BBL3H2RSIStdStrategy

# pragma pylint: disable=missing-docstring, invalid-name
# pragma pyline: disable=pointless-string-statement

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy.interface import IStrategy

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class BBL3H2RSIStdStrategy(IStrategy):
    """
    Strategy: optimized for standard loss function

        Buy hyperspace params:
        {   'mfi-enabled': True,
            'mfi-value': 37,
            'rsi-enabled': True,
            'rsi-value': 31,
            'trigger': 'bb_lower3'}
        Sell hyperspace params:
        {   'sell-mfi-enabled': True,
            'sell-mfi-value': 92,
            'sell-rsi-enabled': True,
            'sell-rsi-value': 83,
            'sell-trigger': 'sell-bb_high2'}
        ROI table:
        {0: 0.26832, 469: 0.10558, 1177: 0.04734, 2554: 0}
        Stoploss: -0.26355
    """

    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    minimal_roi = {
        "0": 0.26832,
        "469": 0.10558,
        "1177": 0.04734,
        "2554": 0
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.26355

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Optimal ticker interval for the strategy.
    ticker_interval = '1h'

    # 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 = 20

    # 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': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "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/USDT", "15m"),
                            ]
        """
        return []

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

        :param dataframe: Raw data from the exchange and parsed by
                          parse_ticker_dataframe()
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

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

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

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

        # # Minus Directional Indicator / Movement
        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)

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

        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0]
        # 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
        # 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
        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
                                             window=20, stds=3)
        dataframe['bb_lowerband2'] = bollinger3['lower']

        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
                                             window=20, stds=2)
        dataframe['bb_upperband1'] = bollinger2['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)

        # # 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['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)
        # # Morningstar: values [0, 100]
        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe)
        # # Three White Soldiers: values [0, 100]
        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)

        # 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)
        # # Engulfing: values [0, -100, 100]
        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)
        # # Harami: values [0, -100, 100]
        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe)
        # # Three Outside Up/Down: values [0, -100, 100]
        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)
        # # Three Inside Up/Down: values [0, -100, 100]
        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)

        # # 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
        # ------------------------------------
        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode in ('live', 'dry_run'):
                ob = self.dp.orderbook(metadata['pair'], 1)
                dataframe['best_bid'] = ob['bids'][0][0]
                dataframe['best_ask'] = ob['asks'][0][0]
        """

        return dataframe

    def populate_buy_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
        """
        dataframe.loc[
            (
                (dataframe['close'] < dataframe['bb_lowerband3']) &
                (dataframe['rsi'] <= 31) &
                (dataframe['mfi'] <= 37)
            ),
            '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 populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['close'] > dataframe['bb_upperband2']) &
                (dataframe['mfi'] >= 92) &
                (dataframe['rsi'] >= 83)
            ),
            'sell'] = 1
        return dataframe