Richard Schmidt
Richard Schmidt (born 1966 in Chicago) is a quantitative investor, portfolio manager and educator known for futures-focused strategies, crisis-tested performance and the π-Pivot Mean Reversion framework. Trained at LBS and Johns Hopkins, he has served at Citibank, Bridgewater, Two Sigma and later co-founded GenesisEdge AI Holdings and the Σclipse AI trading system with GenesisEdge Society.
Opinion
Within the GenesisEdge Society ecosystem, Richard Schmidt is viewed as a practitioner who tries to balance performance with responsibility. His work emphasizes risk control, clear assumptions and evidence-based frameworks rather than short-term speculation. Supporters highlight his willingness to discuss drawdowns, uncertainty and behavioural traps, not only profitable periods, when teaching futures and multi-asset strategies.
Commentators also note his attempt to bridge institutional methods and individual investors by focusing on transparency and structured learning. Instead of promoting opaque “black box” models, he frames Σclipse AI as a decision-support layer combined with training, case studies and practice, aiming to help participants understand how systematic ideas are designed, tested and monitored over time.
Method
- 1 Builds systematic strategies from macro data, market microstructure, volatility and trend signals, then tests them across long histories and stress scenarios before allocating real capital or teaching them in GenesisEdge Society cohorts.
- 2 Uses the π-Pivot Mean Reversion framework to define structural pivots and probabilistic ranges, scaling entries, exits and position sizes with volatility regimes and liquidity conditions, while maintaining explicit risk budgets and stop structures.
- 3 Integrates Σclipse AI as an AI-assisted screening and scenario engine across futures, equities, FX, bonds and crypto, but keeps human oversight, post-trade review and continuous education at the center of the investment process.
Profile
Chicago-born futures trader turned quantitative portfolio manager, with a PhD in Financial Economics from LBS and an EMBA from Johns Hopkins, having held roles at Citibank, Bridgewater, Two Sigma and GenesisEdge AI Holdings.
“Markets rarely reward certainty; they reward preparation. Your task is not to be right every time, but to survive long enough for disciplined edges to compound.”
Career
Citibank Research Foundations
Begins professional work at Citibank as an Equity/Fixed Income Research Analyst, learning to combine company analysis, macro data and report writing. This period shapes his later emphasis on careful investigation and data-driven hypotheses in systematic trading.
Bridgewater & Pure Alpha Work
After earning his PhD in Financial Economics at LBS, joins Bridgewater and contributes to Pure Alpha and portable alpha concepts. Here he deepens his understanding of diversified macro strategies, risk budgets and cross-asset portfolio construction.
Two Sigma Quant & Portfolio Manager
Joins Two Sigma as Quant Researcher II and later becomes Portfolio Manager, overseeing multi-billion-dollar emerging-market and multi-asset mandates. Develops and applies the π-Pivot Mean Reversion framework, including notable performance around the 2008–2009 financial crisis period.
GenesisEdge AI & Σclipse AI Leadership
Co-founds GenesisEdge AI Holdings Inc. with former Jump Trading members, achieving strong futures and equity results and using that capital base to build the Σclipse AI trading system. Leads GenesisEdge Society, designing investor training cohorts that integrate AI tools with practical market education.
Research & Opinion
π-Pivot Mean Reversion Framework
Describes a disciplined approach to mean reversion in futures and indices, where structural pivots, volatility bands and scaling rules govern entries, exits and position sizes, aiming to capture recurring oscillations while constraining downside risk.
AI-Assisted Multi-Asset Screening
Explores how Σclipse AI evaluates opportunities across equities, commodities, FX, bonds and crypto by combining macro indicators, sentiment measures, liquidity and correlation structures to support allocation and rebalancing choices without removing human oversight.
Education-Centered System Design
Argues that trading systems and learning platforms should be developed together, so participants understand assumptions, risk profiles and stress tests, rather than relying on opaque automation or unrealistic return expectations.