from __future__ import annotations
import math
import pandas as pd
from typing import Dict

def portfolio_metrics(equity: pd.Series) -> Dict[str, float]:
    if equity.empty or equity.iloc[0] <= 0:
        return {"max_dd": 0.0, "sharpe": 0.0, "cagr": 0.0}

    roll_max = equity.cummax()
    dd = (equity/roll_max) - 1.0
    max_dd = float(dd.min())

    rets = equity.pct_change().dropna()
    if rets.empty or rets.std(ddof=0) == 0:
        sharpe = 0.0
    else:
        periods_per_year = 365*24*60  # dla 1m
        sharpe = float((rets.mean()/rets.std(ddof=0)) * math.sqrt(periods_per_year))

    t0 = pd.to_datetime(equity.index[0], unit="ms", utc=True)
    t1 = pd.to_datetime(equity.index[-1], unit="ms", utc=True)
    years = max((t1 - t0).total_seconds() / (365.25*24*3600), 1e-9)
    cagr = float((equity.iloc[-1]/equity.iloc[0])**(1/years) - 1.0)

    return {"max_dd": max_dd, "sharpe": sharpe, "cagr": cagr}