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The next tearsheet challenge is here: come up with factors for the guidance dataset
Thomas Wiecki
posted
Contest Using Data Challenges

Our recent estimates data challenge was a huge success, so we are excited to run the next one with very similar parameters. This round we are looking for your best guidance factors. We think that this challenge will be more difficult than the last ones and more creativity will be required in finding alpha, but we are looking forward to seeing what you come up with.

Here are the rules, pretty much the same as before:

  • There is no submission or live-updated leaderboard like for the contest. To enter this challenge, simply post an alpha tearsheet as a reply to this thread. For this, you would run a backtest on your factor and run the alpha notebook which loads in your backtest results.
  • The deadline to submit a factor is October 21, 2019.
  • There is no hold-out testing, just post your best factor starting on June 1, 2015 until Oct 1, 2018.
  • We will look at all tearsheet submissions and manually determine 5 best algorithms according to our discretion. Each winner gets a $100 prize.
  • There is no limit on the number of submissions.

Algorithm requirements to enter the challenge:

  • Use the guidance data set as your primary signal source. It is OK to combine guidance signals with other sources if there are predictive interactions between them.
  • Use TargetWeights in the optimizer and do not put any constraints on common risk exposures.
  • Use the QTU.

When selecting a winner, we will primarily look at:

  • Specific Sharpe Ratio (IR) over the first 5 days in the alpha decay analysis (higher is better).
  • Turnover (lower is better).
  • Not driven mainly by common risk (but no reason to try and artificially reduce your exposures, ideally your idea is dissimilar enough from common factors that it will be naturally uncorrelated).
  • Universe size (larger is better)
  • For more examples on what we look for, check out our last live tearsheet review.

These rules are mostly derived from our updated guidelines on getting an allocation and are based on many community members' feedback. Thank you for all your input and creative suggestions. If this challenge is well-received, we will continue to offer more experimental challenges.
Ultimately, we want to keep improving our ability to find your best ideas and fund them!

Good luck and happy coding!

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.

80 responses
Vladimir

Thomas,

As it stated above this challenge is for guidance dataset.
But only 17% of QTU has not null guidance records.
Is this challenge only for this universe of around 380 stocks?

Thomas Wiecki

Vladimir: Yes, that's correct as most companies wouldn't provide guidance estimates.

Joakim Arvidsson (Cream Mongoose)

Single Guidance derived factor:

Shiv Chawla

First Entry!!

Deleted User

Is it allowed to mix the Guidance factor with other factors in order to obtain a larger universe (within the QTU)?

Deleted User

In the meanwhile, single factor algo.
First Commit :)

Thomas Wiecki

@Gregorio: You can mix in other factors but only if there are predictive interactions between your non-guidance and your guidance factor. Thus, you won't be able to increase your universe size this way as all factors need to start with guidance.

Deleted User

Thank you Thomas, now everything is clear to me.
By the way, the factor that I posted is based on Guidance, so it is compliant with the rules on this contest.

James Villa

@Gregorio and @Shiv,

Nice first cut for a single guidance factor. However, I noticed that your number of positions exceeds 500 names while the guidance datasets have only approximately 400 names. I suggest you go back to your code and check why this is happening. I'm sure with a slight modification in the way you process the factor, you'll be good to go. Good Luck!

Deleted User

@James
Thanks for your feedback. I checked the code for being sure and actually, in my opinion, it meets the contest criteria stated by Thomas.
However, the ultimate decision will be taken by Quantopian of course: in case it won't meet the criteria, I will be disqualified for sure.

Happy coding :)

Deleted User

Multi factor algo
-> First Commit

Deleted User

Single factor algo
Second Commit

Joakim Arvidsson (Cream Mongoose)

My second model. Struggling to score all 350+ names with Guidance data. :(

Daniel Cascio (Blue Robin)

Here's my first take on a Guidance strategy.

Shiv Chawla

@Arun, You have posted the incorrect notebook. Use this one: alpha tearsheet

Shiv Chawla

Another attempt with one additional factor.

Deleted User

My third model, multi factor model.

Deleted User

My fourth model, again multi factor.

Deleted User

I will make also my fifth submission.
This one is less performing but it has a larger universe and (I think) it is compliant with the rules of this contest.

James Villa

My first cut.

Kuwa Wen

Revised Model 01

James Villa

Model 2

Joakim Arvidsson (Cream Mongoose)

Third model's a charm?

Emiliano Fraticelli

@Thomas Can we re use this one as a template?

Kyle M

I was curious as to what ranges of universe size to expect, so I wrote this little notebook to as a tool to estimate exactly that. Depending on how you do your factor combination, you may or may not drop vs. fill NaNs. In either case, this can help you to make sure your universe isn't wildly larger than expected!

Arun

@Shiv Thanks for pointing that out.

Vladimir

Natural set, profitable and stable.

Vladimir

Natural set with artwork.

Daniel Cascio (Blue Robin)

My 2nd submission using only 1 Guidance Factor. Not the highest IR in the forum but the exposures were decent.

Kuwa Wen

Model 02 - Two Guidance Factors and Actuals
Note: High Returns (70% - with 300 assets)

James Villa

Model 3

James Villa

Model 4

Kuwa Wen

Model 03 - More Stocks

Kuwa Wen

Model 04

James Villa

Model 5 - ML

James Villa

Model 6 - ML (au natural)

Kyle M

Tried to keep it simple here. Pure guidance. No actuals, estimates, etc. Sharpe isn't comparable to other users' entries, but I tried to keep my factors as clean as possible.

Alex Temesov

My first attempt with 560 guidance universe.

Arun

First attempt

Kyle M

Pure guidance v2. No other datasets used.

Kyle M

Pure guidance v3. Same factors as v2 but a wider universe.

Daniel Cascio (Blue Robin)

Submission # 3. This one exclusively uses 2 Pure Guidance Factors.

Vedran Rusman

4 Guidance factors - > removed one questionable factor and avoided overfitting to make it as predictive as possible

Arun

Attempt 2

Bala Sridhar

My first submission. Not pretty but different. Backtest looks better than this notebook!
Uses one guidance factor only.

Start date 2015-06-02
End date 2018-10-01
Total months 40
Backtest
Annual return 7.8%
Cumulative returns 28.4%
Annual volatility 4.3%
Sharpe ratio 1.75
Calmar ratio 3.14
Stability 0.98
Max drawdown -2.5%
Omega ratio 1.37
Sortino ratio 2.66
Skew -0.10
Kurtosis 15.17
Tail ratio 1.12
Daily value at risk -0.5%
Gross leverage 1.01
Daily turnover 9.0%
Alpha 0.06
Beta 0.10

Arun

Attempt 3

Arun

Attempt 4

Emiliano Fraticelli

first attempt

Shiv Chawla

Last Attempt!
This has only 3 factors with reduced drawdown, higher IR an relatively smaller universe in comparison to previous entries. Thanks!

Alex Temesov

My Challenge Submission #2

Arun

Attempt 5.

Vladimir

Some participants in this challenge are concentrating on universe size (larger is better)
This algo has average set size 650.

Joakim Arvidsson (Cream Mongoose)

My 4th model.

[Edit: Improved 4th model below.]

Bala Sridhar

second attempt

Bala Sridhar

third and last

g s rao

My very first contribution. Thank you Quantopian and its members for creating this eco system for learning Quant finance.

g s rao

Second model

Vladimir

Here is my attempt to utilize intersection of QTU with union of four Guidance qf

Leo c

Objective: Develop as pure Guidance factor as possible and uncorrelated signal to rest of the peers

Joakim Arvidsson (Cream Mongoose)

4th model refined. Factors use both quarterly and annual EPS and Sales guidance.

Vladimir

Intersection of QTU with union of two Guidance quarterly factors.

Indigo Monkey

First entry

Indigo Monkey

Second entry using different technique to rank guidance estimates

Indigo Monkey

Version 2 of second entry

Alan Coppola

Better late than never...combined guidance and consensus surprises.
PercHoldings requires investigation.

Thomas Wiecki

Thanks for submitting everyone, we're very excited about all the entrants to the challenge! Over the next few weeks we will look through all of them and then do another live webinar to review the submissions and announce the winners. Stay tuned!

Bala Sridhar

ok this is it for me

Vladimir

Here is my attempt to use the intersection of QTU with a combination of four Guidance quarterly and annual factors.
Thanks to Joakim Arvidsson for the tip.

Alex Temesov

Increased universe size.

Enigma Variation

Unfortunately, I didn't have time to prepare something in time. But for the sake of the exercise, here is my contribution. The theme was to generate a signal for every asset in the Quantopian Universe.

Ang Petrozzino

The tearsheet review and winners announcement webinar for this challenge will be at 9am EST on Monday, November 18th, 2019. You can watch the webinar here: http://bit.ly/guidanceweb.

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.

Ang Petrozzino

Our tearsheet review and winners announcement webinar is happening now. Tune in at http://bit.ly/guidanceweb.

Kuwa Wen

Hi Thomas and Ang ,

Just had the chance to watch the tearsheet review. Although ranked number 6, I am glad to be mentioned multiple times and the potential opportunity of licenced algos, particularly for the fact that three of my four models landed in 3 different algo clusters, and two of them continually performed well in the out-sample data. I am relatively new as a python programmer and learned a lot from everyone here at the Q site. Thanks a lot for the opportunities and am looking forward to contribute more in the near future!

Thomas Wiecki

@Kuwa Wen: Surprise! You are also getting a $100 honorary prize (in addition to us wanting to license the factor from you) because we really liked your algorithm and want to welcome new users that show great potential like yourself. Glad to hear that your experience here so far was a positive one of learning.

The other winners are:

  • Vedran Rusman
  • Vladimir
  • Indigo Monkey
  • James Villa
  • Antony Jackson

Please join me in congratulating them on the amazing intellectual work that goes into cracking these challenges. This of course extends to everyone who submitted to this challenge, so thank you all and we hope to see you all for next challenge: https://www.quantopian.com/posts/financials-tearsheet-challenge.

Shiv Chawla

Using cluster correlation to weed out other equally good entries in the same cluster doesn't make clear cut sense. If one entry is correlated with another entry but is an independent submission, why they should get a pie of the prize or the allocation.

At the end of the day, it's Q's discretion but it undermines the effort made. Do you agree?

Vladimir

@Thomas Wiecki,
То restore a justice, may I request to share my second prize with others in my correlation cluster?

Arun

@Thomas
Can you share how you combined the algorithms? I think this would be interest to everyone on this thread.

Thomas Wiecki

@Antony: Yes, you're right, although the number of times it can be tested is limited.

@Arun: I just equal weighted them.

g s rao

Thomas,

I have revisited my submission which was eliminated based on the mean of alpha decay in earlier tear sheet review and tried improving it.
Is there any chance to re look at same. Attached the improved version tear sheet.

Appreciate the feedback.

g s rao

Dear Thomas,
Attached the slightly improved version of the above tear sheet. Appreciate your review and feedback.
first five sharpes_specific mean: 2.29
first five sharpes_total mean: 2.21
mean turnover: 0.059
Regards,
g s rao

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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.