from __future__ import annotations

from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, cast, Generator, List

import pandas as pd

from pt_strategy.trading_pair import TradingPair

@dataclass
class Prediction:
    tstamp_: pd.Timestamp
    disequilibrium_: float
    scaled_disequilibrium_: float
    pair_: TradingPair

    def to_dict(self) -> Dict[str, Any]:
        return {
            "tstamp": self.tstamp_,
            "disequilibrium": self.disequilibrium_,
            "signed_scaled_disequilibrium": self.scaled_disequilibrium_,
            "scaled_disequilibrium": abs(self.scaled_disequilibrium_),
            "pair": self.pair_,
        }
    def to_pd_series(self) -> pd.Series:
        return pd.DataFrame([self.to_dict()]).iloc[0]
    
class PairsTradingModel(ABC):

    @abstractmethod
    def predict(self, pair: TradingPair) -> Prediction:
        ...
    
    @staticmethod
    def create(config: Dict[str, Any]) -> PairsTradingModel:
        import importlib
        
        model_class_name = config.get("model_class", None)
        assert model_class_name is not None
        module_name, class_name = model_class_name.rsplit(".", 1)
        module = importlib.import_module(module_name)
        model_object = getattr(module, class_name)()
        return cast(PairsTradingModel, model_object)