Welcome to my research blog! 👋 Here, I share my experiences, insights, and the latest updates on my projects and resume.
I've developed an open-source Python library for financial machine learning. It focuses on raw trade data preprocessing and training data preparation for ML model learning. Find out more about that in this post.
This essay examines the role of trust in monetary systems, comparing fiat currencies and Bitcoin. Fiat relies on trust in governments, while Bitcoin derives value from its decentralized protocol and utility. It challenges the notion that Bitcoin lacks intrinsic value, arguing that all money depends on belief.
The Exponential Weighted Moving Average (EMA) is ubiquitous in signal processing, especially in the algorithmic trading scene. It serves to filter out noise from the signal and compute an average/expected value, with recent samples carrying more significant weight in the calculation. Although this is a fundamental tool, the underlying theory can be confusing. This post attempts to make things clear regarding the theory and practical usage of EMA.
This post contains a collection of World Quant University proficiency test examples along with their solutions, gathered from publicly available sources.
This guide explores building a scalable training pipeline using PyTorch Lightning and LightningCLI. It covers handling dataset preparation, where different transformations for training and validation are applied. The post details how Lightning organizes code into data and model modules, streamlining training but complicating parameter sharing. By using LightningCLI and link_arguments, key parameters are passed between modules. Based on the discussed aspects a final blueprint is proposed.