@davidavr thank you for the kind words and @run-out appreciate your responsiveness and helpfulness.

I'll lurk in these forums for a bit more and read more into these articles Davidarr mentioned. Thank you both very much and happy Thursday.

@run-out Thank you so much for providing some actionable guidance!

I am entering this backtesting exercise with intermediate python experience (i.e. I can write scripts to automate some of my recurring workflows and have no problem using Pandas/numpy and the standard ML libraries). As I'm digging deeper, I am feeling like this material may be a bit "over my head".

Is there a good uDemy/Coursera or other online course you'd recommend to gain a stronger familiarity working with this package? Given my background, I dont think the existing documentation alone will get me to pick this up.

@davidavr thank you for the kind words and @run-out appreciate your responsiveness and helpfulness.

I'll lurk in these forums for a bit more and read more into these articles Davidarr mentioned. Thank you both very much and happy Thursday.

@run-out Thank you so much for providing some actionable guidance!

I am entering this backtesting exercise with intermediate python experience (i.e. I can write scripts to automate some of my recurring workflows and have no problem using Pandas/numpy and the standard ML libraries). As I'm digging deeper, I am feeling like this material may be a bit "over my head".

Is there a good uDemy/Coursera or other online course you'd recommend to gain a stronger familiarity working with this package? Given my background, I dont think the existing documentation alone will get me to pick this up.

@run-out How should I change it to get it do what I like? And @ab_trader I have reviewed the quickstart but not much detail is provided about logging specifically

TL;DR: I used backtrader boilerplate code with small adjustments to prepare a prototype that shows me the dates on which an indicator was triggered but it's not working. Please help!

Hi all,

Hope everyone is safe and healthy here. I am trying to build a backtest that makes trades based on how many days have passed since an indicator was triggered. In order to build this, I began by creating a simple prototype to ensure I am capturing these dates in the next() method thats part of the strategy class.

Below you can find my code. I am just trying to get the Cerebro instance to print out the dates on which the indicator was triggered but am not getting the relevant output. I include my code below along with the output. I use python version 3.7.4 for this. Most of this was prepared using boilerplate code in the backtrader documentation.

Code:

#Import libraries
import numpy as np
import pandas as pd
import backtrader as bt
import backtrader.analyzers as btanalyzers
import matplotlib
from IPython.display import display, Image
from datetime import datetime
import yfinance as yf
import sys

#We instantiate a class used to inform QQQ call buying and selling
class LocalPeakStrategy(bt.Strategy):

    #Hoping to log date in which local peaks are triggered
    #We use this data to inform our actual buying
    def log(self, dt=None):
        
        dt = dt or self.datas[0].datetime.date(0)
        print('%s' % (dt.isoformat()))
    
    #We calculate different indicators/signals that inform buying/selling
    def __init__(self): 
        
        local_peak_threshold = 0.08
        
        one_day_rate_of_change =\
            bt.ind.RateOfChange(period = 1)
        two_day_rate_of_change =\
            bt.ind.RateOfChange(period = 2)
        three_day_rate_of_change =\
            bt.ind.RateOfChange(period = 3)
        
        if one_day_rate_of_change >= local_peak_threshold:
            self.local_peak = one_day_rate_of_change
        
        elif two_day_rate_of_change >= local_peak_threshold:
            self.local_peak = two_day_rate_of_change
            
        elif three_day_rate_of_change >= local_peak_threshold:
            self.local_peak = three_day_rate_of_change
        
        else:
            self.local_peak = 0

    #We tell backtrader when to buy and sell
    def next(self):
              
        #Testing to see if I can at least get the dates of when local peaks are set
        if self.local_peak > 0:
            self.log()

#Instantiate Cerebro object
cerebro = bt.Cerebro()

#Important relevant data
qqq = yf.Ticker("QQQ")
columns_to_use = ['Open', 'High', 'Low', 'Close', 'Volume']
df_qqq = qqq.history(period = 'max')[columns_to_use]
df_qqq['Open Interest'] = -1 #We are missing this data

#Add data feed
feed = bt.feeds.PandasData(dataname = df_qqq)
cerebro.adddata(feed)

#Set cash position and run
cerebro.broker.setcash(5000)
display(cerebro.run())

Output:

[<backtrader.strategy.Strategy at 0x7fdd236e5450>]

Any feedback deeply appreciated. Thanks for making this far.