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Adventures in ML trading - Part 1

ML Trading
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.

Exploring Code LLMs - Instruction fine-tuning, models and quantization

AI
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.

Getting Things Done with LogSeq

Management
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”...

Understanding GPT - Transformers

AI
Part 2/3 - Understanding how modern LLMS work. From RNNs, to transformers, towards modern scaling laws.

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Syncing historical data from IBKR

ML Trading

Syncing Historical Data from IBKR: A Comprehensive Guide In this post, we’ll walk through a complete workflow for downloading historical data from Interactive Brokers (IBKR) and preparing it for analysis and backtesting. Why download from broker ? The core assumption is that we sync data directly from the broker, ensuring its accuracy while trading and backtesting. Once this data is downloaded, we can build ML data batches for training models....

February 1, 2025

Statistical learnings from a failed 2024 santa rally

ML Trading

Intro Santa Claus Rally is a well-known narrative in the stock market, where it is claimed that investors often see positive returns during the final week of the year, from December 25th to January 2nd. But is it a real pattern or just a market myth ? It is also claimed that next years returns are positively correlated to the Santa rally. But is it a real pattern or just a market myth ?...

January 1, 2025

Adventures in ML trading - Part 2

ML Trading

Preface In my previous post, I developed a simple mean-reversion strategy based on an oscillating signal calculated from a stock’s distance to its 50-day simple moving average. However, the results revealed a key shortcoming: the algorithm struggled to account for momentum, leading to poorly timed exits during parabolic moves—either too early or too late. In this post, we’ll dive into momentum and conduct an analysis to validate our assumption. If we can confirm that incorporating momentum enhances the strategy, we’ll move forward with developing a more advanced approach to leverage it effectively....

December 25, 2024

Adventures in ML trading - Part 1

ML Trading

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.

December 19, 2024