import backtrader as bt

class ShortInterestEffect(bt.Strategy):
    def __init__(self):
        self.sir = dict()

    def prenext(self):
        self.next()

    def next(self):
        if self.datetime.date().day != 1:
            return
        short_interest_ratios = list()
        for data in self.datas:
            if data._name in self.sir:
                short_interest_ratios.append((data, self.sir[data._name]))
        short_interest_ratios.sort(key=lambda x: x[1])
        top_percentile = int(len(short_interest_ratios) * 0.01)
        selected_stocks = short_interest_ratios[:top_percentile]
        target_weight = 1.0 / len(selected_stocks)
        for data, _ in selected_stocks:
            self.order_target_percent(data, target_weight)
        for data in self.datas:
            if data not in [stock for stock, _ in selected_stocks]:
                self.order_target_percent(data, 0.0)

    def notify_data(self, data, status, *args, **kwargs):
        if status == data.LIVE:
            self.sir[data._name] = data.sir[0]

def run_backtest(feed_list, start_cash, fromdate, todate):
    cerebro = bt.Cerebro()
    cerebro.broker.set_cash(start_cash)
    for feed in feed_list:
        data = bt.feeds.PandasData(dataname=feed['data'], short_interest_ratio=feed['short_interest_ratio'])
        cerebro.adddata(data, name=feed['symbol'])
    cerebro.addstrategy(ShortInterestEffect)
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
    cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
    cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='annual_return')
    cerebro.run(stdstats=False, fromdate=fromdate, todate=todate)
    return cerebro

{    'symbol': 'ticker',    'data': pandas_dataframe,    'short_interest_ratio': 'column_name'}