Hello,

I am trying to run an algorithm with zipeline involving SP500, however I have got the following error whether I use the sid of symbol function.
My backtest begins on January 2008 until December 2016.
Do you have any idea what is wrong ?

Thank you for your help.

Symbol 'SPY' was not found.

SidsNotFound: No asset found for sid: 8554.

Following is my code:

# Companies file directory Wikipedia  
companies_file_path='https://docs.google.com/spreadsheets/d/12u_q25eaTF455JOcg8kIh4g6YQzK0cfjc_W5jANxc5U/pub?output=csv'  
#economic termns file directory  
economic_file_path='https://docs.google.com/spreadsheets/d/1sx6CQlWBuRmqz2X7dnwOI95JWq8aLsgrbaUQ-1Vjow0/pub?output=csv'  

from zipline.api import (symbol,sid, order, record, fetch_csv,set_benchmark,schedule_function,  
        date_rules,time_rules,order_target_percent,order_target,get_datetime,set_commission,commission )  
from zipline import run_algorithm  
import numpy as np  
import datetime  
import matplotlib.pyplot as plt  
import pytz  
from datetime import datetime  
import pandas as pd  
#from quantopian.pipeline.data.sentdex import sentiment_free

def drop_na(df):  
    return df.fillna(0)

def backtest(ticker,delta_t1,delta_t2):  
    def initialize(context):  
        # This is the search query we are using, this is tied to the csv file.  
        context.query = str(ticker)  #column name for each stock in our universe  
        # User fetcher to get data. I uploaded this csv file manually.  
        # Note that this data is daily averages.  
        fetch_csv(companies_file_path,  
                  date_format='%Y-%m-%d',  
                  pre_func =drop_na,  
                  symbol='wiki',  
                  skiprows=0  
        )  
        set_commission(commission.PerTrade(cost=0.000 ))  
         #Define our securities, global variables, and schedule when to trade.  
        context.security = sid(8554)  
        #set_benchmark(symbol(ticker)) # S&P5000 # stock refering to context.query 

        schedule_function(rebalance,  
                          date_rule=date_rules.every_day(), #day or week  
                          time_rule=time_rules.market_open())

        context.past_queries_t1 = []  
        context.past_queries_t2 = []

    def rebalance(context, data):  
        c = context  
        dt=get_datetime()  
        # Extract weekly average of search query.  
        indicator = data.current('wiki',c.query);  #c.query=nom de la colonne dans data  
        '''Compute the moving average t1'''  
        # Track our fetched values in a context variable to build up history.  
        context.past_queries_t1.append(indicator)

        if len(context.past_queries_t1) > delta_t1:  
            del context.past_queries_t1[0]

        if len(context.past_queries_t1) == delta_t1:  
            # Compute average over weeks in range  
            mean_indicator_t1 = np.mean(context.past_queries_t1)  
        ''' Compute the moving average t2'''  
        # Track our fetched values in a context variable to build up history.  
        context.past_queries_t2.append(indicator)

        if len(context.past_queries_t2) > delta_t2:  
            del context.past_queries_t2[0]

        if len(context.past_queries_t2) == delta_t2:  
            # Compute average over weeks in range  
            mean_indicator_t2 = np.mean(context.past_queries_t2)  
            # Long or short depending on whether debt search frequency  
            # went down or up, respectively.  
            if np.isnan(mean_indicator_t2):  
                order_target_percent(c.security, 0)  
            elif mean_indicator_t1>mean_indicator_t2 and mean_indicator_t2>10 and data.can_trade(c.security):  
                order_target_percent(c.security, -1.0)

            elif mean_indicator_t1<mean_indicator_t2 and mean_indicator_t2>10 and data.can_trade(c.security):  
                order_target_percent(c.security, 1.0)  
            else:  
                order_target_percent(c.security, 0)  
    # Set up the stuff for running the trading simulation  
    start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)  
    end = datetime(2016, 12, 31, 0, 0, 0, 0, pytz.utc)  
    base_capital = 10000

    # run the trading algorithm and save the results in perf  
    perf = run_algorithm(start, end, initialize, base_capital,bundle = 'quantopian-quandl')  
    return perf