Run any ticker through multiple frontier AI models. Each reasons independently. An arbiter measures consensus. Every signal is tracked to outcome — publicly and timestamped.
How TradeHorde turns raw market data into scored conviction.
Live prices, technicals, regime context, and fundamentals assembled per ticker.
Frontier models analyze independently. Each builds bull and bear cases.
Calibration-weighted scoring. Agreement + accuracy = conviction.
Tracked against live prices to public resolution. Wins and losses.
Live prices, technicals, regime context, and fundamentals assembled per ticker.
Frontier models analyze independently. Each builds bull and bear cases.
Calibration-weighted scoring. Agreement + accuracy = conviction.
Tracked against live prices to public resolution. Wins and losses.
When models align with high conviction, the signal is stronger. Here's how conviction tiers perform on real market outcomes.
179 resolved signals · Data updates hourly
View full track record →Three problems every LLM has when you ask it for market analysis.
They hallucinate prices, invent support levels, and guess at volume. TradeHorde injects live quotes, technicals, volume profiles, earnings dates, and market regime before any model sees the ticker.
"On one hand... on the other hand." Every answer is a non-answer. TradeHorde forces each model to make a directional call with exact entry, target, and stop levels — plus a full bull and bear thesis.
Conversations disappear. Nobody tracks whether that "bullish setup" actually played out. TradeHorde monitors every signal against real prices and publishes outcomes — wins and losses — publicly.
The science of forecasting, applied to markets.
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.
Research on forecasting 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.
Each model sees the same data but reasons differently. No model sees what others said before committing.
A model that says "70% bullish" should win ~70% of the time. We track this. Models that are overconfident get down-weighted.
High conviction means multiple models, analyzing independently, reached the same conclusion with high confidence. Disagreement or abstention lowers conviction.
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.
We don't claim to predict the future. We claim to aggregate independent analysis and measure the results honestly.
Run any ticker through multiple frontier models. See where they agree and get calibrated conviction.
Sign Up Free