"""Shared definition of a 'qualified' (actionable) trade setup.

A single predicate, driven by the admin activation config, used by the
performance stats (server) and mirrored on the frontend. Beyond raw R:R and
confidence, an actionable setup must show genuine conviction: a high-conviction
recommended action, a clean (conflict-free) read, and a probable primary target.
"""

from __future__ import annotations

from typing import Any

HIGH_CONVICTION_ACTIONS = {"LONG_HIGH", "SHORT_HIGH"}


def best_target_probability(setup: Any) -> float:
    """Highest probability among a setup's targets, 0 if none."""
    targets = getattr(setup, "targets", None) or []
    probs = [float(t.get("probability", 0.0)) for t in targets if isinstance(t, dict)]
    return max(probs, default=0.0)


def setup_qualifies(setup: Any, config: dict) -> bool:
    """Whether a setup clears the activation gate.

    ``setup`` is duck-typed: any object exposing rr_ratio, confidence_score,
    recommended_action, risk_level and a ``targets`` list of dicts.
    """
    if setup.rr_ratio < config["min_rr"]:
        return False
    if (setup.confidence_score or 0.0) < config["min_confidence"]:
        return False
    if config.get("require_high_conviction"):
        if (setup.recommended_action or "") not in HIGH_CONVICTION_ACTIONS:
            return False
    if config.get("exclude_conflicts"):
        if (setup.risk_level or "") != "Low":
            return False
    min_tp = float(config.get("min_target_probability", 0.0))
    if min_tp > 0 and best_target_probability(setup) < min_tp:
        return False
    return True