Python for commodity trading

Based on the most helpful WSO content, here are some tips and ideas for a Python project related to commodities:

Python Skills to Focus On:

• Understanding Object Types: Strings, lists, tuples, dictionaries. Use the type() function or the variable explorer.
• Installing Modules: Learn how to install modules using pip install.
• Data Sourcing: Write connection strings and use libraries like mySQL, pyodbc, cx_oracle.
• Data Manipulation: Master Pandas for data manipulation.
• Emailing from Your Machine: Use win32com and learn basic HTML with inline styling for better email formatting.
• Advanced Tools (if you have more time):
  • numpy for numerical operations.
  • seaborn for data visualization.
  • pyfolio for portfolio risk management.
  • Web scraping for data collection.

Project Idea:

Commodity Price Analysis and Prediction

Steps:

1. Data Collection:

   • Use APIs to collect historical commodity prices (e.g., oil, gold, silver).
   • Store the data in a database using mySQL or pyodbc.

2. Data Cleaning and Manipulation:

   • Use Pandas to clean and manipulate the data.
   • Handle missing values, normalize data, and create new features if necessary.

3. Exploratory Data Analysis (EDA):

   • Use seaborn and matplotlib to visualize trends, correlations, and patterns in the data.

4. Model Building:

   • Implement machine learning models to predict future commodity prices.
   • Use libraries like scikit-learn for regression models.

5. Risk Management:

   • Use pyfolio to analyze the risk and performance of your predictions.

6. Visualization and Reporting:

   • Create dashboards using Tableau or Power BI to present your findings.
   • Automate email reports using win32com.

This project will not only help you learn Python but also give you practical insights into commodity trading. Happy coding!