3. Bug using Pandas hdf?
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Bug using Pandas hdf?

  • C

    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)
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  • T

    As far as I am aware, BT data feeds require all timestamps / dates to be datetime objects. So that is why you need to convert the TS before adding it.

    In one of my scripts I do this by:

    df.index = pd.to_datetime(df.index, format='%Y-%m-%dT%H:%M:%S.%fZ')

    Some more notes here:
    https://www.backtrader.com/docu/pandas-datafeed/pandas-datafeed.html?highlight=pandas

    Another thing you could look at is the datetime keyword argument in the link above. It looks like your timestamp is not the index.

    datetime (default: None)
    None : datetime is the “index” in the Pandas Dataframe
    -1 : autodetect position or case-wise equal name
    = 0 : numeric index to the colum in the pandas dataframe
    string : column name (as index) in the pandas dataframe

    Having said all that, you now have it working! So it does not matter too much :)

    0
  • C

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

    0