REGRET — Counterfactual Trading Intelligence

REGRET is an autonomous decision-intelligence system built for Alpaca paper trading. Most trading agents only evaluate positions they execute; REGRET also preserves rejected opportunities as ShadowTrades and later measures what would have happened. Each candidate moves through an AI analyst, an adversarial critic, consensus, and a deterministic Risk Gate that remains the final authority. Rejected trades are evaluated counterfactually and classified as AVOIDED_LOSS or MISSED_ALPHA. Accepted trades can be routed to Alpaca Paper, reconciled to confirmed fills, monitored against the original target, stop-loss, and time horizon, then closed automatically. Realized executions are classified as CORRECT_EXECUTION or BAD_EXECUTION. REGRET combines these outcomes into Decision Value: a measure of the value created or protected by both action and inaction. In the verified demo ledger, 20 evaluated decisions produced +$3,452.97 of net Decision Value. The system also completed a genuine autonomous TSLA paper lifecycle: ACCEPT → BUY FILLED → TIME EXIT → SELL FILLED → BAD_EXECUTION. The hosted dashboard is a read-only audit terminal with bilingual EN/ID replay, structured analyst/critic evidence, execution history, counterfactual outcomes, and safety status. Public execution is disabled in the hosted demo, while the verified Alpaca Paper evidence remains persisted and replayable.