algo-electricity-trading

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algo-electricity-trading

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Intraday algorithmic trading on the German electricity market

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# Algorithmic Trading on German Electricity Markets

## Project Overview

This project leverages **ensemble tree based machine learning** models to predict the direction of the **spread** (imbalance vs. day-ahead prices) in the German Electricity Market, with the goal of maximizing **cumulative Profit & Loss (P&L)**.

The dataset consists of **15-minute interval observations** with the primary features being:  
- **Wind generation forecasts -**  Impact supply variability from renewable sources.  
- **Solar generation forecasts -** Capture solar energy’s contribution to the electricity grid.
- **Load (demand) forecasts -** Reflect consumer demand patterns that influence electricity prices.

### Motivation  
The spread reflects the dynamics of electricity supply and demand:  
- When **supply exceeds demand**, imbalance prices tend to **decrease** to account for the excess.  
- When **demand surpasses supply**, imbalance prices generally **increase**.

By modeling these relationships, the project aims to develop a **systematic trading strategy** to exploit these price differences.

## **Instructions**  
Clone the repository:  
```bash
git clone https://github.com/anthonymakarewicz/algo-electricity-trading.git electricity_trading
cd electricity_trading
```

## Usage
### **1. Exploratory Data Analysis (EDA)**  
**Notebook:** `eda.ipynb`  
- Performs an in-depth analysis of the dataset:
  - Analysis over several frequencies (Daily, Weekly, Monthly)
  - Outlier Detection using Boxplots
  - Seasonality analysis using line plot and Autocorrelation Function

---

### **2. Data Processing**  
**Notebook:** `data_processing.ipynb`
- Handles outliers using winsorization and hampel filter.  
- Compares and applies several transformations techniques:
  - Log Transformation
  - Box-Cox Transformation
  - Yeo-Johnson Transformation

---

### **3. Feature Engineering & Selection**  
**Notebook:** `feature_engineering_selection.ipynb`  
- Generate new features to extract relevent information including:
    - Time/Dates features
    - Cyclical features
    - Interaction effects
    - Rolling features

- Leverage various feature selection techniques to remove irrelevent features including:
    - Correlation Filter  
    - Recursive Feature Elimination (RFE)  
    - Embedded Methods (Random Forest Feature Importance)

---

### **4. Model Evaluation**  
**Notebook:** `model_evaluation.ipynb`
- Trains and evaluates ensemble models for spread prediction such as:  
    - XGBoost  
    - LightGBM  
    - Random Forest
- Perform hyperparameter tuning using Grid Search to avoid overfitting
- Compares models using **cumulative profit & loss (P&L)** curves.

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