The Evolution of Trading Algorithms
Quantitative trading has roots dating back to the late 20th century, with its earliest forms emerging in the 1970s and gaining momentum in the following decades. The earliest form of quantitative trading was the adoption of basic statistical models and mathematical algorithms to identify patterns that would inform the optimal time to execute a particular trading strategy. Over the years, the field has experienced various paradigm shifts, with strategies evolving from strictly fundamental to highly sophisticated approaches driven by the advent of Quants who helped usher in quantitative trading strategies powered by mathematical and statistical modeling.
In more recent times Artificial Intelligence has emerged as another means of engineering algorithms powered by Machine learning techniques. The development and implementation of Machine learning techniques however have been implemented in silos with certain key factors making it prohibitive for mass market adoption. Variables such as access to AI talent, Data, hardware cost and compute power were a few of the barriers to entry. Recent advancements in chip manufacturing, compute power, data providers, and commercially available machine learning operation platforms have led to better economies of scale enabling a growing number of funds to explore machine learning models to simulate trade and investment ideas via Deep Learning models signaling another important evolution of quantitative trading algorithms.
Understanding Deep Learning
Deep learning, while seemingly a complex and obscure concept, is more accessible than its name might suggest. Many key aspects of deep learning for trading can be unraveled and understood, even by those without a technical background. By familiarizing themselves with essential terms and concepts, individuals can grasp how deep learning is applied to design Quantitative strategies for areas like stock price prediction and portfolio optimization.
It is advantageous to think of machine learning as a set of algorithms inspired by the structure and function of the brain, known as artificial neural networks. These algorithms process vast amounts of data to learn and identify patterns, constantly improving with experience, much like how our brains learn from new information.
Deep learning, a subset of machine learning, typically involves more complex structures. It uses layers of these artificial neural networks, each designed for specific tasks. Recurrent Neural Networks (RNNs) excel in processing sequential data, making them useful for tasks where past information influences the current output, such as language translation. Convolutional Neural Networks (CNNs), on the other hand, are adept at analyzing spatial hierarchies in data, which makes them ideal for image recognition and similar tasks where the recognition of spatial patterns is crucial.
So, how does deep learning architecture apply to trading strategies? It essentially acts as a toolkit to take in different types of financial information, analyze it, and use the patterns it picks up on to make predictions. Machine learning models can be used to analyze things such as stock price data, news events, web traffic data, and social media trends.
Let’s say you apply deep learning to a price chart for a particular security or investment. The machine learning brain will quickly — we’re talking milliseconds or even nanoseconds — recognize recurring patterns in the chart, even subtle ones difficult to discern by a human analyst. It can use these patterns to suggest, or even execute on its own, trades based on likely stock market moves.
With enough data at its disposal, deep learning can also highlight irregularities and anomalies that signal a shift in the stock market is likely to occur before the occurrence giving its human counterparts an edge. Due to market sensitivity to news and historical data, it can capture shifting sentiments among consumers and investors and use this insight to inform trade execution.
Deep learning models are definitely an area worthy of further exploration and we intend to take a deeper dive to explore its utility.
All Models aren’t created Equally.
In the context of quantitative trading, traditional statistical models and deep learning approaches offer contrasting capabilities. Traditional statistical models, such as linear regression and ARIMA, have long been staples in quantitative finance due to their transparency, interpretability, and well-understood theoretical foundations. These models excel in scenarios where relationships between variables are linear or when the underlying assumptions of the model closely match the data. However, their effectiveness can be limited in handling complex, non-linear patterns and high-dimensional datasets common in today’s financial markets.
Deep learning, on the other hand, shines in these complex environments. With its ability to learn hierarchical representations and capture non-linear relationships through layers of artificial neural networks, deep learning can uncover intricate patterns in vast datasets, including unstructured data like news feeds and social media sentiment. This makes it particularly powerful for formulating long/short trading strategies. Traditional models might miss subtle, yet potentially lucrative signals. However, this capability historically has come at the cost of reduced interpretability and a greater need for computational resources, posing challenges in understanding the model’s decision-making process and in meeting the stringent computational demands of real-time trading.
While the simplicity and explainability of models like linear regression and ARIMA has its advantages, especially for practitioners who aren’t familiar with the higher complexity and difficulty of deep learning, it is pretty trivial to demonstrate that even simple deep learning models can significantly outperform them given sufficient data. When dealing with complicated nonlinear relationships (like financial markets and most other real world datasets of interest), the higher complexity of deep learning models becomes necessary to achieve the highest levels of accuracy and performance. For instance, long short-term memory networks (or LSTM for short) have special parts of their neuron structure that are responsible for holding memory of the data and patterns that they have seen. SlowFast networks have different neural pathways dedicated to signals of different frequencies that can communicate with each other to solve problems that require information to be compiled and processed from a variety of different frequencies.
XAI – AI Explainability
Model explainability has become table stakes for fund managers looking to harness the power of AI to generate alpha. While pure performance is attractive, most asset management firms and their investors need to be able to fully explain how results are generated before they can responsibly deploy these methods at scale. XAI is a new acronym – XAI or Explainable Artificial Intelligence that has recently entered the public lexicon as a way to categorize the research and development occurring within the field. Causal and Counterfactual explanations have emerged as reliable methods to demystify predictions being made by Deep learning Models. Casual or Counterfactual explanations by definition describe a causal situation in the form: “If X had not occurred, Y would not have occurred”.
Addressing the Problem of Model Explainability
LIT AI MLOps platform addresses the model explainability problem head on through its innovative platform features. Access to XAI tools provides granular insights into how deep learning trading algorithms are constructed and how it reaches its decisions to enhance trust, bolster confidence and satisfy regulatory and compliance requirements.
Prediction Insight is a feature that illuminates the AI decision-making process. Instead of just providing the end predictions or decisions, it allows users to see the exact data points and factors the model is considering when making a prediction. This transparency is vital for users to understand the AI’s functionality, especially in a sector where decisions can have significant financial implications.
Prediction Compare further enhances this transparency by enabling users to analyze the consistency of the AI model. It allows users to compare real-time data feeds with post-market data, ensuring the model’s predictions are consistent and reliable over time. This feature is particularly important in the dynamic and often volatile world of finance, where market conditions can change rapidly.
Applying LIT AI’s strategy builder is a great way to belt and suspender the decision to act on the trade signals generated by your models. LIT AI’s proprietary strategy builder turns raw model output into interpretable signals. The trade signals being produced by the Deep learning model can be assessed in real time to increase confidence in trade decisions and inspect signals for degradation.
Choosing the right deep-learning architecture is key. MLOps providers such as LIT AI provide market-ready solutions to help quant funds Build, Train, and Deploy Deep Learning quantitative trading strategies in as little as four weeks depending on model complexity.
To learn more about how deep learning can maximize your trading operation, contact LIT AI today.