Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/

Crude oil analysis on SQL cleaned data

Crack spread is the price differential between crude oils and refined products. "Crack" refers to the catalyzing and heating process that results in the breaking down of the carbon bonds, hence the name "cracking". The spreads represent industry refining margins and lend insight into economic activities. The spreads are thought to be seasonal. T…

React-based dashboard for real-time crude oil price forecasting powered by a hybrid ARIMA | GRU | XGBoost backend with live news sentiment integration.

Quantitative Analysis of Strait of Hormuz Disruption on Global Capital Markets & Commodity Trades

LLM-assisted options trading system for crude oil futures with rules-based exits, five-tier strategy promotion, and human-in-the-loop safety gates

Code and walkthrough for reproducibility of the Case Study (Crude Oil Price Prediction using Artificial Neural Network) presented in class.

Methods for Identification - academic project

Group Project: we intend to develop a robust machine learning model that will deliver a one-year projection for the price of West Texas Intermediate (WTI) Crude Oil.

🚀 AI Forex Trend Pullback EA 2026 - High Sharpe Ratio Strategy ⚡

VECM analysis of the 3-2-1 crack spread: demand-pull and cost-push price transmission between crude oil, gasoline, and heating oil (BENV0122 UCL)

This repository contains three machine learning projects examining price forecasting across different asset classes. Each project isolates a specific question: Which models work best under different market regimes?

Crude Oil trend pullback algorithmic trading system— EMA crossover, ATR-based stops, 5.2x return/drawdon ratio

Accurate crude oil prices with full historical data. Integrate in under 10 minutes.

This repository contains the analysis and results of a study on the estimation of Value at Risk (VaR) for Brent crude oil. The analysis compares classical VaR methods with advanced Machine Learning models based on Quantile Regression, aiming to evaluate the risk associated with this highly volatile and globally significant commodity.

Crude oil chart