To understand AI’s effect on trading, you need to understand why exactly algorithm trading can and can’t do. AI stock experiment beat the market in simulation. But the question is, how much of this claim is true? You can use AI for credit decision making, personalized banking, and account security.
But one of the most prominent uses of AI in finance is in the stock market. AI has created a deep impact on the finance sector. It has created many convenient ways through which customers can spend, trade, and invest their money.
What AI can do?
AI trading is particularly useful for situations where you don’t require the human motion context or psychological bias. You can also run an automation spectrum, i.e., perform a wide array of tasks without human intervention.
Additionally, you need to ensure that the inherent uncertainty for that model should be less, and the model is continually evolving to reduce that uncertainty. Also, make sure that the operator performs his operations based on specified trading and scientific principles.
Of course, to do that, you need a large amount of data. The denser the primary dataset, the better prediction an AI model can do. It can save from fat-finger trading mistakes. It can run test trades based on its knowledge of mathematics and probability, hence decreasing the overall volatility related to a particular trade.
Why AI can’t do everything?
First, the AI has to track, understand, and predict how the market moves. They do that by taking all the technical factors into accounts such as market fluctuations, insights, and trending companies. Trades fluctuate because of the collective actions of machines, algorithms, and humans.
You also need to understand the relationship between the size of the transaction and stock performance. The AI algorithms also suffer from the black box problem – they aren’t as cognitive as humans. Algorithms can be trained in-laws and protocols, but they can’t be taught emotions and reflexes.
The model needs to continuously improve itself. It’s even more difficult for day trading since the data generated for day trading is significantly scarcer. If you want to train an AI model, the training dataset has to be massive. The ultimate requirement for an algorithm is to focus on a fixed point and keep training itself.
Companies that are using AI for trading
1. Algoriz
You can use English to communicate your strategy to the system. You should have the basic knowledge of quantitative analysis and algorithmic trading to create a strategy to use this.
You can also automate your ideas and get updates when there is a change in the market. Created by Goldman Sachs and Millenium partners, this platform works on two domains: equity, and cryptocurrency. Users can build and test their own strategies.
2. TechTrader
It claims to do at scale what traders do for individual stocks. This is an autonomous stock trading tool that has been training itself for seven years. Developed by the maker of pftq, it combines the trading knowledge of thousands of traders with the power of machines.
3. Intelligent Cross
It minimizes market impact (measures in BPS) and adverse selection. It reduces post-trade response and optimizes itself from a sweet spot of liquidity. Created by engineers and traders from Imperative Execution Inc, Intelligent cross is an AI platform that optimized trading for U.S. equities.
