Quantum Machine Learning powered trading bot detected BTC crash on 3rd December

Quantabotics
3 min readDec 4, 2021

For many, the huge +15% crash of Bitcoin over the 3rd/4th December is a stark reminder of how unmanaged risk and market volatility can spell disaster for BTC day traders.

With the odd exception around the 27th of November the BTC market had enjoyed a relative quiet period since the 19th of the month following a predictable fall in value after the All Time High (ATH) a week earlier. Some call these conditions a “sideways market”. But, as it happens something was lurking in the shadows…

The latter half of November enjoyed a relative quite period

Despite the apparent calm, on the 3rd of December 2021 at 04:05am an algorithm tied to a Toridion Quantum Inspired Neural Network was suddenly awoken by a complex pattern of data. The pattern was strong enough to cause the algorithm to issue a “Bear Market Warning”. The warning, which was quickly issued automatically to trading bot clients.

Below is a log extract from the Quantabotics system showing the Bear Market Warnings issued at 04:05, 18:05.19:05,20:05 and 21:05. For me, the later warnings whilst indeed useful, are less interesting than the warning sent at 04:05am.

Take a look at the chart below. Would you have made that call? I don’t think I would have. What we see demonstrated here is the sheer power of predictive analytics married with quantum inspired machine learning technologies able to extract signals hidden deep in noisy data. Clearly the Neural Network could see something hidden there that is very much hidden to the naked eye.

As a time limited human trying to make your way in the crypto world, having a robotic ally on your side could be the difference between entering or exiting a sideways market before it surges or crashes spectacularly (as it did today).

Quantabotics is a quantum inspired machine learning plug in app for the Cryptohopper trading platform.

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Quantabotics

A machine learning driven trading bot integration for Cryptohopper