# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from datetime import datetime

import numpy as np  # noqa
import pandas as pd  # noqa
from freqtrade.strategy.parameters import  DecimalParameter, BooleanParameter, IntParameter
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 StrategyPierrick41213(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(0.00, 0.18, decimals=2, default=0.065, 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_step = IntParameter(1, 8, default=3, space="buy")
    buy_rolling = IntParameter(-20, 0, default=-6, 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 = -1
    trailing_stop = False
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.0275  # 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': 'red'},
            'bb_upperband': {'color': 'green'},
            'sma100': {'color': 'blue'},
            'sma500': {'color': 'pink'},
            'sma10': {'color': 'yellow'},
            'min': {'color': 'white'},
            'max': {'color': 'white'},
            'sma20': {'color': 'cyan'}

        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "BB": {
                'bb_width': {'color': 'white'},
            },
            "Rsi": {
                'rsi': {'color': 'pink'},
            },
            # "ADX": {
            #     'adx': {'color': 'white'},
            #     'minus_dm': {'color': 'blue'},
            #     'plus_dm': {'color': 'red'}
            # },
            "rolling": {
                'bb_rolling': {'color': '#87e470'},
                "bb_rolling_min": {'color': '#ac3e2a'}
            }
        }
    }

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        # print("proposed_stake=", proposed_stake, " max_stake=", max_stake)
        # if current_candle['bb_width'] > 0.05:
        #     print("use more stake", pair, " ", proposed_stake * 2)
        #     return min(max_stake, proposed_stake * 2)
        #
        # if current_candle['bb_width'] > 0.035:
        #     print("use more stake", pair, " ", proposed_stake * 1.5)
        #     return min(max_stake, proposed_stake * 1.5)

        # if current_candle['bb_width'] < 0.020:
        #     print("use less stake", pair, " ", proposed_stake / 2)
        #     return min(max_stake, proposed_stake / 2)
            # if self.config['stake_amount'] == 'unlimited':
            #     # Use entire available wallet during favorable conditions when in compounding mode.
            #     return max_stake
            # else:
            #     # Compound profits during favorable conditions instead of using a static stake.
            #     return self.wallets.get_total_stake_amount() / self.config['max_open_trades']


        # Use default stake amount.
        return proposed_stake

    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        previous_last_candle = dataframe.iloc[-2].squeeze()
        previous_5_candle = dataframe.iloc[-5].squeeze()

        # description trade
        # Trade(id=0, pair=CAKE/USDT, amount=4.19815281, open_rate=11.91000000, open_since=2021-12-22 17:55:00)
        # print(last_candle)
        if (current_profit > 0) \
                & ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \
                & (previous_5_candle['sma20'] > last_candle['sma20']):
                # & (last_candle['bb_upperband'] > last_candle['max']) \
            print("over_bb_band_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'over_bb_band_sma20_desc'

        if (current_profit > 0) \
                & (last_candle['rsi'] > 80):
            print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'over_rsi'

        # if (current_profit > 0) \
        #         & (last_candle['close'] < last_candle['open']) \
        #         & (previous_last_candle['bb_upperband'] < previous_last_candle['close']) \
        #         & ((current_time - trade.open_date_utc).seconds >= 3600) \
        #         & ((previous_last_candle['sma10'] - last_candle['sma10']) / last_candle['sma10'] < 0.1):
        #         # & (previous_last_candle['sma100'] > last_candle['sma100']) \
        #     print("close_over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
        #     return 'close_over_bb_band_sma10_desc'

        if 0.05 < current_profit < 0.1:
            if (
                    (previous_last_candle['sma10'] > last_candle['sma10']) &
                    (current_time - trade.open_date_utc).seconds >= 3600 * 3
                ) | (
                    (current_time - trade.open_date_utc).seconds >= 3600 * 6
            ):
                print("profit_3h_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
                return 'profit_3h_sma10_desc'

        if (0 < current_profit < 0.1) \
                & (previous_last_candle['sma20'] > last_candle['sma20']) \
                & ((current_time - trade.open_date_utc).seconds >= 3600 * 5):
            print("profit_5h_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'profit_5h_sma20_desc'

        # if (current_profit < -0.025) \
        #         & (previous_last_candle['sma20'] > last_candle['sma20']) \
        #         & ((current_time - trade.open_date_utc).seconds >= 3600 * 3):
        #     print("stop_loss_3h", pair, trade, " profit=", current_profit, " rate=", current_rate)
        #     return 'stop_loss_3h'

    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['sma20'] = ta.SMA(dataframe, timeperiod=20)
        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"] - dataframe["bb_lowerband"]) / dataframe["bb_lowerband"]).rolling(
            3).mean()
        dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"]
        dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=10)
        dataframe['bb_buy'] = (dataframe['min'] + (dataframe['max'] - dataframe['min']) / 3)
        # 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:
        step = self.buy_step.value
        bb_rolling_max = self.buy_rolling.value
        condition_bb_rolling = [
            # dataframe['bb_width'] >= self.buy_bollinger.value,
            # (100 * (dataframe['sma100'].shift(12) - dataframe['sma100']) / dataframe['sma100']) < 0.1,
            (dataframe['close'] < dataframe['sma10']),
            (dataframe['bb_rolling_min'].shift(step) <= bb_rolling_max),
            (dataframe['bb_rolling_min'].shift(step) == dataframe['bb_rolling'].shift(step)),
            # (100 * ((dataframe['sma100'].shift(12) - dataframe['sma100']) / dataframe['sma100']) < 0.25),
            (dataframe['close'].shift(step) < dataframe['min'].shift(step) + (
                        dataframe['max'].shift(step) - dataframe['min'].shift(step)) / 3),
            (dataframe['min'].shift(step) == dataframe['min']),
            (dataframe['rsi'] <= 35)

        ]
        condition_bb_rolling2 = reduce(lambda x, y: x & y, condition_bb_rolling)

        conditions = [
            dataframe['bb_width'] >= 0.065, #self.buy_bollinger.value,
            (dataframe['close'] < dataframe['bb_lowerband']),
            (dataframe['rsi'] <= 35)
        ]

        dataframe.loc[
            (
                (
                        # condition_bb_rolling2
                        (reduce(lambda x, y: x & y, conditions))
                ) & (
                    dataframe['volume'] > 0
                )
            ), '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
