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# My Quantitative Finance Lab
Welcome to my Quantitative Finance Lab, a comprehensive repository showcasing a diverse range of financial modeling, algorithmic trading, and data analysis projects. This lab serves as a platform for exploring and implementing various concepts in quantitative finance, machine learning, and data science applied to financial markets.
## Repository Highlights:
### Portfolio Optimization and Risk Management
- Monte Carlo simulation for portfolio optimization
- Value at Risk (VaR) and Conditional Value at Risk (CVaR) calculations
- Optimal portfolio construction combining risk-free assets and market portfolios
### Algorithmic Trading Strategies
- Reinforcement Learning agent for market making
- RSI-based trading simulation
- Advanced trading strategy using Bollinger Bands, RSI, and MACD indicators for NVIDIA stock
### Time Series Analysis and Forecasting
- Bitcoin price prediction models
- S&P 500 predictor
- Time series analysis on Microsoft stock
### Machine Learning Applications
- Sentiment analysis for financial texts
- K-Means clustering for stock similarity analysis
- Multiple regression analysis on economic indicators
### Data Analysis and Visualization
- Exploratory data analysis on various datasets including weather data
- Visualizations and statistical analysis of financial data
### Financial Tools and Utilities
- Simple expense tracker application
- Genetic algorithm for trade optimization
## Technologies and Methods Used:
- Python for all implementations
- Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn
- Statistical methods: Linear and multiple regression, time series analysis
- Machine Learning: Supervised and unsupervised learning techniques
- Deep Learning: Neural networks for predictive modeling
- Optimization algorithms: Monte Carlo simulations, genetic algorithms
This lab represents an ongoing journey into the world of quantitative finance, continuously updated with new projects, improved methodologies, and cutting-edge techniques in financial modeling and analysis.
Feel free to explore, provide feedback, or reach out for collaborations!