# By Remiotore (Jorge F. F.)
# Espero poder darte una buena vida algún día...

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple
from freqtrade.strategy import (
    IStrategy,
    #Trade,
    #Order,
    #PairLocks,
    #informative,
    #BooleanParameter,
    #CategoricalParameter,
    #DecimalParameter,
    #IntParameter,
    #RealParameter,
    #timeframe_to_minutes,
    #timeframe_to_next_date,
    #timeframe_to_prev_date,
    merge_informative_pair,
    #stoploss_from_absolute,
    #stoploss_from_open,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib


class ZaratustraV4(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'
    can_short = True
    use_exit_signal = False
    exit_profit_only = True
    exit_profit_offset = 0.05
    inf_times = ["5m", "15m",]

    # ROI table:
    minimal_roi = {
        "0": 1,
        "7200": 0
    }

    # Stoploss:
    stoploss = -0.296

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.013
    trailing_stop_positive_offset = 0.071
    trailing_only_offset_is_reached = True
    
    def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float:
        return 10.0
    
    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = []
        for inf_time in self.inf_times:
            for pair in pairs:
                informative_pairs.append((pair, inf_time))
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.dp:
            return dataframe

        for inf_time in self.inf_times:
            informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_time)
            
            # Directional Indicator
            informative['pdi'] = ta.PLUS_DI(informative)
            informative['mdi'] = ta.MINUS_DI(informative)

            # Bollinger Bands
            bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
            informative['bbu'] = bollinger['upper']
            informative['bbm'] = bollinger['mid']
            informative['bbl'] = bollinger['lower']

            dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_time, ffill=True)

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['signal'] = macd['macdsignal']

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

        dataframe.loc[
            (
                # Directional Indicator
                (dataframe['pdi_15m'] > 25) &
                (dataframe['pdi_5m']  > 25) &
                # Bollinger Bands
                (dataframe['close_15m'] > dataframe['bbm_15m']) &
                (dataframe['close_5m']  > dataframe['bbm_5m']) &
                # MACD
                (dataframe['macd']   > dataframe['signal']) & 
                (dataframe['signal'] > 0) & 
                (dataframe['macd']   > 0)
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Bullish trend')

        dataframe.loc[
            (
                # Directional Indicator
                (dataframe['mdi_15m'] > 25) &
                (dataframe['mdi_5m']  > 25) &
                # Bollinger Bands
                (dataframe['close_15m'] < dataframe['bbm_15m']) &
                (dataframe['close_5m']  < dataframe['bbm_5m']) &
                # MACD
                (dataframe['macd']   < dataframe['signal']) & 
                (dataframe['signal'] < 0) & 
                (dataframe['macd']   < 0)
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Bearish trend')
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe