BTCEMABounce

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair

from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
import math

# --------------------------------
# Add your lib to import here


class BTCEMABounce(IStrategy):
    """
    Applies the EMABounce strategy, but using BTC/USD data to find buy points
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """

    #buy_long_period = CategoricalParameter([20, 30, 30, 50, 60, 70, 80, 90, 100], default=50, space="buy")
    buy_long_period = IntParameter(20, 100, default=50, space="buy")
    buy_short_period = IntParameter(5, 15, default=10, space="buy")
    buy_diff = DecimalParameter(0.01, 0.10, decimals=3, default=0.065, space="buy")
    #buy_diff = DecimalParameter(0.01, 0.10, decimals=3, default=0.02, space="buy") # for testing
    buy_macd_enabled = CategoricalParameter([True, False], default=False, space="buy")

    sell_diff = DecimalParameter(0.01, 0.10, decimals=3, default=0.057, space="sell")
    sell_hold = CategoricalParameter([True, False], default=False, space="sell")

    # ROI table:
    minimal_roi = {
        "0": 0.161,
        "15": 0.088,
        "54": 0.028,
        "82": 0
    }

    # Stoploss:
    stoploss = -0.144

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.199
    trailing_stop_positive_offset = 0.201
    trailing_only_offset_is_reached = True

    # Optimal timeframe 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 = True
    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': {
            'ema': {},
            'sma': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "MACD": {
                'macdhist': {'color': 'blue'}
            },
            "RSI": {
                'rsi': {'color': 'red'},
            },
        }
    }

    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/ETH", "15m"),
                            ]
        """
        return [("BTC/USD", "5m")]

    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.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # NOTE: we are applying this to the BTC/USD dataframe, not the normal dataframe (or in addition to anyway)
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return dataframe

        # get BTC dataframe
        inf_tf = '5m'
        btc_dataframe = self.dp.get_pair_dataframe(pair="BTC/USD", timeframe=inf_tf)

        # populate BTC dataframe (see main logic for comments)

        btc_macd = ta.MACD(btc_dataframe)
        btc_dataframe['macd'] = btc_macd['macd']
        btc_dataframe['macdsignal'] = btc_macd['macdsignal']
        btc_dataframe['macdhist'] = btc_macd['macdhist']

        btc_dataframe['ema'] = ta.EMA(btc_dataframe, timeperiod=self.buy_long_period.value)
        btc_dataframe['ema_short'] = ta.EMA(btc_dataframe, timeperiod=self.buy_short_period.value)
        btc_dataframe['ema_angle'] = ta.LINEARREG_SLOPE(btc_dataframe['ema_short'], timeperiod=3) / (2.0 * math.pi)
        btc_dataframe['ema_diff'] = (((btc_dataframe['ema'] - btc_dataframe['close']) /
                                      btc_dataframe['ema'])) \
                                    - self.buy_diff.value

        # merge into main dataframe. This will create columns with a "_5m" suffix for the BTC data
        dataframe = merge_informative_pair(dataframe, btc_dataframe, self.timeframe, "5m", ffill=True)


        # Momentum Indicators
        # ------------------------------------

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

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

        # Minus Directional Indicator / Movement
        # 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']

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

        # # Awesome Oscillator
        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)

        # # Keltner Channel
        # keltner = qtpylib.keltner_channel(dataframe)
        # dataframe["kc_upperband"] = keltner["upper"]
        # dataframe["kc_lowerband"] = keltner["lower"]
        # dataframe["kc_middleband"] = keltner["mid"]
        # dataframe["kc_percent"] = (
        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
        # )
        # dataframe["kc_width"] = (
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
        # )

        # # Ultimate Oscillator
        # dataframe['uo'] = ta.ULTOSC(dataframe)

        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
        # dataframe['cci'] = ta.CCI(dataframe)

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


        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        # rsi = 0.1 * (dataframe['rsi'] - 50)
        # dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)

        # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)

        # # Stochastic Slow
        # stoch = ta.STOCH(dataframe)
        # dataframe['slowd'] = stoch['slowd']
        # dataframe['slowk'] = stoch['slowk']

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

        # # Stochastic RSI
        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
        # stoch_rsi = ta.STOCHRSI(dataframe)
        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
        # dataframe['fastk_rsi'] = stoch_rsi['fastk']

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

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

        # # ROC
        # dataframe['roc'] = ta.ROC(dataframe)

        # Overlap Studies
        # ------------------------------------

        # 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"]
        )

        # Bollinger Bands - Weighted (EMA based instead of SMA)
        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
        #     qtpylib.typical_price(dataframe), window=20, stds=2
        # )
        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
        # dataframe["wbb_percent"] = (
        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
        # )
        # dataframe["wbb_width"] = (
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
        # )

        # # 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)
        #dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)

        # dataframe['ema7'] = ta.EMA(dataframe, timeperiod=7)
        # dataframe['ema25'] = ta.EMA(dataframe, timeperiod=25)

        dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.buy_long_period.value)

        # use a shorter frame EMA to find turnaround point
        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.buy_short_period.value)
        dataframe['ema_angle'] = ta.LINEARREG_SLOPE(dataframe['ema_short'], timeperiod=3) / (2.0 * math.pi)

        # zero-based %age diff relative to goal (easier to visualise)
        dataframe['ema_diff'] = (((dataframe['ema'] - dataframe['close']) / dataframe['ema'])) - self.buy_diff.value

        # # SMA - Simple Moving Average
        dataframe['sma'] = ta.SMA(dataframe, timeperiod=self.buy_long_period.value)
        # 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)

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

        # TEMA - Triple Exponential Moving Average
        # dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)

        # Cycle Indicator
        # ------------------------------------
        # # Hilbert Transform Indicator - SineWave
        # hilbert = ta.HT_SINE(dataframe)
        # dataframe['htsine'] = hilbert['sine']
        # dataframe['htleadsine'] = hilbert['leadsine']

        # Pattern Recognition - Bullish candlestick patterns
        # ------------------------------------
        # # Hammer: values [0, 100]
        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
        # # Inverted Hammer: values [0, 100]
        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
        # # Dragonfly Doji: values [0, 100]
        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
        # # Piercing Line: values [0, 100]
        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
        # # Morningstar: values [0, 100]
        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
        # # Three White Soldiers: values [0, 100]
        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]

        # Pattern Recognition - Bearish candlestick patterns
        # ------------------------------------
        # # Hanging Man: values [0, 100]
        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
        # # Shooting Star: values [0, 100]
        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
        # # Gravestone Doji: values [0, 100]
        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
        # # Dark Cloud Cover: values [0, 100]
        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
        # # Evening Doji Star: values [0, 100]
        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
        # # Evening Star: values [0, 100]
        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)

        # Pattern Recognition - Bullish/Bearish candlestick patterns
        # ------------------------------------
        # # Three Line Strike: values [0, -100, 100]
        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
        # # Spinning Top: values [0, -100, 100]
        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
        # # Engulfing: values [0, -100, 100]
        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
        # # Harami: values [0, -100, 100]
        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
        # # Three Outside Up/Down: values [0, -100, 100]
        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
        # # Three Inside Up/Down: values [0, -100, 100]
        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]

        # # Chart type
        # # ------------------------------------
        # # Heikin Ashi Strategy
        # heikinashi = qtpylib.heikinashi(dataframe)
        # dataframe['ha_open'] = heikinashi['open']
        # dataframe['ha_close'] = heikinashi['close']
        # dataframe['ha_high'] = heikinashi['high']
        # dataframe['ha_low'] = heikinashi['low']

        # Retrieve best bid and best ask from the orderbook
        # ------------------------------------


        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """

        conditions = []

        # GUARDS AND TRENDS


        # check that volume is not 0
        conditions.append(dataframe['volume'] > 0)

        # MACD OK?
        if self.buy_macd_enabled.value:
            conditions.append(dataframe['macdhist'] >= 0)

        # TRIGGERS

        # (BTC) EMA flattened?
        conditions.append(qtpylib.crossed_above(dataframe['ema_angle_5m'], 0))

        # buy if price is far enough below EMA (note: NOT the BTC ema)
        conditions.append(dataframe['ema_diff'] > 0.0)

        # build the dataframe using the conditions
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """

        conditions = []
        # if hold, then don't set a sell signal
        if self.sell_hold.value:
            dataframe.loc[(dataframe['close_5m'].notnull() ), 'sell'] = 0

        else:

            # (BTC) EMA flattened?
            conditions.append(qtpylib.crossed_below(dataframe['ema_angle_5m'], 0))

            # price above EMA
            conditions.append(dataframe['close'] > dataframe['ema'])

            # sell if price is far enough above EMA
            conditions.append(dataframe['ema_diff'] >= -self.sell_diff.value)

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

        return dataframe