A gamified learning platform to help BSc Math students become top 1% Quant Researchers with Ben 10 theme
Solutions to Daily Coding Problem questions
MLP approach to predict real-time financial market data and select the right trades to execute.
Algorithmic trading client and strategies for a Jane Street–style electronic exchange: pennying market-making, micro-MACD, bond maker, ETF/ADR arbitrage.
An example of web application written in OCAML
Jane Street Real-Time Market Data Forecasting 2025: https://www.kaggle.com/competitions/jane-street-real-time-market-data-forecasting
My answers to problem sets for Aizu, AdventofCode, Janestreet Puzzles, CodeChef, Codeforces, CSES, Deep-ML, DMOJ, EOlymp, HackerRank, Kattis, KiloNova, Leetcode, LibreOJ, Perl Weekly Review, Project Euler, SPOJ and Timus judges.
End-to-end Python regression pipeline for Jane Street market data forecasting with Optuna tuning, chronological cross-validation, custom weighted R2 evaluation, model comparison, plots, SHAP explainability, and Docker support.
A hardware-first exploration of Advent of Code 2025, implementing each puzzle as a cycle-accurate FPGA design in Hardcaml, using streaming UART I/O, explicit memory pipelines, and finite-state schedulers instead of software control flow.
📉Market Forecasting 🧠ML for Trading 📊Time Series 🧮Ensemble Learning
This project implements three Advent of Code 2025 puzzles in a single FPGA design, written in Verilog.
High-performance FIX and SBE protocol implementation for low-latency trading systems.
"Para usar este agente necesitas una cuenta gratuita en Claude.ai — sin tarjeta de crédito para el plan básico.". Democratizing the logic of the world's largest quant trading firms · by Claude
Solutions to Jane Street Monthly Puzzles
🚀 Implement algorithmic trading strategies for a Jane Street-style exchange, featuring market-making, arbitrage, and momentum signals.
jane street's "dropped a neural net" puzzle
Compile OCaml code without leaving behind temporary files
Solutions to Jane Street monthly puzzles
Spring Insight participant Jane Street. Software Engineering Department. Ocaml workshop
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