Hi everyon,

This algo monitored the behavior for each day and print 'Buy', 'Sell', 'No trade' correspondingly. In this algo, it should sell BAC on 2006-05-29 (I tested it using local python). However, it didn't trade on that day, even not shown in the output and the backtest results.

I double checked that the datapoint on 2006-05-29 was imported into quantopian.

Anyone can help? Thank you all so much!

My code is below.

import pandas

def initialize(context):  
    # context.width = 1.9  
    # context.window = 350  
    context.BAC = sid(700)  
    context.amount = 100  
    context.longs = context.shorts = 0  
    # import the external data  
    url = 'https://docs.google.com/spreadsheets/d/1dlHaM4QhybACe20gY6Udu0LXcdoQWUQjNXQRFHcXADQ/pub?gid=1314643233&single=true&output=csv'  
    fetch_csv(url,  
        symbol='BAC',  
        date_column='Date',  
        pre_func = preview,  
        post_func = rename_col,  
        date_format='%Y-%m-%d')  
    schedule_function(BBbands,  
                      date_rules.every_day(),  
                      time_rules.market_open()  
                     )  
    # Record variables at the end of each day.  
    schedule_function(record_vars,  
                      date_rules.every_day(),  
                      time_rules.market_close())  


def preview(df):  
    log.info(' \n %s ' % df.dropna().head())  
    log.info(' \n %s ' % df.tail())  
    return df    

def rename_col(df):  
    width = 1.9  
    window = 350  
    df = df.rename(columns={'PX_CLOSE_1D': 'Close'})  
    df = df.rename(columns={'PX_OPEN': 'Open'})  
    df = df[['Open', 'Close', 'sid']]  
    df['Close_SMA'] = df['Close'].rolling(window).mean()  
    df['Close_STD'] = df['Close'].rolling(window).std()  
    df['Upperband'] = df['Close_SMA'] + width * df['Close_STD']  
    df['Lowerband'] = df['Close_SMA'] - width * df['Close_STD']  
    # df = df.fillna(method='ffill')  
    df.dropna(inplace=True)  
    # # Correct look-ahead bias in mapping data to times  
    # df = df.tshift(1, freq='b')  
    log.info(' \n %s ' % df.head())  
    log.info(' \n %s ' % df.tail())  
    return df

def handle_data(context, data):  
    pass


def BBbands(context,data):  
    upperband = data.current('BAC','Upperband')  
    lowerband = data.current('BAC','Lowerband')  
    open_price = data.current('BAC','Open')  
    log.info(get_datetime())  
    log.info('cur_price: %s --- [upperbb: %s,lowerbb: b %s]' %(open_price, upperband, lowerband))  
    if open_price < lowerband:  
        order(context.BAC, context.amount)  
        log.info('Buying %d shares of %s' %(context.portfolio.portfolio_value/open_price, 'BAC'))  
    elif open_price > upperband:  
        order(context.BAC, -context.amount)  
        log.info('Selling %d shares of %s' %(context.portfolio.portfolio_value/open_price, 'BAC'))  
    else:  
        log.info('No trade today')  
        pass  
    record(Upperband=upperband, Lowerband=lowerband, CurrentPrice=open_price)  
def record_vars(context, data):  
    """  
    This function is called at the end of each day and plots our leverage as well  
    as the number of long and short positions we are holding.  
    """

    # Check how many long and short positions we have.  
    for position in context.portfolio.positions.itervalues():  
        if position.amount > 0:  
            context.longs += 1  
        elif position.amount < 0:  
            context.shorts += 1    

    log.info('longs: %s' %context.longs)  
    log.info('shorts: %s\n\n' %context.shorts)