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Quantitative analysis for power markets

Project description

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octoanalytics 📊⚡

Energy consumption forecasting & risk premium analysis for the French electricity market


Description

octoanalytics is a Python toolkit for:

  • Retrieving and smoothing weather data via Open-Meteo API.
  • Training and evaluating load forecasting models (MW) using Random Forest.
  • Generating interactive visualizations comparing actual vs. forecasted values.
  • Accessing spot and forward price data (annual, monthly, PFC) from Databricks SQL.
  • Computing volume and shape risk premiums, key for energy portfolio management.

Installation

pip install octoanalytics

⚠️ You need a valid Databricks access token to retrieve market data.


Dependencies

  • pandas
  • numpy
  • scikit-learn
  • plotly
  • matplotlib
  • tqdm
  • requests
  • tentaclio
  • yaspin
  • holidays
  • dotenv
  • databricks-sql-connector

Main Features

🔁 Weather data retrieval and smoothing

from octoanalytics import get_temp_smoothed_fr

temp_df = get_temp_smoothed_fr(start_date="2024-01-01", end_date="2024-12-31")

⚡ Load forecasting

from octoanalytics import eval_forecast

forecast_df = eval_forecast(df=load_df(), temp_df=temp_df, cal_year=2024)

💰 Risk premium calculation

Volume Risk

from octoanalytics import calculate_prem_risk_vol

premium = calculate_prem_risk_vol(forecast_df, spot_df, forward_df)

Shape Risk

from octoanalytics import calculate_prem_risk_shape

shape_risk = calculate_prem_risk_shape(forecast_df, pfc_df, spot_df)

🔌 Databricks SQL connections

from octoanalytics import get_spot_price_fr, get_forward_price_fr_annual

spot_df = get_spot_price_fr(token=DB_TOKEN, start_date="2024-01-01", end_date="2024-12-31")
forward_df = get_forward_price_fr_annual(token=DB_TOKEN, cal_year=2025)

Package Structure

octoanalytics/
│
├── __init__.py
├── core.py              # Main logic
├── ...

Authors

Jean Bertin
📧 jean.bertin@octopusenergy.fr

Thomas Maaza
📧 thomas.maaza@octoenergy.com


License

MIT – free to use, modify, and distribute.


Roadmap

  • Add XGBoost model
  • Load anomaly detection
  • Flask REST API deployment
  • Automatic PDF report generation

Full Demo

To be included in examples/forecast_demo.ipynb.

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