SmoothOperator

# --- Do not remove these libs ---
import numpy  # noqa
import talib.abstract as ta
from pandas import DataFrame

import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy.interface import IStrategy

# --------------------------------


# DO NOT USE, just playing with smooting and graphs!


class SmoothOperator(IStrategy):
    """

    author@: Gert Wohlgemuth

    idea:

    The concept is about combining several common indicators, with a heavily smoothing,
    while trying to detect
    a none completed peak shape.
    """

    # Minimal ROI designed for the strategy.
    # we only sell after 100%, unless our sell points are found before
    minimal_roi = {"0": 0.10}

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should be converted to a trailing stop loss
    stoploss = -0.05

    # Optimal timeframe for the strategy
    timeframe = "5m"

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ##################################################################################
        # required for entry and exit
        # CCI
        dataframe["cci"] = ta.CCI(dataframe, timeperiod=20)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe)
        dataframe["mfi"] = ta.MFI(dataframe)
        dataframe["mfi_smooth"] = ta.EMA(dataframe, timeperiod=11, price="mfi")
        dataframe["cci_smooth"] = ta.EMA(dataframe, timeperiod=11, price="cci")
        dataframe["rsi_smooth"] = ta.EMA(dataframe, timeperiod=11, price="rsi")

        ##################################################################################
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_middleband"] = bollinger["mid"]

        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        ##################################################################################
        # required for entry
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=1.6)
        dataframe["entry_bb_lowerband"] = bollinger["lower"]
        dataframe["entry_bb_upperband"] = bollinger["upper"]
        dataframe["entry_bb_middleband"] = bollinger["mid"]

        dataframe["bpercent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"])
            / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
            * 100
        )

        dataframe["bsharp"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / (
            dataframe["bb_middleband"]
        )

        # these seem to be kind useful to measure when bands widen
        # but than they are directly based on the moving average
        dataframe["bsharp_slow"] = ta.SMA(dataframe, price="bsharp", timeperiod=11)
        dataframe["bsharp_medium"] = ta.SMA(dataframe, price="bsharp", timeperiod=8)
        dataframe["bsharp_fast"] = ta.SMA(dataframe, price="bsharp", timeperiod=5)

        ##################################################################################
        # rsi and mfi are slightly weighted
        dataframe["mfi_rsi_cci_smooth"] = (
            dataframe["rsi_smooth"] * 1.125
            + dataframe["mfi_smooth"] * 1.125
            + dataframe["cci_smooth"]
        ) / 3

        dataframe["mfi_rsi_cci_smooth"] = ta.TEMA(
            dataframe, timeperiod=21, price="mfi_rsi_cci_smooth"
        )

        # playgound
        dataframe["candle_size"] = (
            (dataframe["close"] - dataframe["open"]) * (dataframe["close"] - dataframe["open"]) / 2
        )

        # helps with pattern recognition
        dataframe["average"] = (
            dataframe["close"] + dataframe["open"] + dataframe["high"] + dataframe["low"]
        ) / 4
        dataframe["sma_slow"] = ta.SMA(dataframe, timeperiod=200, price="close")
        dataframe["sma_medium"] = ta.SMA(dataframe, timeperiod=100, price="close")
        dataframe["sma_fast"] = ta.SMA(dataframe, timeperiod=50, price="close")

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # protection against pump and dump
                #     (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean()\
                #      .shift(1) * 20))
                #
                #     & (dataframe['macd'] < dataframe['macdsignal'])
                #     & (dataframe['macd'] > 0)
                # # spike below entry band for 3 consecutive ticks
                # & (dataframe['low'] < dataframe['entry_bb_lowerband'])
                # & (dataframe['low'].shift(1) < dataframe['bb_lowerband'].shift(1))
                # & (dataframe['low'].shift(2) < dataframe['bb_lowerband'].shift(2))
                # # pattern recognition
                # & (
                #         (dataframe['close'] > dataframe['open'])
                #         | (dataframe['CDLHAMMER'] == 100)
                #         | (dataframe['CDLINVERTEDHAMMER'] == 100)
                #         | (dataframe['CDLDRAGONFLYDOJI'] == 100)
                # )
                # bottom curve detection
                # & (dataframe['mfi_rsi_cci_smooth'] < 0)
                #
                # |
                (
                    # simple v bottom shape (lopsided to the left to increase reactivity)
                    # which has to be below a very slow average
                    # this pattern only catches a few, but normally very good buy points
                    (
                        (dataframe["average"].shift(5) > dataframe["average"].shift(4))
                        & (dataframe["average"].shift(4) > dataframe["average"].shift(3))
                        & (dataframe["average"].shift(3) > dataframe["average"].shift(2))
                        & (dataframe["average"].shift(2) > dataframe["average"].shift(1))
                        & (dataframe["average"].shift(1) < dataframe["average"].shift(0))
                        & (dataframe["low"].shift(1) < dataframe["bb_middleband"])
                        & (dataframe["cci"].shift(1) < -100)
                        & (dataframe["rsi"].shift(1) < 30)
                    )
                    |
                    # buy in very oversold conditions
                    (
                        (dataframe["low"] < dataframe["bb_middleband"])
                        & (dataframe["cci"] < -200)
                        & (dataframe["rsi"] < 30)
                        & (dataframe["mfi"] < 30)
                    )
                    |
                    # etc tends to trade like this
                    # over very long periods of slowly building up coins
                    # does not happen often, but once in a while
                    (
                        (dataframe["mfi"] < 10)
                        & (dataframe["cci"] < -150)
                        & (dataframe["rsi"] < dataframe["mfi"])
                    )
                )
                &
                # ensure we have an overall uptrend
                (dataframe["close"] > dataframe["close"].shift())
            ),
            "buy",
        ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # different strategy used for sell points, due to be able to duplicate it to 100%
        dataframe.loc[
            (
                (
                    #   This generates very nice sale points, and mostly sit's one stop behind
                    #   the top of the peak
                    (
                        (dataframe["mfi_rsi_cci_smooth"] > 100)
                        & (
                            dataframe["mfi_rsi_cci_smooth"].shift(1)
                            > dataframe["mfi_rsi_cci_smooth"]
                        )
                        & (
                            dataframe["mfi_rsi_cci_smooth"].shift(2)
                            < dataframe["mfi_rsi_cci_smooth"].shift(1)
                        )
                        & (
                            dataframe["mfi_rsi_cci_smooth"].shift(3)
                            < dataframe["mfi_rsi_cci_smooth"].shift(2)
                        )
                    )
                    |
                    #   This helps with very long, sideways trends, to get out of a market before
                    #   it dumps
                    (StrategyHelper.eight_green_candles(dataframe))
                    |
                    # in case of very overbought market, like some one pumping
                    # sell
                    ((dataframe["cci"] > 200) & (dataframe["rsi"] > 70))
                )
            ),
            "sell",
        ] = 1
        return dataframe


class StrategyHelper:
    """
    simple helper class to predefine a couple of patterns for our
    strategy
    """

    @staticmethod
    def seven_green_candles(dataframe):
        """
            evaluates if we are having 7 green candles in a row
        :param self:
        :param dataframe:
        :return:
        """
        return (
            (dataframe["open"] < dataframe["close"])
            & (dataframe["open"].shift(1) < dataframe["close"].shift(1))
            & (dataframe["open"].shift(2) < dataframe["close"].shift(2))
            & (dataframe["open"].shift(3) < dataframe["close"].shift(3))
            & (dataframe["open"].shift(4) < dataframe["close"].shift(4))
            & (dataframe["open"].shift(5) < dataframe["close"].shift(5))
            & (dataframe["open"].shift(6) < dataframe["close"].shift(6))
            & (dataframe["open"].shift(7) < dataframe["close"].shift(7))
        )

    @staticmethod
    def eight_green_candles(dataframe):
        """
            evaluates if we are having 8 green candles in a row
        :param self:
        :param dataframe:
        :return:
        """
        return (
            (dataframe["open"] < dataframe["close"])
            & (dataframe["open"].shift(1) < dataframe["close"].shift(1))
            & (dataframe["open"].shift(2) < dataframe["close"].shift(2))
            & (dataframe["open"].shift(3) < dataframe["close"].shift(3))
            & (dataframe["open"].shift(4) < dataframe["close"].shift(4))
            & (dataframe["open"].shift(5) < dataframe["close"].shift(5))
            & (dataframe["open"].shift(6) < dataframe["close"].shift(6))
            & (dataframe["open"].shift(7) < dataframe["close"].shift(7))
            & (dataframe["open"].shift(8) < dataframe["close"].shift(8))
        )

    @staticmethod
    def eight_red_candles(dataframe, shift=0):
        """
            evaluates if we are having 8 red candles in a row
        :param self:
        :param dataframe:
        :param shift: shift the pattern by n
        :return:
        """
        return (
            (dataframe["open"].shift(shift) > dataframe["close"].shift(shift))
            & (dataframe["open"].shift(1 + shift) > dataframe["close"].shift(1 + shift))
            & (dataframe["open"].shift(2 + shift) > dataframe["close"].shift(2 + shift))
            & (dataframe["open"].shift(3 + shift) > dataframe["close"].shift(3 + shift))
            & (dataframe["open"].shift(4 + shift) > dataframe["close"].shift(4 + shift))
            & (dataframe["open"].shift(5 + shift) > dataframe["close"].shift(5 + shift))
            & (dataframe["open"].shift(6 + shift) > dataframe["close"].shift(6 + shift))
            & (dataframe["open"].shift(7 + shift) > dataframe["close"].shift(7 + shift))
            & (dataframe["open"].shift(8 + shift) > dataframe["close"].shift(8 + shift))
        )

    @staticmethod
    def four_green_one_red_candle(dataframe):
        """
            evaluates if we are having a red candle and 4 previous green
        :param self:
        :param dataframe:
        :return:
        """
        return (
            (dataframe["open"] > dataframe["close"])
            & (dataframe["open"].shift(1) < dataframe["close"].shift(1))
            & (dataframe["open"].shift(2) < dataframe["close"].shift(2))
            & (dataframe["open"].shift(3) < dataframe["close"].shift(3))
            & (dataframe["open"].shift(4) < dataframe["close"].shift(4))
        )

    @staticmethod
    def four_red_one_green_candle(dataframe):
        """
            evaluates if we are having a green candle and 4 previous red
        :param self:
        :param dataframe:
        :return:
        """
        return (
            (dataframe["open"] < dataframe["close"])
            & (dataframe["open"].shift(1) > dataframe["close"].shift(1))
            & (dataframe["open"].shift(2) > dataframe["close"].shift(2))
            & (dataframe["open"].shift(3) > dataframe["close"].shift(3))
            & (dataframe["open"].shift(4) > dataframe["close"].shift(4))
        )