Part 1/3 - Exporing the mathematical, statistical, and probabilistic nature of the market. Specifically, I attempt building a mean-reversion probability model, backtesting it against historical data, and understanding where/why it falls short. The results explain why simple statistical models fail to capture the complex beast that is the financial market. Nevertheless, this helps with foundational understanding and there is much to learn that I then iterate in the subsequent posts on the topic of ML based Trading.
Part 1/3 - Evaluating LLM’s that are specialised in code generation tasks, and evaluating their performance on writing code. This post starts with concepts and theory, while the next 2 parts evaluate specific code models.
Introduction I was first introduced to the concept of “second-brain” from Tobi Lutke, the founder of Shopify. The topic started because someone asked whether he still codes - now that he is a founder of such a large company. Tobi went on to explain that he spent the weekend writing some code to customise Logseq to his preferences, and that he’s an active member of the Logseq community.
The following weekend, I setup Logseq and learnt its weird ways of working, and have since been an ardent user and fan of the Logseq/Obsidian methodology of building a “second-brain”...
Part 2/3 - Understanding how modern LLMS work. From RNNs, to transformers, towards modern scaling laws.