I'm testing the fetch_csv function. The code I'm using is the sample code provided in https://www.quantopian.com/help with small modification.

Basically I'm renaming 'Hong Kong 14:00' to 'price' and getting it by data.current('gold', 'price'), and then logging to console.

While in backtest it can return correct data, but in paper testing it always returns NaN. data.current() should return the last available entry of the series, did I miss anything?

Sample code below:

import pandas

def rename_col(df):  
    df = df.rename(columns={'Hong Kong 14:00': 'price'})  
    df = df.rename(columns={'Value': 'price'})  
    df = df.fillna(method='ffill')  
    df = df[['price', 'sid']]  
    # Correct look-ahead bias in mapping data to times  
    df = df.tshift(1, freq='b')  
    #log.info(' \n %s ' % df.head())  
    return df

def preview(df):  
    log.info(' \n %s ' % df.head())  
    return df  
def initialize(context):  
    # import the external data  
    fetch_csv('https://www.quandl.com/api/v1/datasets/JOHNMATT/PALL.csv?trim_start=2012-01-01',  
        date_column='Date',  
        symbol='palladium',  
        pre_func = preview,  
        post_func=rename_col,  
        date_format='%Y-%m-%d')

    fetch_csv('https://www.quandl.com/api/v1/datasets/BUNDESBANK/BBK01_WT5511.csv?trim_start=2012-01-01',  
        date_column='Date',  
        symbol='gold',  
        pre_func = preview,  
        post_func=rename_col,  
        date_format='%Y-%m-%d')  
    # Tiffany  
    context.stock = sid(7447)  
    schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())



def rebalance(context, data):  
    # Invest 100% of the portfolio in Tiffany stock when the price of gold is low.  
    # Decrease the Tiffany position to 50% of portfolio when the price of gold is high.  
    current_gold_price = data.current('gold', 'price')  
    log.info('cur gold price = ' + str(current_gold_price))  
    if (current_gold_price < 1600):  
       order_target_percent(context.stock, 1.00)  
    if (current_gold_price > 1750):  
       order_target_percent(context.stock, 0.50)

    # Current prices of palladium (from .csv file) and TIF  
    current_pd_price = data.current('palladium', 'price')  
    current_tif_price = data.current(context.stock, 'price')  
    # Record the variables  
    record(palladium=current_pd_price, gold=current_gold_price, tif=current_tif_price)