"""Moving-average crossover — predefined Python strategy."""

from __future__ import annotations

import numpy as np
import pandas as pd
import vectorbt as vbt

STRATEGY_KEY = "ma_crossover"
DISPLAY_NAME = "MA Crossover"
DESCRIPTION = "Enter when fast MA crosses above slow MA; exit on cross below."

PARAM_GRID = {
    "window_pool": list(range(5, 101, 5)),
}

DEFAULT_PARAMS = {
    "fast_window": 20,
    "slow_window": 50,
}


def generate_signals(
    close: pd.Series,
    high: pd.Series,
    low: pd.Series,
    volume: pd.Series,
    fast_window: int = 20,
    slow_window: int = 50,
    **_kwargs,
) -> tuple[pd.Series, pd.Series]:
    if fast_window >= slow_window:
        raise ValueError("fast_window must be smaller than slow_window")

    fast_ma = vbt.MA.run(close, fast_window).ma
    slow_ma = vbt.MA.run(close, slow_window).ma
    entries = fast_ma.vbt.crossed_above(slow_ma).fillna(False)
    exits = fast_ma.vbt.crossed_below(slow_ma).fillna(False)
    return entries, exits


def optimize_vectorized(
    close: pd.Series,
    window_pool: list[int] | None = None,
    init_cash: float = 10_000.0,
    fees: float = 0.001,
    metric: str = "sharpe_ratio",
) -> pd.DataFrame:
    """VectorBT combinatorial scan across all fast/slow pairs (fast < slow)."""
    pool = np.array(window_pool or PARAM_GRID["window_pool"], dtype=int)
    fast_ma, slow_ma = vbt.MA.run_combs(close, pool, r=2, short_names=["fast", "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",
    )

    metric_fn = {
        "sharpe_ratio": portfolio.sharpe_ratio,
        "sortino_ratio": portfolio.sortino_ratio,
        "total_return": portfolio.total_return,
        "max_drawdown": lambda **_: portfolio.max_drawdown(group_by=False),
    }.get(metric, portfolio.sharpe_ratio)

    scores = metric_fn(group_by=False)
    max_dd = portfolio.max_drawdown(group_by=False)
    total_ret = portfolio.total_return(group_by=False)
    sortino = portfolio.sortino_ratio(group_by=False)
    trades = portfolio.trades.count(group_by=False)

    rows = []
    for idx, score in scores.items():
        fast_w, slow_w = int(idx[0]), int(idx[1])
        rows.append(
            {
                "fast_window": fast_w,
                "slow_window": slow_w,
                "score": float(score) if score == score else float("nan"),
                "sharpe_ratio": float(scores[idx]) if scores[idx] == scores[idx] else float("nan"),
                "sortino_ratio": float(sortino[idx]) if sortino[idx] == sortino[idx] else float("nan"),
                "total_return": float(total_ret[idx]),
                "max_drawdown": float(max_dd[idx]),
                "total_trades": int(trades[idx]),
            }
        )

    return pd.DataFrame(rows).sort_values("score", ascending=False, na_position="last")