You are right!
When switching to a verified good data:

from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt import backtrader.indicators as btind import backtrader.feeds as btfeeds # Create a Stratey class NoStrategy(bt.Strategy): def __init__(self): btind.ATR(self.datas[0]) if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(NoStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere #modpath = os.path.dirname(os.path.abspath(sys.argv[0])) #datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed # data1 = bt.feeds.YahooFinanceData( # dataname="SPY", # # Do not pass values before this date # fromdate=datetime.datetime(2000, 1, 1), # # Do not pass values before this date # todate=datetime.datetime(2017, 10, 31), # # Do not pass values after this date # reverse=True) # Create a Data Feed data1 = btfeeds.GenericCSVData(dataname = '../data/spy-2000-2017.txt', fromdate = datetime.datetime(2000, 1, 1), todate = datetime.datetime(2017, 10, 31), dtformat=('%m/%d/%Y'), nullvalue = 0.0, datetime = 0, high = 3, low = 4, open = 2, close = 5, volume = 6, openinterest = 7) # Add the Data Feed to Cerebro cerebro.adddata(data1) # Run over everything cerebro.run() # Plot the result cerebro.plot()

ATR seems to be fine!
0_1511542021885_3.png

Sorry just realised this might not be right sub-forum, i tried to delete the post but cannot, if mods could move if required.

Apologies

This is very helpful, thanks.

I believe the solution was found long ago (actually implemented long ago). It's called *replay.

https://www.backtrader.com/docu/data-replay/data-replay.html

https://community.backtrader.com/topic/413/release-1-9-48-116

PR #307 Fractal study added to studies/contributions

It's an study, hence: bt.studies.Fractal

You create an array by either

Using a for loop

or

Using a (list) comprehension

The best way to check gradually (and is not directly related to jupyter notebooks) is to start by adding only 1 item to the array and then gradually add more.

@Alessandro-Di-Piazza said in Portfolio situation:

How can I print the portfolio situation in a table ?

Go over the positions with getposition(xxx) where xxx is a data feed.

https://www.backtrader.com/docu/strategy.html

The sum of portfolio and the cash value must be equal to the getvalue results?

Should be.

Well, you are correct! Thank you for the response.

Not mine.

@Paska-Houso

Thanks for the detailed reply!

cerebro.broker.set_checksubmit(checksubmit=False)

Fixed the problem. Thank you so much! :)

Reposted code -- Fixed errors in the calculation for Puts.

Also found an issue in compatibility with Python 2.7 assigning alpha1 and alpha2. Suggest using numpy.power instead of ** to convert error to warning in line 259 and 260.

A screenshot showing theta:

0_1515265502854_15f709b0-5fd7-4a56-8de3-46a3ca85714d-image.png file:///home/bigdavediode/Pictures/testtheta.png

You get the idea.

@Cheez
Sorry I missed your question. Probably both options will work, but better test it.

There was a very similar question a couple of days ago

https://community.backtrader.com/topic/823/how-to-plot-renko

A quick glance would seem to indicate that your problem is similar but in the other direction. You have size=0.0008 and the price moves in that range, triggering the up/downs (you have instances of more than one in the same direction)

... 2017-07-04, Close, 1.1352 2017-07-04, Close, 1.1360 2017-07-04, Close, 1.1344 2017-07-04, Close, 1.1360 2017-07-04, Close, 1.1344 2017-07-04, Close, 1.1360 2017-07-04, Close, 1.1344 ...

1.1360 - 1.1352 = 0.0008 which is the size and the rest are above the size (in absolute value), each time in an opposite directions.

Play with a different size and you will see different results.

@polr said in HTTP Rest Datafeed:

My question is this -- if I want to do a test across WTI for example from 1995 to 2010 I have 180 data feeds to enter (12 * 15). Then if I want to test the portfolio across 25 products I get into the thousands of feeds quite quickly. Am I correct in my understanding?

12 * 15 seems quite right. If you have 25 different assets and you want to backtest of all them at the same time, yes, you will have 12 * 15 * 25

If

@polr said in HTTP Rest Datafeed:

I have a function that takes a datetime object as a parameter and returns a list of the front, first roll, second roll etc for the given date, among other information. I was wondering if it was possible to provide a function as input to a data feed such that the data feed provided to the Strategy object is in essence a symbol and a rolling function (in other words, it doesn't yet hold data but knows what to do when the strategy iteratively provides it a date during the implementation of the strategy.

Such a data feed would be possible, but it doesn't exist at the moment. You simply have to override _load, and when called provide the data. From inside _load you can decide where the next batch of data is coming from.

In any case you end with the same problem as above: you need to be able to fetch 12 * 15 data feeds. Whereas above you do it in a loop/list comprehension (quite easily if the naming is consistent), you pass the complexity into a different beast if you make a data feed.

Example with a list comprehension

basename = `myfeed-{}.txt` dfeeds = [bt.feeds.GenericCSVData(dataname=basename.format(i) for i in range(1995, 2011)] cerebro.rollover(*dfeeds)

That should do the trick (you may obviously do extra things, like setting the format of the datetime field or others and it may be better to unroll the comprehension and have a loop with a couple extra lines)

Have a look at the Cerebro return values

https://www.backtrader.com/docu/cerebro.html (Section: Returning the results)

result = cerebro.run(**kwargs)

The format of result returned by run will vary depending on whether optimization is used (a strategy was added with optstrategy):

All strategies added with addstrategy

result will be a list of the instances run during the backtesting

1 or more strategies were added with optstrategy

result will be a list of list. Each internal list will contain the strategies after each optimization run

Thanks. Nice to see you again on this blog, I was starting to think that you intended to abandon the project!

@backtrader

I am trying:

X = ret_1m_mat.join(ret_3m_mat).join(ret_6m_mat).join(ret_12m_mat).dropna()

However, getting error saying:

AttributeError: 'list' object has no attribute 'join'

Thanks for the help in advance as always..........

A long shot: you are running from inside one of the fancy python shell wrappers/ides that get in your way, and this is not the same python with which you are checking the installed packages.