A.
Market Data Infrastructure
Normalized, high-resolution datasets across major centralized exchanges, derivatives venues, and on-chain sources.
- CEX data
- Order flow
- Liquidity metrics
- Trading behavior
Crypto Quantitative Research
Building systematic crypto strategies through advanced factor research, market data intelligence, and quantitative modeling.
+1,286.3%
Cumulative Returnsince Jan 2024
5.04
Sharpe Ratioannualized
−9.3%
Max Drawdownpeak-to-trough
+56.4%
Live Returnsince Apr 2026 · 1x gross
Live
Strategy Livesince Apr 2026
* Track record from January 2024; figures prior to April 2026 are simulated backtest results, thereafter live. 1x gross exposure. Past performance is not indicative of future results.
01 — The Firm
Theta Prime Capital is a quantitative investment firm built for digital assets. We pair academic rigor in financial economics with years of live systematic trading in equity markets — and apply that discipline of factor research, portfolio construction, and risk control to a market that never closes.
Fully rules-based execution. Every position is the output of a defined model — no discretionary overrides, no narrative trades.
Continuous research into cross-sectional and time-series signals across centralized exchanges, derivatives, and on-chain data.
Statistical and machine-learning models combined with disciplined portfolio construction and transaction-cost awareness.
Exposure limits, drawdown budgets, and real-time monitoring are embedded in the strategy itself — not applied after the fact.
02 — Research Framework
Our research pipeline is organized into four tightly integrated modules. Each stage is versioned, reproducible, and subject to the same engineering discipline as the production trading system.
Normalized, high-resolution datasets across major centralized exchanges, derivatives venues, and on-chain sources.
Systematic discovery and validation of return-predictive signals, with strict out-of-sample testing and capacity analysis.
Signals are combined through statistical and machine-learning models, then translated into portfolios with explicit cost modeling.
Position-level and portfolio-level risk limits enforced systematically, with continuous drawdown monitoring.
03 — Strategy Performance
A market-neutral, cross-sectional multi-factor strategy across liquid digital assets. Returns are driven by factor premia rather than directional exposure to the crypto market. Live since April 2026.
+1,286.3%
Cumulative Return
+184.5%
CAGR
5.04
Sharpe Ratio
21.2%
Volatility
−9.3%
Max Drawdown
59.5%
Win Rate
2024-01 → 2026-07 · Daily NAV rebased to 1.00 · Final NAV 13.86x
Dashed line marks the start of the live track record (April 2026); earlier figures are simulated backtest results.
* Figures from April 2026 onward reflect the live track record; earlier figures are simulated backtest results. Shown at 1x gross exposure, net of estimated transaction costs and before management and performance fees. Past performance — actual or simulated — is not indicative of future results. Digital asset trading involves substantial risk of loss.
04 — Investment Philosophy
Decisions follow evidence, not narratives. Every assumption is tested against data before it is allowed to influence a portfolio.
Rules replace discretion. A defined process is repeatable, auditable, and improves with every research cycle.
The strategy is never finished. Most of our work is research that never reaches production — that selectivity is the edge.
Return is only meaningful relative to the risk taken to earn it. Drawdown discipline takes precedence over return targets.
05 — Team & Technology
We are a small, research-led team combining academic finance and live trading experience. Our background spans financial academia, systematic equity trading, and crypto market microstructure. We publish selectively and let the track record speak for itself.
Professor-level research background in financial economics, grounding every model we deploy in rigorous asset-pricing and factor theory.
Years of live systematic trading in equity markets — execution quality, cost control, and factor crowding learned in production, not in backtests.
Deep familiarity with exchange microstructure, funding dynamics, and on-chain data across major venues and networks.
Technology Stack
06 — Contact
We welcome inquiries from qualified investors, allocators, and institutional partners. Please reach out through either channel below.