I am new to Quantopian . I have a csv file with date as index and 2 columns long and short each with a list of stocks : example:-
20130104 ['BABA','GOOGL','TSLA'] ['FB','TQ','ADBE']
20130105 ['FB','TSLA','QQQ'] ['TBE','QEB','SEC']

I want my program to go over the dates and buy and short the respective stocks according to column labels . In the below code how to replace the rsi with my own stocks or use the csv file to build an algo from scratch

def make_pipeline():  
    rsi = RSI()  
    return Pipeline(  
        columns={  
            'longs': rsi.top(3),  
            'shorts': rsi.bottom(3),  
        },  
    )


def rebalance(context, data):

    # Pipeline data will be a dataframe with boolean columns named 'longs' and  
    # 'shorts'.  
    pipeline_data = context.pipeline_data  
    all_assets = pipeline_data.index

    longs = all_assets[pipeline_data.longs]  
    shorts = all_assets[pipeline_data.shorts]

    record(universe_size=len(all_assets))

    # Build a 2x-leveraged, equal-weight, long-short portfolio.  
    one_third = 1.0 / 3.0  
    for asset in longs:  
        order_target_percent(asset, one_third)

    for asset in shorts:  
        order_target_percent(asset, -one_third)

    # Remove any assets that should no longer be in our portfolio.  
    portfolio_assets = longs | shorts  
    positions = context.portfolio.positions  
    for asset in viewkeys(positions) - set(portfolio_assets):  
        # This will fail if the asset was removed from our portfolio because it  
        # was delisted.  
        if data.can_trade(asset):  
            order_target_percent(asset, 0)


def initialize(context):  
    attach_pipeline(make_pipeline(), 'my_pipeline')

    # Rebalance each day.  In daily mode, this is equivalent to putting  
    # `rebalance` in our handle_data, but in minute mode, it's equivalent to  
    # running at the start of the day each day.  
    schedule_function(rebalance, date_rules.every_day())

    # Explicitly set the commission/slippage to the "old" value until we can  
    # rebuild example data.  
    # github.com/quantopian/zipline/blob/master/tests/resources/  
    # rebuild_example_data#L105  
    context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))  
    context.set_slippage(slippage.VolumeShareSlippage())


def before_trading_start(context, data):  
    context.pipeline_data = pipeline_output('my_pipeline')


def _test_args():  
    """  
    Extra arguments to use when zipline's automated tests run this example.  
    Notes for testers:  
    Gross leverage should be roughly 2.0 on every day except the first.  
    Net leverage should be roughly 2.0 on every day except the first.  
    Longs Count should always be 3 after the first day.  
    Shorts Count should be 3 after the first day, except on 2013-10-30, when it  
    dips to 2 for a day because DELL is delisted.  
    """  
    import pandas as pd

    return {  
        # We run through october of 2013 because DELL is in the test data and  
        # it went private on 2013-10-29.  
        'start': pd.Timestamp('2013-10-07', tz='utc'),  
        'end': pd.Timestamp('2013-11-30', tz='utc'),  
        'capital_base': 100000,  
    }  

Thanks in Advance