from typing import Dict, Tuple
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score


def compute_price_series_from_log_returns(start_price: float, log_returns: np.ndarray) -> np.ndarray:
    """Reconstruct price series from log returns starting at start_price."""
    prices = [start_price]
    for r in log_returns:
        prices.append(prices[-1] * float(np.exp(r)))
    return np.asarray(prices[1:])


def compute_metrics_from_prices(actual_prices: np.ndarray, predicted_prices: np.ndarray) -> Dict[str, float]:
    """Compute RMSE, MAPE, R2, and directional accuracy given price series."""
    rmse = float(np.sqrt(mean_squared_error(actual_prices, predicted_prices)))
    with np.errstate(divide='ignore', invalid='ignore'):
        mape_arr = np.abs((actual_prices - predicted_prices) / np.where(actual_prices == 0, np.nan, actual_prices))
    mape = float(np.nanmean(mape_arr) * 100.0)
    r2 = float(r2_score(actual_prices, predicted_prices))
    direction_actual = np.sign(np.diff(actual_prices))
    direction_pred = np.sign(np.diff(predicted_prices))
    dir_acc = float((direction_actual == direction_pred).mean()) if len(direction_actual) > 0 else 0.0
    return {"rmse": rmse, "mape": mape, "r2": r2, "directional_accuracy": dir_acc}