Quantopian's community platform is shutting down. Please read this post for more information and download your code.
Back to Community
FactSet Estimates Example Algorithm

The algorithm attached here is a great example of how FactSet Estimates can be used. It's a revamped version of the algorithm found here. There are differences between the two versions:
- Unlike the original, this version uses the QTradableStocksUS universe and also Optimize since they are required for the contest.
- This version uses FactSet Estimates instead of Zacks and EventVestor data.
- This version performs better by going short on positive surprises and going long on the misses, which is the opposite of the original.

Disclaimer

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.

4 responses

Good work Saba. Easily written and quick.

I actually made a quick adjustment to the metrics that made a decent improvement. Thanks for making the algo easy to read and organized to do so.

Summary:
- Raised the beta by 0.01,
- Lowered the max drawdown by about 4%
- 4% Higher returns.
- Better sortino with SPY.

Made the adjustment all based on a twitter quote I saw years ago by a hedge fund manager. And he got the quote from another hedge fund manager even more years ago, "Amateurs trade before 10:30." Now taking that with a grain of salt and taking out the egotistical sentiment behind it, in practice, I've seen volatile trades happen in the first hour or so, and likely mean revert later on in the day. I at least believed this held true. I believe it definitely holds true in high volume large caps after an earnings jump. With a mean reversion volatility strategy like this, I felt it might actually make a quantifiable difference and appears as if so. Simply had the trading occur 2 hours and 29 minutes into the market open. (29 minutes because of maybe other algos out there trading at the 30 minute mark back then, because it's a nice round number). Here it is!

Note: May play around some more with extra changes, but here is a small change you can incorporate into this and many other algorithms to get seemingly better results, rather easily.

Saba your algo isn't usable in the contest as it is, it fails to pass many risk tests, such as sector exposure

Hi,

Is there a way to get FactSet Estimates data post 11/08/2018? In case that it's not possible, is there any substitute accessible through the Quantopian API?

I guess this restriction is part of Quantopian's agreements with the data provider (and that's why they provide data for free)?