Artificial Intelligence is a contemporary concept that we all have heard of and maybe even know about it. Since this is the article you chose to read through, you will surely benefit from the trading aspects of AI and ML that we have covered ahead. In today's time, it is a must to know how they have given a nudge to the profitable trading. Find out all about trading with help of AI & ML. So, the content in this article covers:
- What is Artificial Intelligence and how is it Used in Trading?
- Types of Artificial Intelligence
- The Impact of Artificial Intelligence and Machine Learning on Trading
- Implementations and Applications of AI and ML in Trading
What is Artificial Intelligence and how is it Used in Trading?
Basically, Artificial Intelligence (AI) is the science and engineering of making intelligent machines. Specifically, it takes into account intelligent computer programs to calculate, reason, learn from experience, adapt to new situations and solve complex problems. Artificial Intelligence (AI) is mainly based on disciplines such as Computer Science, Psychology, Linguistics, Mathematics, Biology and Engineering.
Since AI is shaping the future of stock trading drastically, it is going to continue making trading profitable in the coming time. For instance, Robo-advisers are automated to analyse millions of data points in as little time as possible and forecast the prices on the basis of the same. Further, it executes trades at the most profitable time because of its ability to carry out several trades in every second in the stock market. Hence, for accurate analysis, forecasting, timely execution of the trades and for mitigating the risks, AI plays an important role.
Now, let us see how exactly AI is used in trading:
- Pattern Formation
- Predictive Trading (Sentiment Based)
- Increased Trading Speed
Artificial Intelligence is a powerful technology which helps to analyse numerous data points within seconds. This way it can identify those trading patterns rapidly which are historical and replicating for smart trading. Whereas, humans can not identify and build patterns with such speed.
Predictive Trading (Sentiment Based)
Based on the analysis of news headlines, social media comments, and other platforms, AI is able to forecast the moves of other traders along with the direction of stocks with the help of sentiment analysis.
Increased Trading Speed
Since it is the era of fast-paced technology-oriented functioning, AI helps as it facilitates trading every millisecond. Also, AI leads to such fast-paced automated trading which needs no human intervention.
Great! Now let us move to the types of Artificial Intelligence and see the main categories covered in AI.
Types of Artificial Intelligence
Now, you must know that both Rules-Based Systems and Machine Learning help to infer the input data. Both types are individually important for particular situations. Now let us see how these types are different from each other.
Rules-Based Systems are considered the simple kind of Artificial Intelligence. They only need to be fed with the statements that comply with either THIS or THAT for making the system come to a conclusion. Hence, it consists of some IF-THEN rules along with a set of facts. There are two main principles it functions on, which are:
- A set of facts
- A set of rules
A set of facts
These are the set of general facts on which the data depends. For instance, the price of a book is INR 100 or is more than INR 100.
A set of rules
These are the engines for facts since they decide what the outcome will be in both cases of facts. For example, if the price of the book is 10$ then you buy it.
So, since you are clear with both the concepts, let us see another example. The AI is fed with the rules-based information to recommend which colour of shoes to wear every day. In this scenario, there will be facts supporting the same. The facts can differ for several reasons that particular day, like:
- It is raining
- It is a sports day
- It is a celebration day
Based on the facts above, the system will conclude each day accordingly.
Here, it is also important to note that the decisions are fed to the system with the help of a group of human experts in the particular field.
Also, rules are easy to write since you need only an addition of a rule to be given to the system in case of any additional fact in the decision-making process that you did not consider earlier. Another important point to note here is that the rules are deterministic and hence, not putting the rules in place appropriately can lead to false outcomes. Moreover, there can be occasions where changes in real-life scenarios may be faster than updates in the system. This also can make the outcomes faulty.
To read more, you can refer to the research paper here.
Machine Learning is another approach but an improved one which helps to do away with the issues in Rules-Based Systems. In this, the machine is fed with information about the outcome of each data point and not the decision-making process.
For instance, in case the scholarship applications were refused for some applicants out of 1000, then the system will only feed the outcome and not the entire process.
This way, the automated system learns to make more accurate decisions as compared to Rules-Based Systems.
Hence, it operates on the basis of historic outcomes and predicts what future outcome can be. Also, apart from the historic outcomes, it takes into consideration other parameters or factors impacting the decision.
According to another example, the output here can be something as simple as ‘Whether I should carry an umbrella today or not?’ or something as complicated as ‘predicting stock prices’. Hence, there can be as many input variables or features as required. Although the input variables and outputs are very much the real-world scenarios, it still becomes difficult to explain the several factors playing a role in between.
Let us now see where the machine learning process may not work.
Here, an example can be helping to decide the attire for an occasion. In such a case, there are so many factors affecting the decision, and one of them is the ‘temperature on a particular day’. The system will check the temperature on the same day a year ago to base its outcome on.
But, here the factor may be not aligned. It is so because on a particular day this year, the temperature may be more or less. And hence, to decide according to the current temperature, the system will have to depend on the facts of that day.
It is also important to mention that Machine Learning also has a ‘Decision tree’ method which resembles Rule-Based Systems. In this, you need to feed the system with a single statement at the start and follow through the decisions made later. But, there is a difference between the Decision tree and Rules-Based system which is with the information fed. The Rules-Based system comes via input from human experts, whereas, the decisions in a Decision tree are made by the machine learning process.
Now, as you are clear about the types of Artificial Intelligence, let us move ahead and find out the Impact of Artificial Intelligence and Machine Learning on Trading.
The Impact of Artificial Intelligence and Machine Learning on Trading
Factually speaking, Artificial Intelligence and Machine Learning have the power to solve large-scale problems in the trading domain. These situations or problems are usually with regard to optimization, analysis, and forecasting. With this power, AI and ML have impacted trading in the following ways:
Identification and Analysis of Predictors (Factors) of Stock Prices
AI and ML use neural networks and several learning methods for identifying and analysing factors leading to particular stock prices. These factors are also known as predictors or features. Based on these factors, AI and ML predict future stock prices. Also, this application of AI is an example of Machine Learning.
Artificial Intelligence is an automated system, which makes fact-based decisions unlike human beings, whose decisions are driven by emotions like fear, greed, hopes and agendas. With these fact-based decisions, trading has become more profitable for market participants.
Change in Recruitment Patterns in Trading Domain
With the advent of fact-based trading, Artificial Intelligence has also brought the need for human beings to help manage the same. Since trading based on AI and ML requires individuals skilled in Maths, Computer programming and so on, now the trading domain is recruiting employees from various related fields.
Use of Chatbots
AI and ML have significantly added value to the day-to-day lives of the traders with several advantageous inclusions, for example, chatbots. Chatbots have improved the way trading takes place since it is easier for traders to not only communicate with the chatbot but also has access to the history of the statements. Moreover, chatbots learn themselves and do not require any human intervention.
Here, let us take an example. Suppose you, as a trader, send a message to the bot to know about the trading offers. In this scenario, the bot will update you with the current prices and also will confirm the size of trade you are looking at. Now, the bot will provide you with the potential offers and also will consider the responses of other traders. Once all the offers have got collected, it will provide you with the best one.
Simulated Risk Scenarios
Since AI helps to forecast the stock prices in the trading domain, it is by far the best tool for the stock market. With accurate predictions of risk, the trader can make wise decisions. AI has the ability to gather mass data to analyse the same with exceptional speed and accuracy. With this ability, it is possible for it to maximise the potential gains and simulate risk scenarios. Hence, AI and ML have turned the trading business towards being more profitable for the traders.
As you can see, AI and ML have impacted the market culture with much more profitability than ever before.
Now, let us move ahead and see the Implementations and Applications of AI and ML in Trading.
Implementations and Applications of AI and ML in Trading
Artificial Intelligence and Machine Learning are playing an important role in the trading domain since the new technology has made trading faster and simpler.
Machine Learning is a subfield of Artificial Intelligence, and it has offered an exceptional innovation to the world of trading.
Machine Learning has several implementations in the trading domain. We have shortlisted some below:
- Historical Data-Based Prediction of Stock Prices
- Accelerates the Search for Effective Algorithmic Trading Strategies
- The Number of Markets to Monitor
Historical Data-Based Prediction of Stock Prices
Machine Learning implies feeding the historical data to the system for it to base its decision on them in the future. Hence, for predicting the stock prices which are called target variables, Machine Learning uses historical data which is called predictor variables. For doing so, the algorithm in ML learns to apply predictor variables for forecasting the target variables.
Accelerates the Search for Effective Algorithmic Trading Strategies
Machine Learning is also implemented to accelerate the search for effective Algorithmic Trading Strategies. Since it provides an automated approach, it is much better than the manual process. These Algorithmic Trading Strategies help traders by optimizing their profits and simulating risks. Anyway, there is a competitive advantage if you have automation to support you for any task. For instance, there are several strategies which make use of Machine Learning for optimizing algorithms, like linear regressions, deep learning, neural networks and so on.
The Number of Markets to Monitor
Machine Learning also helps increase the number of markets to monitor by the individual and to respond to. More the number of markets, the better the chances of a trader to go for the most profitable one. Hence, you can increase your opportunities with this implementation of Machine Learning.
There are several well-known companies such as Renaissance Technologies and Citadel which are using Machine Learning for their investment decision making.
As an application of Machine Learning, XGBoost is the best example of the same. An XGBoost model is actually the booster for a gradient model. Thus, it enhances the performance of the same with the help of Machine Learning.
Let us take an example and build a portfolio of five companies. Now, on this portfolio, we will apply the XGBoost model to create a trading strategy. The five companies were Apple, Amazon, Netflix, Nvidia and Microsoft. And here’s what we got.
Great! We have come to the end of this article and have covered quite a lot of important aspects of AI and ML in trading.
Coming to the conclusion, this article consists of the elaborative understanding of Artificial Intelligence and Machine Learning from the trading perspective. First, we understood the concept of Artificial Intelligence, its types and the impact AI and ML have on Trading. We also covered the implementation and application sides of the same. Hence, by the end of this article, we have a fair understanding of the topic and its use in trading.
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