Quantopian's community platform is shutting down. Please read this post for more information and download your code.
Back to Community
Same Day Pricing Data Availability for Algorithms
Daryck Brown
posted May 9, 2020
API Using Data Tools and Tips Q Updates Pipeline

Hi Everyone,

I'm relatively new to Quantopian and have spent the last month performing research creating an algorithm that implements close-to-close strategy. My algorithm re-balances 20 minutes before close.

I've got it working pretty well but I'm missing the last important part... I need to get the most recent pricing available for "today" (the day that the my algorithm is being run) for my pipeline. I need this in order to get the best estimate of the close-close return for that day since I won't be able to get the actual close-close return since it'll re-balance before close.

For example: Running my pipeline in the notebook research environment on at 3:40pm on 5/11/2020, is there a way for me to retrieve pricing that same day closest to 3:40pm (whether it's at 12,1,2,3pm).

And if i'm running my algorithm in the backtest environment - collect the pricing at a specific time on the current day the back test is simulating.

Summary of my question: How often is pricing data available from the current date/time? and how to retrieve that data for that specific time closest to 3:40pm?

Can anyone provide suggestions or point me in the right direction?
I apologize if this question is unclear.
Thank you,

2 responses
Daryck Brown
May 9, 2020

I actually believe the code below may help solve my issue. Would this work the way I described previously? If yes, in the case of backtesting, would I have to call this function using a separate schedule function or call this function in my rebalance function that is already being called at 3:40pm?

def before_trading_start(context, data):  
    # Get a dataframe of our pipe data.  
    context.output = pipeline_output('my_pipe')  


def enter_buy_sell_orders(context, data):  
    # Get the current prices for all stocks returned by our pipe (ie context.output.index)  
    # Add those prices as a new column named 'current_price' in 'context.data'  
    context.output['current_price'] = data.current(context.output.index, 'price')

    # Do any logic you wish on any of the columns in the pipe dataframe output  
     stocks_to_buy = context.output.query('current_price < yesterday_close').head(TARGET_STOCKS).index
Daryck Brown
May 9, 2020

Would appreciate any feedback.

About Quantopian
Careers
Community
Events
Documentation
Academia
Lectures
Status
Facebook
Twitter
LinkedIn
YouTube
Terms of Use
Privacy Policy

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.