In the high-tech world, with everyday disruptive innovations presented to humankind, one may find it hard to keep pace with the changes. But to remain competitive, people should embrace new technological products, especially if they promise good returns. This is precisely the case with stock trading, which is evolving at high speed, becoming more accessible to masses of users and revolutionizing with technological advancements at the same time.
The global trading market is indeed immense. In 2020, over $32 trillion of global equity are being traded worldwide, compared to a bit more than $25 trillion in 2009. Only the U.S. stock exchanges NYSE and NASDAQ account for 39% of the global stock market value, with their market capitalization exceeding $31 trillion altogether. Within the past 20 years, the holders of the NASDAQ 100 index have increased their fortune by 300%, with the next-best performing one being the Dow Jones Industrial Average (a 196% increase).
As you can see, stock markets are always about big money, no matter the crises, pandemics, and revolutions. Everyone wants to get a share of that delicious financial pie, but is it possible to earn on stock trading without specialized knowledge and years of experience? Fortunately, with the advent of innovative technologies for trends analytics and smart data-based decision-making, the dream is becoming a reality, with new automated artificial intelligence (AI) trading solutions at hand.
Do you want to learn more about AI-based stock trading? Read on to see how AI is transforming the industry, what the pros and cons of using AI for trading are, and how laypersons without in-depth technical knowledge can utilize the power of AI algorithms for lucrative stock trading and investment. We also cover the threats of over-reliance on AI and explain the limitations of this technology in prediction accuracy.
In simple words, AI means the use of computer software for mimicking cognitive functions, such as learning and modeling human-like decision-making based on the provided data input. A subdivision of AI, machine learning (ML), has also enabled machines to further advance their cognitive functions, learning not only on the given input but also on their experience with making right and wrong decisions as well. Thus, at present, the potential of AI (specifically ML) is actively explored in all spheres of human activities, such as:
AI is everywhere, and stock trading AI is also gaining momentum as a "lazy trading" solution. Here's how it fits the industry specifics and enables traders to derive profits from automated dealings.
Artificial intelligence trading is booming now because its features fit the world of finance ideally. AI solutions are capable of counting numbers rapidly and making optimal decisions based on big masses of data, which is highly applicable to the stock market realities. Machine learning for trading allows financial firms to get a complete image of the stock market situation with the help of in-depth, continuous stock price fluctuation analysis and unstructured data processing. It also proves useful in complex trading pattern identification, informing the right selling/buying decisions in real-time.
Artificial intelligence trading strategies get increasingly sophisticated as the systems learn from their own experience. Thus, today, they offer indisputable benefits to users by allowing:
These benefits speak in favor of using AI in your investment activities. Here are some ways of applying AI in day-to-day trading routines.
The best outcome of using AI in stock market trading is a trading signal. Such signals are the result of AI systems' big data analysis on particular assets providing precise recommendations for successful trading decisions, such as the best entry price, stop loss, and profit margins. These signals thus allow much better asset risk management, preventing traders from going too far below the loss margin in the hope of the price recovery.
Trading signals are produced by AI systems based on the advanced analysis of numerous indicators, such as price action, currency valuation, and even analysis of data about the particular asset in the news and social media. The technical analysis of stock price dynamics is also included in the dataset.
Where to take AI trading signals? As a rule, companies that have invested in setting up their custom software for real-time stock market analysis don't disclose their secrets and sell signals on a subscription basis. Here are some pros and cons of relying on trading signals:
As you can see, trading signals offer some benefits to investors, but they contain certain risks you should be aware of before entrusting your money to machines. To use or not to use these signals, depends on your subjective perceptions of the stock market risks and your desire to try out new lazy investment solutions.
The profit you can make from AI software is indisputable. It has risen from only $9.51 billion in 2018 to $22.59 billion in 2020 and is expected to grow to unthinkable heights, $118.6 billion in 2025 (which is only 5 years from now). So, today it's high time to take the benefit of AI technology in trading stocks to multiply your wealth and manage stock trading-related risks proactively.
The development of an advanced AI trading platform is a more time- and labor-intensive process, but it's still possible, as many existing end-to-end products show. You'll need some investment to realize this project, both as a client and as a coder, as it's an expensive software development task. However, such a product promises more in-depth analysis and more sustainable recommendations suitable for long-term investors.
Finally, you may seek a customized AI solution for trading a specific asset or asset group. In this case, if you have particular experience with this object, you can order a tailor-made AI app from a qualified coder to fit your needs and the specifics of the asset of interest.
AI trading software isn't perfect; neither is it flawless. Thus, the challenge that most trading AI software developers are striving to overcome today is the inverse relationship between performance and capacity of a program. According to it, the higher the returns from a trading algorithm are, the less sustainable they will be. Besides, machines analyze risks in their way distinct from that of humans, so the balance between mechanical sobriety and human opportunism is yet to be achieved.
Many people praise the power of AI to analyze big data and predict patterns, which allows making "lazy money" on correct stock decisions. But the sobering truth is that good strategy is quickly recognized and copied, becoming obsolete too quickly to make enough money on them. Thus, a genuinely ideal AI algorithm should be good not only at analytics but also at adaptation to quickly changing market conditions.
Besides, the trading AI software can't help traders overcome the large-size trading limitations; it's the rule of the dynamic stock market you will never override. In practice, selling 100 shares at a recommended price is possible, but if you have 1,500 shares or more, the price will react to the bulk sale, and a part of your sale will take place at a much different price. That's what an AI algorithm still can't predict precisely, so this limitation remains the task of humans to manage.
The development of customized, plug-and-play AI solutions is becoming more accessible. This is what Datrics can do for you, applying data science and innovative software development to deliver demand forecasting, sentiment analysis, and customer analytics products to customers. Whether you're planning to use some simple intraday trading software or wish to develop longer-term trading advice platforms, Datrics can provide turnkey solutions for any development task. Contact our managers today to tame the power of AI and apply it to your trading aspirations.
Yes, AI is currently widely applied in the field of stock trading and investment due to the ability of AI systems to process vast masses of information and analyze them in the real-time mode. Besides, ML algorithms are ideally suited to trend prediction and accurate sentiment analysis because of their advanced learning potential. Hence, AI applications in trading are diverse and potentially lucrative.
There are many ways to apply AI in the trading industry; you can use the variety of smart trading advisors and analytical software sold online or order the development of customized robots or apps for your individual trading purposes, giving you signals and making operations based on your trading strategy.
Algorithmic trading, or algo-trading, is not quite the same as AI. Algo-trading refers to the application of specific trading rules into a program conducting trading operations for the user. The programmer determines the rules, so they are not necessarily based on AI, often being the trader's intuition. In contrast to algo-trading, AI solutions involve machine learning and analysis of complex human behavior by the machines for achieving more accurate sentiment prediction and making trading decisions. In other words, algo-trading is done by the rules established by a person, while AI trading takes place by the rules learned by the ML systems from the data input provided by people.
Yes, AI systems can produce accurate forecasting based on pattern analysis, but with serious limitations to that capacity. People over-relying on AI technology should keep in mind that such systems' potential is limited to technical analysis only. At the same time, human beings often tend to shift their trading sentiments based on the fundamental analysis outcomes and on the real-time price movement.
For instance, an AI trading algorithm sees a good chance of profit-making on the asset's current price. It gives a trading signal to the user to sell or buy stocks at a given price. Still, if the user has a vast volume of this asset (e.g., 1000+ shares), the sale of this amount will affect the stock price, which an average ML system can't predict. Thus, a portion of the sale/purchase can be completed at a recommended price, in which 30-40% of the volume will still be sold at a reduced/increased price that the user themselves initiated.
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