#python 2.7 ; matplotlib

All active altcoins were analyzed at poloniex.com; past 3 years of 2h data
2 hour simple moving averages of period 5 through 1000, stepping by 5, were drawn
Each moving average was colored in rainbow format; slowest on one end of spectrum; fastest on other
Rainbows were then sliced into 2 hour chunks and indexed by coin name and unix stamp
Several hundred thousand such sliced png images 2 pixels by 100 pixels were created occupying several gigs of space
Approximately 50 days of quad core desktop cpu time were used to create the rainbow slices
An additional 2x10 pixels of grey scale were added to each image relative to the breadth of the rainbow moving average mesh
The same process was repeated for BTC/USDT
The current slice 2x110 pixels of rainbow for BTC/USDT was compared using computer vision to each of the quarter million altcoin slices back in time
The best matches were sorted by highest correlation coeff
The most recent 120 days of BTC/USDT data is then plotted in white
In yellow, best matching historical altcoin data from match date to 60 days of projection was plotted
The intensity of the yellow represents the correlation of each historic altcoin to the past 1000 2h candles of BTC/USDT data
Each altcoin was scaled to btc price scale by dividing its beginning price by BTC/USDT last price then multiplying the array by the ratio
The result is a 60 day ensemble model projection for BTC/USDT based on 3 year historical performance of poloniex altcoins

litepresence

April 2017