AiewStraV2

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

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

from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from scipy.signal import argrelextrema
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class AiewStraV2(IStrategy):
    """
    This is a strategy template to get you started.
    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_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.05

    # 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 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 = True

    # 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': 'limit',
        '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': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "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

        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
        """

        # 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] (https://goo.gl/2JGGoy)
        # 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] (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, window_slow=27,
                       window_fast=11, window_sign=4,)
        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['ema2'] = ta.EMA(dataframe, timeperiod=2)
        dataframe['ema11'] = ta.EMA(dataframe, timeperiod=11)
        dataframe['ema27'] = ta.EMA(dataframe, timeperiod=27)
        # 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['sma4'] = ta.SMA(dataframe, timeperiod=4)
        # 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
        # ------------------------------------
        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode.value 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]
        """

        dataframe_copy = dataframe.copy()
        frame_size = 250
        len_df = len(dataframe)
        dataframe['buy_signal'] = False
        dataframe['sell_signal'] = False
        lookback_size = 50
        # Let's calculate argrelextrema on separated data slices and get only last result to avoid lookahead bias!
        for i in range(len_df):
            if i + frame_size < len_df:
                slice = dataframe_copy[i: i+frame_size]
                min_peaks = argrelextrema(
                    slice['close'].values, np.less, order=lookback_size)
                max_peaks = argrelextrema(
                    slice['close'].values, np.greater, order=lookback_size)
                # Somehow we never getting last index of a frame as min or max. What a surprise :)
                # So lets take penultimate result and use it as a signal to buy/sell.
                if len(min_peaks[0]) and min_peaks[0][-1] == frame_size - 2:
                    # signal that penultimate candle is min
                    # lets buy here
                    dataframe.at[i + frame_size, 'buy_signal'] = True
                if len(max_peaks[0]) and max_peaks[0][-1] == frame_size - 2:
                    # oh it seams that penultimate candle is max
                    # lets sell ASAP
                    dataframe.at[i + frame_size, 'sell_signal'] = True

                if i + frame_size == len_df - 1:
                    pass
                    # print(min_peaks)

        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[
            (
                # Signal: RSI crosses above 30
                dataframe['buy_signal'] &
                # # Guard: tema below BB middle
                (dataframe['tema'] <= dataframe['bb_middleband']) &
                # # Guard: tema is raising
                (dataframe['tema'] > dataframe['tema'].shift(1)) &
                (qtpylib.crossed_above(dataframe['rsi'], 30)) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            '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[
            (
                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(dataframe['rsi'], 70)) &
                # Guard: tema above BB middle
                (dataframe['tema'] > dataframe['bb_middleband']) &
                # Guard: tema is falling
                (dataframe['tema'] < dataframe['tema'].shift(1)) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe