"""Risk guardrails. Generous-but-present defaults for a paper account.

These are enforced programmatically in ``execution/guardrails.py`` — the LLM's
requested sizing and stop/take levels are inputs that get clamped here, never trusted.
"""

from __future__ import annotations

from pydantic import BaseModel


class RiskConfig(BaseModel):
    max_position_pct: float = 0.10  # cap per single position, % of equity
    max_symbol_exposure_pct: float = 0.15  # aggregate cap per ticker across all agents
    max_trades_per_agent: int = 4  # 4 trades/day mandate (one of them an option)
    # NOTE: plan_orders runs once per persona against its own account, so this "day" cap
    # acts PER ACCOUNT. Kept equal to the per-agent cap on purpose.
    max_trades_per_day: int = 4
    allow_shorts: bool = True  # shorts enabled; persona prompts decide when to use them
    default_stop_pct: float = 0.07
    default_take_pct: float = 0.15
    min_price: float = 3.0  # skip sub-$3 names
    max_price: float | None = None
    # --- options quality gate (the "high probability" mandate, enforced not just prompted) ---
    # Premium notional cap per option trade (% of equity). 0.10 so one large-cap 30-45DTE
    # contract (~$600-1000 premium) fits a $10k book — 0.05 made most contracts qty<1.
    max_option_weight_pct: float = 0.10
    min_option_conviction: float = 0.6  # drop option picks the agent itself isn't sure about


DEFAULT_RISK = RiskConfig()