📅 2024-01-15 — Session: Developed and Visualized Investment Strategies
🕒 03:50–07:45
🏷️ Labels: Investment, Data Analysis, Visualization, Python, Trading
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and develop investment strategies utilizing financial data analysis and visualization techniques.
Key Activities
- Investment Strategies: Discussed various strategies such as trend following, momentum investing, and dollar-cost averaging, emphasizing the need for backtesting.
- Data Processing: Outlined steps for processing monthly price data and testing trading strategies, including data acquisition and iterative refinement.
- Data Analysis: Developed methods for analyzing investment opportunities by calculating period returns and aligning them with observations.
- [[Data Visualization]]: Implemented Python code to visualize stock data using Matplotlib, including scatter plots and color maps for enhanced analysis.
- Troubleshooting: Addressed common issues with Pandas styling in VS Code, providing solutions for rendering styled DataFrames.
Achievements
- Successfully outlined and began implementing investment strategies with a focus on backtesting.
- Developed Python scripts for data processing, analysis, and visualization, enhancing the understanding of stock trends.
- Identified and resolved issues related to DataFrame styling in VS Code.
Pending Tasks
- Complete the implementation of backtesting frameworks for the discussed investment strategies.
- Further refine data visualization techniques to better represent financial data insights.