Quantitative Problem Solving and Visualization in Finance

2 min read

📅 2024-12-05 — Session: Quantitative Problem Solving and Visualization in Finance

🕒 13:05–14:05
🏷️ Labels: Capital_Management, Data_Visualization, Bayesian_Inference, Financial_Analysis, Python, Candlestick_Charts
📂 Project: Business
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore quantitative problem-solving approaches in capital management, focusing on data-driven decision-making, uncertainty modeling, and risk-reward balancing. Additionally, it involved practical implementations for financial analysis and visualization using Python libraries.

Key Activities

  • Introduced quantitative problem-solving approaches in capital management, emphasizing Bayesian reasoning, game theory, and pairs trading analysis.
  • Conducted correlation analysis across various time windows to understand market relationships and implications for trading strategies.
  • Analyzed the relative performance of AAPL and MSFT over a 90-day rolling return window, highlighting trading signals for pairs trading strategies.
  • Provided instructions for installing and using the yfinance library for financial analysis.
  • Developed and customized candlestick charts for AAPL and MSFT using the mplfinance library, focusing on aesthetics and annotations.
  • Explored Bayesian inference for modeling uncertainty in decision-making contexts.

Achievements

  • Successfully introduced and outlined frameworks for quantitative problem-solving in capital management.
  • Implemented and customized candlestick charts for financial data visualization, enhancing analysis capabilities.

Pending Tasks

  • Further exploration of Bayesian inference applications in financial decision-making.
  • Continuous refinement of visualization techniques for improved clarity and insight.

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