Return dataframe of 1's (event happened) and NaNs (no event), 1 column for each symbol

def find_events(symbols, d_data, market_sym='$SPX', trigger=drop_below, trigger_kwargs={}):
    '''Return dataframe of 1's (event happened) and NaNs (no event), 1 column for each symbol'''

    df_close = d_data['actual_close']
    ts_market = df_close[market_sym]

    print "Finding `{0}` events with kwargs={1} for {2} ticker symbols".format(trigger.func_name, trigger_kwargs, len(symbols))
    print 'Trigger docstring says:\n\n{0}\n\n'.format(trigger.func_doc)

    # Creating an empty dataframe
    df_events = copy.deepcopy(df_close)
    df_events = df_events * np.NAN

    # Time stamps for the event range
    ldt_timestamps = df_close.index

    for s_sym in symbols:
        if s_sym == market_sym:
            continue
        for i in range(1, len(ldt_timestamps)):
            # Calculating the returns for this timestamp
            kwargs = dict(trigger_kwargs)
            kwargs['price_today'] = df_close[s_sym].ix[ldt_timestamps[i]]
            kwargs['price_yest'] = df_close[s_sym].ix[ldt_timestamps[i - 1]]
            kwargs['return_today'] = (kwargs['price_today'] / (kwargs['price_yest'] or 1.)) - 1
            kwargs['market_price_today'] = ts_market.ix[ldt_timestamps[i]]
            kwargs['market_price_yest'] = ts_market.ix[ldt_timestamps[i - 1]]
            kwargs['market_return_today'] = (kwargs['market_price_today'] / (kwargs['market_price_yest'] or 1.)) - 1

            if trigger(**kwargs):
                df_events[s_sym].ix[ldt_timestamps[i]] = 1
    print 'Found {0} events where priced dropped below {1}.'.format(df_events.sum(axis=1).sum(axis=0), trigger_kwargs['threshold'])
    return df_events