@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:

time coordinate asset field

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

@xiaolan Hello, the transformation is very simple. Let us suppose that you load the data, and select some field, for example the close, from the native xarray structure using:

import qnt.data as qndata data = qndata.cryptodaily.load_data(min_date="2016-01-01") close = data.sel(field="close")

You can convert to pandas using:

close_pandaized = close.to_pandas()

Then you can work with pandas. Let us come to the final weight, and let us say they are a pandas dataframe. You should convert them to the final xarray data structure using:

weights = weights_pandaized.unstack().to_xarray()

@anthony_m Sorry for the confusion. Correct, there are 3 APIs.

qnt.data.cryptodaily.load_data

doc: https://quantiacs.com/documentation/en/reference/data_load_functions.html#loading-cryptocurrency-daily-data

This API call loads data for all cryptocurrencies which were at least once among the top 10 in terms of market capitalizazion since Jan 1, 2014. These data are relevant for the Q16 crypto contest.

qnt.data.cryptofutures.load_data

doc: https://quantiacs.com/documentation/en/reference/data_load_functions.html#loading-bitcoin-futures-data

This API call loads data for the futures contracts on BTC. Used for the Q15 BTC Futures contest.

qnt.data.crypto.load_data

doc: https://quantiacs.com/documentation/en/reference/data_load_functions.html#loading-cryptocurrency-data

This API call loads hourly-resolution datas for 7 cryptocurrencies; they can be used as external indicators.

@pink-seel Super that you found it, please do not hesitate to ask for support!

@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.

@xiaolan That is correct, but the logic can be easily re-used. The only novel element will be the introduction of the liquidity filter at intermediate stages/at the final stage for the selection of the weights.

@nikos84 Hi Nikos, the first source for information in the documentation at:

https://quantiacs.com/documentation/en/

However, the most update resource is the source code itself. As you can see in your personal area, you have a "root" directory for each strategy. The strategy file is called "strategy.ipynb", but many other files/directories are available to you:

Screenshot from 2021-09-08 11-34-54.png

The backtester, including the functions for loading data, is included in the "qnt" directory. The "qnt" directory itself includes several sub-directories and files:

Screenshot from 2021-09-08 11-35-44.png

Data functions can be inspected in the "data" folder:

Screenshot from 2021-09-08 11-36-36.png

You will see datasets for previous/current contests:

blockchaincom: "fundamental" data for BTC; blsgov: macro data from the Bureau of Labor Statistics; crypto: crypto data for a subset of cryptocurrencies, hourly resolution; cryptodaily: crypto data for all cryptos with largest market capitalization, adjusted for avoiding survivorship bias (Q16 contest); cryptofutures: BTC data for the Q15 crypto contest; futures: futures data for the Q15 managed futures contest; imf: data from the International Monetary Fund (commodities and spot fx).

@support Thank you very much for fixing it!

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

@support ok, I see, all my systems look processed btw

@support great, thank you, yes, this is what I was looking for

@antinomy In the end we followed your advise and changed a little bit the algorithm for adding data, once a cryptocurrency is in the top10 we include it with its past history and go on with the update (now DASH and XMR are being updated). Of course once the crypto is not among the top 10, the liquidity tag for the filter is "zero". Thank you!

@anthony_m Hi, sorry and thanks for the note, it is a bug we have fixed recently. If you try to submit your system now (a copy of the Q16 template) you will get an error message telling that the system is not eligible for a prize because it correlates too much with a template.

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....

@maxime Yes, of course, you can use our optimizer:

https://quantiacs.com/community/topic/29/optimizing-and-monitoring-a-trading-system-with-quantiacs

Be careful with optimization as it will lead to overfitting....

@support Thank you, yes, this is what I was looking for

@nikos84 Hi, no, you should not provide your own data for defining the top10 cryptocurrencies according to market capitalization. Each asset for this contest has a field called "is_liquid" which gets values 0 (not in the top 10 according to market capitalization) or 1 (in the top 10 according to market capitalization). You can read it using:

is_liquid = data.sel(field="is_liquid")

When you define allocation weights later you can simply apply the filter as follows:

weights = weights * is_liquid

where "weights" are the allocation weights. The filter will set to zero weights for assets which do not belong to the top 10 according to market capitalization at a specific point in time.