Quantopian fetch_csv

[Hassan Gharehassanloo]

Hi Everyone,
Does Zipline provide any functionality like fetch_csv in Quantipian or its equivalent?

Best Regards,
Hasan Gharehasanloo

[Erick Gomez]

Hassan,

Have you figure out how to run zipline in live trading? 

[Hasan Gharehasanloo]

Erik,

Unfortunately, not yet. It needs a lot of changes in Zipline.

------------------------------------------------
Best Regards

Hasan Gharehasanloo

Front-end Web Developer

Email: h_g...@yahoo.com
Cell: (0098) 912-462-4557
LinkedIn: linkedin.com/fa/hghare

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[Mark Francis]

It's just a modified pandas dataframe. Look at how load_bars_from_yahoo works. I ended up writing (hacking) my own function to import from my personal database. I'm sure this is really crude but it works for me. This might give you a starting point (replace from_sql to from_csv pandas functions):

def load_bars_from_database(start, end, stocks=[]):


    db = pymysql.connect(host="",
                         user="",
                         passwd="",
                         db="")
    df_dict = {}


    for stock in stocks:


        sql = "select date, open, high, low, close, volume from data \
        where symbol = '{0}' and date > '{1}' and date < '{2}'".format(
            stock, start, end)


        data_raw = pd.read_sql(sql, con=db)


        #Setting the date index
        data_raw.index = pd.to_datetime(data_raw['date'])
        data = data_raw.tz_localize('UTC')


        # It might not be needed to drop the date column after converted to index
        #data.drop('date', axis=1, inplace=True)


        #setting the price column to be the close, this is how i think yahoo\
        #finance deals with splits and such
        data['price'] = data['close']


        # Adding this stock to the dict of dataframes
        df_dict[stock] = data


    # Convert dict to a panel
    panel_return = pd.Panel(df_dict)


    return panel_return