Hi there,

The set of 15 selected systems displayed on page: https://quantiacs.com/personalpage/submissions/tab_in_contest
or on page: https://quantiacs.com/leaderboard
is not the same that I have selected (before October 31, 2021) on page: https://quantiacs.com/personalpage/submissions/tab_candidates
to be my final set of systems to compete on the Q16 contest (for example: Sun73_Q16_4f).

Also, there are systems not listed anymore on the latter page (such as: Sun73_Q16_3a) which I have previously selected to compete on Q16.

Could you please check this (selection) issue? Many thanks!

Kind regards,
Sun73

Hi there,

Since we're approaching the end of the submission phase for the Q16 contest, is it possible for us quants to already know a little bit about the upcoming competitions?

Will the Q17 contest be designed similarly to Q16? In other words, will Q17 be based only on crypto trading (or will it allow for futures trading too)? Only long-positions allowed (or short-positions too)?

I would suggest (if feasible) to put in place a new contest allowing to trade both cryptos and futures, besides trading long and short in any asset, but limiting the overall leverage of the portfolio. For example, adopting a 130/30 portfolio structure, which is a structure with a net long position, but permits leverage with long and short positions up to a maximum amount of leverage. In the case of a 130/30 portfolio, the leverage is 30%.

This broader and combined setup would allow us to design enhanced portfolios and might (potentially) increase the out-of-sample Sharpe ratios. Thank you!

Best regards,
Sun73

Hi @support,

Thank you for the prompt response. My set of Q16 systems displayed on the website https://quantiacs.com/leaderboard/16 is the following: Sun73_Q16_1c , Sun73_Q16_1d , Sun73_Q16_1e , Sun73_Q16_2b , Sun73_Q16_2b_v2 , Sun73_Q16_2b_v3 , Sun73_Q16_2c , Sun73_Q16_2e , Sun73_Q16_2f , Sun73_Q16_3a , Sun73_Q16_3b , Sun73_Q16_3c , Sun73_Q16_3d , Sun73_Q16_3g , Sun73_Q16_3h.

However, my chosen set of selected systems is the following: Sun73_Q16_2c , Sun73_Q16_2e , Sun73_Q16_2f , Sun73_Q16_3a , Sun73_Q16_3b , Sun73_Q16_3c , Sun73_Q16_3d , Sun73_Q16_3e , Sun73_Q16_3f , Sun73_Q16_3g , Sun73_Q16_3h , Sun73_Q16_3i , Sun73_Q16_3j , Sun73_Q16_4d , Sun73_Q16_4f.

All systems above were submitted before the Q16 deadline. Since there is an issue with the auto/manual selection, could you please select the 15 systems above for the Q16 contest? Many thanks once again!

Regards,
Sun73

Hi there,

The new function backtest_ml() implemented in the example: "Machine Learning with a Voting Classifier" is a great way of getting a fast feedback from a given model.

Is it possible to use it with multiple datasets (for example, using "futures" and "commodity" data, as done in the template: "Futures - IMF Commodity") ?

Thank you!

@support Yes, I can load now the stocks data. Thank you once again!

@support Great! This route opens new possibilities in terms of model design. Thanks a lot!

Hi there,

I submitted a strategy for crypto-futures, called Sun73_Q15_BTC_1f that was not accepted in the contest due to a Sharpe ratio lower than 1.

However, the in-sample Sharpe ratio obtained with the JupyterLab does not match the one displayed online. Both prints are shown below.

It seems that the online system almost does not trade Bitcoin after March 2017 (please see the first graph below), whereas the original system from JupyterLab trades Bitcoin on a regular basis since 2014 (as shown in last graph).

Could you please verify this mismatch?

Thanks a lot!!

@support Thank you for the prompt response. I appreciate if you could reprocess the strategy Sun73_Q15_BTC_1f after solving this cache issue. You guys are the best!

@qida1995 Hi qida1995, please take a look at the documentation available in the Quantiacs' website (https://quantiacs.com/documentation/en/), which is very useful not only for beginners but also for experienced users. Best of luck!

Hi @support,

I modified the retraining interval to 1 day and it worked. Thank you for the help.

You guys rock!

Hi @support,
What is the ticker for cash in the Q19 contest? I've tried 'NAS:CASH' but it didn't work.
Thanks!

Hi @support, you guys are doing a great job by improving the platform still further at each contest!

A couple of suggestions/wishes:

(i) A contest that allows to go long (or short) on an enlarged set of assets (for instance, nasdaq and nyse, stocks and futures…)

(ii) A system optimization example based on a multi-pass strategy (instead of single-pass);

(iii) Some extra plots (histograms) generated by the backtest functions (qnbt.backtest and qnbt.backtest_ml) with the best (or worst) assets of the backtested portfolio, for instance, by ranking the average returns (or individual sharpe ratios) of the top 10 assets (or worst 10 assets), using the “get_best_instruments” function discussed in the “Q18 quick start” template.

Many thanks once again!

Hi @support,

Any clue on the asset class (futures, crypto, nasdaq...) of the next contest? Long-only or long-short?

Thanks!

Hi @support,

My last submitted strategies are all receiving the error: "Calculation time exceeded".

By running on a JupyterLab environment, it takes roughly one second to run each day (and roughly 1.5 hour to run the strategy since 2006).

The rule about execution time limited to 5 minutes is valid for each day (and not the whole sample from 2006 until 2022), right?

Thanks!

@alphastar Hi, I've got the same error trying to clone an old strategy.

@support Great news, thanks!

Hi @support,

Regarding the new contest (NASDAQ-100 Stock Long-Short), could you please provide us a Quick Start example to show how to properly read the NASDAQ-100 data?

Thanks!

Hi @support,

I modified the retraining interval to 1 day and it worked. Thank you for the help.

You guys rock!

By the way, I submitted another system yesterday, called "Sun73_Q17_1b", with an in-sample SR=2, but today the system is online in the website with an in-sample SR=0.7

Hi @support, I backtested the system in JupyterLab two days ago and submitted on the same day. The submission went online yesterday.

Since I am trying to use a multi-pass strategy based on the function "qnbt.backtest_ml", maybe the difference is due to difference parameters of this function considered in Jupyter and the online filters of the quantiacs website.

Are the parameters of the function "qnbt.backtest_ml" exactly the same in those cases?

For example, I run the code using retrain_interval=90 days. When the system is evaluated online is it retrained every day since 2014 (or every 90 days in this example)? In other words, the parameter "retrain_interval" is forced to 1 when evaluated online?

Many thanks again!