Financial Models & Tools
Quantitative models, trading strategies, and portfolio management tools
Featured Models
Risk-Adjusted Momentum Strategy
Tech Stack: Python, pandas, NumPy, scikit-learn, Backtrader
Description: A quantitative trading model that combines momentum factors with dynamic risk management. The strategy identifies trending assets while implementing volatility-based position sizing and drawdown controls.
Key Features: - Multi-timeframe momentum scoring algorithm - Dynamic position sizing based on volatility forecasting - Automated risk management with stop-loss and position limits - Backtesting framework with transaction cost modeling
Performance: Sharpe Ratio: 1.24 | Max Drawdown: 8.3% | Annual Return: 16.7%
Links: GitHub | Backtest Results | Documentation
Portfolio Optimization Engine
Tech Stack: Python, CVXOpt, Yahoo Finance API, Plotly
Description: Modern portfolio theory implementation with advanced optimization techniques. The tool constructs efficient frontiers, performs risk budgeting, and implements Black-Litterman model for enhanced expected returns.
Key Features: - Mean-variance optimization with multiple constraints - Risk parity and risk budgeting strategies - Black-Litterman model integration - Interactive visualization of efficient frontiers
Performance: Optimized portfolios show 15% lower volatility vs equal-weight benchmark
Options Pricing & Greeks Calculator
Tech Stack: Python, NumPy, SciPy, Black-Scholes, Monte Carlo
Description: Comprehensive options pricing toolkit implementing multiple models including Black-Scholes, binomial trees, and Monte Carlo simulations. Features real-time Greeks calculation and implied volatility analysis.
Key Features: - Multiple pricing models (Black-Scholes, Binomial, Monte Carlo) - Real-time Greeks computation (Delta, Gamma, Theta, Vega, Rho) - Implied volatility surface modeling - Risk scenario analysis and stress testing
Links: GitHub | Jupyter Notebooks
Analytical Tools & Dashboards
Market Sentiment Tracker
Tech: Python, NLTK, Twitter API
Real-time sentiment analysis of financial news and social media to gauge market sentiment and predict short-term price movements.
Earnings Beat Predictor
Tech: Python, Random Forest, XGBoost
Machine learning model that predicts earnings beats/misses using fundamental and technical indicators with 68% accuracy.
Crypto Arbitrage Scanner
Tech: Python, WebSockets, Multiple APIs
Real-time arbitrage opportunity scanner across major cryptocurrency exchanges with automated alert system.
ESG Scoring Framework
Tech: Python, NLP, Alternative Data
Comprehensive ESG scoring system using alternative data sources including satellite imagery, news sentiment, and regulatory filings.
Technologies I Work With
Programming Languages: Python, R, MATLAB, SQL, C++, VBA
Financial Libraries: NumPy, pandas, SciPy, QuantLib, Zipline, PyPortfolioOpt
Machine Learning: scikit-learn, XGBoost, TensorFlow, PyTorch, Keras
Data Sources: Bloomberg API, Yahoo Finance, Alpha Vantage, Quandl, FRED
Visualization: Plotly, Matplotlib, Seaborn, Bokeh, Dash
Databases: PostgreSQL, MongoDB, InfluxDB, Redis, MS SQL Server
Platforms: AWS, Google Cloud, Azure, Docker, Kubernetes
Trading Platforms: Interactive Brokers API, MetaTrader, TradingView
Model Categories
- Quantitative Strategies: Algorithmic trading and systematic investment approaches
- Risk Management: Portfolio risk assessment, VaR models, and stress testing
- Derivatives Pricing: Options, futures, and exotic derivatives valuation
- Portfolio Optimization: Modern portfolio theory and factor-based investing
- Alternative Data: ESG scoring, sentiment analysis, and satellite data
- Market Microstructure: High-frequency trading and market making strategies
Interested in collaborating on a project? Get in touch!