AutoTrade Experts — Multi-Asset Signal AI

Most retail 'signal' apps are random walks dressed in confidence bars. They publish their best week and ghost their worst quarter. Anyone who's spent five minutes near financial markets sees through it. The bar for an honest product is high: it has to actually beat baseline, and it has to publish accuracy figures the user can audit.

Multi-asset coverage compounds the problem. Stock signals don't transfer to currency markets; currency models don't generalise to commodities. Each asset class has its own volatility regime, its own news-sentiment shape, and its own macro drivers. One model can't do all three well.

The team also wanted to ship something they'd put their own money behind. That meant six months of paper-trading and adversarial backtesting before any user-facing accuracy claim — a delay most signal-app teams refuse to accept.

Ensemble of asset-specific models on top of public market data, news sentiment from established feeds (Reuters, Bloomberg public APIs), and macro indicator series. Time-series forecasting (LSTM + transformer hybrid) per asset, with meta-classifier deciding which model to trust on a given day's volatility regime.

Honesty by design: the dashboard publishes the rolling 30-day, 90-day, and lifetime success rate per asset class. No 'hide last week' switches. Failed calls are visible alongside successful ones, with the model's reasoning preserved.

Six-month paper-trading discipline: no public release until live accuracy crosses 80%+ on out-of-sample data across at least two volatility regimes. R&D status is the product positioning, not a delay we're hiding from.