Machine learning bitcoin tradingFive Machine Learning Methods Crypto Traders Should Know About - CoinDesk
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You'll need this for the best Bitcoin trading strategy and how to use it:. Uk threatens to shut down popular bitcoin investment site bitconnect Malaysia. 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. Semi-supervised learning is analogous to a teacher presenting a few concepts to a group of students and leaves the other concepts to homework and self-study.
In our sample scenario, imagine that we train a model with a small set of labeled trades from OTC desks and a large set of unlabeled ones. Our semi-supervised learning model will learn key features from the labeled dataset such as trade size or frequency and will use the unlabeled dataset to expand the training.
Feature extraction and selection are a key component of any quant machine learning model and is particularly relevant in problems that are not very well understood such as crypto asset predictions.
Imagine that we are trying to build a predictive model for the price of bitcoin based on order book records. One of the most important aspects of our effort is to determine which attributes or features can act as predictors. Is it the mid-price, the volume or a hundred other factors? The traditional approach is to rely on subject matter experts to handcraft these features but that can become hard to scale and maintain over time.
Representation learning is an area of dep learning focused on automating the learning of solid representations or features in order to build more effective models. Instead of relying on human feature modeling, representation learning tries to extrapolate features directly from unlabeled datasets. In our example, a representation learning method could analyze the order book and identify hundreds of thousands of potential features that can act as predictors for the Bitcoin prices.