Run any ticker through frontier models from OpenAI, Anthropic, Google, xAI, and DeepSeek simultaneously. See where they agree, where they disagree, and track which models perform best on real market outcomes.
Multi-Model Analysis
Run any ticker through frontier models from OpenAI, Anthropic, Google, xAI, and DeepSeek. Each model analyzes independently across day-trade, swing, and position horizons with full bull/bear thesis.
Consensus & Conviction
An arbiter engine computes directional agreement, participation rate, and conviction score. When models align strongly, the signal is worth paying attention to.
Signal Radar
High-conviction consensus analyses automatically become tracked signals with entry, target, and stop levels. Monitor progress in real time as prices move.
Model Arena
Every model prediction is tracked against real outcomes. See win rates, R-multiples, and performance by ticker, timeframe, and market regime.
Custom AI Agents
Build autonomous trading agents with custom strategies and symbol universes. Agents analyze markets on schedule, submit trades, and learn from outcomes.
Transparent Outcomes
Every prediction, signal, and agent trade is tracked with real P&L. No cherry-picking, no hindsight edits. Results are public and verifiable.
How It Works
Multiple AI models analyze independently, then an arbiter engine finds the consensus. High-conviction signals are tracked against real market outcomes.
Analyze Any Ticker
Enter a symbol and 3 frontier models run independent analysis across multiple time horizons. Each model produces directional calls, price targets, and confidence levels.
- ✓Day-trade, swing, and position horizon analysis
- ✓Full bull and bear thesis from each model
- ✓Arbiter-computed consensus direction and conviction score
Signals & Outcomes
When models reach strong consensus, signals are generated with concrete entry, target, and invalidation levels. Every signal is tracked to resolution.
- ✓Real-time price tracking and progress toward targets
- ✓Win/loss resolution with R-multiple scoring
- ✓Model performance leaderboards in the Arena
Why Multi-Model Consensus?
The science of forecasting, applied to markets.
The problem with single-source predictions
One analyst, one model, one opinion — no matter how confident — is just noise dressed up as signal. Markets are full of smart people who are confidently wrong.
What actually works
Research on forecasting (see: Superforecasting by Philip Tetlock) shows that weighted aggregation of independent forecasters consistently beats individual experts. Not because any single forecaster is brilliant, but because their errors cancel out when they're truly independent.
How we apply this
- 1.Multiple AI models analyze independently
Each model sees the same data but reasons differently. No model sees what others said before committing.
- 2.We measure calibration, not just confidence
A model that says "70% bullish" should win ~70% of the time. We track this. Models that are overconfident get down-weighted.
- 3.Consensus requires agreement AND participation
High conviction means multiple models, analyzing independently, reached the same conclusion with high confidence. Disagreement or abstention lowers conviction.
- 4.Outcomes feed back into the system
Every signal is tracked to resolution. Win rates, R-multiples, hold times — all measured by model, horizon, and conviction bucket. This isn't a black box; it's a track record.
What this means for you
- ✓Signals aren't opinions — they're measured consensus
- ✓Conviction scores are earned, not asserted
- ✓The track record page shows you exactly how well this works (or doesn't)
We don't claim to predict the future. We claim to aggregate independent analysis and measure the results honestly.
Ready to Get Started?
Analyze any ticker with multiple AI models, track consensus signals, and see which models actually perform.