# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class WorkingStrategy(IStrategy):
    """
    Simple working EMA crossover strategy for testing
    """

    # Strategy interface version
    INTERFACE_VERSION = 3

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }

    # Optimal stoploss
    stoploss = -0.10

    # Optimal timeframe for the strategy
    timeframe = '1h'

    # These values can be overridden in the config
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds EMA indicators
        """
        # Calculate EMAs
        dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=12)
        dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=26)
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        EMA crossover entry signal
        """
        # Buy when fast EMA crosses above slow EMA
        dataframe.loc[
            (
                (dataframe['ema_fast'] > dataframe['ema_slow']) &
                (dataframe['ema_fast'].shift(1) <= dataframe['ema_slow'].shift(1)) &
                (dataframe['volume'] > 0)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        EMA crossover exit signal
        """
        # Sell when fast EMA crosses below slow EMA
        dataframe.loc[
            (
                (dataframe['ema_fast'] < dataframe['ema_slow']) &
                (dataframe['ema_fast'].shift(1) >= dataframe['ema_slow'].shift(1)) &
                (dataframe['volume'] > 0)
            ),
            'exit_long'] = 1

        return dataframe