get_ai_prediction
Get a data-driven probability estimate for the next trading day's direction using an ensemble of gradient-boosted trees, LSTM, and quantum-classical hybrid models. Delivers up/down prediction, confidence, and model votes.
Instructions
Get an AI/ML directional prediction for a stock's next-session move.
Use this tool when:
You want a data-driven probability estimate for the next trading day's direction (up vs. down).
You need the individual model votes (ensemble breakdown) to assess consensus strength.
You want to compare model confidence against current IV pricing.
The prediction engine uses an ensemble of gradient-boosted trees, an LSTM, and a VQC (quantum-classical hybrid) model. Features include VIX, relative strength, Treasury rates, and options flow signals.
Parameters
symbol : str Exchange ticker in uppercase, e.g. "NVDA", "META", "QQQ". Per-ticker model accuracy varies; META and QQQ have shown above- baseline hit rates in backtests.
Returns
dict with keys: symbol : str — normalized ticker prediction : str — "Up" | "Down" | "Neutral" up_probability : float — 0.0–1.0 probability of upward close confidence : float — 0.0–1.0 ensemble agreement score model_votes : dict — per-model predictions and probabilities regime : str — "Bull" | "Bear" | "Chop" market regime signal_strength : str — "Strong" | "Moderate" | "Weak"
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Exchange ticker in uppercase, e.g. 'NVDA', 'META', 'QQQ'. Do NOT pass company names — use official tickers only. Per-ticker model accuracy varies; META and QQQ have shown above-baseline hit rates in backtests. |