AppsGeyser

Before the transition to electronic trading in the late 20th century, floor trading was the dominant method, with traders physically present on the trading floor exchanges. The advancements in modern technology democratized trading and made it accessible for a wider audience. The shift was so dramatic that it changed the markets so much that many floor traders lost their edge and gave up their profession. 

Today, we’re probably witnessing another major shift in the markets from manual to automatic trading. Even at this very moment, most trades are done by trading algorithms. According to most estimates, between 60-75% of trades are conducted by trading algorithms, and this number keeps growing. Trading robots are getting a boost from Artificial Intelligence (AI), and Machine Learning (ML). 

Proprietary (prop) firms invest heavily in cutting-edge AI technologies to help their traders find their edge in the market. They train their traders thoroughly and offer them career opportunities. You can check top 10 prop firm companies in case you are interested in proprietary trading.

The Rise of Machine Learning in Trading

Machine learning in trading represents a notable evolution in financial markets. With the advent of algorithmic trading software powered by machine learning and Artificial Intelligence, it’s possible to analyze vast amounts of data quickly and find patterns and trends easily. Advanced trading algorithms are generally utilized by financial institutions that have all the resources needed for developing AI based predictive algorithms and trading bots. 

Technology is advancing at a rapid rate and it is expected that more and more financial trading will be done by robots. Robots have no human emotions that are often viewed as drawbacks, such as greed, fear of opening a trade, desire to revenge trade, or laziness. Robots do not feel tired, and they do not need to take a vacation from work. And, they are becoming increasingly smarter. If we take a look at the events from history, we can see how rapidly the technology is advancing: 

• May 11, 1997 – Deep Blue (developed by IBM) defeats world chess champion Garry Kasparov in a six-game match, marking a significant milestone in artificial intelligence.
• March 9, 2016 – A computer program called AlphaGo (developed by DeepMind) defeats world champion Go player Lee Sedol. The match consisted of 5 games. This event is significant because Go is a complex game.
• June 2018 – OpenAI Five (developed by OpenAI) defeats professional players in the highly complex battle game Dota 2.

Artificial intelligence and machine learning are actively used in various fields, such as medicine, and especially financial trading. AI based trading algorithms are competing against human traders and other algorithms. Human capacity to improve its approach to the markets and execute trades is finite, you can only keep an eye on a couple of trades simultaneously, but analytical and trade execution capabilities of an AI is still in its infancy, and already dominating the financial industry. In the near future, human traders might have a zero chance at competing against these machines in the market. 

AI-Driven Trading Apps

More and more traders see the significance of AI-driven trading analysis and trading algorithms. Which results in increased interest in AI powered trading tools and apps. 

• Kensho: Kensho’s platform utilizes the power of AI and machine learning to comprehensively analyze financial markets. This innovative system excels in real-time event recognition and forecasting. Which gives traders timely insights into market developments. Kensho was purchased by S&P Global back in 2018 for 550 million US Dollars.
• AlphaSense: AlphaSense stands out as an AI-powered search engine specially crafted for financial professionals. Using advanced natural language processing, it efficiently scans through a wide range of financial information such as documents, reports, and news sources. This smart system is great at accurately analyzing and pulling out important information. It helps financial professionals quickly get the key insights they need to stay on top of the ever-changing world of finance.
• Alpha Vantage: Alpha vantage offers a set of financial market data APIs that use machine learning to study and understand market trends. It provides different data sets, technical indicators, and tools for traders and developers.

Benefits for Prop Traders

Proprietary (prop) traders use machine learning and artificial intelligence in the financial markets in various ways. Prop trading firms invest a lot of money in providing their traders cutting-edge technologies and tools for market research and analysis. The most common uses of AI in trading include: 

• Developing algorithmic trading strategies
• Predictive analysis: Machine learning models play a major role in analyzing extensive datasets encompassing market prices, economic indicators, and pertinent variables. Utilizing predictive analytics, traders leverage these models to foresee market trends, pinpoint potential trading opportunities, and enhance risk management strategies with greater effectiveness and efficiency.
• Sentiment Analysis: Generative AI is increasingly used in sentiment analysis due to its ability to effectively analyze large amounts of information quickly.
• High-Frequency Trading (HFT): prop trading firms that are specialized in HFT trading vastly rely on use of AI and machine learning. HFT requires fast decision making, high speed internet and proper hardware.
• Portfolio Optimization: AI-driven tools assist traders in fine-tuning their portfolios. They take into account factors like risk tolerance, return goals, and current market conditions. These tools are valuable for creating diversified portfolios that match the trader’s specific objectives.
• Pattern recognition: AI-powered tools are actively used in pattern recognition.

Wrapping Up

To sum it all up, AI and machine learning are dominating the financial world. Most trades are done by trading algorithms, and the numbers keep growing. Prop firms use AI-driven tools and apps in multiple ways. Some of the notable ones are: Developing algorithmic trading strategies, Predictive analysis, Sentiment Analysis, High-Frequency Trading (HFT), Portfolio Optimization, and Pattern recognition.