Solutions to Daily Coding Problem questions

MLP approach to predict real-time financial market data and select the right trades to execute.

A gamified learning platform to help BSc Math students become top 1% Quant Researchers with Ben 10 theme

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

📉Market Forecasting 🧠ML for Trading 📊Time Series 🧮Ensemble Learning

Jane Street Real-Time Market Data Forecasting 2025: https://www.kaggle.com/competitions/jane-street-real-time-market-data-forecasting

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.

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.

Solutions to Jane Street Monthly Puzzles

My attempt at Jane Street's puzzles

This project implements three Advent of Code 2025 puzzles in a single FPGA design, written in Verilog.

Some things may be broken, work has stalled, you know what this is though :)

🚀 Implement algorithmic trading strategies for a Jane Street-style exchange, featuring market-making, arbitrage, and momentum signals.

Solves a machine learning problem using three different models (XGBoost, Neural Network, Logistic Regression)

jane street's "dropped a neural net" puzzle

Distributed, type-safe, incremental stream processing framework in OCaml. 250ns stabilization, 2M+ events/sec, heap-based dirty propagation, idempotent delta algebra.

A statistics display for the Jane Street trading game, Figgie.

Spring Insight participant Jane Street. Software Engineering Department. Ocaml workshop

Solutions to Jane Street monthly puzzles