from __future__ import annotations
from typing import List
from .dsl import MarketSignal, PlanDelta

class Backtester:
    """
    Tiny deterministic replay engine.
    Applies PlanDelta actions to a simple PnL model.
    """
    def __init__(self, initial_cash: float = 100000.0):
        self.cash = initial_cash
        self.positions: List[dict] = []

    def apply(self, signals: List[MarketSignal], plan: PlanDelta) -> float:
        # Very simple PnL: sum(action.size * current_price) and adjust cash
        pnl = 0.0
        for act in plan.delta:
            symbol = act.get("symbol") or act.get("asset") or "UNKNOWN"
            size = act.get("size", 0.0)
            price = act.get("price") or act.get("premium") or 0.0
            if price is None:
                price = 0.0
            pnl += size * price
            self.positions.append({"symbol": symbol, "size": size, "price": price})
        self.cash += pnl
        return self.cash