Machine Learning In Trading

Machine learning in trading refers to the use of complex algorithms and AI to detect patterns, technical indicators, and socio-economic movements of different markets to decide on the viability of an investment or trade. 

Trading or investing in any security is heavily determined by the efficiency of the trader’s pattern detection skills. These patterns can be figured out by analyzing a massive amount of data, including financial statements, market conditions, recent events, macroeconomic expectations, and so on. 

Therefore, it can become tedious and time-consuming to do all this without the help of technology. Various algorithms help traders study a stock or any other financial security thoroughly. In fact, most ML and AI algorithms analyze news headlines and social media posts to understand the sentiment around the underlying asset or company. 

Therefore, machine learning in trading strategies is a combination of rules that flag a buy or sell trigger whenever a set of conditions are met. The traders or fund managers set these conditions at their discretion. 

With changing times, it is crucial to adopt methodologies that optimize currently monotonous activities. In trading, the usage of AI and ML has helped traders and other market players facilitate more efficient, faster traders who tend to generate higher alpha by recognizing opportunities in patterns that the human eye might have missed.

The three fundamental types of ML in trading are:

• Supervised Learning: When the output of a training model is predetermined, it is commonly referred to as supervised learning. It is used for forecasting stock values or finding unidentified or under-identified trading opportunities in the market. 
• Unsupervised Learning: The outcome of unsupervised learning is not predetermined. Therefore, it is trained with unlabelled data. It can be used to spot hidden structures or patterns in the financial data of a stock or a group of stocks that move similarly when a particular market variable shifts.
• Reinforcement Learning: The third fundamental type of learning is modeled so that the algorithm takes actions in situations that can either maximize the trade's profits or minimize its losses within a specific period.

Multiple machine learning in trading books shows that the use of this technology can improve the process multifold. Therefore, an increasing number of algorithms have been curated to follow these models. A few trading algorithms that use machine learning models are:

• Linear Regression: This model uses historical data, conducts financial analysis, and predicts a stock's future price. 
• Decision Trees: It is considered a viable alternative for constructing manual trading strategy rules. Using the decision tree model, traders can curate customized trading rules for each investment or trade. These interpretable models allow them to save time and generate better returns.
• K-means Clustering is one of the most common models used for splitting or partitioning stocks according to their time series data. However, after the partition, linear regression has to be applied for further analysis within each partitioned cluster.
• PCA: The Principal Component Analysis (PCA) is an unsupervised learning model. It is the most commonly used algorithm for exploratory data analysis and information compression.
• Monte Carlo Tree Search: This is a form of reinforcement learning model used for optimizing trading strategies. Traders and other market players can use it to set parameters that trigger a buy or sell action based on market conditions. 
• Q-learning: It is another common form of reinforcement learning model that communicates with market movements and learns patterns that maximize profits and limit losses through trial and error.

A few of the best ways to use machine learning in trading and finance include:

• Predictive Trading: Fortunately or unfortunately, markets move more because of sentiments rather than actual performances. Hence, ML can be effectively used to conduct sentiment analysis by studying news, social media, and headlines.
• Pattern Formation: Given the massive amount of data that has to be analyzed before a trade can be executed, machine learning is adept at processing massive amounts of data within seconds and finding actionable patterns. 
• Finding Patterns: The number of stocks in a market is rising with every passing week. Therefore, finding an unidentified stock to maximize gains or become an alpha generator can be a challenge. Therefore, ML models can help find patterns that human eyes can easily miss.

Now that the theoretical aspects of machine learning in trading strategies are sorted, it is time to establish the concept's practical applicability through the examples below. 

Example #1

Finch is an IT engineer who started trading a few months back. He watches videos on the internet regularly to understand the nitty-gritty of the market. He has heard the term timing the market way too often. However, when he actually tries to time the market, he never gets the hang of it.

Being an IT engineer, he decided to use ML models to ensure that the algorithm helped him maximize his profits while ensuring that the model was also able to limit the losses in particular situations. He used the reinforcement learning model and created a model that helped him trade in a way that could be perceived as time the market in a way. 

Example #2

Apart from trading stocks, ML models have also been helping forex traders. The forex industry has the highest liquidity among all financial markets. The market's daily trading volume is in excess of $7.5 trillion, a significant 14% increase year-on-year. 

Since the market's volume and competitiveness are incredibly high, traders must constantly innovate and recalibrate their trading actions. Therefore, ML and AI have been the epicenter of their innovation strategies. These models streamline trades and provide traders with fresh perspectives to boost their operations. 

Out of the multitude of advantages of machine learning in trading strategies, a few of the most prominent ones are:

• The foremost advantage is that it automates a process that otherwise requires traders' time, effort, and resources on a daily basis.
• Most trading security is defined by the research and analysis conducted by the investor or trader. Therefore, AI's assistance in that department helps these traders save a lot of time. 
• ML models can process millions of data points in seconds. Moreover, these models constantly learn about every market trend or movement they study. As a result, they can find patterns that might miss the human eye.
• Given the models’ ability to study a wide range of movements, their predictive abilities are enhanced, which can make the trader's decision-making process much more manageable.