Energy Data Chart Automation

I need a Python-based routine that will pull several energy-market data sources into pandas DataFrames, then automatically generate three chart types—line, candle, and spread—for every delivery month or contract pair I feed it.

Core workflow
1. Ingest & store
• Price time-series (at least two years)
• Weather data (last week and this week)
• EIA storage reports with clear time-stamps for withdrawals/injections, plus hi/lo/avg values
• Transco critical-note files or similar operational alerts

2. Chart production
For each dataset the script should:
• Colour the background by moving-average regime
• Overlay rate-of-change and average lines
• Draw vertical “alert” markers for EIA releases, pipeline notices, or any future events parsed from the critical notes
• Output the full set—line, candle, and spread—into a dated folder (PNG and interactive HTML preferred)

3. Event & calendar layer
The routine must scan the critical-note text, detect dates of upcoming maintenance or flow changes, and write them both to:
• a CSV/JSON calendar file
• a visual calendar plot or Gantt-style chart so I can see what’s coming

Acceptance criteria
• Reproducible Jupyter notebook or .py script with clear function blocks
• Sample charts for at least one complete delivery month and one spread pair
• All raw and processed data saved to DataFrames and pickled/CSV’d
• README explaining set-up, dependencies, and how to point the code at new data paths

Libraries I normally work with are pandas, numpy, matplotlib/plotly, seaborn, mplfinance, and yfinance/requests for scraping, but I’m open to your preferred stack as long as it’s Python.

Please build the solution so I can run it locally on Windows; any external APIs or credentials should be parameterised in a config file.