Machine Learning on Jorge Vega

Trading Pipelines — End-to-End Financial ML Framework

Thu, 09 Jul 2026 20:00:00 +0200

A proof-of-concept project that proposes an end-to-end architecture for a financial machine learning framework. It covers the full lifecycle — from raw market data ingestion and feature engineering to model training, hyperparameter optimization, model serving, and interactive visualization — all orchestrated as reproducible pipelines.

Architecture

The system is composed of five main layers:

Data Ingestion

Raw financial data is loaded into a local DuckDB database using dlt incremental pipelines, orchestrated by Apache Airflow:

Stable Diffusion custom workflow for rendering video

Mon, 03 Apr 2023 16:29:41 +0200

Developed a Python script that leverages Stable Diffusion models to enhance video quality by applying stylized post-processing to each frame. Using Gradio and Stable Diffusion 1.5, I discovered that combining diffusion-based rendering with simple shading techniques can significantly improve the visual style of rendered output.

I then integrated frame interpolation into the workflow along with custom color correction to reduce flickering between frames. By automatically generating these intermediate frames, the resulting videos have smoother and more natural motion while maintaining better overall structural consistency.

Minimal Neuron

Wed, 03 Apr 2019 16:29:41 +0200

A minimal implementation of a sigmoid neural network written entirely in C. Because it’s built from scratch with no external dependencies, it can be easily ported to embedded systems, microcontrollers, and other resource-constrained environments.

The library currently supports feedforward inference: given a pre-trained neural network definition (weights and biases), it computes the output. It has been tested against various models trained on the MNIST dataset.

Code can be downloaded HERE