📅 2024-11-16 — Session: Developed Comprehensive Trading Strategy Framework
🕒 18:00–20:30
🏷️ Labels: Trading, Risk Management, Simulation, Data Visualization, Strategy
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop a comprehensive trading strategy framework by understanding various trading principles, analyzing performance metrics, and designing simulation tools.
Key Activities
- Understanding the Kelly Criterion: Explored the mathematical formula for optimal bet sizing in trading and gambling, focusing on risk management and position sizing.
- Structured Plan for Day Trading: Outlined a plan leveraging data science and software skills, addressing opportunities, challenges, and risk management in day trading.
- Trader Performance Analysis: Analyzed trade distributions by price and volume, providing insights for strategy optimization.
- Trading Scale Analysis: Detailed capital requirements and implications for different scales of trading, offering practical advice on scaling.
- Structured Trading Plan: Outlined a five-stage trading plan with objectives, capital requirements, and growth metrics.
- Python Simulation Tool: Developed a simulation tool to model trading performance, including implementation code and feature suggestions.
- Visualization and Analysis: Enhanced data visualization using Seaborn and analyzed growth paths and risks in compounding strategies.
Achievements
- Developed a comprehensive trading strategy framework incorporating risk management, performance analysis, and simulation tools.
- Enhanced understanding of trading scales, risk-reward dynamics, and optimal bet sizing using the Kelly Criterion.
- Improved data visualization techniques for financial data analysis.
Pending Tasks
- Further refine the Python simulation tool with additional features and capabilities.
- Conduct more extensive testing of trading strategies using the developed framework.
- Explore additional data visualization enhancements for clearer insights.