Using unsupervised machine learning to label FT articles
Why article clustering is important
FT is one of the largest providers of financial news in the world. We publish hundreds of articles every day. One of the most challenging tasks is consistently categorising these articles.
While FT journalists tag the articles manually, it is hard to…
Identifying signals using time-series analysis and unsupervised machine learning.
Understanding the preferences of Financial Times readers is crucial for improving user experience and maintaining engagement with our products. Having accurate indicators showing which area is increasingly important can augment journalists’ work, by helping them to focus on topics of interest.
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This story explains how to implement the moving average trading algorithm with R. If you’re interested in setting up your automated trading pipeline, you should first read this article. This story is a purely technical guide focusing on programming and statistics, not financial advice.
Throughout this story, we will build…
This article explains how to create a trading pipeline using R. The trading pipeline consists of 4 main elements
- Connecting with Google API and loading current holdings data
- Connecting with Robinhood API and getting current stock prices
- Getting historic market data using Yahoo API
- Decision algorithm and executing an order
Documentation is an important part of being a data scientist. I propose to use R Markdown and LaTeX to document data science models.
There are several desirable properties of good documentation
- easily accessible
- readable
- consistent across projects
- reproducible
Using LaTeX inside R Markdown allows users to use consistent LaTeX formatting…
