End-to-end financial data pipeline and trading automation project using historical stock data, GRU-based price prediction, feature engineering, and Alpaca API integration. The system selects tickers, generates predictions, and automates order placement using scheduled scripts.
This is the Angular frontend for the Otero Ediciones landing page. It displays a dynamic book catalog and a brief history of the editorial.
A tactical turn-based board game where two players duel 1v1 using movement cards instead of fixed rules. Each turn, cards are exchanged, forcing constant adaptation as you maneuver your pawns to capture the enemy King or invade their Base before they outplay you.
Free online audio tools built with Go, templ, HTMX, AlpineJS and Tailwind.
A lightweight school management platform built in pure Go, designed for managing users, classes, assignments, and submissions. The project focuses on simplicity, performance, and clean architecture without external frameworks.
Machine learning library written in Go, implementing regression, classification, evaluation metrics, and model persistence from scratch. The project explores the internal structure of ML algorithms without relying on external dependencies.
Deep learning image classification project for detecting malignant skin moles from medical images. The model was built with TensorFlow/Keras using transfer learning, data augmentation, and a recall-first evaluation approach to reduce the risk of missing malignant cases.
Financial sentiment analysis platform that collects stock-related news and classifies sentiment using an NLP microservice. The system combines a C# API, Python FastAPI service, and React frontend to deliver market-related insights through a responsive dashboard.
DataFrame library for Go focused on efficient data manipulation, typed columns, aggregation, statistical operations, and parallelized processing using goroutines. The project was built to explore dataframe internals and query-style data operations.
Regression-based machine learning project for predicting house prices from structured property data. The project compares multiple models, applies preprocessing and feature engineering, and evaluates performance using standard regression metrics.
Time series forecasting project analyzing Microsoft stock prices using exploratory data analysis, statistical inspection, moving averages, and an LSTM neural network built with Keras.