# 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  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

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

from freqtrade.strategy import merge_informative_pair

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

# This class is a sample. Feel free to customize it.
class STRAT001B(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

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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        #"60": 0.01,
        #"30": 0.01,
        "0": 0.02,
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.25/16

    # 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

    # Optimal timeframe for the strategy.
    timeframe = '15m'

    # 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 = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 26

    # 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'
    }

    def informative_pairs(self):
        return []

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

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        # Définition de l'indicateur Bollinger
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1.5)
        # Ajout de la bande supérieure
        dataframe['bb_lowerband'] = bollinger['lower'] 
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        return dataframe

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

        dataframe.loc[
            (
                # Entrée long Lorsque le RSI est inférieur à 30
                (dataframe['rsi'] < 30) &
                # Lorsque la clôture du cours est sous la bande inférieure
                (dataframe['close'] < dataframe['bb_lowerband'])
            ),
            'enter_long'] = 1

        dataframe.loc[
            (
                # Entrée short lorsque le RSI est supérieur à 70
                (dataframe['rsi'] > 70) &
                # Lorsque la clôture du cours est au-dessus de la bande supérieure
                (dataframe['close'] > dataframe['bb_upperband'])
            ),
            'enter_short'] = 1

        #dataframe.loc[
            #( -
            #    (qtpylib.crossed_above(dataframe['ICH_KS'], dataframe['ICH_TS']))
            #),
            #'enter_short'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # Sortie du long lorsque la bb du milieu est atteinte
                (dataframe['close'] >= dataframe['bb_middleband'] )
            ),
            'exit_long'] = 1

        dataframe.loc[
            (
                # Sortie du short lorsque la bb du milieu est atteinte
                (dataframe['close'] <= dataframe['bb_middleband'])
            ),
            'exit_short'] = 1

        #dataframe.loc[
        #    (
                # Signal: RSI crosses above 70
        #        (qtpylib.crossed_above(dataframe['ICH_TS'], dataframe['ICH_KS']))
        #    ),

        #    'exit_short'] = 1


        return dataframe



# freqtrade backtesting -c config-gateio.json --strategy SampleStrategy --timerange=20220101-20221007 --timeframe="15m"