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ChiragMirani/README.md

Chirag Mirani (@ChiragMirani)

Investment manager and quantitative researcher in Suwanee, Georgia, with about 20 years in quantitative finance (Master of Science in Computational Finance, Carnegie Mellon University). I build macro investment strategies across rates, currencies, options and volatility, and the forecasting models behind them, including macroeconomic and prediction-market models.

Research and portfolio · LinkedIn · SSRN · Kaggle

Apps

Coming soon: Off Grid Intelligence (OGI). I'm building offline technology to help save lives, with enormous potential for defense and emergency response.

I also build iPhone and iPad apps and scripture readers. See my apps.

Selected research portfolio (private)

A focused subset of in-production research. Code, methodology, and live PnL stay private. Happy to discuss under NDA.

  1. US macro release-day trading stack. House forecasts, surprise classifiers, and prediction-market consensus calibration for Core CPI, Core PCE, NFP, ADP, UR, and weekly jobless claims. The public proof point is the live dashboard and its honest track record. The bet sheet and position layer stay private.

  2. Term-structure butterfly trading across UST, SOFR, and EUR rates. Z-score and carry-driven 2s5s10s, 2s3s5s, and broader fly engines built on Bloomberg, with multi-curve cross-sectional scoring and regime overlays.

  3. Prediction-market alpha on Kalshi and Polymarket. Real-time monitoring on 100+ series, with delta engines, cross-platform price discovery, and event-calendar-aware alerting around Fed decisions, macro prints, and market-moving headlines.

  4. 0DTE SPX program. Live short-dated option strategies (iron flies, delta-hedged structures, calendar and ratio overlays) with automated MES futures hedging through IBKR. Vol-premium-aware sizing with intraday risk controls.

  5. Cross-asset factor stack across TLT, QQQ, GLD, and USO. Multi-asset signal generator combining macro, seasonality, and trend or persistence factors, backed by an expanding-window backtest grid that has evaluated 170K+ weight configurations.

Published research

Exposure-Time Normalized Performance: Strategy Evaluation Under Binding Exposure Constraints (Feb 2026). Formalizes exposure-time as a measurable input in strategy evaluation, defines exposure-normalized Sharpe and MAR ratios, and shows in Monte Carlo that exposure-normalized rankings reverse versus raw Sharpe in 69.5% of simulations. SSRN.

A Predictive Model for 10-Year Treasury Futures (independent research, 2025). PDF.

Public work

MacroForecastbyCM is a live, daily-updated dashboard with house forecasts and Kalshi market consensus for every major US macro release: Core CPI, Core PCE, NFP, ADP, Unemployment Rate, and Weekly Jobless Claims. It also keeps a public track record of how my forecasts have done against Kalshi and the actual prints. Source at github.com/ChiragMirani/macro-dashboard.

On Kaggle as cmirani for 9 years. Silver-medal notebook on a leakage-free time-series training and test pipeline (77 upvotes). Recently finished top 22% (379 of 1711 teams) in the MITSUI Commodity Prediction Challenge. Currently competing in ARC Prize 2026 (ARC-AGI-2) and the NVIDIA Nemotron Model Reasoning Challenge.

Reach out

Open to investment, consultation, collaboration or trading-research conversations. Find me on LinkedIn.

Pinned Loading

  1. macro-dashboard macro-dashboard Public

    Live US macro release schedule with house forecasts and Kalshi consensus by Chirag Mirani

    Python 4

  2. zoes-vocabulary zoes-vocabulary Public

    Zoe's Vocabulary - K-12 flashcard PWA

    HTML 1

  3. ChiragMirani.github.io ChiragMirani.github.io Public

    Portfolio site for Chirag Mirani, data scientist and quantitative researcher: macro forecasting dashboard, research and apps.

    HTML

  4. CPISeasonality CPISeasonality Public

    Python and Jupyter code for estimating consumer price index (CPI) seasonality from five years of data per series.

    Jupyter Notebook

  5. ReinforcementLearningSpaceInvaders ReinforcementLearningSpaceInvaders Public

    Reinforcement learning agent trained to play Atari Space Invaders, in Jupyter notebooks.

    Jupyter Notebook 2