from __future__ import annotations
import pandas as pd
import yfinance as yf
from core.market_data import get_quote, get_history


def calculate_pnl(positions: list[dict], quotes: dict[str, dict]) -> list[dict]:
    result = []
    for pos in positions:
        sym = pos["symbol"]
        q = quotes.get(sym, {})
        price = q.get("price", 0.0)
        shares = pos.get("shares", 0.0)
        avg_cost = pos.get("avg_cost", 0.0)
        market_value = price * shares
        cost_basis = avg_cost * shares
        pnl = market_value - cost_basis
        pnl_pct = (pnl / cost_basis * 100) if cost_basis else 0.0
        result.append({
            **pos,
            "price": price,
            "market_value": market_value,
            "cost_basis": cost_basis,
            "pnl": pnl,
            "pnl_pct": pnl_pct,
            "change_pct": q.get("change_pct", 0.0),
        })
    return result


def get_benchmark_performance(period: str = "1y") -> pd.DataFrame:
    return get_history("SPY", period=period)


def calculate_sector_allocation(positions: list[dict]) -> dict[str, float]:
    sector_values: dict[str, float] = {}
    for pos in positions:
        sector = pos.get("sector", "Unknown") or "Unknown"
        value = pos.get("market_value", 0.0)
        sector_values[sector] = sector_values.get(sector, 0.0) + value
    total = sum(sector_values.values())
    if total == 0:
        return {}
    return {s: (v / total * 100) for s, v in sorted(sector_values.items(), key=lambda x: -x[1])}


def get_portfolio_performance(positions: list[dict], period: str = "1y") -> pd.DataFrame:
    if not positions:
        return pd.DataFrame()

    symbols = [p["symbol"] for p in positions]
    weights = {}
    total_value = sum(p.get("market_value", 0) for p in positions)

    if total_value == 0:
        return pd.DataFrame()

    for p in positions:
        weights[p["symbol"]] = p.get("market_value", 0) / total_value

    frames = []
    for sym in symbols:
        hist = get_history(sym, period=period)
        if not hist.empty:
            pct = hist["Close"].pct_change().fillna(0)
            pct.name = sym
            frames.append(pct * weights.get(sym, 0))

    if not frames:
        return pd.DataFrame()

    combined = pd.concat(frames, axis=1).fillna(0)
    portfolio_returns = combined.sum(axis=1)
    portfolio_cumulative = (1 + portfolio_returns).cumprod() - 1

    spy = get_history("SPY", period=period)
    if not spy.empty:
        spy_returns = spy["Close"].pct_change().fillna(0)
        spy_cumulative = (1 + spy_returns).cumprod() - 1
        return pd.DataFrame({
            "portfolio": portfolio_cumulative,
            "spy": spy_cumulative,
        }).dropna()

    return pd.DataFrame({"portfolio": portfolio_cumulative}).dropna()