from __future__ import annotations

"""Toy adapters to bootstrap interoperability between venues and the core engine.

- PriceFeedAdapter: simulates a price feed (e.g., equities, spot, etc.).
- OptionsVenueAdapter: simulates an options venue with basic option data.

These are deliberately simple and deterministic to serve as MVP building blocks.
"""

from typing import Dict, List
from .dsl import Asset, MarketSignal


class PriceFeedAdapter:
    def __init__(self, assets: List[Asset]):
        self.assets = assets
        # deterministic seed values per asset
        self.prices: Dict[str, float] = {a.symbol: 100.0 for a in assets}

    def update(self) -> List[MarketSignal]:
        # simple random walk with fixed seed for deterministic tests
        signals: List[MarketSignal] = []
        for a in self.assets:
            price = self.prices[a.symbol] * 1.0005  # tiny drift
            self.prices[a.symbol] = price
            signals.append(MarketSignal(asset=a, signal_type="price", value=price))
        return signals


class OptionsVenueAdapter:
    def __init__(self, underlying: Asset, strikes: List[float], maturities: List[float]):
        self.underlying = underlying
        self.strikes = strikes
        self.maturities = maturities

    def quote(self) -> List[MarketSignal]:
        # toy: simple intrinsic value proxy for options across strikes
        signals: List[MarketSignal] = []
        for k in self.strikes:
            delta = max(0.0,  self.underlying_asset_spot() - k)
            signals.append(MarketSignal(asset=self.underlying, signal_type=f"option_delta_{k}", value=delta))
        return signals

    def underlying_asset_spot(self) -> float:
        # deterministic spot via a fixed function of symbol hash
        return float(sum(ord(c) for c in self.underlying.symbol)) % 200 + 50