Five-Month Simulated Returns from the QScore Artificial Intelligence Stock Trading Algorithm

The TRADEnosis Q-Score stock trading strategy uses advanced machine learning (AI) algorithms trained to recognize situations where the price of a stock, over the next two weeks, will be unusually high or low compared to historical returns. Basically, QScore generates signals to let you know when to enter a long or short trade. It can be used for discretionary trading, where you review and confirm prospective trades each morning, or as a fully-automated trading system.

Over the last 5 months, the stock market has had an unusually strong, albeit volatile, rise of about 11% as measured from the low in early October 2022 to the last full week of February 2023. Conversely, the QScore stock portfolio is showing a collective return of over 90% during this period! The charts in the above graphic show the amazing simulation results we are seeing on individual stocks in our early beta testing. Of course, not every stock performed as well, which is why QScore diversifies trades across a basket of 200 stocks from the S&P100 and NDQ100 plus a few popular smaller companies.

QScore is the first algorithmic trading system to be developed on the TRADEnosis platform; we hope to train other AI models and explore other markets in the future, particularly using alternative data sources that, historically, have not been available to the individual trader.

QScore is currently in an early-beta release. If you have interest in participating in the beta program, please contact our partner, Nirvana Systems. Their OmniTrader platform connects to TRADEnosis and provides the ability to trade QScore in a live broker environment. OmniTrader is what we are using to generate the simulated historic returns shown on this site. Soon, our beta testers will have real-world results to post here that include commissions, slippage and similar costs not yet included herein.