"""VectorBT backtest engine reading local Parquet OHLCV data."""

from __future__ import annotations

import os
from dataclasses import dataclass
from pathlib import Path

import pandas as pd
import vectorbt as vbt


@dataclass(frozen=True)
class BacktestResult:
    ticker: str
    fast_window: int
    slow_window: int
    sharpe_ratio: float
    max_drawdown: float
    total_return: float
    equity_curve: pd.Series
    price: pd.Series


def parquet_dir() -> Path:
    return Path(os.environ.get("PARQUET_DIR", "/data/parquet"))


def load_ohlcv(ticker: str) -> pd.DataFrame:
    path = parquet_dir() / f"{ticker.upper()}.parquet"
    if not path.exists():
        raise FileNotFoundError(f"No Parquet file for {ticker.upper()} at {path}")

    df = pd.read_parquet(path)
    if "Date" in df.columns:
        df = df.set_index("Date")
    df.index = pd.to_datetime(df.index)
    df = df.sort_index()
    return df


def run_ma_crossover(
    ticker: str,
    fast_window: int,
    slow_window: int,
    init_cash: float = 10_000.0,
    fees: float = 0.001,
) -> BacktestResult:
    if fast_window >= slow_window:
        raise ValueError("Fast MA window must be smaller than slow MA window")

    ohlcv = load_ohlcv(ticker)
    close = ohlcv["Close"].astype(float)

    fast_ma = vbt.MA.run(close, fast_window, short_name="fast")
    slow_ma = vbt.MA.run(close, slow_window, short_name="slow")

    entries = fast_ma.ma_crossed_above(slow_ma)
    exits = fast_ma.ma_crossed_below(slow_ma)

    portfolio = vbt.Portfolio.from_signals(
        close,
        entries=entries,
        exits=exits,
        init_cash=init_cash,
        fees=fees,
        freq="1D",
    )

    stats = portfolio.stats()
    sharpe = float(stats.get("Sharpe Ratio", 0.0) or 0.0)
    max_dd = float(stats.get("Max Drawdown [%]", 0.0) or 0.0) / 100.0
    total_return = float(stats.get("Total Return [%]", 0.0) or 0.0) / 100.0

    equity = portfolio.value()
    if isinstance(equity, pd.DataFrame):
        equity = equity.iloc[:, 0]

    return BacktestResult(
        ticker=ticker.upper(),
        fast_window=fast_window,
        slow_window=slow_window,
        sharpe_ratio=sharpe,
        max_drawdown=max_dd,
        total_return=total_return,
        equity_curve=equity,
        price=close,
    )