Hi Simon,
That's an awesome idea. There are a few ways to go about doing this, the way that I would normally do is to load in my data through fetch_csv, define in the pre_func parameter a method that simply copies the date column as another name like "date_copy", set my universe of stocks to be the stocks found in the CSV (using fetcher's set_universe) function, and finally in handle_data(context, data) check whether or not the current date (get_datetime()) matches the date_column of the CSV I pulled in and trade if it does match.
Here are a few code snippets that I think will help you get in the right direction.
def pre_func(df):
#: We're going to make a copy of the date column to make sure we know when to trade at the appropriate date
#: This is where we make a copy of the date column
df['date_copy'] = df['date']
return df
def initialize(context):
#: This preserves the extra date_column that we'll need for each ticker
fetch_csv(url,
pre_func=pre_func,
symbol_column='ticker')
#: This simply loads our stocks into our universe
fetch_csv(
url,
pre_func=pre_func,
symbol_column='ticker',
universe_func=my_universe)
and later in handle_data I use this check before trading a stock:
if 'date_column' in data[stock]:
date = data[stock]['date_copy']
#: Convert date to datetime object using something like
date = datetime.strptime(date, date_format)
if date.month == get_datetime().month and date.day == get_datetime().day and date.year = get_datetime().year:
#: Order here
order_me
If this is working properly, you should be able to see the trade data for each of those securities in the backtesting results window (https://www.quantopian.com/help#backtests-results)
I realize this is a little vague, but try starting out with these bits of code and let's keep this thread going with more questions!
Seong
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