import numpy as np

import pandas as pd

from pandas import Series, DataFrame

from pandas.compat import zip

from pandas.types.generic import ABCSeries, ABCIndex

from pandas.types.missing import isnull, notnull

from pandas.types.common import (is_bool_dtype,

                                 is_categorical_dtype,

                                 is_integer)

from pandas.core.common import _values_from_object

from pandas.core.algorithms import take_1d

import pandas.compat as compat

from pandas.core.base import AccessorProperty, NoNewAttributesMixin

from pandas.util.decorators import Appender

import re

import pandas.lib as lib

import warnings

import os

import sys

import datetime as dt

import pandas

import numpy as np

def load_data_frame_from_hdf5(fullname):

    store = pandas.HDFStore(fullname, mode='r')

data=load_data_frame_from_hdf5(hdf5FileName)

cp=pandas.DataFrame(data.cp)

op=pandas.DataFrame(data.op)

adj_cp=pandas.DataFrame(data.adj_cp)

#load all other tickers

    cp=pandas.merge(cp, pandas.DataFrame(data.cp), left_index=True, right_index=True, how='outer')

    op=pandas.merge(op, pandas.DataFrame(data.op), left_index=True, right_index=True, how='outer')

import copy

import itertools

import re

import numpy as np

from pandas.core.base import PandasObject

from pandas.core.common import (_possibly_downcast_to_dtype, isnull,

                                _possibly_infer_to_datetimelike)

from pandas.core.index import Index, MultiIndex, _ensure_index

from pandas.core.indexing import (_maybe_convert_indices, _length_of_indexer)

import pandas.core.common as com

from pandas.sparse.array import _maybe_to_sparse, SparseArray

import pandas.lib as lib

import pandas.tslib as tslib

import pandas.computation.expressions as expressions

import datetime as dt

import pandas

import numpy as np

import os

import matplotlib.pyplot as plt

import sys

path="/backtest_data/1second/teams_data/team3/data/"

sent=pandas.load(path+"recorded_future.bin")

cp=pandas.load(path+"adj_close_px_all_from_09.bin") #need this to merge with the date index used in price files

#flatten out the sentiment data

w30=pandas.DataFrame(sent['w30'].ix[tickers[0]])

w24h=pandas.DataFrame(sent['w24h'].ix[tickers[0]])

count=pandas.DataFrame(sent['Count'].ix[tickers[0]])

import nose

from numpy import nan

import numpy as np

from pandas import Index, isnull, Timestamp

from pandas.util.testing import assert_almost_equal

import pandas.util.testing as tm

from pandas.compat import range, lrange, zip

import pandas.lib as lib

import pandas.algos as algos

def test_series_grouper():

    from pandas import Series

def test_series_bin_grouper():

    from pandas import Series

    def test_generate_bins(self):

        from pandas.core.groupby import generate_bins_generic

import numpy as np

import pandas.tslib as tslib

import pandas.lib as lib

import pandas._join as _join

import pandas.algos as _algos

import pandas.index as _index

from pandas.lib import Timestamp, Timedelta, is_datetime_array

from pandas.compat import range, u

from pandas.compat.numpy import function as nv

from pandas import compat

from pandas.types.generic import ABCSeries, ABCMultiIndex, ABCPeriodIndex

from pandas.types.missing import isnull, array_equivalent

import os

import sys

import datetime as dt

import pandas

import numpy as np

def load_data_frame_from_hdf5(fullname):

    store = pandas.HDFStore(fullname, mode='r')

cp=pandas.DataFrame(data['cp'].ix[start:end])

op=pandas.DataFrame(data['op'].ix[start:end])

vol=cp=pandas.DataFrame(data['vol'].ix[start:end])

    cp=pandas.merge(cp, pandas.DataFrame(data['cp'].ix[start:end]), left_index=True, right_index=True, how='outer')

    op=pandas.merge(op, pandas.DataFrame(data['op'].ix[start:end]), left_index=True, right_index=True, how='outer')

    vol=pandas.merge(vol, pandas.DataFrame(data['vol'].ix[start:end]), left_index=True, right_index=True, how='outer')

import statsmodels.base.model as base

from statsmodels.base import data

import statsmodels.base.wrapper as wrap

from statsmodels.tsa.base import datetools

from numpy import arange, asarray

from pandas import Index

from pandas import datetools as pandas_datetools

import datetime

_freq_to_pandas = datetools._freq_to_pandas

dates : array-like of datetime, optional

    An array-like object of datetime objects. If a pandas object is given

                self._data.predict_end = dtend

                # for pandas 0.7.x vs 0.8.x

import numpy as np

import pandas.lib as lib

from pandas.types.common import (is_number,

                                 _ensure_object)

from pandas.types.cast import _possibly_downcast_to_dtype

import pandas as pd

from pandas.compat import reduce

from pandas.core.index import Index

from pandas.core import common as com

    """

    Numpy version of itertools.product or pandas.compat.product.

    >>> import pandas as pd

import os

import nose

import numpy as np

from pandas.compat import zip

from pandas import DataFrame, Series, unique

import pandas.util.testing as tm

from pandas.util.testing import assertRaisesRegexp

import pandas.core.common as com

from pandas.core.algorithms import quantile

from pandas.tools.tile import cut, qcut

import pandas.tools.tile as tmod

import nose

from datetime import datetime

from numpy.random import randn

import numpy as np

from pandas.core.api import Series, DataFrame, date_range

from pandas.util.testing import assert_almost_equal

import pandas.core.datetools as datetools

import pandas.stats.moments as mom

import pandas.util.testing as tm

import pandas.stats.math as pmath

import pandas.tests.test_series as ts

from pandas import ols

import re

from datetime import datetime, timedelta

import numpy as np

import pandas.compat as compat

import pandas as pd

from pandas.compat import u, StringIO

from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin

from pandas.util.testing import assertRaisesRegexp, assert_isinstance

from pandas import Series, Index, Int64Index, DatetimeIndex, PeriodIndex

from pandas import _np_version_under1p7

import pandas.tslib as tslib

import nose

import pandas.util.testing as tm

        exp1 = """<class 'pandas.tseries.index.DatetimeIndex'>

Length: 0, Freq: D, Timezone: None"""

from dateutil.relativedelta import relativedelta

from pandas.compat import range, iteritems

from pandas import compat

import nose

from pandas.compat.numpy import np_datetime64_compat

from pandas.core.datetools import (bday, BDay, CDay, BQuarterEnd, BMonthEnd,

                                   BusinessHour, CustomBusinessHour,

from pandas.core.series import Series

from pandas.tseries.frequencies import (_offset_map, get_freq_code,

                                        _get_freq_str, _INVALID_FREQ_ERROR)

from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache

from pandas.tseries.tools import parse_time_string, DateParseError

import pandas.tseries.offsets as offsets

import sys

import numpy as np

from pandas import *

import pandas.core.sparse as spm

import pandas.compat as compat

reload(spm)

from pandas.core.sparse import *

if 'linux' in sys.platform:

    pth = '/home/wesm/code/pandas/example'

else:

# import hotshot, hotshot.stats

import pandas.stats.plm as plm

import pandas.stats.interface as face

reload(plm)

from pandas import tslib, lib

import pandas._period as period

import datetime

import pandas as pd

from pandas.core.api import (Timestamp, Index, Series, Timedelta, Period,

                             to_datetime)

from pandas.tslib import get_timezone

from pandas._period import period_asfreq, period_ordinal

from pandas.tseries.index import date_range, DatetimeIndex

from pandas.tseries.frequencies import (

    get_freq,

)

import pandas.tseries.tools as tools

import pandas.tseries.offsets as offsets

import nose

from pandas import compat

from pandas.compat import u, PY3

from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,

                    date_range, period_range, Index, Categorical)

from pandas.core.common import PerformanceWarning

from pandas.io.packers import to_msgpack, read_msgpack

import pandas.util.testing as tm

from pandas.util.testing import (ensure_clean,

                                 assert_categorical_equal,

                                 patch)

from pandas.tests.test_panel import assert_panel_equal

import pandas

import numpy as np

import pandas.index as _index

from pandas import compat

from pandas.compat.numpy import function as nv

from pandas.types.generic import ABCCategorical, ABCSeries

from pandas.types.common import (is_categorical_dtype,

                                 _ensure_platform_int,

                                 is_scalar)

from pandas.types.missing import array_equivalent

from pandas.util.decorators import (Appender, cache_readonly,

                                    deprecate_kwarg)

from pandas.core.config import get_option

from pandas.indexes.base import Index, _index_shared_docs

import numpy as np

import pandas.lib as lib

import pandas._join as _join

import pandas.algos as _algos

import pandas.index as _index

from pandas.types.common import (is_dtype_equal, pandas_dtype,

                                 is_integer_dtype, is_scalar)

from pandas.types.missing import array_equivalent, isnull

from pandas.core.common import _values_from_object

from pandas import compat

from pandas.indexes.base import Index, InvalidIndexError, _index_shared_docs

from pandas.util.decorators import Appender, cache_readonly

import pandas.indexes.base as ibase

import sys

import numpy as np

from pandas import *

import pandas.core.sparse as spm

reload(spm)

from pandas.core.sparse import *

else:

    pth = '/Users/wesm/code/pandas/example'

# import hotshot, hotshot.stats

import pandas.stats.plm as plm

import pandas.stats.interface as face

reload(plm)