Indicator for Log Returns of data feeds

jf

Dear Community, dear @backtrader

I try to create an indicator that lets me calculate the log-returns for a given period for each "data". The following code failed, I assume that I made a mistake with ApplyN?

class LogReturns(bt.Indicator):
    lines = ('logreturns',)

    def __init__(self):
        self.lines.logreturns = bt.ind.ApplyN(bt.ind.PercentChange(self.data, period=1) + 1, func=lambda x: math.log(x[0]))

Can you kindly show me how I can correctly calculate a log-return indicator?

Thanks!
J

run-out

This analyzer exists if you are ok using an analyzer.

https://www.backtrader.com/docu/analyzers-reference/#logreturnsrolling

Which means you can look at the code for creating the log return analyzer to help making your own:
https://github.com/backtrader2/backtrader/blob/master/backtrader/analyzers/logreturnsrolling.py

jf

@run-out

Thanks for pointing this out. I also came across the analyzer, but as far as I understand the backtrader platform this approach doesn't work as analyzers are "a different type of animal" when compared to indicators. Or have you been able to use an analyzer as an input inside next?

run-out

I took the liberty of writing up the code.

import datetime
import backtrader as bt
import math

class LogReturns(bt.Indicator):
    params = (
        ("log_period", 1),
    )
    lines = ('logreturns',)

    def next(self):
        # This makes sure enough bars have passed before trying to calcualte the log return.
        if len(self) < self.p.log_period:
            return
        self.l.logreturns[0] = (math.log(self.datas[0].close[0] / self.datas[0].close[-1]) / self.p.log_period) * 100


class Strat(bt.Strategy):
    params = dict(log_period=1)

    def log(self, txt, dt=None):
        """ Logging function fot this strategy"""
        dt = dt or self.data.datetime[0]
        if isinstance(dt, float):
            dt = bt.num2date(dt)
        print("%s, %s" % (dt.date(), txt))

    # Added log return to the signal printout.
    def print_signal(self):
        self.log(
            "o {:7.2f}\th {:7.2f}\tl {:7.2f}\tc {:7.2f}\tlog_return {:3.3f}".format(
                self.datas[0].open[0],
                self.datas[0].high[0],
                self.datas[0].low[0],
                self.datas[0].close[0],
                self.lr[0],
            )
        )

    def __init__(self):
        self.lr = LogReturns(log_period=self.p.log_period)


    def next(self):
        self.print_signal()


if __name__ == "__main__":

    cerebro = bt.Cerebro()

    for s in ['AAPL']:
        data = bt.feeds.YahooFinanceData(
            dataname=s,
            timeframe=bt.TimeFrame.Days,
            fromdate=datetime.datetime(2019, 11, 4),
            todate=datetime.datetime(2020, 11, 4),
            reverse=False,
        )
        cerebro.adddata(data)

    cerebro.addstrategy(Strat)

    # Execute
    cerebro.run()

Which prints out something like:

2019-11-04, o   63.07	h   63.20	l   62.60	c   63.12	log_return -55.771
2019-11-05, o   63.01	h   63.29	l   62.83	c   63.03	log_return -0.143
2019-11-06, o   62.94	h   63.11	l   62.59	c   63.05	log_return 0.032
2019-11-07, o   63.61	h   64.01	l   63.46	c   63.78	log_return 1.151
2019-11-08, o   63.60	h   64.03	l   63.15	c   63.95	log_return 0.266
2019-11-11, o   63.50	h   64.53	l   63.50	c   64.46	log_return 0.794

jf

@run-out Amazing, thanks a lot! Your help in this regard is highly appreciated!