Mo Rahmani

Quantitative researcher — systematic FX forecasting, rigorous backtesting, and research infrastructure.

About

I build end-to-end quantitative research pipelines: from raw market and macro data through feature engineering, rolling out-of-sample model evaluation, and portfolio-level analysis. My work emphasizes reproducible backtests, explicit risk overlays, and communication of methodology — not black-box performance claims.

Currently focused on multi-timeframe FX directional forecasting (daily through intraday), expanded-universe backtests, and integrated research dashboards for signal monitoring and production workflows.

Research principles

  • Out-of-sample evaluation first — rolling holdout and walk-forward protocols, not in-sample fit.
  • Transparent data lineage — versioned parquet pipelines, manifest reports, reproducible run scripts.
  • Risk-aware framing — stop-loss overlays, transaction-cost sensitivity, and clear limitation statements.

Selected research

US sector allocation by rate, inflation & business cycle

Long-history study of which US equity industries outperform under different macro regimes, using Ken French 12 industry portfolios with Shiller long rates, headline CPI, and industrial production. Maps conditional sector returns across rate direction, inflation level, and business-cycle phase.

Ken French Macro regimes Sector allocation

FX directional forecasting & data pipeline

End-to-end FX research stack: automated OHLC and FRED macro collection, feature engineering, and multi-timeframe binary/ternary directional models with rolling out-of-sample evaluation across daily through intraday bars.

Rolling OOS FRED / ETL Python H1 / H4 / Daily

Integrated forecasting dashboard

Streamlit research workspace unifying FX forecast signals, data refresh controls, and cross-asset analytics for daily monitoring and production workflows.

Streamlit Research ops Automation

Tools & methods

Python pandas / NumPy scikit-learn Time-series CV Backtesting Parquet / ETL Macro data (FRED) FX microstructure PCA / factor analysis PowerShell automation Streamlit Git

Contact

Open to senior quantitative analyst and quant researcher roles in investment management. Reach me at mr.mh.rahmani@gmail.com or via LinkedIn.