The GNY Range Report Expands to Include Coverage of nano XNO

GNY is excited to share that we are expanding the GNY Range Report to include coverage of nano. That coverage includes machine learning-driven volatility forecasts, AI-powered token summaries, plus all the essential traditional charts & indicators.

Here are nano’s key values:

– With Token Data stretching back to 2015, nano is one of the most mature crypto projects out there, so has a rich history of data to train our machine learning models on.

– nano, which started as RaiBlocks, has a completely distributed coin supply, and no inflation which makes for a dynamic trading community that is eager for data-driven insights.

– With zero fees and instant transactions GNY recognizes nano’s position and potential as a global blockchain project.

– Volunteer-led operating model

GNY’s LSTM model for nano is currently predicting the cryptocurrency’s price volatility with an impressively high accuracy rate. The historical accuracy chart and all the accompanying forecast and indicator charts for nano can be found on the GNY Range Report HERE.  

The nano Experiment

To celebrate the addition of nano ($XNO) as the latest cryptocurrency to feature on the GNY Range Report, GNY is hosting a Wisdom of the Crowd competition for the nano community.

From 0001 UTC, Monday December 11, 2023 to 2359 UTC, Wednesday December 13, 2023 users are invited to guess the high and low price for nano of the day of December 18, 2023 as it will appear on

The first 200 user submissions are rewarded with Airdrops of up to 300 $GNY BEP20 tokens. The closest guess within 3% of the actual closing prices, which will constitute an average of the MAPE of the high and low guess, will win 150 $XNO tokens. The winner of the nano WOTC competition as well as the overall results of the competition will be announced on the week of December 21, 2023.

The competition can be accessed at from 0001 UTC, Monday December 11, 2023.

GNY hopes to see a range of submission values that closely flank the future actual closing prices and that would come within 3% of the closing prices, as that is as good or better than our standard accuracy rate using our machine learning LSTM model for nano.

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