Eagle AI,
At Institutional Scale
Infinite Point Capital, a US-regulated, CFTC-overseen hedge fund, chose Eagle AI Labs as its exclusive AI infrastructure partner. Deployed on live CME Bitcoin futures. This is what institutional confidence in our models looks like.
Brad McGill
Founder, Infinite Point Capital — IPC
Brad McGill is a career institutional markets professional. With decades operating across capital markets and quantitative strategy, he understands what separates a genuine statistical edge from noise.
After independently discovering Eagle AI Labs' models, Brad ran structured performance validation across multiple market regimes. The accuracy, confidence calibration, and drawdown profile all passed the institutional bar, and led to a significant decision: build a regulated fund around them. That is not a marketing claim. That is an institutional operator putting capital and regulatory commitment behind what our models produce.
What Our Models
Demonstrated
Repeatable Edge
Directional accuracy held across bull, bear, and ranging markets, not curve-fitted to a single regime. A non-random edge is the hardest thing to find in quantitative finance. Our models have it.
Calibrated Confidence
When the model signals high confidence, accuracy is materially higher. When it signals uncertainty, it says so. That calibration, rare in AI systems, is what makes the output usable at institutional scale.
Institutional Deployment
The validation didn't end in a spreadsheet. A regulated US hedge fund was launched specifically to trade live capital on the back of Eagle AI's signal output. Results, not projections.
What Eagle AI
Delivers at Scale
Probabilistic Forecasting
Not a price target, a directional probability. Across 30m, 1H, 4H, and daily timeframes, every output is a calibrated number a quant or portfolio manager can act on without interpretation.
Core OutputSignal Quality Tiers
Every signal carries a three-tier quality rating. Strong signals historically outperform. Uncertain ones say so explicitly. That transparency enables disciplined position sizing, a non-negotiable in institutional risk management.
Risk FrameworkContinuous Improvement
Models retrain weekly on a rolling 24-month dataset. A new checkpoint only replaces the live model if it outperforms, meaning the system structurally improves over time without manual intervention or degradation risk.
Adaptive SystemCleared on the
Chicago Mercantile Exchange
The fact that our models are running inside a fund that operates on the CME says something about their quality. The CME doesn't care about compelling narratives, it clears trades. The CFTC doesn't care about marketing, it regulates conduct. Our models passed both filters.
- CFTC oversight, the highest US derivatives regulatory standard
- Centrally cleared, every position audited and reported
- Same venue used by BlackRock, Goldman, and sovereign wealth funds
- Institutional liquidity depth, no slippage, no counterparty risk
Institutional Models.
No Institution Required.
The same signal infrastructure that a regulated US hedge fund built its strategy around is available to individual traders via Eagle AI Labs. Same models, same outputs, same weekly retraining cycle.