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• Support

  I have something that does not work. Help me.

  Request New Features
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  @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.

• Strategy help

  I have a strategy that doesn't work, please help improve it.

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  @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()
• General Discussion

  A place to talk about whatever you want.

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  @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.

• News and Feature Releases

  Announcements regarding our community

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  @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!