').get_analysis()
bench_time_return = strat.analyzers.getbyname('BenchTimeReturn').get_analysis()

print("--------------- TimeReturn -----------------")
print(time_return)
print("--------------- BenchTimeReturn -----------------")
print(bench_time_return)

Output log ：

--------------- TimeReturn -----------------
OrderedDict([(datetime.date(2009, 12, 31), 0.0), (datetime.date(2010, 12, 31), 0.0055023006637382466), (datetime.date(2011, 12, 31), 0.0015355496623354892), (datetime.date(2012, 12, 31), 0.004218277850025043), (datetime.date(2013, 12, 31), 0.09220659214070048), (datetime.date(2014, 12, 31), 0.05213380413050861), (datetime.date(2015, 12, 31), 0.012115724348371826), (datetime.date(2016, 12, 31), -0.017901621371985033), (datetime.date(2017, 12, 31), 0.06763449420829182), (datetime.date(2018, 12, 31), 0.01391412496311406), (datetime.date(2019, 12, 31), 0.05472002239157425), (datetime.date(2020, 12, 31), 1.8864865026259587), (datetime.date(2021, 12, 31), 0.36892175167157526), (datetime.date(2022, 12, 31), -0.2711252801992251)])
--------------- SpyTimeReturn -----------------
OrderedDict([(datetime.date(2009, 12, 31), -0.011793865755532318), (datetime.date(2010, 12, 31), 0.12840988195524994), (datetime.date(2011, 12, 31), -0.001988071570576566), (datetime.date(2012, 12, 31), 0.134741065036728), (datetime.date(2013, 12, 31), 0.2968892471880906), (datetime.date(2014, 12, 31), 0.11289182180470925), (datetime.date(2015, 12, 31), -0.008124930541476227), (datetime.date(2016, 12, 31), 0.09643402233275422), (datetime.date(2017, 12, 31), 0.19384416771302204), (datetime.date(2018, 12, 31), -0.06347893319524989), (datetime.date(2019, 12, 31), 0.28785206349908), (datetime.date(2020, 12, 31), 0.16162313396749006), (datetime.date(2021, 12, 31), 0.2703540848726258), (datetime.date(2022, 12, 31), -0.13563244077211734)])

Note that not all Analyzers Analyzers support Benchmark contrast .

Conclusion & communication

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