Matt Matt - 1 year ago 255
Python Question

Panda runtime warning Cannot compare type 'Timestamp' with type 'str', sort order is undefined for incomparable objects

I am currently working on homework 2 for the coursera computational finance.

While executing this line:

ep.eventprofiler(df_events, d_data, i_lookback=20, i_lookforward=20,
s_filename=report_filename, b_market_neutral=True, b_errorbars=True,

I get the error:

anaconda/lib/python2.7/site-packages/pandas/indexes/ RuntimeWarning: Cannot compare type 'Timestamp' with type 'str', sort order is undefined for incomparable objects
return this.join(other, how=how, return_indexers=return_indexers)

Which creates the pdf file, shows the number of events occurred but not actually draw the events. I am not sure why this occurs. I am using pandas 0.18.0

Any ideas? I appreciate the help.

df_events.dtypes sample:

ALTR float64
ALXN float64
AMAT float64
AMD float64
AMGN float64
AMP float64
AMT float64
WDC float64
WEC float64
WFC float64
WFM float64
WHR float64
WIN float64
WLP float64
WM float64
WMB float64
WMT float64
XLNX float64
XOM float64
XRAY float64
XRX float64
XYL float64
YHOO float64
YUM float64
ZION float64
ZMH float64
SPY float64
dtype: object

Here is the d_data.dtypes log sample:

2008-01-02 16:00:00 23.72 37.88 45.29 66.29 144.93
2008-01-03 16:00:00 23.84 37.35 44.38 66.36 144.86
2008-01-04 16:00:00 23.16 36.82 42.40 66.50 141.31
2008-01-07 16:00:00 23.18 37.68 43.28 68.66 141.19

I get

*** AttributeError: 'dict' object has no attribute 'dtypes'

when I try to print out the d_data dtypes.

Answer Source

the trouble is caused by the line:

df_rets = df_rets - df_rets[s_market_sym]

in these couple of lines:

if b_market_neutral == True:
    df_rets = df_rets - df_rets[s_market_sym]
    del df_rets[s_market_sym]
    del df_events[s_market_sym]

in eventprofiler(...) function. Quite frankly I think the line is a bug and it should be put as a comment, to say at least - as the logic behind escapes my understanding. Others are just fine.

If you set the argument b_market_neutral to False, it will give you a nice graph, but that also takes into account the SPY market data when calculating the mean return. So the workaround, in order to use a "proper" logic when calculating mean values, would be to comment this line and recompile QSTK with this modification.

Hope this helps.

Regards, Daniel

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