@xiaolan Hello, first of all conversion xarray -> pandas -> xarray is very easy, as we answered you in the other thread you opened.

The basic reason is that xarray works really well with array with an arbitrary number of dimensions. For financial data we have normally 3 dimensions:

and xarray works really well with cross-sectional indicators ranging over several assets.

@anthony_m Yes, you are right, we are using a simplified model for slippage. In reality slippage will depend on (among other things) the traded volume. More liquid assets will be prone to less slippage.

However, for speeding up the evaluation, we are using a simplified model based on the gaps, it fits well with historical data.

Clearly slippage will be a function of the allocated USD capital to a strategy.

@xiaolan Yes, allocations are translate to orders internally, it is enough to check the variation in the allocations and transform it into number of contracts bought/sold. When we designed the toolbox the goal was to simplify development as much as possible for the users.

@support I see, need to push them to submit then....

Hello, welcome! Well, you can use both, a nice discussion about pros and contras can be found at stack overflow. As far as the documentation is concerned, the official page at https://jupyter.org/ is definitely the best starting point.

@support thanks, yes....

@magenta-grimer Hello, the 34 M USD have been allocated to the winning strategies according to the contest rules.

Other strategies have been funded, and agreements are in place between quantiacs, investors and quants. We cannot disclose more details now, sorry.

5M USD is a reasonable capacity a strategy could handle, yes.

@sheikh Hi,

it simply means that your system should make a new global high before you are entitled for a payment. If your system makes 1000 at the end of January, 800 at the end of Februray, 900 at the end of March and 1100 at the end of April, your profit will be generated at the end of April and they will amount to 1100-100=100;

no, once the system starts being traded, it will be traded in the form it was at submission time, i.e. the quant will not be allowed to update parameters/change details. Of course a submitted system can have an adaptive logic, by changing parameters according to the value of some meta-indicator. If the quant believes there is some big change to be made, it is ok to re-submit the changed system, but it will need again to accumulate a track record before being traded.

@support
oh I see now what you mean.
15 strategies PER USER are selected.
At first, I thought you were only going to select 15 strategies total for all users.
Thanks.

@support ok, thank you!

@penrose-moore Thank you for the idea. For the Bitcoin Futures contest we are indeed patching the Bitcoin Futures data with the BTC spot price to build a meaningful time series. For the other Futures contracts, for the moment we will keep the futures histories only, but add spot prices + patching with spot prices to increase the length of the time series to our to-do list.

@support yes coarse heuristics work well as long as you are conservative. For shorter term models I have started using minute bars despite the computational hit, because it helps in a lot of other ways.

I may enter this contest, I am pretty rusty on predictive modelling and I am not sure I can do a good job using just daily prices, there is not a lot of data. I used to work at a CTA and I feel like we wasted a lot of man years using only prices, hoping better models would acheive more alpha. in the end the sharpe is similar to the S&P but uncorrelated, but you have gotten there with some simpler models and enjoyed life.

I have some other questions about the platform and the contest that I will post here.

Best
P.M.

@magenta-grimer That is correct, but you can for example train a model locally on your machine, and then submit a code which uses the results of the training (for example stored in numerical form) but does not reveal the underlying idea.