How to Use Artificial Intelligence for Stock Trading in 2024

1. Introduction

Given the nature of AI and its benefits, it is probably unrealistic to expect that AI stock trading will be halted before a definitive demonstration of a powerful and independent act of trading in itself has been carried out. This is in contrast to the modern period where sophisticated trading software has allowed human traders to execute trades based on tips from a research department, and much of the trading act is still carried out by humans. AI trading would likely provide more cost-efficient methods of executing trades, such as through direct market access, and various algorithms have the potential for greater market impact through increased automation and the absence of the human emotion, often an obstacle for trading strategies. All in all, the likely outcome is that AI trading will continue to progress, and we will reach a stage where certain actions will be difficult to reverse due to the desire to perceive the varying impacts of AI on different market conditions.

Many people are surprised to hear that artificial intelligence is being used to pick stocks, but few find it shocking that it's happening. AI technology has advanced to a stage where the likelihood of any competition from humans in the high-risk trading area is non-existent. So, whether you think the idea of using AI to trade stocks is a wonderful breakthrough or the beginning of the end, there are issues and arguments that you should be aware of.

1.1 Benefits of Using AI in Stock Trading

- AI can process and learn from enormous quantities of data. It has the potential to far surpass human ability in this area. It can learn from past market data, building and testing hypotheses using methods of statistical inference. This is very difficult for humans to do. We tend to read into data what we want to see and cannot possibly process all the available data for a large-scale decision. AI takes the emotional human element out of the equation and rationalizes a decision based on the probability of an outcome. This method of making decisions is proven to be superior in environments of risk and uncertainty. An AI decision is only as good as its probability, and it is possible to quantify the exact probability of its decisions.

- AI can be everywhere at the time, humans cannot. AI has no intrinsic circadian rhythm, it does not get tired, and it does not have responsibilities away from the market. It can constantly and tirelessly scan the market looking for opportunities, which is highly advantageous. An AI can monitor many more stocks and their price movements than a human.

As artificial intelligence is developed and adopted, its advantages become more apparent. AI is deployed in a myriad of problems and fields. A few top benefits of carrying out trade with AI are:

1.2 Ethical Considerations of AI Trading

(High-Tech Powerhouse, 200X) With the ever-increasing technological advancements of the modern era, our society has become extremely reliant on the use of technology. As technology continues to become more and more advanced, people are constantly searching for newer, more efficient methods of completing tasks. In the business world, this newer and more efficient method can be found with the use of artificial intelligence (AI) to automate various business processes. With the marketing of various different AI software promising to increase efficiency and productivity of your business, there has been a recent shift of various businesses towards AI automation, and the financial sector is no different. This brings us to the topic of AI and its use in stock trading and the ethical considerations of AI trading. The purpose of this white paper is to give an overview of the pros and cons of AI from an ethical perspective then transition into the stock market and the issues faced in applying AI to stock trading as a prediction informing method. The central thesis is that though AI has significant potential to increase information efficiency in the stock market, its value as a prediction tool should not lead to mass utilization to the point that stock prices lose connection with the fundamental values of the underlying companies.

2. AI Trading Platforms

Trading Technologies is one of the market leaders in providing professional trading software to professional traders, proprietary traders, banks and brokers and now, thanks to the recent proliferation in the availability of robust automated trading systems, the tools are available to a wider audience than ever. X_TRADER is the company’s flagship platform and as one would expect is a high-performance trading platform that is designed for professional traders. The platform is an open platform that allows users to build, test, and deploy automated trading strategies via its Electronic Eye functionality. Users can use the server-based Auto Trader to connect to the various APIs available through TT to automate their trading strategies and furthermore the data and execution can all be contained within the TT network which reduces concerns about system requirements. Investment Technology Group is an agency brokerage firm that has attempted to provide an array of tools to algorithmic traders. POSIT is perhaps ITG’s best-known platform as it is an alternative trading system that lets buy-side traders or institutions trade large orders anonymously. The system breaks down large orders into smaller chunks and searches for a market for potentially adverse flow (i.e. the buys and sells that would drive the market away from an intended price) then continues to either work the order or the adverse portion with a Rules Based Management (RBM) process using algorithms. Unlike with the latest price before applying it to the actual order. This prevents information or expected price leakage that drives up the cost of the trade. ITG has learned that not all of their institutional clients have the same experience or technological resources to trade the POSIT way on their own, therefore they released the Macgregor X tools which lets a trader implement POSIT-like algorithms on POSIT and POSIT Alert through an interface with queues and detailed information about the algorithms used.

2.1 Features and Capabilities of AI Trading Platforms

In order to trade, an AI program must have a method of determining what action to take at a given time. This requires a decision-making system, preferably a logical system that is able to deduce whether an action is true or false. The logic involved can range from informal methods of trial and error to formal symbolic logic. Also involved is a testing and implementation platform that the AI will use to interact with the market. This may be in the form of a simpler program that takes the inputs of the AI and executes orders, or the purchase of a direct market access (DMA) platform that integrates the AI with the broker's infrastructure and bypasses a broker's order desk. In general, the success of the AI is largely dependent on the quality of its system relative to that of other traders. This is due to the fact that in a market with an inefficient allocation of resources, the trader that makes better use of the information available will make a greater profit.

An AI trading program is one that is able to generate and execute buy and sell orders in a security. It is reliant on intelligence that is able to learn from its mistakes and past actions. This type of program is an example of weak AI in that it is only able to perform in the narrow task of trading and does not possess human-like general intelligence. The learning process can be in the form of supervised learning where the AI is given a set of example input/output pairs and learns a function that maps the input to the output, or unsupervised learning where the AI is given only input data and the goal is to learn the structure of the data. Reinforcement learning is another option that has the AI learn what to do in a particular situation in order to maximize a reward function. This is the most suitable learning method for trading because of the maximization of profit in the form of a reward function.

E-commerce and technological advancements in the 21st century have led to a surge in automation and electronic trading. These may take the form of algorithmic trading and 'black box' trading carried out by hedge funds and proprietary trading shops. One of the results is a greater emphasis on trading software. There are a wide range of trading programs out there that are targeted towards different trading needs. These include off-the-shelf programs such as Ninja Trader, TradeStation, and AmiBroker that cater to self-traders, through to programs that are tailored to specific brokers and are used as the platform offered to clients. However, the emergence of artificial intelligence (AI) trading programs that can think and learn like a human poses a real threat to the plethora of human traders out there. The onus is now on us to match the learning machines.

2.2 Best Brokers for AI Trading

When identifying the best brokers for AI trading, it is important to know the differentiators between AI trading offered. AI can trade tactical algorithms or statistical, which can then be transformed into trade signals. A signal would suggest a buy or sell, and then the traders would execute the trade based upon the signal. The statistical approach is the most popular among AI trading. So, with statistical analysis in mind, it would be beneficial to find a broker that offers historical data. This is important for trading strategy development, backtesting, and implementation. Another aspect of statistical trading is probability and predictive analysis. Look to see if the broker offers tools and software for predictive analysis. Measure the likelihood of an outcome in trading is a format that closely resembles trading probability. Boole and Babbage, a developer of trading algorithms, have an ongoing project to develop predictive analysis algorithms and are working with the Chicago Mercantile Exchange to trade these algorithms on their electronic E-mini S&P 500 futures contract. This leads us onto the probability of whether the broker will still be around in the future. Bootstrapping, a historical method of statistical analysis, has determined that the lack of survivorship bias of managed futures and forex traders is around 20% annually. Survivorship bias is the tendency for failed traders to be excluded from a trading sample, and it is estimated that in the future, AI will increase evasion of survivorship bias due to the fact that the strategies are consistently duplicated and are unlikely to fall victim to any impulsive, ill-informed decisions. Last but not least, on the topic of statistical analysis, AI has the potential to create and sell trade signals and strategies to investors. This is a long-term project, and the AI trader would essentially be acting as a trading consultant for individual investors, analyzing their portfolio and suggesting investment and allocation strategies. If this is a project of interest, it would be wise to trade in an environment close to where the signals would be sold.

3. AI Stock Picks and Machine Learning Algorithms

A simple example of a commonly used machine learning method for predicting trade outcomes is the logistic regression. This is just a regression of the true or false result of a trade being profitable, with the input data being the features of the trade and stock prior to the trade. Another technical analysis method might be to predict certain price patterns that are known to be profitable. An example of this would be a neural network, trained on various inputs. The neural network attempts to predict the value of the pattern in question and other patterns like it in order to give a buy/sell signal with a high probability of success.

By using historical data from the stock in context (for instance, your trade signals and for how long you kept a trade), a machine learning model can learn what would have been the best action to take at each step in the data. This is referred to as the Markov Decision Process. For a given model of the state (S) and action (A) at that state, the objective is to find the sequence of actions that will maximize the expected sum of future rewards. In this case, a reward is a profitable trade or a high probability of a profitable trade.

In general, a trader would seek to make a system that is more accurate than a 50/50 coin flip in determining which trades will be profitable. This can be formalized as trying to maximize the return of an investment strategy that takes a value of 1 when a trade is profitable and -1 when it is not. A trader would also put constraints on the acceptable risk of his strategy. The most common measurement is to maximize the probability of a positive return, rather than maximizing the total return of the strategy.

Stock picking is a time-consuming and often uncertain task for stock traders. AI techniques like machine learning can be a tool in forecasting which trades will perform best. Various online brokerage sites as well as independent researchers have developed AI packages that make buy/sell recommendations. These AI applications range from the simple decision tree classifier to more complex neural network and reinforcement learning models.

3.1 Using AI for Technical Analysis

Machine learning involves the training of machines to learn and understand patterns in data rather than simply following a fixed program. In order to apply machine learning to trading, the machine must be able to identify patterns that can be used to predict the future. In trading, a basic example of machine learning is a technical trading system. This might involve identifying a pattern in a price chart and then simulating a trade using that pattern. If the result is better than average, the machine has learned a successful pattern. We at first make the assumption that the patterns and relationships identified by a data mining algorithm or machine learning method will continue to hold in the future. This assumption is based on the nature of financial market data: that price movements are the result of a chain of events which are more or less linear and do not randomly and spontaneously occur. High frequency trading firms using news reading algorithms exploit the same factor and can be very successful if they are able to correctly interpret the news before other market participants do. In that case, they will be mimicking trend following as they will be looking to take long positions in stocks that are going up and short positions in stocks headed down. Machine learning can significantly aid in the realm of pattern recognition by automatically identifying relationships between input data and a given result. An example of this would be classifying the cause of movement in an equity's price as the result of an event which can be defined by an array of inputs. The machine can then test different combinations of inputs to identify the ones that result in the best price movement prediction. Pattern recognition is often a complex task and there are various forms of machine learning which can be applied to it. High frequency trading firms have recently been using hidden Markov models to build algorithms which are able to more accurately predict short-term price movements.

3.2 AI for Risk Management in Trading

AI for risk management in trading: AI can be a useful tool to help human traders manage their risk. Risk management is one of the most important and yet often neglected aspects of trading. A common adage in trading is "cut your losses and let your profits run." Many traders neglect this simple saying and let losses get out of control, depleting their accounts or ruining their chances of creating a substantial income from trading. On the other hand, most traders who have been in the market for some time will have experienced winning streaks where they have made a substantial amount of money, but sooner or later gave it all back to the market and in some cases much more. This is a failure to control the urge for greed. Both of the foregoing examples are failures to manage risk, be it on the losing or winning side. The objective of risk management is to define the acceptable level of risk for a trade and a winning streak, make sure that it does not exceed this level, and secure the gains. AI can help traders manage risk through its use in developing trend-following systems and mean reversion strategies. It is the latter which a study by Hong Kong Polytechnic University feels is more suited to AI. The study used an artificial intelligence technique known as genetic programming to build a mean-reverting trading rule on the S&P 500. This compares to trend following, which aims to find and follow a trend and is more high risk as successful trend following usually occurs at turning points but it is difficult to distinguish a turning point from the temporal noise in the market.

3.3 Can AI Outperform Human Traders?

On this basis, it is highly probable that an AI system can make consistently superior decisions to humans. Given that it will be continuously optimizing many strategies to find the best one and always acting on them without any behavioral slippage. Data-driven decisions can also involve machine learning from past decisions and outcomes. In the future, it is not inconceivable to see algorithmic agents running simulations with fake money to learn how to best beat human adversaries.

The other angle is that of data-driven decisions. Human psychology and decision-making are riddled with various cognitive biases, heuristics, emotions, and stress. This can often lead to impulsive decisions or deviation from strategy. Modern behavioral economics has largely been a study of why humans deviate from a rational model. As an AI system is always rational, the decision made will be the best fit for its model and never swayed by emotional suffering from a bad day in the market or the shrewdness of a hedge fund manager aiming to buy a depreciated asset.

One important distinction to make is between skill and a data-driven process being used. Humans are able to adapt and refine new strategies based on current situations and the market around them. AI will be bound by its programming, having to be re-trained to update its strategies. Although AI can constantly multi-agent to find the best strategies, it could end up avoiding overfitting and modeling strategy in a prudent and low-risk way, similar to 'indexing'. It is a good strategy that future AI systems will aim to replicate.

Outperforming the human average on long-term strategy will be a definitive test for AI's success in the stock market for the reasons mentioned above. There is no consensus among financial professionals as to whether markets at any point in time are efficient. Assuming they are not, an AI system that has successfully learned to model the market and make a profit would be evidence that it has surpassed human traders.

4. The Future of AI in Finance

This carries large systemic risks, where AI systems could become too complex and intertwined, making it hard to understand the impact of failures, and we would essentially be trusting our complex financial systems to know what will be highly intelligent but fallible machines.

The use of machine learning in the form of AI for decision making in the financial industry is not without its critics, many of whom are concerned it could bring about an AI arms race in financial markets. This is because the nature of machine learning and AI is that as it becomes more widespread it will become necessary as a competitive tool. As markets are often a sum game (net profits equal zero), using AI to achieve even a slight edge could result in substantial gains, and given the AI arms race scenario, opponents not utilizing AI might end up at a significant disadvantage.

The financial sector is seeing a growing interest in AI and its future development and impact. AI in finance is not new, in the sense that it has been applied to rule-based systems for trading for a couple of decades. What is different today is the growing influence of AI technologies that exhibit broad spectrum intelligence, learn from the data they operate on, and are capable of making autonomous decisions. These capabilities promise an era of disruptive change.

4.1 Trends and Predictions for AI in Stock Trading

As alluded to in the beginning of this article, AI applications have been heavily focused on automation. As this technology continues to progress, it is likely to represent fully autonomous trading agents that make decisions in the place of humans. While depending on one's perspective, this could be seen as a significant success in the development of AI, it may also lead to catastrophic market events. Given a program's ability to rapidly execute trades in response to changing market environments, there is potential for markets to experience increased volatility as more trading is executed with minimal human intervention. This could culminate in a flash crash, a sudden deep and large drop in stock prices, due to a chain reaction of automated decisions on volatile stocks. The 2010 Flash Crash was caused by an order that executed in 20 minutes and only represented 4% of US stock trading volume at the time. With the increasing prevalence of AI agents as market participants, we must proactively determine how to control the level of AI market participation to prevent possible destabilization of financial markets.

According to a Forbes article, "AI-related developments in stock trading took off in the mid-1980s." Since then, algorithmic and high-frequency trading have come to represent a significant portion of activity in capital markets, often up to half of a given market's trading volume, largely due to the effectiveness and efficiency of automation brought on by AI. AI has continued to develop since then, with modern machine learning making use of complex models and prediction on big data to craft trading strategies. "Robo-advisors" represent a newer development in using AI for trading: algorithmic financial planning and investment platforms that cater to consumers without the expense and complexity of traditional advisors.

4.2 Implications and Challenges of AI Adoption in Finance

Step up the level of complexity for AI use and we may come to machines making decisions themselves. These machines are loaded with AI programs that enable them to make decisions with various levels of intelligence. At its most complex, AI decision-making machines can replace humans in some decision-making and implementation tasks, enabling the automation of certain jobs. An example would be the automation of an algorithm for trading in the stock market. Development in AI up till today have already seen the use of DSS and some forms of decision-making machines. But there has been a wanting for more and more sophisticated AI to be used in finance, arguing that the potential benefits are highly attractive. And thus as AI's use in finance increases, so will the implications and challenges of this increased use.

AI has come a long way from its inception into the annals of modernity. AI has been known to have penetrated into many sectors that are of relevance to us, and one of these sectors that we are concerned with in this essay is the economy and finance sector. In the context of finance, AI has been used in various forms. Its simplest form is through the use of decision-support systems (DSS) that serve as recommendations for humans to make decisions. The idea here is that since AI is able to store and process huge amounts of data, this is particularly useful when dealing with data pertaining to stock market investments where it can make recommendations based on data and thus free humans from making repetitive and mundane types of decisions.

Implications and challenges for the adoption of AI in finance