ComboHold

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter
import math
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
from freqtrade.strategy.strategy_helper import merge_informative_pair

import Config

class ComboHold(IStrategy):
    """
    Combines several buy strategies, and just holds until ROI kicks in
    This version doesn't issue a sell signal, just holds until ROI or stoploss kicks in

    How to use it?
    > python3 ./freqtrade/main.py -s ComboHold
    """


    buy_params = Config.strategyParameters["ComboHold"]
    print("Exchange: ", Config.exchange_name)

    buy_bbbhold_enabled = CategoricalParameter([True, False], default=True, space="buy")
    buy_bigdrop_enabled = CategoricalParameter([True, False], default=False, space="buy")
    buy_btcjump_enabled = CategoricalParameter([True, False], default=False, space="buy")
    buy_btcndrop_enabled = CategoricalParameter([True, False], default=False, space="buy")
    buy_btcnseq_enabled = CategoricalParameter([True, False], default=False, space="buy")
    buy_emabounce_enabled = CategoricalParameter([True, False], default=True, space="buy")
    buy_macdcross_enabled = CategoricalParameter([True, False], default=False, space="buy")
    buy_fisherbb_enabled = CategoricalParameter([True, False], default=True, space="buy")
    buy_ndrop_enabled = CategoricalParameter([True, False], default=True, space="buy")
    buy_nseq_enabled = CategoricalParameter([True, False], default=False, space="buy")








    buy_bbbhold_bb_gain = Config.strategyParameters["BBBHold"]["buy_bb_gain"]
    buy_bbbhold_fisher = Config.strategyParameters["BBBHold"]["buy_fisher"]
    buy_bbbhold_fisher_enabled = Config.strategyParameters["BBBHold"]["buy_fisher_enabled"]
    buy_bbbhold_mfi = Config.strategyParameters["BBBHold"]["buy_mfi"]
    buy_bbbhold_mfi_enabled = Config.strategyParameters["BBBHold"]["buy_mfi_enabled"]

    buy_bigdrop_bb_enabled = Config.strategyParameters["BigDrop"]["buy_bb_enabled"]
    buy_bigdrop_drop = Config.strategyParameters["BigDrop"]["buy_drop"]
    buy_bigdrop_fisher = Config.strategyParameters["BigDrop"]["buy_fisher"]
    buy_bigdrop_fisher_enabled = Config.strategyParameters["BigDrop"]["buy_fisher_enabled"]
    buy_bigdrop_mfi = Config.strategyParameters["BigDrop"]["buy_mfi"]
    buy_bigdrop_mfi_enabled = Config.strategyParameters["BigDrop"]["buy_mfi_enabled"]
    buy_bigdrop_num_candles = Config.strategyParameters["BigDrop"]["buy_num_candles"]

    buy_btcjump_bb_gain = Config.strategyParameters["BTCJump"]["buy_bb_gain"]
    buy_btcjump_btc_jump = Config.strategyParameters["BTCJump"]["buy_btc_jump"]
    buy_btcjump_fisher = Config.strategyParameters["BTCJump"]["buy_fisher"]

    buy_btcndrop_bb_enabled = Config.strategyParameters["BTCNDrop"]["buy_bb_enabled"]
    buy_btcndrop_drop = Config.strategyParameters["BTCNDrop"]["buy_drop"]
    buy_btcndrop_fisher = Config.strategyParameters["BTCNDrop"]["buy_fisher"]
    buy_btcndrop_fisher_enabled = Config.strategyParameters["BTCNDrop"]["buy_fisher_enabled"]
    buy_btcndrop_mfi = Config.strategyParameters["BTCNDrop"]["buy_mfi"]
    buy_btcndrop_mfi_enabled = Config.strategyParameters["BTCNDrop"]["buy_mfi_enabled"]
    buy_btcndrop_num_candles = Config.strategyParameters["BTCNDrop"]["buy_num_candles"]

    buy_btcnseq_bb_enabled = Config.strategyParameters["BTCNSeq"]["buy_bb_enabled"]
    buy_btcnseq_bb_gain = Config.strategyParameters["BTCNSeq"]["buy_bb_gain"]
    buy_btcnseq_drop = Config.strategyParameters["BTCNSeq"]["buy_drop"]
    buy_btcnseq_fisher = Config.strategyParameters["BTCNSeq"]["buy_fisher"]
    buy_btcnseq_fisher_enabled = Config.strategyParameters["BTCNSeq"]["buy_fisher_enabled"]
    buy_btcnseq_num_candles = Config.strategyParameters["BTCNSeq"]["buy_num_candles"]

    buy_emabounce_long_period = Config.strategyParameters["EMABounce"]["buy_long_period"]
    buy_emabounce_short_period = Config.strategyParameters["EMABounce"]["buy_short_period"]
    buy_emabounce_diff = Config.strategyParameters["EMABounce"]["buy_diff"]

    buy_fisherbb_bb_gain = Config.strategyParameters["FisherBB"]["buy_bb_gain"]
    buy_fisherbb_fisher = Config.strategyParameters["FisherBB"]["buy_fisher"]

    buy_macdcross_adx = Config.strategyParameters["MACDCross"]["buy_adx"]
    buy_macdcross_adx_enabled = Config.strategyParameters["MACDCross"]["buy_adx_enabled"]
    buy_macdcross_bb_enabled = Config.strategyParameters["MACDCross"]["buy_bb_enabled"]
    buy_macdcross_bb_gain = Config.strategyParameters["MACDCross"]["buy_bb_gain"]
    buy_macdcross_dm_enabled = Config.strategyParameters["MACDCross"]["buy_dm_enabled"]
    buy_macdcross_fisher = Config.strategyParameters["MACDCross"]["buy_fisher"]
    buy_macdcross_fisher_enabled = Config.strategyParameters["MACDCross"]["buy_fisher_enabled"]
    buy_macdcross_mfi = Config.strategyParameters["MACDCross"]["buy_mfi"]
    buy_macdcross_mfi_enabled = Config.strategyParameters["MACDCross"]["buy_mfi_enabled"]
    buy_macdcross_neg_macd_enabled = Config.strategyParameters["MACDCross"]["buy_neg_macd_enabled"]
    buy_macdcross_period = Config.strategyParameters["MACDCross"]["buy_period"]
    buy_macdcross_sar_enabled = Config.strategyParameters["MACDCross"]["buy_sar_enabled"]

    buy_ndrop_bb_enabled = Config.strategyParameters["NDrop"]["buy_bb_enabled"]
    buy_ndrop_drop = Config.strategyParameters["NDrop"]["buy_drop"]
    buy_ndrop_fisher = Config.strategyParameters["NDrop"]["buy_fisher"]
    buy_ndrop_fisher_enabled = Config.strategyParameters["NDrop"]["buy_fisher_enabled"]
    buy_ndrop_mfi = Config.strategyParameters["NDrop"]["buy_mfi"]
    buy_ndrop_mfi_enabled = Config.strategyParameters["NDrop"]["buy_mfi_enabled"]
    buy_ndrop_num_candles = Config.strategyParameters["NDrop"]["buy_num_candles"]

    buy_nseq_bb_enabled = Config.strategyParameters["NSeq"]["buy_bb_enabled"]
    buy_nseq_drop = Config.strategyParameters["NSeq"]["buy_drop"]
    buy_nseq_fisher = Config.strategyParameters["NSeq"]["buy_fisher"]
    buy_nseq_fisher_enabled = Config.strategyParameters["NSeq"]["buy_fisher_enabled"]
    buy_nseq_mfi = Config.strategyParameters["NSeq"]["buy_mfi"]
    buy_nseq_mfi_enabled = Config.strategyParameters["NSeq"]["buy_mfi_enabled"]
    buy_nseq_num_candles = Config.strategyParameters["NSeq"]["buy_num_candles"]


    startup_candle_count = max(buy_emabounce_long_period, 20)

    minimal_roi = Config.minimal_roi
    trailing_stop = Config.trailing_stop
    trailing_stop_positive = Config.trailing_stop_positive
    trailing_stop_positive_offset = Config.trailing_stop_positive_offset
    trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached
    stoploss = Config.stoploss
    timeframe = Config.timeframe
    process_only_new_candles = Config.process_only_new_candles
    use_sell_signal = Config.use_sell_signal
    sell_profit_only = Config.sell_profit_only
    ignore_roi_if_buy_signal = Config.ignore_roi_if_buy_signal
    order_types = Config.order_types

    class HyperOpt:

        def roi_space() -> List[Dimension]:
            return [
                Integer(10, 240, name='roi_t1'),
                Integer(10, 120, name='roi_t2'),
                Integer(10, 80, name='roi_t3'),
                SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
                SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
                SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
            ]

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        """


        if not self.dp:

            return dataframe

        inf_tf = '5m'
        btc_dataframe = self.dp.get_pair_dataframe(pair=Config.informative_pair, timeframe=inf_tf)

        dataframe = merge_informative_pair(dataframe, btc_dataframe, self.timeframe, "5m", ffill=True)

        dataframe['btc_gain'] = (dataframe['close_5m'] - dataframe['open_5m']) / dataframe['open_5m']
        dataframe['btc_zgain'] = dataframe['btc_gain'] - self.buy_btcjump_btc_jump

        dataframe['adx'] = ta.ADX(dataframe)

        dataframe['dm_plus'] = ta.PLUS_DM(dataframe)
        dataframe['di_plus'] = ta.PLUS_DI(dataframe)

        dataframe['dm_minus'] = ta.MINUS_DM(dataframe)
        dataframe['di_minus'] = ta.MINUS_DI(dataframe)
        dataframe['dm_delta'] = dataframe['dm_plus'] - dataframe['dm_minus']
        dataframe['di_delta'] = dataframe['di_plus'] - dataframe['di_minus']

        dataframe['mfi'] = ta.MFI(dataframe)

        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']

        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

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

        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)


        bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"])

        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.buy_emabounce_long_period)
        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.buy_emabounce_short_period)
        dataframe['ema_angle'] = ta.LINEARREG_SLOPE(dataframe['ema_short'], timeperiod=3) / (2.0 * math.pi)
        dataframe['ema_diff'] = (((dataframe['ema'] - dataframe['close']) /
                                      dataframe['ema'])) \
                                    - self.buy_emabounce_diff

        dataframe['sar'] = ta.SAR(dataframe)

        return dataframe

    def NDrop_conditions(self, dataframe: DataFrame):
        conditions = []

        if self.buy_ndrop_mfi_enabled:
            conditions.append(dataframe['mfi'] <= self.buy_ndrop_mfi)

        if self.buy_ndrop_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] < self.buy_ndrop_fisher)

        if self.buy_ndrop_bb_enabled:
            conditions.append(dataframe['close'] <= dataframe['bb_lowerband'])


        if self.buy_ndrop_num_candles >= 1:
            for i in range(self.buy_ndrop_num_candles):
                conditions.append(dataframe['close'].shift(i) <= dataframe['open'].shift(i))

        conditions.append(
            (((dataframe['open'].shift(self.buy_ndrop_num_candles - 1) - dataframe['close']) /
              dataframe['open'].shift(self.buy_ndrop_num_candles - 1)) >= self.buy_ndrop_drop)
        )
        return conditions


    def NSeq_conditions(self, dataframe: DataFrame):
        conditions = []

        if self.buy_nseq_mfi_enabled:
            conditions.append(dataframe['mfi'] <= self.buy_nseq_mfi)

        if self.buy_nseq_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] < self.buy_nseq_fisher)

        if self.buy_nseq_bb_enabled:
            conditions.append(dataframe['close'] <= dataframe['bb_lowerband'])


        conditions.append(dataframe['close'] >= dataframe['open'])

        if self.buy_nseq_num_candles >= 1:
            for i in range(self.buy_nseq_num_candles):
                conditions.append(dataframe['close'].shift(i+1) <= dataframe['open'].shift(i+1))

        conditions.append(
            (((dataframe['open'].shift(self.buy_nseq_num_candles) - dataframe['close']) /
            dataframe['open'].shift(self.buy_nseq_num_candles)) >= self.buy_nseq_drop)
        )
        return conditions

    def EMABounce_conditions(self, dataframe: DataFrame):
        conditions = []



        conditions.append(qtpylib.crossed_above(dataframe['ema_angle'], 0))

        conditions.append(dataframe['ema_diff'] > 0.0)

        return conditions

    def MACDCross_conditions(self, dataframe: DataFrame):
        conditions = []


        if self.buy_macdcross_adx_enabled:
            conditions.append(dataframe['adx'] >= self.buy_macdcross_adx)

        if self.buy_macdcross_dm_enabled:
            conditions.append(dataframe['dm_delta'] > 0)

        if self.buy_macdcross_mfi_enabled:
            conditions.append(dataframe['mfi'] > self.buy_macdcross_mfi)

        if self.buy_macdcross_sar_enabled:
            conditions.append(dataframe['close'] < dataframe['sar'])

        if self.buy_macdcross_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] < self.buy_macdcross_fisher)

        if self.buy_macdcross_neg_macd_enabled:
            conditions.append(dataframe['macd'] < 0.0)

        if self.buy_macdcross_bb_enabled:
            conditions.append(dataframe['bb_gain'] >= self.buy_macdcross_bb_gain)

        conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))

        conditions.append(dataframe['volume'] > 0)

        return conditions


    def BigDrop_conditions(self, dataframe: DataFrame):
        conditions = []

        if self.buy_bigdrop_mfi_enabled:
            conditions.append(dataframe['mfi'] <= self.buy_bigdrop_mfi)

        if self.buy_bigdrop_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] < self.buy_bigdrop_fisher)

        if self.buy_bigdrop_bb_enabled:
            conditions.append(dataframe['close'] <= dataframe['bb_lowerband'])


        conditions.append(
            (((dataframe['open'].shift(self.buy_bigdrop_num_candles - 1) - dataframe['close']) /
              dataframe['open'].shift(self.buy_bigdrop_num_candles - 1)) >= self.buy_bigdrop_drop)
        )
        return conditions


    def FisherBB_conditions(self, dataframe: DataFrame):
        conditions = []


        conditions.append(dataframe['fisher_rsi'] <= self.buy_fisherbb_fisher)
        conditions.append(dataframe['bb_gain'] >= self.buy_fisherbb_bb_gain)



        return conditions


    def BBBHold_conditions(self, dataframe: DataFrame):
        conditions = []

        if self.buy_bbbhold_mfi_enabled:
            conditions.append(dataframe['mfi'] <= self.buy_bbbhold_mfi)

        if self.buy_bbbhold_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] < self.buy_bbbhold_fisher)

        conditions.append(dataframe['volume'] > 0)


        conditions.append(dataframe['bb_gain'] >= self.buy_bbbhold_bb_gain)

        conditions.append(dataframe['close'] > dataframe['open'])

        conditions.append(
            (dataframe['open'] < dataframe['bb_lowerband']) &
            (dataframe['close'] >= dataframe['bb_lowerband'])
        )

        return conditions


    def BTCNDrop_conditions(self, dataframe: DataFrame):
        conditions = []

        if self.buy_btcndrop_mfi_enabled:
            conditions.append(dataframe['mfi'] <= self.buy_btcndrop_mfi)

        if self.buy_btcndrop_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] <= self.buy_btcndrop_fisher)

        if self.buy_btcndrop_bb_enabled:
            conditions.append(dataframe['close'] <= dataframe['bb_lowerband'])


        if self.buy_btcndrop_num_candles >= 1:
            for i in range(self.buy_btcndrop_num_candles):
                conditions.append(dataframe['close_5m'].shift(i) <= dataframe['open_5m'].shift(i))

        conditions.append(
            (((dataframe['open_5m'].shift(self.buy_btcndrop_num_candles-1) - dataframe['close_5m']) /
            dataframe['open_5m'].shift(self.buy_btcndrop_num_candles-1)) >= self.buy_btcndrop_drop)
        )

        return conditions


    def BTCNSeq_conditions(self, dataframe: DataFrame):
        conditions = []


        if self.buy_btcnseq_fisher_enabled:
            conditions.append(dataframe['fisher_rsi'] <= self.buy_btcnseq_fisher)

        if self.buy_btcnseq_bb_enabled:
            conditions.append(dataframe['bb_gain'] >= self.buy_btcnseq_bb_gain)


        conditions.append(dataframe['close_5m'] > dataframe['open_5m'])
        if self.buy_btcnseq_num_candles >= 1:
            for i in range(self.buy_btcnseq_num_candles):
                conditions.append(dataframe['close_5m'].shift(i+1) <= dataframe['open_5m'].shift(i+1))

        conditions.append(
            (((dataframe['open_5m'].shift(self.buy_btcnseq_num_candles-1) - dataframe['close_5m']) /
            dataframe['open_5m'].shift(self.buy_btcnseq_num_candles-1)) >= self.buy_btcnseq_drop)
        )

        return conditions


    def BTCJump_conditions(self, dataframe: DataFrame):
        conditions = []

        conditions.append(dataframe['fisher_rsi'] <= self.buy_btcjump_fisher)
        conditions.append(dataframe['bb_gain'] >= self.buy_btcjump_bb_gain)


        conditions.append(qtpylib.crossed_above(dataframe['btc_zgain'], 0))

        return conditions

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


        conditions = []
        c = []
        if self.buy_ndrop_enabled.value:
            c = self.NDrop_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_nseq_enabled.value:
            c = self.NSeq_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_emabounce_enabled.value:
            c = self.EMABounce_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_macdcross_enabled.value:
            c = self.MACDCross_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_bigdrop_enabled.value:
            c = self.BigDrop_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_fisherbb_enabled.value:
            c = self.FisherBB_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_bbbhold_enabled.value:
            c = self.BBBHold_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_btcndrop_enabled.value:
            c = self.BTCNDrop_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_btcnseq_enabled.value:
            c = self.BTCNSeq_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        if self.buy_btcjump_enabled.value:
            c = self.BTCJump_conditions(dataframe)
            if c:
                conditions.append(reduce(lambda x, y: x & y, c))

        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:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column

        """

        dataframe.loc[(dataframe['close'] >= 0), 'sell'] = 0
        return dataframe