This repository contains a research project focused on developing an intraday trading strategy for the GB electricity market. The strategy leverages insights from day-ahead and intraday price dynamics, incorporating weather-driven factors to optimize electricity trading for profit maximization and risk mitigation.
This study analyzes electricity price data from the first week of December 2024, focusing on intraday price patterns and their correlation with temperature. The project explores three trading strategies:
- Intraday Trading – Reacting to real-time price fluctuations by buying during off-peak hours and selling during peak hours.
- Volatility-Based Scalping – Capitalizing on price fluctuations during volatile periods for short-term profits.
- Seasonal Arbitrage – Exploiting seasonal trends and cold snaps for long-term gains.
- Price Trends – Intraday prices are significantly higher than day-ahead prices, peaking during evening hours.
- Temperature Correlation – Lower temperatures increase demand, driving higher prices.
- Volatility Patterns – Volatility spikes during rapid temperature changes and peak demand periods.
- 📊 Data – GB day-ahead and intraday electricity price datasets:
GB_dayahead_pricedata.csvGB_intraday_pricedata.csv
- 📜 Code – Python notebook implementing data analysis & trading strategy simulations:
electricity_analysis.py
- 📖 Documentation – Research proposal outlining methodology & findings:
findings_RP.pdf
- Clone the repository:
git clone https://github.com/vybhav345/Electricity-Trading-Optimization.git