@Ed-Bartosh will you implement both backtesting and live? That would be great!

Actually I'd even pay something as a donation or whatever to @backtrader to implement ccxt as a new broker, to assure that it is correctly done. I don't rely too much on my coding skills to do it.

I'm currently using ccxt and I'd love to have it in backtrader :)

Humm... your one-line solution seems more elegant than mine. Thanks! :)

@mpskowron May be that this helps: https://www.backtrader.com/docu/mixing-timeframes/indicators-mixing-timeframes.html

I'm not sure if it is applicable also to length datas, but it is designed for timeframes of different length

Hi, using the standard example for Pandas, but using hdf files, I'm getting an error and I'm not sure if its due to a problem in the data or an error in Backtrader itself:

  File "b2.py", line 69, in <module>
    runstrat()
  File "b2.py", line 46, in runstrat
    cerebro.run()
  File "/Library/Python/2.7/site-packages/backtrader/cerebro.py", line 1127, in run
    runstrat = self.runstrategies(iterstrat)
  File "/Library/Python/2.7/site-packages/backtrader/cerebro.py", line 1209, in runstrategies
    data.preload()
  File "/Library/Python/2.7/site-packages/backtrader/feed.py", line 435, in preload
    while self.load():
  File "/Library/Python/2.7/site-packages/backtrader/feed.py", line 476, in load
    _loadret = self._load()
  File "/Library/Python/2.7/site-packages/backtrader/feeds/pandafeed.py", line 262, in _load
    dt = tstamp.to_pydatetime()
AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime'

It crashes in Cerebro.run()

This is the code (basically, copied from example, but using hdf instead of csv). Here goes also the pandas data, just in case it has some error: https://drive.google.com/open?id=0ByTv1orrPKKPYkR2d1NYRHVhSUU

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import argparse

import backtrader as bt
import backtrader.feeds as btfeeds

import pandas


def runstrat():
    args = parse_args()

    # Create a cerebro entity
    cerebro = bt.Cerebro(stdstats=False)

    # Add a strategy
    cerebro.addstrategy(bt.Strategy)

    # Get a pandas dataframe
    datapath = ('datos1.pand')

    # Simulate the header row isn't there if noheaders requested
    skiprows = 1 if args.noheaders else 0
    header = None if args.noheaders else 0

    dataframe = pandas.read_hdf(datapath,"table",
                                skiprows=skiprows,
                                header=header,
                                parse_dates=True,
                                index_col=0)

    if not args.noprint:
        print('--------------------------------------------------')
        print(dataframe)
        print('--------------------------------------------------')

    # Pass it to the backtrader datafeed and add it to the cerebro
    data = bt.feeds.PandasData(dataname=dataframe)
    cerebro.adddata(data)

    # Run over everything
    cerebro.run()

    # Plot the result
    cerebro.plot(style='bar')


def parse_args():
    parser = argparse.ArgumentParser(
        description='Pandas test script')

    parser.add_argument('--noheaders', action='store_true', default=False,
                        required=False,
                        help='Do not use header rows')


    parser.add_argument('--noprint', action='store_true', default=False,
                        help='Print the dataframe')

    return parser.parse_args()


if __name__ == '__main__':
    runstrat()

EDIT: I think the data is correct because it loads correctly when I do print(dataframe):

--------------------------------------------------
       Timestamp      Open      High       Low     Close      Volume
0     1483228800  0.008335  0.008377  0.008332  0.008368   22.678130
1     1483229100  0.008379  0.008412  0.008373  0.008400   23.101541
2     1483229400  0.008400  0.008463  0.008388  0.008450   42.334102
...          ...       ...       ...       ...       ...         ...
8927  1485906900  0.011050  0.011067  0.011035  0.011046    4.529571
8928  1485907200  0.011046  0.011091  0.011036  0.011090    9.032207

[8929 rows x 6 columns]
--------------------------------------------------

EDIT2: Ok, I found how to fix this. I don't fully understand why this is needed but now it works. It is needed to transform the timestamp into a python Timestamp object:

def to_datetimeindex(unixtimestamp):
    t=datetime.datetime.fromtimestamp(unixtimestamp)
    return t.strftime("%Y-%m-%d %H:%M:%S")

def to_Timestamp (stringtime):
    return pandas.Timestamp(stringtime)


def runstrat():
    args = parse_args()

    # Create a cerebro entity
    cerebro = bt.Cerebro(stdstats=False)

    # Add a strategy
    cerebro.addstrategy(bt.Strategy)

    # Get a pandas dataframe
    datapath = ('datos1.pand')

    # Simulate the header row isn't there if noheaders requested
    skiprows = 1 if args.noheaders else 0
    header = None if args.noheaders else 0

    dataframe = pandas.read_hdf(datapath,"table",openinterest=-1)

    dataframe["Timestamp"] = dataframe["Timestamp"].apply(to_datetimeindex)
    dataframe["Timestamp"] = dataframe["Timestamp"].apply(to_Timestamp)