I will build financial models, portfolio optimization using python
Unlock Insights with Expert Python and R Data Analysis
About this Gig
I deliver institutional-grade financial modeling using Python, R, and SQL.
I specialize in Modern Portfolio Theory (Markowitz, Black-Litterman, Risk Parity), time-series forecasting (ARIMA, GARCH, LSTM), and panel data econometrics techniques I apply at the State Bank of Pakistan.
Services:
Portfolio Optimization Multi-asset allocation with liquidity, drawdown, and currency constraints. Backtested.
Econometric Modeling Fixed/Random Effects, IV, Diff-in-Diff, dynamic panels. Full diagnostics.
Time-Series Forecasting ARIMA, GARCH, VAR/VECM, LSTM for macro indicators, FX, bonds, commodities.
FX & Reserve Analytics Currency composition, reserve adequacy (IMF ARA), sovereign bond analysis.
Written Interpretation Technical appendix with model choice, assumptions, and policy implications.
Deliverables: Jupyter/R Markdown, commented code, CSV/Excel exports, charts (matplotlib, Plotly, ggplot2), Report in Latex format PDF.
Data: World Bank, FRED, IMF, Bloomberg, FAO AQUASTAT, or your proprietary dataset.
FAQ
Can you work with central bank or sovereign wealth fund data?
Yes. I currently model Pakistan's FX reserves and international bond portfolio at the State Bank of Pakistan. I understand liquidity, safety, and return constraints unique to official institutions.
What data do you need from me?
Ideally a CSV/Excel of historical prices/variables with clear column headers. If you don't have data, I can source macro-financial data from World Bank, FRED, or IMF databases.
Can you explain the model to my team or supervisor?
Absolutely. Every Premium package includes a 15-minute walkthrough video or written technical appendix. I have taught Econometrics and Calculus at the university level — I explain without jargon.
What is your revision policy
Revisions cover parameter adjustments, additional tests, or formatting changes. They do not include entirely new model specifications unless agreed upon.

