AI-Powered Trading Strategies: Are They Trustworthy?

—TechRound does not recommend or endorse any financial or investment advice. All articles are purely informational—

Artificial technology (AI) is the buzzword on everyone’s lips, and it is affecting almost every industry. The online trading of financial markets is no exception. Despite the hype around AI, this technology is still in its infancy.

Although it’s emerging, AI in online trading is predicted to improve vastly over time. Let’s learn more about AI in online trading and its many applications.


A Brief Overview of AI in Trading


Artificial intelligence simulates human perception and activities through computer systems. Many people link algorithmic trading to AI. While it relies on sophisticated computer-based algorithms, not all algorithmic trading is AI-based

As AI gains traction, algorithmic trading will use human perception while improving its ability to analyse massive datasets accurately and quickly.

Other uses of artificial intelligence in trading include high-frequency trading, arbitrage, quantitative analysis, and sentiment analysis.


Key Elements of AI in Online Trading


AI is an umbrella term for computerised systems that are as smart as humans and automatically mimic their actions in traded markets. Thus, it helps to understand artificial intelligence’s key branches or components.

  • Machine Learning: This describes computers using algorithms to learn and adapt to constantly changing data without manual intervention. These algorithms can analyse significantly more data than humans while making astute trading decisions
  • Natural Language Processing (NLP): Another branch of AI refers to how computers understand and manipulate human language or semantics. It’s necessary to analyse news articles and social media feeds and extract information from financial reports
  • Big Data Analytics: This term describes the process of computers or algorithms observing vast amounts of complex data to uncover hidden patterns, market trends, and correlations

Knowing these elements helps us understand how artificial intelligence broadly works in trading.


The Practical Uses and Strategies of AI in Online Trading


Let’s now study the many use cases for AI in online trading.

Algorithmic Trading

The first widespread use of AI in trading is algorithmic trading, which describes software with pre-coded rules and instructions to execute trades automatically. The coding is guided by parameters the developer derives from historical and current market data.

A charting platform like TradingView is valuable for backtesting and necessary for automating strategies.

Altogether, algorithmic trading can automate straightforward long-term trend-following and scalping or day trading-based strategies. It can also use more advanced methods such as:

  • High-Frequency Trading (HFT): HFT involves executing significant market positions at ultra-fast rates using high-speed data networks. Massive financial institutions like hedge funds mainly utilise this side of AI in trading
  • Quant Trading: Quant trading refers to incorporating quantitative analysis, which means mathematical or statistical data models to determine the probability of a specific outcome. The goal is to identify exploitable patterns or trends in the markets
  • Arbitrage: Arbitrage capitalises on minor price differences by buying and selling several markets across different trading providers to earn a profit
  • News Trading: Traded markets always move based on impactful economic announcements. AI can produce highly accurate news trading using data from social media feeds and financial websites

Sentiment Analysis

Sentiment analysis involves observing market sentiment and determining the mood or attitude of investors trading a specific financial asset. Learning about the sentiment offers traders a clue about the eventual buying or selling activity.

Using NLP, artificial intelligence can analyse news articles, social media posts, and other data to gauge market sentiment. This analysis helps traders foster more informed decisions about bullish or bearish bias.

Sentiment analysis is a necessary factor in news trading, as discussed earlier.

Predictive Models

Another focus area for artificial intelligence in trading is using machine learning to predict price movements and trends. These predictive models can incorporate some of the concepts mentioned, like quantitative and sentiment analysis, to make their decisions.

A related area to predictive models is reinforcement learning, where strategies can algorithmically learn from their experiences and adjust to changing market conditions in real time.

Risk Management

Thus far, AI has been discussed in finding profitable opportunities. Yet, this technology is also necessary in risk management when trading or investing in the financial markets.

Like predictive models, risk management models are in place to assess the outcomes of numerous trading scenarios using historical data from platforms like TradingView.

Another AI-based risk management technique is stress testing. In this method, AI rigorously subjects a particular trading strategy to countless simulated or market scenarios to identify weaknesses and improve performance.


The Limitations of AI


The benefits of AI are clear: speed, big data processing, automation, forecasting, scalability, accuracy, non-emotional bias, and hidden pattern discovery.

Despite these perks, developers have to address many challenges before it becomes mainstream.

Dependence on Data Quality

Financial markets don’t always move according to past events. Essentially, they are unpredictable. So, the AI is only as effective as the data it processes. Hence, it can duplicate inaccurate information for traders instead of providing unique insights.

Risk of Over-Optimisation

AI-informed or algorithmic trading strategies often suffer from excessive curve fitting or tweaking. This means traders will over-optimise such strategies based on historical data, which will cause them to fail in live trading. It’s attempting an approach that provides near-perfect results, an impossible feat. Over-optimisation with AI means a trading system is less adaptable to new situations.

Ethical Concerns

The first ethical concern is inequality since the development and implementation of AI strategies are reserved for wealthy individuals and companies. Hence, only a select few have this privileged information.

Another area for improvement is privacy. AI processes vast amounts of data from many sources, which may violate data protection policies.


AI Still Has Some Way To Go Before Wider Adoption


Artificial intelligence has made some decent leaps and bounds. The technology boasts numerous benefits, including algorithmic trading, sentiment analysis, and risk management.

Despite this, AI has only scratched the surface of its true potential and is far from ‘taking over’ the trading world. However, each passing year brings the technology closer to possessing the instinct of human decision-making in trading but with better speed, accuracy, scalability, and automation.

—TechRound does not recommend or endorse any financial or investment advice. All articles are purely informational—