Category Archives: Code

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

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Tip and Tricks to correct a Cuda Toolkit installation in Conda

On the eastern side of Oxfordshire are the Cotswolds, a pleasant hill range with a curious etymology: the hills of the goddess Cuda (maybe, see footnote). Cuda is a powerful yet wrathful goddess, and to be in her good side it does feel like druidry. The first druidic test is getting software to work: the wild magic makes the rules of this test change continually. Therefore, I am writing a summary of what works as of Late 2023.

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The stuff MDAnalysis didn’t implement: CPU Parallel HOLE conductance analysis

Some time ago, I needed to find a way to computationally estimate conductance values for every protein frame from several molecular dynamics (MD) trajectories.

In a previous post, I wrote about how to clean the resulting instant conductance timeseries from outliers. But, I never described how I generated these timeseries.

In this post, I will show how you can parallelise the computation of instant conductance given an MD trajectory. I will touch on the difficulties of this process. And why I had to implement a custom tool for it given that MDAnalysis seems to already have implemented a routine of this sort. Finally, I will provide two Python scripts that you can easily adapt to run your parallel calculations – for which I’ll provide some important notes you don’t wanna skip.

Violin plots of conductance distributions from 64 molecular dynamic trajectories with 1000 frames each.
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Taking Equivariance in deep learning for a spin?

I recently went to Sheh Zaidi‘s brilliant introduction to Equivariance and Spherical Harmonics and I thought it would be useful to cement my understanding of it with a practical example. In this blog post I’m going to start with serotonin in two coordinate frames, and build a small equivariant neural network that featurises it.

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Some useful pandas functions

Pandas is one of the most used packages for data analysis in python. The library provides functionalities that allow to perfrom complex data manipulation operations in a few lines of code. However, as the number of functions provided is huge, it is impossible to keep track of all of them. More often than we’d like to admit we end up wiriting lines and lines of code only to later on discover that the same operation can be performed with a single pandas function.

To help avoiding this problem in the future, I will run through some of my favourite pandas functions and demonstrate their use on an example data set containing information of crystal structures in the PDB.

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Let your library design blosum

During the lead optimisation stage of the drug discovery pipeline, we might wish to make mutations to an initially identified binding antibody to improve properties such as developability, immunogenicity, and affinity.

There are many ways we could go about suggesting these mutations including using Large Language Models e.g. ESM and AbLang, or Inverse Folding methods e.g. ProteinMPNN and AntiFold. However, some of our recent work (soon to be pre-printed) has shown that classical non-Machine Learning approaches, such as BLOSUM, could also be worth considering at this stage.

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Converting pandas DataFrames into Publication-Ready Tables

Analysing, comparing and communicating the predictive performance of machine learning models is a crucial component of any empirical research effort. Pandas, a staple in the Python data analysis stack, not only helps with the data wrangling itself, but also provides efficient solutions for data presentation. Two of its lesser-known yet incredibly useful features are df.to_markdown() and df.to_latex(), which allow for a seamless transition from DataFrames to publication-ready tables. Here’s how you can use them!

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What 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.

What’s a TPU?

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Deploying a Flask app part II: using an Apache reverse proxy

I recently wrote about serving a Flask web application on localhost using gunicorn. This is sufficient to get an app up and running locally using a production-ready WSGI server, but we still need to add a HTTP proxy server in front to securely handle HTTP requests coming from external clients. Here we’ll cover configuring a simple reverse proxy using the Apache web server, though of course you could do the same with another HTTP server such as nginx.

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