Developed Investment Strategy and Analyzed Stock Data

• Day: 2024-01-13
• Time: 03:15 to 07:30
• Project: Business
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Investment, Data Analysis, Python, Financial Strategy, Stock Analysis

Description

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.

Evidence

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• event_ids: []