Tools & Technologies
My analytical stack prioritizes reproducibility, statistical coherence, and clear reporting. The focus is on Python-based workflows for financial analytics and quantitative portfolio diagnostics.
Python-Based Financial Analytics
Python is the primary environment for data preprocessing, time-series analytics, and portfolio diagnostics. Deliverables can include notebooks, scripts, and structured outputs.
- Pandas for data manipulation and preprocessing
- NumPy for numerical computation
- Statistical workflows for returns, risk, and performance metrics
Statistical and Quantitative Methods
Quantitative methods are selected based on data properties, stability, and interpretability. Definitions and assumptions are explicitly stated.
- Time-series analysis: returns, volatility, correlation structures
- Risk analytics: drawdowns, Value-at-Risk (VaR), downside measures
- Portfolio analytics: diversification diagnostics, drift analysis, benchmarking
Visualization and Reporting
Visualization is used to improve interpretability and highlight risk dynamics and portfolio behavior. Reports are structured and suitable for academic or professional contexts.
- Time-series plots, risk charts, and diagnostic dashboards
- Summary tables for performance and risk indicators
- Export-ready reporting formats
Reproducibility and Documentation
Workflows are designed to be auditable and repeatable. Outputs include documented computation steps, definitions, and clear limitations.
- Structured analysis pipeline
- Versionable scripts and notebooks
- Consistent metric definitions and assumptions