🏦 J.P. Morgan Quantitative Finance Virtual Experience (Forage)

This repository contains my solutions to the Quantitative Finance Virtual Internship offered by J.P. Morgan & Co. via Forage. It simulates real-world tasks performed by a quant analyst working on a trading desk, focusing on time series forecasting and commodity pricing models.

---

📌 Overview

✅ Task 1 – Natural Gas Price Forecasting

Objective:
Estimate and extrapolate the purchase price of natural gas using historical data.

Deliverables:

• Loaded and cleaned monthly natural gas price data (from Oct 2020 to Sep 2024).
• Conducted exploratory data analysis (EDA) to observe seasonal patterns and trends.
• Built a SARIMA model to forecast gas prices.
• Performed hyperparameter tuning for optimal performance.
• Extrapolated prices for an additional 12-month period to support long-term contract pricing.
• Created a function to return predicted price based on any date input.

📈 Key Skills:

Time Series Analysis, SARIMA, EDA, Forecasting, Matplotlib, Pandas, Statsmodels

---

✅ Task 2 – Gas Storage Contract Pricing Model

Objective:
Build a prototype pricing model for gas storage contracts by simulating various injection and withdrawal scenarios.

Approach:

• Developed a pricing function that accounts for:
  • Injection and withdrawal dates
  • Commodity price on those dates
  • Flow rates (injection/withdrawal)
  • Storage limits
  • Storage cost
• Simulated cash flows for various strategies and calculated the contract value.
• Assumed ideal market conditions (no transport delays, zero interest rate, no holidays).
• Designed logic for both manual scenario testing and future automation.

💡 Key Skills:

Pricing Models, Time Series Analysis, Python, Machine-Learning

---

🚀 How to Use This Repository

You can run this project directly on Google Colab without setting up anything locally:

✅ Steps:

Download the datset, from the link beleow....

1. Clone the repository to your system:

   git clone <repository-url>

   Replace <repository-url> with the actual link to this repo.

2. Go to Google Colab

3. Click on File > Upload notebook
   and upload the .ipynb file from the cloned folder and run the file

   ---

   DATASET

   https://drive.google.com/file/d/1-tWYPmv9bD8VXKCthHEaU1UHsbJeWSA6/view?usp=drive_link