Data can often naturally be represented in a graph format and being able to directly employ a deep learning architecture on that data without finding a different representation is an appealing idea. Graph neural networks (GNNs) have become a standard part of the ML toolbox but navigating the world of different architectures available out-of-the-box can be a daunting task. A great place to start looking for architectures is with PyTorch Geometric, which provides an extensive list of readily available GNN layers and tutorials on how to use them in your standard PyTorch models. There are many things to consider when choosing a GNN layer, but the two considerations that I think are a great place to start are expressiveness and edge feature handling. In general, it is hard to predict what will work best for the task at hand and hence it’s optimal to try a wide range of different layers. This blogpost is meant as a brief introduction for what I would find useful to know before I started using GNNs, and a starting point for exploring the GNN literature.
Continue readingCategory Archives: PyTorch
A Seq2Seq model for ETF forecasting
Owing to the misguided belief that I can achieve the impossible, I decided to build a model with the goal of beating the stock market.
Strap in, we’re about to get rich.
Machine learning is increasingly being employed by hedge funds to help mitigate risk and identify patterns and opportunities, whether this is for optimisation of algo trading strategies, fraud detection, high-frequency trading, or sentiment analysis. Arguably the most obvious, difficult, and naïve application of fintech ML is direct stock market forecasting – sounds like the perfect place to start.
Target
First things first, we need to decide on a stock to forecast. Volatility provides opportunities, but predictable volatility is even better. We need a security that swings in response to actual, reported events, and one whose trends roughly move somehow with other stocks – our hypothesis being that wider events in the market can be used to forecast a single security. SPDR GLD seems like a reasonable option – gold is such a popular hedge against global instability it’s price usually moves in the opposite direction to stocks such as DJIA or SP500 and moves with global disaster.
Gold price (/oz) in Pounds from 1980-2024
Continue readingWhat the heck are TPUs?
I recently became curious about TPUs, a specialised hardware for training Machine- and Deep-Learning models, where TPU stands for Tensor Processing Unit. This fancy chip can provide very high gains for anyone aiming to perform really massive parallelisation of AI tasks such as training, fine-tuning, and inference.
In this blog post, I will touch on what a TPU is, why it could be useful for AI applications when compared to GPUs and briefly discuss associated opportunity costs.