Quantopian's community platform is shutting down. Please read this post for more information and download your code.
Back to Community
Pipeline Implementation
Delman Woodrum
posted Feb 28, 2017
Seeking Help Getting Started Pipeline

I had posted earlier today about using an Excel spreadsheet to execute my screened stocks using Quantopian. The screener parameters follow below. Is this feasible to implement with Pipeline? I've been trying to get it to work, but am not familiar enough with Python or using the correct syntax to get it 'just right'

MktCapM is >= US$ 300 million (basis year 2000) adjusted yearly
Rank all stocks according to Price 1-Day ago / TTM Free Cash Flow Per Share (lower is better)
Rank all stocks according to ( TTM Cash from Operations – TTM Net Income) / latest Filing Market Cap (higher is better)
Rank all stocks according to TTM Cash from Financing Activities / latest Filing Market Cap (lower is better)
Rank all stocks according to Latest FY Return on Invested Capital (higher is better)
Rank all stocks according to 26 week Excess Total return (higher is better)
Take the 200 stocks with the smallest sum of these ranks added up.

3 responses
Delman Woodrum
Feb 28, 2017

Attached below is a backtest that sort of follows the general theory of the screener, however, this does not accurately reflect my trades or performance on the live market.

Delman Woodrum
Mar 2, 2017

After thinking about it for a while, is it possible to give the stocks a score using something along the lines of "context.score", tally that up, and then take the top stocks out of that using the pipeline parameters?

Dan Whitnable
Mar 2, 2017

Maybe take a look at this post for a simple way to combine ranks and get a 'context.score' as you noted https://www.quantopian.com/posts/how-to-combine-factors-in-alphalens .

Also, Trailing Twelve Month (TTM) fundamental data isn't directly available in the Morningstar dataset. The Morningstar data returns whatever was reported on the latest quarterly filing. This may or may not impact your algorithm.

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.