It is commonly accepted that the finance industry was the first area to apply artificial intelligence and machine learning in the early ‘90s. The movement, which started to gather up speed in early 2016, has not only reshaped the whole trading environment, but also revolutionised the complete financial ecosystem.
AI application development in general, including trading, is about teaching machines to learn, think, reason, quantify sentiment, analyse data and news, generate insights and make decisions automatically with high accuracy and speed.
Machine learning and deep learning are becoming more significant in optimising trading portfolios to meet strategic and risk objectives. Automated trading systems, in fact, may eliminate all of the flaws of human trading. Traders no longer need to strain their optic and brain nerves non-stop during a trading session. Thomson Reuters estimates that algorithmic trading systems now handle 75% of trades worldwide.
The Investor Perspective
You want your algorithm to know when to enter and exit a trade like an investor. As a directional trader, you want your algorithm to respond to breaking news or financial data that can move the markets quicker and smarter than your competitors.
As an arbitrageur, you want your algorithm to be the fastest at simultaneously buying and selling in two markets before your competitors trade away a desirable price differential. Trading companies strive to maximise trading profits while minimising operational and market risks.
Many quant trading firms use machine learning algorithms on data feeds for automated trades. It makes perfect sense that they are protective of their AI algorithm properties and high-speed technologies – everyone is looking to capture the same handful of market prices that appear and disappear in nanoseconds.
How AI is Taking Over Humans
While humans remain an important element of the trading equation, artificial intelligence is becoming increasingly important. Electronic trades account for about 45 percent of cash equities trading revenues, according to a recent analysis by Coalition, a U.K. research group. While hedge funds are wary of automation, many of them utilise AI-powered analysis to generate investment ideas and build portfolios.
AI in Stock Trading
AI is shaping the future of stock trading. Using AI, robo-advisers analyse millions of data points and execute trades at the optimal price, analysts forecast markets with greater accuracy and trading firms efficiently mitigate risk to provide for higher returns.
Let’s take a look at an example of how Trading Technologies, a company based in Chicago, uses AI.
Through its acquisition of Neurensic, Trading Technologies now has an AI platform that identifies complex trading patterns on a massive scale across multiple markets in real time. Combining machine learning technology with high-speed, big data processing power, the company provides clients with an ongoing assessment of compliance risk.
AI in Crypto World
The extreme volatility of cryptocurrencies raises the degree of dangers of crypto trading while also making it more rewarding than any other type of investing. Prices fluctuate often during the day, allowing traders to generate consistent revenue if properly calculated. To calculate the patterns of the dynamically changing crypto market, it is necessary to process vast amounts of information, which artificial intelligence and machine learning systems can help with.
Advantages of AI and Machine Learning:
Ability to analyse large data
High work speed