import numpy as np
import pandas as pd
from abc import ABC, abstractmethod

def validate_history_and_horizon(history, horizon: int, model_name: str) -> None:
    if horizon < 1:
        raise ValueError(f'{model_name}: horizon must be >= 1, got {horizon}.')
    values = np.asarray(pd.Series(history).astype(float), dtype=float)
    if not np.all(np.isfinite(values)):
        raise ValueError(f'{model_name}: price history contains NaN/inf. This usually means a data glitch left a missing or malformed candle -- re-fetch the history and try again.')

class BaseForecastModel(ABC):
    name = 'base'

    @abstractmethod
    def predict(self, history: pd.Series, horizon: int=1, features: pd.DataFrame=None) -> list:
        raise NotImplementedError