AI in trading today

Trading has grown more complex. The explanation is in the capability difference: while conventional exchange bots just analyze the input data, AI imitates thinking of a financial analyst who collects and processes data.

Compared with legacy trading technologies, AI not just captures data from financial news, social media, or exchange indicators, but carries out regular retesting. This is how AI “learns” to understand global market trends and adjust forecasts based on the optimized and updated data. Besides, AI cannot experience human emotions such as greed and fear, or recline on irrational guesses.

Conceptual model of algorithmic trading

What AI is capable of to date ?

Over the recent years, the number of projects heavily employing artificial intelligence has grown significantly.

There are several types of operations exercised by AI for trading companies and their customers:

  • Developing exchange algorithms based on technical analysis.
    Searching for new big data processing methods to use them in traditional analysis.
  • Combining machine learning with financial examination to design robotized decision-making advisors.
  • Developing investment strategies for hedge funds.
  • “Unmanned” end-to-end investment fund management.
  • Analyzing big data captured on social media and thematic websites – including customer sentiment and reviews – for further forecasting.
  • Compiling analyst rating and processing their forecast for further selection of the best trading strategies.
  • Developing economical models during high volatility and upon market disruptions.
  • Detecting market conspiracy and manipulation.
  • Profitability with involvement of AI significantly exceeds average market profitability. AI algorithms turn out to be more efficient than a passive “buy and hold” principle.

Conceptual model of algorithmic trading

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