Hello all,
I have been trying to figure out a way to load data from before the start date so there is no data lag for the first days of trading. I would like to load a csv with the prior few months of prices so I only need it the one time to set some initial parameters. My problem is that the fetcher will go get the same old data daily which seems like a waste and there doesn't seem to be a way around this.
Questions:

1. Is there a way to call fetch_csv once?
   
2. can the fetcher be given several urls or be called several times per cycle?
   
3. Is zipline.utils.factory.load_from_yahoo supported???? I get Runtime exception: TypeError: a float is required
   
4. Will there be URL support outside of the fetcher?? I assume not for security reasons.
   

Im following this template because I think it's a great step in the right direction.

import datetime  
import math  
import numpy as np  
import pandas as pd

# I'd like to use the fetcher in this sort of way.

class Thing():

    def __init__(self, context):  
        self.context = context  
    def pre_fetcher(self, df):  
        # modify df and update context here  
        log.info(str(df))  
        return df  
    def post_fetcher(self, df):  
        # And/or here  
        log.info(str(df))  
        return df


class YahooCSV():  
    """  
    This is a tool to get the url for the current quote of a company  
    or the historical data over a time period. They have sector and  
    industry data as well.  
    To extend this there's more info here,  
    http://code.google.com/p/yahoo-finance-managed/wiki/CSVAPI  
    """

    def quote(self, symbol, stat='all'):  
        url = "http://download.finance.yahoo.com/d/quotes.csv?s=%s&f=" %(symbol)  
        all_stats = 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7a0b0'  
        if not stat == 'all':  
            url += stat  
        else:  
            url += all_stats  
        return url  
    def history(self, sym, start_date, end_date):  
        #  
        # Date format must be 'YYYY-MM-DD'  
        # str(data[sym].datetime) works for this  
        #  
        url = 'http://ichart.yahoo.com/table.csv?'  
        url += 's={}&a={}&b={}&c={}&d={}&e={}&f={}'.format(  
            sym,  
            str(int(start_date[5:7]) - 1),  
            str(int(start_date[8:10])),  
            str(int(start_date[0:4])),  
            str(int(end_date[5:7]) - 1),  
            str(int(end_date[8:10])),  
            str(int(end_date[0:4]))  
        )  
        url += '&g=d&ignore=.csv'  
        return url  
def initialize(context):  
    context.stocks = [sid(16841), sid(8229)]  
    start_date = '2012-06-01'  
    end_date = '2013-01-01'  
    url = YahooCSV().history('AMZN', start_date, end_date)

    fetch_csv(  
        url,  
        date_column = 'Date',  
        symbol = 'AMZN',  
        pre_func = Thing(context).pre_fetcher,  
        post_func = Thing(context).post_fetcher,  
#      one_time_fetch = True           this would be awesome to have!!  
    )

########################################################  
#                                ALTERNATIVE  
########################################################

def initialize(context):  
    #  
    #  
    context.handle_back_data = True  
    context.back_data_start_date = datetime.datetime(2012,1,1)  
    context.back_data_end_date = datetime.datetime(2012,6,1)  
    #  
    #Build the Universe  
    #Add SPDR SPY  
    context.SPY = sid(8554)  
    context.SHY = sid(23911)

    #Set constraints on borrowing  
    context.pct_invested_threshold = 0.95 #Set limit on percent invested (as a decimal)  
    context.init_margin=1.50 #Set initial margin requirement  
    context.maint_margin=1.25 #Set the maintenance margin requirement  
def handle_data(context, data):  
    if context.handle_back_data:  
        handle_back_data(context)  
        context.handle_back_data = False

    # Update new frame  
    update_newFrame(context, data)  

    #Apply Trade Logic  
    trade_logic(context,data)

def handle_back_data(context):  
    start = context.back_data_start_date  
    end = context.back_data_end_date  
    back_data = load_from_yahoo(  
        stocks=['SPY', 'SHY'],  
        indexes={},  
        start=start,  
        end=end,  
        adjusted=False  
    )  
    # Mess with back data here:  

I actually like the second way more because load_from_yahoo takes a list of stocks.