Public API method that returns a dataframe containing the requested
        history window.  Data is fully adjusted.

        Parameters
        ----------
        assets : list of zipline.data.Asset objects
            The assets whose data is desired.

        bar_count: int
            The number of bars desired.

        frequency: string
            "1d" or "1m"

        field: string
            The desired field of the asset.

        data_frequency: string
            The frequency of the data to query; i.e. whether the data is
            'daily' or 'minute' bars.

        ffill: boolean
            Forward-fill missing values. Only has effect if field
            is 'price'.

        Returns
        -------
        A dataframe containing the requested data.

def get_history_window(self,
                           assets,
                           end_dt,
                           bar_count,
                           frequency,
                           field,
                           data_frequency,
                           ffill=True):
        """
        Public API method that returns a dataframe containing the requested
        history window.  Data is fully adjusted.

        Parameters
        ----------
        assets : list of zipline.data.Asset objects
            The assets whose data is desired.

        bar_count: int
            The number of bars desired.

        frequency: string
            "1d" or "1m"

        field: string
            The desired field of the asset.

        data_frequency: string
            The frequency of the data to query; i.e. whether the data is
            'daily' or 'minute' bars.

        ffill: boolean
            Forward-fill missing values. Only has effect if field
            is 'price'.

        Returns
        -------
        A dataframe containing the requested data.
        """
        if field not in OHLCVP_FIELDS and field != 'sid':
            raise ValueError("Invalid field: {0}".format(field))

        if bar_count < 1:
            raise ValueError(
                "bar_count must be >= 1, but got {}".format(bar_count)
            )

        if frequency == "1d":
            if field == "price":
                df = self._get_history_daily_window(assets, end_dt, bar_count,
                                                    "close", data_frequency)
            else:
                df = self._get_history_daily_window(assets, end_dt, bar_count,
                                                    field, data_frequency)
        elif frequency == "1m":
            if field == "price":
                df = self._get_history_minute_window(assets, end_dt, bar_count,
                                                     "close")
            else:
                df = self._get_history_minute_window(assets, end_dt, bar_count,
                                                     field)
        else:
            raise ValueError("Invalid frequency: {0}".format(frequency))

        # forward-fill price
        if field == "price":
            if frequency == "1m":
                ffill_data_frequency = 'minute'
            elif frequency == "1d":
                ffill_data_frequency = 'daily'
            else:
                raise Exception(
                    "Only 1d and 1m are supported for forward-filling.")

            assets_with_leading_nan = np.where(isnull(df.iloc[0]))[0]

            history_start, history_end = df.index[[0, -1]]
            if ffill_data_frequency == 'daily' and data_frequency == 'minute':
                # When we're looking for a daily value, but we haven't seen any
                # volume in today's minute bars yet, we need to use the
                # previous day's ffilled daily price. Using today's daily price
                # could yield a value from later today.
                history_start -= self.trading_calendar.day

            initial_values = []
            for asset in df.columns[assets_with_leading_nan]:
                last_traded = self.get_last_traded_dt(
                    asset,
                    history_start,
                    ffill_data_frequency,
                )
                if isnull(last_traded):
                    initial_values.append(nan)
                else:
                    initial_values.append(
                        self.get_adjusted_value(
                            asset,
                            field,
                            dt=last_traded,
                            perspective_dt=history_end,
                            data_frequency=ffill_data_frequency,
                        )
                    )

            # Set leading values for assets that were missing data, then ffill.
            df.ix[0, assets_with_leading_nan] = np.array(
                initial_values,
                dtype=np.float64
            )
            df.fillna(method='ffill', inplace=True)

            # forward-filling will incorrectly produce values after the end of
            # an asset's lifetime, so write NaNs back over the asset's
            # end_date.
            normed_index = df.index.normalize()
            for asset in df.columns:
                if history_end >= asset.end_date:
                    # if the window extends past the asset's end date, set
                    # all post-end-date values to NaN in that asset's series
                    df.loc[normed_index > asset.end_date, asset] = nan
        return df