One of the core elements of the AQTIS ecosystem is our unique use of Quant Tech and Artificial Intelligence.
By combining these two, AQTIS can deliver class leading yield in all different market conditions. But how do we do that?
In this week’s AQTIS Explains, we’ll be walking you through how we use AI, and in what ways it helps fuel the AQTIS ecosystem.
How does AQTIS use AI and Machine Learning?
First, it’s important to understand what we mean by Machine Learning, as the term often is used to describe many different things.
Machine Learning is the branch of Artificial Intelligence and computer science that enables machines to mimic human behavior and thought processes at a very high pace. While machines can simulate human behavior, replicating human emotions is still beyond their capability, distinguishing humans from machines. Machine Learning, a subset of AI, utilizes data and algorithms to imitate human learning processes, progressively enhancing accuracy.
At AQTIS, Machine Learning is used to train algorithms to learn from historical patterns in data and try to find a target we have specified.
When we mean target, this is typically expressed as the theoretical definition of one of the strategies laid out in our What is Quant Tech explainer. For reference, we have eight strategies or targets currently either deployed or in testing. These are:
- Breakout Long
- Breakout Short
- Mean Reversion Long
- Mean Reversion Short
- Trend Following Long
- Trend Following Short
- Momentum Long
- Momentum Short
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So one target could be to identify a breakout, and another to identify a momentum pattern.
Once those models reach a certain level of proficiency, they then generate probability and confidence scores. These are scores based on how likely the learning model can predict what will happen next in the market.
From there, we continue to optimize the model, feeding it with new data, and refining the parameters to ensure the algorithm is profitable and stable.
Now that we understand how AQTIS uses Machine Learning, there are several ways we are currently using the models in our Quant Tech.
AQTIS AI: Market Predictions
One of the key ways we use Machine Learning at AQTIS is to help make market predictions.
Our Quant Tech uses historical data and mathematical models to help our team calculate what the best trades are. We are developing Machine Learning algorithms to help refine these decisions.
In other words, when we’re considering a trade, our Machine Learning algorithms can provide a confidence rating on how likely it is that the trade will be successful.
AQTIS AI: Portfolio Allocation
As part of the AQTIS investment strategy, we have built a number of different trading approaches designed to take advantage of certain market conditions.
We are currently training a model with several parameters to be able to autonomously detect shifts in market conditions, and re-allocate capital into different trading strategies automatically.
Building these models helps improve the performance of our Quant Tech by being able to reduce the time between shifting trading strategies.
❓ Questions or Feedback?
As always, we’re eager to answer all your questions and consider your suggestions. We’d love to hear your thoughts, so please share them in our Telegram or Discord.
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That’s all for now. Stay tuned for more exciting updates, and we’ll catch you in the next one!
