from abc import ABC, abstractmethod
import pandas as pd
import json
import os
import logging
from datetime import datetime, timezone
import sqlite3

from logging_utils import setup_logging

class BaseStrategy(ABC):
    """
    An abstract base class that defines the blueprint for all trading strategies.
    It provides common functionality like loading data, saving status, and state management.
    """
    
    def __init__(self, strategy_name: str, params: dict):
        # Note: log_level is not needed here as logging is set up by the process
        self.strategy_name = strategy_name
        self.params = params
        self.coin = params.get("coin", "N/A")
        self.timeframe = params.get("timeframe", "N/A")
        self.db_path = os.path.join("_data", "market_data.db")
        self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
        
        self.current_signal = "INIT"
        self.last_signal_change_utc = None
        self.signal_price = None

        logging.info(f"Initializing with parameters: {self.params}")

    def load_data(self) -> pd.DataFrame:
        """Loads historical data for the configured coin and timeframe."""
        table_name = f"{self.coin}_{self.timeframe}"
        
        periods = [v for k, v in self.params.items() if 'period' in k or '_ma' in k or 'slow' in k or 'fast' in k]
        limit = max(periods) + 50 if periods else 500

        try:
            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
                query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
                df = pd.read_sql(query, conn, parse_dates=['datetime_utc'])
                if df.empty: return pd.DataFrame()
                df.set_index('datetime_utc', inplace=True)
                df.sort_index(inplace=True)
                return df
        except Exception as e:
            logging.error(f"Failed to load data from table '{table_name}': {e}")
            return pd.DataFrame()

    @abstractmethod
    def calculate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
        """The core logic of the strategy. Must be implemented by child classes."""
        pass
        
    def calculate_signals_and_state(self, df: pd.DataFrame):
        """
        A wrapper that calls the strategy's signal calculation and then
        determines the last signal change from the historical data.
        """
        df_with_signals = self.calculate_signals(df)
        df_with_signals.dropna(inplace=True)
        if df_with_signals.empty: return

        df_with_signals['position_change'] = df_with_signals['signal'].diff()
        
        last_signal = df_with_signals['signal'].iloc[-1]
        if last_signal == 1: self.current_signal = "BUY"
        elif last_signal == -1: self.current_signal = "SELL"
        else: self.current_signal = "HOLD"
        
        last_change_series = df_with_signals[df_with_signals['position_change'] != 0]
        if not last_change_series.empty:
            last_change_row = last_change_series.iloc[-1]
            self.last_signal_change_utc = last_change_row.name.tz_localize('UTC').isoformat()
            self.signal_price = last_change_row['close']

    def _save_status(self):
        """Saves the current strategy state to its JSON file."""
        status = {
            "strategy_name": self.strategy_name,
            "current_signal": self.current_signal,
            "last_signal_change_utc": self.last_signal_change_utc,
            "signal_price": self.signal_price,
            "last_checked_utc": datetime.now(timezone.utc).isoformat()
        }
        try:
            with open(self.status_file_path, 'w', encoding='utf-8') as f:
                json.dump(status, f, indent=4)
        except IOError as e:
            logging.error(f"Failed to write status file for {self.strategy_name}: {e}")