"""Walk-forward check: how good were next-day forecasts over recent sessions?

The model is fitted once on data before the test window. Then, one day at a
time, it forecasts the next close and only afterwards sees the real one. The
parameters stay frozen, so no test data leaks into the fit.

The yardstick is the naive forecast "tomorrow closes where today closed".
Daily prices move little, so a low MAPE on its own says almost nothing. The
question is whether the model beats that.
"""

from __future__ import annotations

from dataclasses import dataclass

import numpy as np
import pandas as pd

from .model import FIT_WINDOW, fit

TEST_DAYS = 20


@dataclass(frozen=True)
class Backtest:
    dates: pd.DatetimeIndex
    actual: np.ndarray
    model: np.ndarray
    naive: np.ndarray
    model_mape: float
    naive_mape: float
    model_mae: float
    naive_mae: float
    direction_hit: float  # share of days where the model got up/down right
    direction_days: int


def _mape(actual: np.ndarray, pred: np.ndarray) -> float:
    return float(np.mean(np.abs((actual - pred) / actual)))


def run(closes: pd.Series, test_days: int = TEST_DAYS) -> Backtest:
    series = closes.iloc[-(FIT_WINDOW + test_days):]
    values = series.to_numpy(dtype=float)
    if len(values) < test_days + 60:
        raise ValueError("not enough history for a backtest")

    train, test = values[:-test_days], values[-test_days:]
    result = fit(train)
    preds = np.empty(test_days)
    for i, actual in enumerate(test):
        preds[i] = float(np.asarray(result.forecast(steps=1))[0])
        result = result.append([actual])  # reveal the real close, keep params

    naive = np.concatenate([[train[-1]], test[:-1]])
    moved = np.sign(test - naive)
    called = np.sign(preds - naive)
    mask = moved != 0  # flat days have no direction to call
    hit = float(np.mean(moved[mask] == called[mask])) if mask.any() else float("nan")

    return Backtest(
        dates=series.index[-test_days:],
        actual=test,
        model=preds,
        naive=naive,
        model_mape=_mape(test, preds),
        naive_mape=_mape(test, naive),
        model_mae=float(np.mean(np.abs(test - preds))),
        naive_mae=float(np.mean(np.abs(test - naive))),
        direction_hit=hit,
        direction_days=int(mask.sum()),
    )