# 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 pandas_ta as pta
from technical import qtpylib


class JodiEMA(IStrategy):
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3

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

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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "60":  0.01,
        "30":  0.03,
        "20":  0.04,
        "0":  0.05
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.10

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Run "populate_indicators()" only for new candle.
    #process_only_new_candles = True

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

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

    # Strategy parameters
    # buy_rsi = IntParameter(10, 40, default=30, space="buy")
    # sell_rsi = IntParameter(60, 90, default=70, space="sell")


    # Optional order type mapping.
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }
    
    # Optional order time in force.
    #order_time_in_force = {
    #    'entry': 'GTC',
    #    'exit': 'GTC'
    #}
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'tema': {},
                'sar': {'color': 'white'},
            },
            'subplots': {
                # Subplots - each dict defines one additional plot
                "MACD": {
                    'macd': {'color': 'blue'},
                    'macdsignal': {'color': 'orange'},
                },
                "RSI": {
                    'rsi': {'color': 'red'},
                }
            }
        }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        EMA_LENGTH = 200
        EMA_SHORT_TERM = 12
        EMA_LONG_TERM = 26

        dataframe['ema'] = ta.EMA(dataframe['close'], timeperiod=self.EMA_LENGTH)
        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(dataframe, timeperiod=self.EMA_SHORT_TERM)
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(dataframe, timeperiod=self.EMA_LONG_TERM)
        
        dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_SHORT_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_SHORT_TERM)
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
            (dataframe['close'] == dataframe['min']) ,
            'enter_long'
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
            (dataframe['close'] >= dataframe['max']) ,
            'exit_long'
        ] = 1

        return dataframe