Developed Investment Strategy and Analyzed Stock Data

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📅 2024-01-13 — Session: Developed Investment Strategy and Analyzed Stock Data

🕒 03:15–07:30
🏷️ Labels: Investment, Data Analysis, Python, Financial Strategy, Stock Analysis
📂 Project: Business
⭐ Priority: MEDIUM

Session Goal

The session aimed to develop a comprehensive investment strategy, including data analysis, strategy formulation, backtesting, and risk management, as well as to perform a detailed analysis of stock data for potential investment opportunities.

Key Activities

  • Outlined a comprehensive investment strategy development plan, focusing on data analysis, strategy formulation, backtesting, and risk management.
  • Created a daily plan for setting up a backtesting environment for investment strategies using Python and Jupyter Notebook.
  • Developed a financial ledger in Google Sheets for budgeting and financial planning.
  • Analyzed 5-year historical stock data for Apple Inc. (AAPL) using Python, including data fetching, visualization, and exponential model fitting.
  • Implemented stock analysis using the yfinance library in Python, focusing on data fetching, plotting, and fitting an exponential model.
  • Created Python scripts for fitting exponential models to stock data and plotting stock prices with volume information.
  • Addressed errors in Pandas resampling and data visualization, ensuring correct plotting of high prices and resampled volume data.
  • Calculated and visualized the ‘Opportunity’ ratio for stock prices, identifying potential buying opportunities.
  • Compiled ticker symbols from Yahoo Finance and retrieved S&P 500 ticker symbols using Python scripts.
  • Fixed DataFrame index issues for resampling in Pandas and ensured proper data manipulation and analysis.

Achievements

  • Successfully developed a structured plan for investment strategy development and backtesting.
  • Completed the analysis of AAPL stock data, identifying potential investment opportunities.
  • Resolved technical issues related to data resampling and visualization in Pandas.

Pending Tasks

  • Further refine the investment strategy based on backtesting results.
  • Explore additional financial data sources for comprehensive analysis.
  • Continue learning and improving data analysis techniques for financial markets.

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