# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from freqtrade.strategy.parameters import  DecimalParameter, BooleanParameter
from pandas import DataFrame
import math
from functools import reduce


from freqtrade.strategy.interface import IStrategy

# --------------------------------
# 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 StrategyPierrick4120(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 = 2

    # valeur de bbwidth pour démarrer
    buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.095, space="buy")
    # Valeur de la deuxième condition bollinger avec condition sma200
    buy_bollinger_2 = DecimalParameter(0.0, 0.08, decimals=2, default=0.04, space="buy")
    buy_min = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy")
    buy_percent = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy")
    # volume à atteindre
    buy_volume = DecimalParameter(0, 50, decimals=1, default=18, space="buy")

    # buy_rsi = IntParameter(20, 40, default=30, space="buy")
    # buy_adx_enabled = BooleanParameter(default=True, space="buy")
    # buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
    # buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")

    # ROI table:
    minimal_roi = {
        # "0": 0.015
        "0": 0.5
    }

    # Stoploss:
    stoploss = -0.1
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.0175  # 0.015
    trailing_only_offset_is_reached = True

    # max_open_trades = 3

    # Optimal ticker interval for the strategy.
    timeframe = '5m'

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

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

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

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'bb_lowerband': {'color': 'white'},
            'bb_upperband': {'color': 'white'},
            'sma100': {'color': 'green'},
            'sma500': {'color': 'blue'},
            'sma10': {'color': 'yellow'},
            'min': {'color': 'green'},
            'max': {'color': 'yellow'},
            'sma20': {'color': 'cyan'}

        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "BB": {
                'bb_width': {'color': 'white'},
            },
            "ADX": {
                'adx': {'color': 'white'},
                'minus_dm': {'color': 'blue'},
                'plus_dm': {'color': 'red'}
            },
            "rolling": {
                'rolling': {'color': 'white'},
            }
        }
    }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MACD
        # macd = ta.MACD(dataframe)
        # dataframe['macd'] = macd['macd']
        # dataframe['macdsignal'] = macd['macdsignal']
        # dataframe['macdhist'] = macd['macdhist']

        # # Plus Directional Indicator / Movement
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)

        # Minus Directional Indicator / Movement
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)

        dataframe['min'] = ta.MIN(dataframe)
        dataframe['max'] = ta.MAX(dataframe)

        # # Aroon, Aroon Oscillator
        # aroon = ta.AROON(dataframe)
        # dataframe['aroonup'] = aroon['aroonup']
        # dataframe['aroondown'] = aroon['aroondown']
        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)

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

        # # EMA - Exponential Moving Average
        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        # # SMA - Simple Moving Average
        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95
        dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98
        dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500)
        # dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9
        # dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95
        # dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2
        dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
        dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200)
        dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200)

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_percent"] = (
                (dataframe["close"] - dataframe["bb_lowerband"]) /
                (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
                (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

        dataframe["rolling"] = (100 * (dataframe["close"].shift(3) - dataframe["bb_lowerband"].shift(3)) / dataframe["bb_lowerband"].shift(3)).rolling(20).mean()
        dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"]
        dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=20)

        # print(dataframe["rolling"].tolist())
        # dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
        #     dataframe['high'],
        #     dataframe['low'],
        #     dataframe['close'],
        #     window=20,
        #     window_atr=10,
        #     fillna=False,
        #     original_version=True
        # )
        #
        # dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
        #     dataframe['high'],
        #     dataframe['low'],
        #     dataframe['close'],
        #     window=10,
        #     offset=0,
        #     fillna=False
        # )

        # dataframe['bb_lower_reg'] = dataframe["bb_lowerband"] - dataframe["bb_lowerband"].shift(1)
        return dataframe

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

        # count = 0
        # for i in range(20):
        #     print("i=", i, " count=", count)
            # c = [(dataframe['close'].shift(i) <= dataframe['bb_lowerband'].shift(i))]
            # value = reduce(lambda x, y: x & y, c)

            # for k, v in value.iteritems():
            #     print("i=", i, " count=", count, " v=", v)
            #     if v:
            #         count = count + 1
        # for i in range(20):
        #     print("i=", i)
        #     a = 1 if bool(dataframe['close'].shift(i) <= dataframe['bb_lowerband'].shift(i)) else 0
        #     print(a)

        conditions_bb_1 = [
            (dataframe['close'].shift(2) < dataframe['bb_lowerband'].shift(2)),
            (dataframe['bb_width'].shift(2) >= 0.09), # & (dataframe['rolling'] < 2))
            (dataframe['volume'].shift(2) >= self.buy_volume.value * 1000),
            (dataframe['open'] < dataframe['close'])
            # (
                # (dataframe['bb_width'] >= 0.119) |
                # (dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
                # ((dataframe['bb_width'].shift(3) >= 0.0) & (dataframe['bb_rolling'].shift(3) < 1))

                # ((dataframe['bb_width'] >= 0.08) & (dataframe['rolling'] < 2))
                # ((dataframe['bb_width'] >= 0.03) & (count >= 6)) |
                # (
                #     (dataframe['bb_width'] >= self.buy_bollinger_2.value) &
                #     (dataframe['close'] / dataframe['min'] <= self.buy_min.value)
                # )
             # ), # self.buy_bollinger.value,
        #    dataframe['volume'] >= self.buy_volume.value * 1000,
        #    dataframe['percent'].shift(3) >= self.buy_percent.value
        ]
        condition_bb = reduce(lambda x, y: x & y, conditions_bb_1)
        conditions_bb_2 = [
            (dataframe['close'].shift(3) < dataframe['bb_lowerband'].shift(3)),
            (dataframe['bb_width'].shift(3) >= 0.09), # & (dataframe['rolling'] < 2))
            (dataframe['volume'].shift(3) >= self.buy_volume.value * 1000),
            (dataframe['open'] < dataframe['close'])
        ]
        condition_bb2 = reduce(lambda x, y: x & y, conditions_bb_2)

        dataframe.loc[
            (
                (condition_bb | condition_bb2) &
                (dataframe['min'] == dataframe['min'].shift(1)) &
                (dataframe['min'].shift(1) == dataframe['min'].shift(2)) &
                (dataframe['min'].shift(2) == dataframe['min'].shift(3))
                    # # courant est positif
                    # (dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
                    # # previous cut BB
                    # (dataframe['close'].shift(4) < dataframe['bb_lowerband'].shift(4))
                    # min
                    # (dataframe['min'].shift(3) * 1.001 >= dataframe['open'].shift(3))
            ), 'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['close'] < dataframe['open']) &
                (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
                (dataframe['close'].shift(2) < dataframe['open'].shift(2)) &
                (dataframe['close'] < dataframe['bb_lowerband']) &
                (((dataframe['bb_lowerband'].shift(2) - dataframe['bb_lowerband']) / dataframe['bb_lowerband']) >= 0.02)
                # (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) >= 0.025)
            ), 'sell'] = 1
        return dataframe
