Developed and Analyzed Investment Strategies

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📅 2024-01-13 — Session: Developed and Analyzed Investment Strategies

🕒 03:15–07:30
🏷️ Labels: Investment, Data Analysis, Backtesting, Risk Management, Python
📂 Project: Business
⭐ Priority: MEDIUM

Session Goal

The aim of the session was to develop a comprehensive plan for investment strategies, including backtesting, risk management, and financial analysis using Python.

Key Activities

  • Outlined a detailed investment strategy development plan, focusing on data analysis, strategy formulation, backtesting, and risk management.
  • Created a daily plan for setting up a backtesting environment using Python and Jupyter Notebook.
  • Developed a financial ledger in Google Sheets for budgeting and financial tracking.
  • 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.
  • Fitted an exponential model to stock data and plotted stock prices with volume information.
  • Corrected errors in Pandas for data visualization and resampling.
  • Calculated and visualized the ‘Opportunity’ ratio for stock prices.
  • Compiled ticker symbols from Yahoo Finance and retrieved S&P 500 ticker symbols using Python.
  • Addressed issues with DataFrame resampling in Pandas, ensuring proper date information retention.

Achievements

  • Successfully developed a structured plan for investment strategy development and backtesting.
  • Enhanced data visualization techniques and corrected errors in data handling using Pandas.
  • Improved financial data analysis by implementing exponential models and calculating opportunity metrics.

Pending Tasks

  • Further refine the investment strategy by integrating additional data sources and refining the backtesting framework.
  • Explore advanced risk management techniques and their integration into the current strategy.

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