"""RSI mean-reversion — predefined Python strategy."""

from __future__ import annotations

import itertools

import pandas as pd
import vectorbt as vbt

STRATEGY_KEY = "rsi_reversion"
DISPLAY_NAME = "RSI Mean Reversion"
DESCRIPTION = "Buy when RSI is oversold; sell when RSI is overbought."

PARAM_GRID = {
    "rsi_period": list(range(7, 22, 2)),
    "oversold": list(range(20, 36, 5)),
    "overbought": list(range(65, 81, 5)),
}

DEFAULT_PARAMS = {
    "rsi_period": 14,
    "oversold": 30,
    "overbought": 70,
}


def generate_signals(
    close: pd.Series,
    high: pd.Series,
    low: pd.Series,
    volume: pd.Series,
    rsi_period: int = 14,
    oversold: float = 30,
    overbought: float = 70,
    **_kwargs,
) -> tuple[pd.Series, pd.Series]:
    if oversold >= overbought:
        raise ValueError("oversold must be less than overbought")

    rsi = vbt.RSI.run(close, window=rsi_period).rsi
    entries = (rsi < oversold).fillna(False)
    exits = (rsi > overbought).fillna(False)
    return entries, exits


def optimize_grid(
    close: pd.Series,
    param_grid: dict | None = None,
    init_cash: float = 10_000.0,
    fees: float = 0.001,
    metric: str = "sharpe_ratio",
) -> pd.DataFrame:
    """Exhaustive grid over RSI parameter space."""
    from metrics import run_from_signals

    grid = param_grid or PARAM_GRID
    keys = list(grid.keys())
    rows = []

    for values in itertools.product(*(grid[k] for k in keys)):
        params = dict(zip(keys, values))
        if params["oversold"] >= params["overbought"]:
            continue
        entries, exits = generate_signals(close, close, close, close, **params)
        result = run_from_signals(
            close=close,
            entries=entries,
            exits=exits,
            init_cash=init_cash,
            fees=fees,
            params=params,
            metric=metric,
        )
        rows.append(result)

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