A quantitative trading dashboard integrating live market data with moving averages, RSI, and volatility indicators to generate real-time Buy/Sell/Hold insights. Using Python, Apps Script, and API triggers, it automates backtesting and visualizes performance trends across multiple assets. The system blends financial research, automation, and algorithmic modeling to showcase practical fintech analytics expertise.

A quantitative portfolio optimization model applying CAPM, Sharpe Ratio, and mean–variance analysis to balance return and volatility. It builds efficient multi-sector portfolios using covariance matrices, beta weighting, and diversification metrics relative to S&P 500 benchmarks. The project reflects real-world asset management methods and highlights strong proficiency in financial modeling, risk analytics, and investment strategy design.

A dynamic machine-learning engine built to forecast equity movements through automated data ingestion, feature generation, and multi-horizon return modeling. It produces 1-day and 5-day predictions, tracks directional accuracy, and surfaces key drivers via Random Forest importance. Integrating volatility, trend, and market indicators, the system supports short-term trading decisions and scenario evaluation. Combining quantitative modeling with an automated Google Sheets dashboard, it streamlines analysis and delivers clear, data-driven market insights.

An Excel-based valuation platform designed to simulate multi-year company performance using DCF, WACC, and Monte Carlo models. It automates IRR scoring, KPI tracking, and scenario comparisons to evaluate investment opportunities across sectors. Built for analytical precision and speed, the engine mirrors professional valuation workflows and demonstrates advanced skills in financial modeling, sensitivity analysis, and corporate finance automation.

A streamlined liquidity-forecasting engine built to support treasury operations through automated cash-flow modeling, balance aggregation, and stress-scenario analysis. It projects 30-day baseline and stressed liquidity, highlights buffer breaches, and quantifies short-term funding risk. Integrating treasury logic with dynamic formulas, the dashboard enhances liquidity planning, improves daily risk visibility, and delivers consistent, data-driven insights for financial decision-making.

A sentiment-driven market intelligence engine designed to map news flow to short-term price behavior. It aggregates headlines, scores sentiment using a custom dictionary, and aligns it with next-day returns, signal direction, and market buckets. By blending NLP logic with dynamic modeling, the dashboard reveals how bullish or bearish news cycles shape trading conditions, surfaces high-impact headlines, and delivers clear, data-driven insights for understanding momentum and market tone.

A dynamic Excel framework built to evaluate property investments through automated rent-roll forecasts, debt waterfalls, and scenario testing. It calculates key metrics including NOI, DSCR, IRR, and NPV to support buy/hold/sell decisions and integrates risk scoring for capital allocation. Combining finance logic with automation, the engine enhances underwriting efficiency and delivers consistent, data-driven real estate analysis.

A dynamic Excel-based investment evaluation engine built to analyze project returns through automated cash-flow modeling, discounted payback calculations, and multi-scenario testing. It computes key financial metrics including NPV, IRR, ROI, Profitability Index, and MIRR to support go/no-go decisions and capital budgeting. Featuring sensitivity heatmaps, scenario toggles, and decision rules, the tool enhances financial planning accuracy and delivers consistent, data-driven investment analysis across a range of business environments.