How AI is Changing Retail Trading

Artificial Intelligence is silently revolutionizing retail trading and considering the pace of change, it’s highly likely that trading is set to change forever. The advent of trading robots and smart trading programs has made it possible to execute high-frequency trades based on a specific pre-defined rule. The advantage of using AI-driven systems is that it can analyze thousands of pieces of complex information in a fraction of a second. Adding AI to trading robots means the system will keep updating its algorithm based on the previous result, thereby improving trading accuracy.   

This has its own set of advantages as the emotional impact on trading is eliminated. As any experienced trader will indicate, these emotional biases constitute great roadblocks to trading performance. 

Here are the various ways in which AI is influencing retail trading: 

  1. Increase in Speed of Execution  

Speed of execution is of paramount importance in retail trading as it ensures good pricing and quick advantage in the market. Executing large orders fast reduces slippage. AI-driven systems use algorithms to execute orders at lightning speed without any human bias. 

Technology has made it possible to analyze the history of trades through a trader and identify certain patterns in their trading behavior, which act as a roadblock to success. These algorithms can now tell whether traders hesitate to take a call when the market is turning. How do they behave in a particular situation? The secret of making profits lies in the answers to these questions. 

Trades are based on a pre-defined strategy. AI is adept in implementing trend-based strategies such as moving averages, channel breakouts, and other technical indicators at the speed of thought. Conventionally, trade execution robots split trading orders into smaller parts to minimize the impact on the stock price. Stealth algorithms can take advantage of sudden price fluctuations caused due to large trade orders.  

Often, a stock may be trading on different prices in two separate markets, enabling traders to take advantage of arbitrage. Based on the fundamental and technical data such as price- and volume-related information, bots can buy and sell different financial assets quickly. This helps bring efficiency in the market.   

  1. Reducing Trading Risk 

Thanks to AI-driven algorithms, traders are able to minimize high-risk margins and bad position sizing attributed to human-induced inefficiencies. In overheated markets buzzing with traders, the problems can become very serious. There is always a tendency to use excessive risk in order to gain large returns, or to try to quickly recover any losses that have been sustained. Such trading behavior commonly leads to more losses.  

AI is effective in detecting such trading behavioral anomalies, providing a cushion against huge losses caused by bad trading behavior in a volatile market. AI-driven trading systems enable the bypass of the human element of emotion, and deploy unemotional methods such as the use of mathematical calculations to perform the trading actions. 

  1. Detect Market Feelings From Large Data 

Excellent in analyzing sentiments, AI processes humongous volumes of information – articles, blogs, video transcriptions, social media posts, comments etc. AI can determine whether the market is feeling positive, negative, or neutral regarding a topic. 

It must be mentioned that many algorithmic strategies and trade systems are all derived from historical market data. AI helps makes sense of unstructured information. Traders can leverage AI to merge vast amounts of data and dig into the insights shared by fellow traders with unparalleled precision. 

  1. Quick Recognition of Patterns 

Neural networks, adept in recognizing patterns swiftly and with efficiency, are swiftly getting popular in the development of training systems. Neural networks are adept in recognizing underlying relationships in a set of data, thus mimicking the human brain.  

Scientists have been continuously working to construct neural networks that are a fraction of the size of the original sets without losing any computational ability. Identifying the contents behind the patterns, they can defeat humans in abstract strategy games. 

Neural networks today are used increasingly for crunching information regarding stock exchange rates and forecasting market movements. 

Now that you’ve a basic idea of how machine learning is changing trading, let’s see a couple of case studies. 

Kavout 

Seattle-based Kavout releases K Scores, which can be used for validating research, constructing portfolios, or creating risk-adjusted investment factors. K Score is stock-ranking rating derived from the processing of diverse sets of data and an array of predictive models. The company provides a daily recommendation of top stocks powered by AI algorithms.  

Epoque 

Based in Switzerland, Epoque Plus is a frontline company in artificial intelligence trading. Epoque provides clients with a fully automated trading service. The system uses three engines – 

  • a strategy engine to observe and analyze potential trades 
  • an order engine to create orders and perform operational actions 
  • a logical engine to handle active orders 

AI trading systems are effective in tracking markets shifting at lightning speed. These systems can spot patterns that the human brain is not capable of, helping you rake in profits. 

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