Meteorologists of Financial Markets:

neuronet ONAtegrum

Importing financial information from the future

This is a powerful tool for traders and investors, providing a glimpse into the future for many periods ahead. Regardless of which market you are interested in,  neuronet ONAtegrum will help you make more informed decisions and improve the outcomes of your financial operations.

ONAtegrum - financial neuronet

ONAtegrum is an innovative financial neural network based on linear regression of instrument prices over time series.

It's important!

ONAtegrum - is not a generative neural network

The result of ONAtegrum’s neural network operation is a glimpse into the future, a forecast for any market where you know in advance what the next time interval on the trading chart will look like in the format of high, low, close. By using the ONAtegrum neural network on an hourly trading chart, you will know what prices will be like in the next hour, and on a daily chart, you will know about the next day.

The capabilities of the ONAtegrum financial neural network provide insights into the future for multiple periods ahead. On a daily chart, you can learn about market movements up to 45 days ahead!

ONAtegrum, the financial neural network, is effective for currency markets, stocks, commodities, futures, cryptocurrencies, and more.

Summary table of prediction accuracy for major currency pairs (during the year on daily charts):

currency pairs

overall accuracy

H (High)

L (Low)

C (Close)

EUR/USD

99.63%

99.70%

99.58%

99.61%

GBP/USD

99.61%

99.68%

99.55%

99.60%

USD/JPY

99.71%

99.78%

99.65%

99.70%

USD/CHF

98.93%

99.05%

98.85%

98.89%

!!! The prediction model demonstrates high accuracy for all currency pairs, most predictions are within 0.5% of the actual values. The Close (C) predictions are particularly accurate, reaching 100% accuracy for all pairs. The Low (L) predictions for USD/CHF is noticeably less accurate than the other predictions, but still achieves an accuracy of over 90%.

ONAtegrum neuronet

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