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In this article, I would like to discuss some novel areas of deep learning that can have a near immediate impact in the quant models applied to crypto. He has held leadership roles at major technology companies and hedge funds.
He is an active investor, speaker, author and guest lecturer at Columbia University in New York. In the last year, there have been active research efforts in quantitative finance exploring how transformer models can be applied to different asset classes. However, the results of these efforts remain sketchy showing that transformers are far from ready to operate in financial datasets and they remain mostly applicable to textual data.
But there is no reason to feel bad. While adapting transformers to financial scenarios remains relatively challenging, other new areas of the deep learning space are showing promise when applied in quant models on various asset classes including crypto. From many angles, crypto seems to be like the perfect asset class for deep learning-based quant models. Quantitative finance has been one of the fastest adopters of new deep learning technologies and research. It is very common for some of the top quant funds in the market to experiment with the same types of ideas coming out of high tech AI research labs such as Facebook, Google or Microsoft.
Some of the most exciting developments in modern quant financing are not coming from flashy techniques like transformers, but from exciting machine learning breakthroughs that are more developed for quant scenarios. Many of those methods are perfectly applicable to crypto-asset quant techniques and are starting to make inroads in crypto quant models. I tried to keep the explanations relatively simple and tailored to crypto scenarios. Blockchain datasets are a unique source of alpha for quant models in the crypto space.
From a structural perspective, blockchain data is intrinsically hierarchical and is represented by a graph with nodes representing addresses connected by edges representing transactions. Imagine a scenario in which a quant model is trying to predict volatility in bitcoin in a given exchange based on the characteristics of addresses transferring funds into the exchange. That kind of model needs to operate efficiently over hierarchical data.
But most machine learning techniques are designed to work with tabular datasets, not graphs. Graph neural networks GNNs are a new deep learning discipline that focuses on models that operate efficiently on graph data structures.
GNNs are a relatively new area of deep learning being invented only in In our sample scenario, a GNN could use a graph as input representing the flows in and out of exchanges and infer relevant knowledge relevant to its impact on price.
In the context of crypto assets, GNNs have the potential of enabling new quant methods based on blockchain datasets. One of the limitations of machine learning quant models is the lack of large historical datasets. Suppose that you are trying to build a predictive model for the price of chainlink LINK based on its historical trading behavior.
While the concept seems appealing, it might prove to be challenging as LINK has a little over a year of historical trading data in exchanges like Coinbase. That small dataset will be insufficient for most deep neural networks to generalize any relevant knowledge. Generative models are a type of deep learning method specialized in generating synthetic data that matches the distribution of a training dataset.
In our scenario, imagine that we train a generative model in the distribution of the link orderbook in Coinbase in order to generate new orders that match the distribution of the real orderbook. Combining the real dataset and the synthetic one, we can build a large enough dataset to train a sophisticated deep learning model. The concept of generative model is not particularly new but has gotten a lot of traction in recent year with the emergence of popular techniques such as generative adversarial neural networks GANs , which have become one of the most popular methods in areas such as image classification and have been used with relevant success with time series financial datasets.
Labeled datasets are scarce in the crypto space and that severely limits the type of machine learning ML quant models that can be built in real world scenarios.
Imagine that we are trying to build an ML model that makes price predictions based on activity of over-the-counter OTC desks. To train that model, we would need a large labeled dataset with addresses belonging to OTC desks which is the type of dataset that only a few entities in the crypto market possesses. Semi-supervised learning is a deep learning technique that focuses on the creation of models that can learn with small labeled datasets and a large volume of unlabeled data.