Interface method.

        Return: pd.Dataframe() with columns MultiIndex [asset -> OHLCV]

def get_bars(self, assets, data_frequency, bar_count=500):
        '''
        Interface method.

        Return: pd.Dataframe() with columns MultiIndex [asset -> OHLCV]
        '''
        assets_is_scalar = not isinstance(assets, (list, set, tuple))
        is_daily = 'd' in data_frequency  # 'daily' or '1d'
        if assets_is_scalar:
            symbols = [assets.symbol]
        else:
            symbols = [asset.symbol for asset in assets]

        symbol_bars = self._symbol_bars(
            symbols, 'day' if is_daily else 'minute', limit=bar_count)

        if is_daily:
            intra_bars = {}
            symbol_bars_minute = self._symbol_bars(
                symbols, 'minute', limit=1000)
            for symbol, df in symbol_bars_minute.items():
                agged = df.resample('1D').agg(dict(
                    open='first',
                    high='max',
                    low='min',
                    close='last',
                    volume='sum',
                )).dropna()
                intra_bars[symbol] = agged

        dfs = []
        for asset in assets if not assets_is_scalar else [assets]:
            symbol = asset.symbol
            df = symbol_bars.get(symbol)
            if df is None:
                dfs.append(pd.DataFrame(
                    [], columns=[
                        'open', 'high', 'low', 'close', 'volume']
                ))
                continue
            if is_daily:
                agged = intra_bars.get(symbol)
                if agged is not None and len(
                        agged.index) > 0 and agged.index[-1] not in df.index:
                    if not (agged.index[-1] > df.index[-1]):
                        log.warn(
                            ('agged.index[-1] = {}, df.index[-1] = {} '
                             'for {}').format(
                                agged.index[-1], df.index[-1], symbol))
                    df = df.append(agged.iloc[-1])
            df.columns = pd.MultiIndex.from_product([[asset, ], df.columns])
            dfs.append(df)

        return pd.concat(dfs, axis=1)