An advanced ML trading dashboard for Nifty 50. JUDAH uses an automated XGBoost pipeline that grid-searches 360 combinations daily across 6 horizons. The live Streamlit engine fuses a 4-pillar probability ensemble to generate mathematically precise, high-conviction options strategies (Spreads/Strangles) and Strike recommendations.

A regime-aware reinforcement learning workbench for synthetic trading research. It combines a hidden-regime market simulator, multiple agent baselines, a live terminal dashboard, and experiment tooling for ablations, OOD sweeps, and artifact-driven analysis.

Multi-asset market regime detection and trading strategy using KMeans, HMM, walk-forward retraining, and macro signals

Educational investment research lab with market regime dashboards, portfolio diagnostics and quantitative research modules.

Quantitative test of the "VIX = fear" narrative. UC3 — Usual Claims Series

Quantitative test of the "bonds always protect" narrative across inflation regimes. UC2 — Usual Claims Series

Structural analysis of the “10 best days” market timing claim using SPX regime clustering and volatility geometry.

Public evidence portfolio for macro-financial research engineering: Python pipelines, real-data ingestion, volatility-regime analysis, ML validation, backtesting logic, and research reporting.

Notebook-based regime modelling pipeline for Indian markets using multi-asset historical analogue analysis.

Python dashboard analysing how BTC, ETH and SOL behave across macro and cross-asset market regimes.

This project simulates wealth accumulation during the pre‑retirement phase using multiple financial return models. The goal is to compare deterministic and stochastic approaches to long‑term portfolio growth.