# source: https://raw.githubusercontent.com/ChaseRun/CryptoBot/f5b40fea758f5434d7b3c98a24d68ba7121ffb43/user_data/strategies/CombinedBinHAndClucV8.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade
from pandas import DataFrame
from datetime import datetime, timedelta
from functools import reduce


###########################################################################################################
##                github_ChaseRun_CryptoBot__CombinedBinHAndClucV8__20220731_152640 by iterativ                                                      ##
##                                                                                                       ##
##    Freqtrade https://github.com/freqtrade/freqtrade                                                   ##
##    The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies  ##
##    V8 by iterativ.                                                                                    ##
##                                                                                                       ##
###########################################################################################################
##               GENERAL RECOMMENDATIONS                                                                 ##
##                                                                                                       ##
##   For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake.        ##
##   A pairlist with 20 to 60 pairs. Volume pairlist works well.                                         ##
##   Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs.                            ##
##   Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc).                    ##
##   Ensure that you don't override any variables in you config.json. Especially                         ##
##   the timeframe (must be 5m) & sell_profit_only (must be true).                                       ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS                                                                               ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                                     ##
##                                                                                                       ##
###########################################################################################################

# SSL Channels
def SSLChannels(dataframe, length=7):
    df = dataframe.copy()
    df["ATR"] = ta.ATR(df, timeperiod=14)
    df["smaHigh"] = df["high"].rolling(length).mean() + df["ATR"]
    df["smaLow"] = df["low"].rolling(length).mean() - df["ATR"]
    df["hlv"] = np.where(
        df["close"] > df["smaHigh"], 1, np.where(df["close"] < df["smaLow"], -1, np.NAN)
    )
    df["hlv"] = df["hlv"].ffill()
    df["sslDown"] = np.where(df["hlv"] < 0, df["smaHigh"], df["smaLow"])
    df["sslUp"] = np.where(df["hlv"] < 0, df["smaLow"], df["smaHigh"])
    return df["sslDown"], df["sslUp"]


class github_ChaseRun_CryptoBot__CombinedBinHAndClucV8__20220731_152640(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {"0": 10}

    stoploss = -0.99  # effectively disabled.

    timeframe = "5m"
    inf_1h = "1h"  # informative tf

    # Sell signal
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = (
        0.001  # it doesn't meant anything, just to guarantee there is a minimal profit.
    )
    ignore_roi_if_entry_signal = True

    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03

    # Custom stoploss
    use_custom_stoploss = True

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

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    # Optional order type mapping.
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    # Buy Hyperopt params

    buy_dip_threshold_0 = DecimalParameter(
        0.001, 0.1, default=0.015, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_1 = DecimalParameter(
        0.08, 0.2, default=0.12, space="buy", decimals=2, optimize=False, load=True
    )
    buy_dip_threshold_2 = DecimalParameter(
        0.02, 0.4, default=0.28, space="buy", decimals=2, optimize=False, load=True
    )
    buy_dip_threshold_3 = DecimalParameter(
        0.25, 0.44, default=0.36, space="buy", decimals=2, optimize=False, load=True
    )

    buy_bb40_bbdelta_close = DecimalParameter(
        0.005, 0.04, default=0.031, space="buy", optimize=True, load=True
    )
    buy_bb40_closedelta_close = DecimalParameter(
        0.01, 0.03, default=0.021, space="buy", optimize=True, load=True
    )
    buy_bb40_tail_bbdelta = DecimalParameter(
        0.2, 0.4, default=0.264, space="buy", optimize=True, load=True
    )

    buy_bb20_close_bblowerband = DecimalParameter(
        0.8, 1.1, default=0.992, space="buy", optimize=True, load=True
    )
    buy_bb20_volume = IntParameter(
        18, 36, default=29, space="buy", optimize=True, load=True
    )

    buy_rsi_diff = DecimalParameter(
        34.0, 60.0, default=50.48, space="buy", decimals=2, optimize=True, load=True
    )

    buy_min_inc = DecimalParameter(
        0.005, 0.05, default=0.01, space="buy", decimals=2, optimize=True, load=True
    )
    buy_rsi_1h = DecimalParameter(
        40.0, 70.0, default=67.0, space="buy", decimals=2, optimize=True, load=True
    )
    buy_rsi = DecimalParameter(
        30.0, 40.0, default=38.5, space="buy", decimals=2, optimize=True, load=True
    )
    buy_mfi = DecimalParameter(
        36.0, 65.0, default=36.0, space="buy", decimals=2, optimize=True, load=True
    )

    buy_volume_1 = DecimalParameter(
        1.0, 10.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_ema_open_mult_1 = DecimalParameter(
        0.01, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True
    )

    # Sell Hyperopt params

    sell_custom_roi_profit_1 = DecimalParameter(
        0.01, 0.03, default=0.01, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_rsi_1 = DecimalParameter(
        40.0, 56.0, default=50, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_profit_2 = DecimalParameter(
        0.01, 0.20, default=0.04, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_rsi_2 = DecimalParameter(
        42.0, 56.0, default=50, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_profit_3 = DecimalParameter(
        0.15, 0.30, default=0.08, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_rsi_3 = DecimalParameter(
        44.0, 58.0, default=56, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_profit_4 = DecimalParameter(
        0.3, 0.7, default=0.14, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_roi_rsi_4 = DecimalParameter(
        44.0, 60.0, default=58, space="sell", decimals=2, optimize=False, load=True
    )

    sell_custom_roi_profit_5 = DecimalParameter(
        0.01, 0.1, default=0.04, space="sell", decimals=2, optimize=False, load=True
    )

    sell_trail_profit_min_1 = DecimalParameter(
        0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_1 = DecimalParameter(
        0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_1 = DecimalParameter(
        0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )

    sell_trail_profit_min_2 = DecimalParameter(
        0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_2 = DecimalParameter(
        0.08, 0.25, default=0.1, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_2 = DecimalParameter(
        0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=False, load=True
    )

    sell_custom_stoploss_1 = DecimalParameter(
        -0.15, -0.03, default=-0.05, space="sell", decimals=2, optimize=False, load=True
    )

    sell_rsi_main = DecimalParameter(
        72.0, 90.0, default=80, space="sell", decimals=2, optimize=True, load=True
    )

    def custom_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs
    ) -> float:
        # Manage losing trades and open room for better ones.
        if (current_profit < 0) & (
            current_time - timedelta(minutes=280) > trade.open_date_utc
        ):
            return 0.01
        elif current_profit < self.sell_custom_stoploss_1.value:
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            last_candle = dataframe.iloc[-1].squeeze()
            if last_candle is not None:
                if (last_candle["sma_200_dec"]) & (last_candle["sma_200_dec_1h"]):
                    return 0.01
        return 0.99

    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()

        if last_candle is not None:
            if (current_profit > self.sell_custom_roi_profit_4.value) & (
                last_candle["rsi"] < self.sell_custom_roi_rsi_4.value
            ):
                return "roi_target_4"
            elif (current_profit > self.sell_custom_roi_profit_3.value) & (
                last_candle["rsi"] < self.sell_custom_roi_rsi_3.value
            ):
                return "roi_target_3"
            elif (current_profit > self.sell_custom_roi_profit_2.value) & (
                last_candle["rsi"] < self.sell_custom_roi_rsi_2.value
            ):
                return "roi_target_2"
            elif (current_profit > self.sell_custom_roi_profit_1.value) & (
                last_candle["rsi"] < self.sell_custom_roi_rsi_1.value
            ):
                return "roi_target_1"
            elif (
                (current_profit > 0)
                & (current_profit < self.sell_custom_roi_profit_5.value)
                & (last_candle["sma_200_dec"])
            ):
                return "roi_target_5"

            elif (
                (current_profit > self.sell_trail_profit_min_1.value)
                & (current_profit < self.sell_trail_profit_max_1.value)
                & (
                    ((trade.max_rate - trade.open_rate) / 100)
                    > (current_profit + self.sell_trail_down_1.value)
                )
            ):
                return "trail_target_1"
            elif (
                (current_profit > self.sell_trail_profit_min_2.value)
                & (current_profit < self.sell_trail_profit_max_2.value)
                & (
                    ((trade.max_rate - trade.open_rate) / 100)
                    > (current_profit + self.sell_trail_down_2.value)
                )
            ):
                return "trail_target_2"

        return None

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.inf_1h) for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(
        self, dataframe: DataFrame, metadata: dict
    ) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=self.inf_1h
        )
        # EMA
        informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200)
        # SMA
        informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h["sma_200_dec"] = informative_1h["sma_200"] < informative_1h[
            "sma_200"
        ].shift(20)
        # RSI
        informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14)
        # SSL Channels
        ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
        informative_1h["ssl_down"] = ssl_down_1h
        informative_1h["ssl_up"] = ssl_up_1h

        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bb_40 = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2)
        dataframe["lower"] = bb_40["lower"]
        dataframe["mid"] = bb_40["mid"]
        dataframe["bbdelta"] = (bb_40["mid"] - dataframe["lower"]).abs()
        dataframe["closedelta"] = (
            dataframe["close"] - dataframe["close"].shift()
        ).abs()
        dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs()

        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["ema_slow"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean()

        # EMA
        dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)
        dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)

        # SMA
        dataframe["sma_5"] = ta.SMA(dataframe, timeperiod=5)
        dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200)

        dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20)

        # MFI
        dataframe["mfi"] = ta.MFI(dataframe, timeperiod=14)

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

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True
        )

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (
                (dataframe["close"] > dataframe["ema_200_1h"])
                & (dataframe["ema_50"] > dataframe["ema_200"])
                & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
                & (
                    (
                        (dataframe["open"].rolling(2).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_1.value
                )
                & (
                    (
                        (dataframe["open"].rolling(12).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_2.value
                )
                & dataframe["lower"].shift().gt(0)
                & dataframe["bbdelta"].gt(
                    dataframe["close"] * self.buy_bb40_bbdelta_close.value
                )
                & dataframe["closedelta"].gt(
                    dataframe["close"] * self.buy_bb40_closedelta_close.value
                )
                & dataframe["tail"].lt(
                    dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta.value
                )
                & dataframe["close"].lt(dataframe["lower"].shift())
                & dataframe["close"].le(dataframe["close"].shift())
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["close"] > dataframe["ema_200_1h"])
                & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
                & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
                & (
                    (
                        (dataframe["open"].rolling(2).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_1.value
                )
                & (
                    (
                        (dataframe["open"].rolling(12).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_2.value
                )
                & (dataframe["close"] < dataframe["ema_slow"])
                & (
                    dataframe["close"]
                    < self.buy_bb20_close_bblowerband.value * dataframe["bb_lowerband"]
                )
                & (
                    dataframe["volume"]
                    < (
                        dataframe["volume_mean_slow"].shift(1)
                        * self.buy_bb20_volume.value
                    )
                )
            )
        )

        conditions.append(
            (
                (dataframe["close"] < dataframe["sma_5"])
                & (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"])
                & (dataframe["ema_50"] > dataframe["ema_200"])
                & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
                & (
                    (
                        (dataframe["open"].rolling(2).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_1.value
                )
                & (
                    (
                        (dataframe["open"].rolling(12).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_2.value
                )
                & (
                    (
                        (dataframe["open"].rolling(144).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_3.value
                )
                & (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_diff.value)
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                (dataframe["sma_200"] > dataframe["sma_200"].shift(20))
                & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(16))
                & (
                    (
                        (dataframe["open"].rolling(2).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_1.value
                )
                & (
                    (
                        (dataframe["open"].rolling(12).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_2.value
                )
                & (
                    (
                        (dataframe["open"].rolling(144).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_3.value
                )
                & (
                    (
                        (dataframe["open"].rolling(24).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    > self.buy_min_inc.value
                )
                & (dataframe["rsi_1h"] > self.buy_rsi_1h.value)
                & (dataframe["rsi"] < self.buy_rsi.value)
                & (dataframe["mfi"] < self.buy_mfi.value)
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                (dataframe["close"] > dataframe["ema_100_1h"])
                & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
                & (
                    (
                        (dataframe["open"].rolling(2).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_1.value
                )
                & (
                    (
                        (dataframe["open"].rolling(12).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_2.value
                )
                & (
                    (
                        (dataframe["open"].rolling(144).max() - dataframe["close"])
                        / dataframe["close"]
                    )
                    < self.buy_dip_threshold_3.value
                )
                & (
                    dataframe["volume"].rolling(4).mean() * self.buy_volume_1.value
                    > dataframe["volume"]
                )
                & (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    (dataframe["ema_26"] - dataframe["ema_12"])
                    > (dataframe["open"] * self.buy_ema_open_mult_1.value)
                )
                & (
                    (dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
                    > (dataframe["open"] / 100)
                )
                & (dataframe["close"] < (dataframe["bb_lowerband"]))
                & (dataframe["volume"] > 0)
            )
        )

        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (
                (dataframe["close"] > dataframe["bb_upperband"])
                & (dataframe["close"].shift(1) > dataframe["bb_upperband"].shift(1))
                & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2))
                & (dataframe["close"].shift(3) > dataframe["bb_upperband"].shift(3))
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            ((dataframe["rsi"] > self.sell_rsi_main.value) & (dataframe["volume"] > 0))
        )

        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1

        return dataframe