# pragma pylint: disable=missing-docstring, invalid-name, 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
_trend_length = 14
_bb_smooth_length = 4

def iff(a, b, c):
    if a:
        return b
    else:
        return c

class STRATEGY_RSI_BB_BOUNDS_CROSS(IStrategy):
    """
    Strategy RSI_BB_BOUNDS_CROSS
    author@: Fractate_Dev
    github@: https://github.com/Fractate/freqbot
    How to use it?
    > python3 ./freqtrade/main.py -s RSI_BB_BOUNDS_CROSS
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3
    # 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 = -0.1
    # Trailing stoploss
    trailing_stop = False
    # 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_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    # Main plot indicators (Moving averages, ...)
    # Subplots - each dict defines one additional plot
    # "MACD": {
    #     'macd': {'color': 'blue'},
    #     'macdsignal': {'color': 'orange'},
    # },
    # "BB": {
    #     'bb_percent': {'color': 'red'},
    #     '1': {},
    #     '0': {},
    # },
    # "RSI": {
    #     'rsi': {'color': 'red'},
    #     '70': {},
    #     '30': {},
    # },
    # "RSI_Percent": {
    #     'rsi_percent': {'color': 'red'},
    #     '1': {},
    #     '0': {},
    # },
    # "bb_above": {
    #     'bb_above_rsi': {}
    # },
    # "bb_below": {
    #     'bb_below_rsi': {}
    # },
    # "bb_minus_rsi_percent": {
    #     'bb_minus_rsi_percent': {},
    #     '0': {},
    # },
    plot_config = {'main_plot': {'tema': {}, 'sar': {'color': 'white'}, 'bb_ub': {'color': 'green'}, 'bb_lb': {'color': 'green'}, 'bb_lb_smoothed': {'color': 'red'}, 'rsi_ub': {'color': 'black'}, 'rsi_lb': {'color': 'black'}, 'rsi_lb_smoothed': {'color': 'orange'}}, 'subplots': {'bb_rsi_count': {'ub_bb_over_rsi_trend': {}, 'lb_bb_under_rsi_trend': {}}}}

    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
        """
        # remove later
        for i in range(1):
            print('')
        # Logic
        # Bollinger Band getting less than RSI should occur after a certain number of candles pass
        # time frame of 1 hour
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=14, stds=2)
        dataframe['bb_lb'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_ub'] = bollinger['upper']
        dataframe['bb_lb_smoothed'] = 0
        for i in range(_bb_smooth_length):
            dataframe['bb_lb_smoothed'] += dataframe['bb_lb'].shift(i) / _bb_smooth_length
        dataframe['bb_percent'] = (dataframe['close'] - bollinger['lower']) / (bollinger['upper'] - bollinger['lower'])
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)  # rsi(prices, period \\ 14)
        dataframe['70'] = 70
        dataframe['30'] = 30
        dataframe['1'] = 1
        dataframe['0'] = 0
        rsi_limit = 30
        dataframe['rsi_percent'] = (dataframe['rsi'] - rsi_limit) / (100 - rsi_limit * 2)
        # Convert RSI Percentage to actual value in candlestick data
        # ep = 2 * length - 1
        # auc = ema( max( src - src[1], 0 ), ep )
        # adc = ema( max( src[1] - src, 0 ), ep )
        # x1 = (length - 1) * ( adc * obLevel / (100-obLevel) - auc)
        # ub = iff( x1 >= 0, src + x1, src + x1 * (100-obLevel)/obLevel )
        # x2 = (length - 1) * ( adc * osLevel / (100-osLevel) - auc)
        # lb = iff( x2 >= 0, src + x2, src + x2 * (100-osLevel)/osLevel )
        length = 14
        ep = 2 * length - 1
        dataframe.loc[dataframe['close'] - dataframe['close'].shift(1) > 0, 'auc1'] = dataframe['close'] - dataframe['close'].shift(1)
        dataframe.loc[dataframe['close'] - dataframe['close'].shift(1) <= 0, 'auc1'] = 0
        dataframe['auc'] = ta.EMA(dataframe['auc1'], ep)
        # dataframe['adc'] = ta.EMA(ta.MAX(dataframe['close'].shift(1) - dataframe['close']), ep)
        dataframe.loc[dataframe['close'].shift(1) - dataframe['close'] > 0, 'adc1'] = dataframe['close'].shift(1) - dataframe['close']
        dataframe.loc[dataframe['close'].shift(1) - dataframe['close'] <= 0, 'adc1'] = 0
        dataframe['adc'] = ta.EMA(dataframe['adc1'], ep)
        dataframe['x1'] = (length - 1) * (dataframe['adc'] * 70 / (100 - 70) - dataframe['auc'])
        dataframe['x1'] = np.nan_to_num(dataframe['x1'])
        # dataframe['ub'] = iff( dataframe['x1'] >= 0, dataframe['close'] + dataframe['x1'], dataframe['close'] + dataframe['x1'] * (100-70)/70 )
        dataframe.loc[dataframe['x1'] >= 0, 'rsi_ub'] = dataframe['close'] + dataframe['x1']
        dataframe.loc[dataframe['x1'] < 0, 'rsi_ub'] = dataframe['close'] + dataframe['x1'] * (100 - 70) / 70
        dataframe['x2'] = (length - 1) * (dataframe['adc'] * 30 / (100 - 30) - dataframe['auc'])
        dataframe.loc[dataframe['x2'] >= 0, 'rsi_lb'] = dataframe['close'] + dataframe['x2']
        dataframe.loc[dataframe['x2'] < 0, 'rsi_lb'] = dataframe['close'] + dataframe['x2'] * (100 - 30) / 30
        dataframe['rsi_lb_smoothed'] = 0
        for i in range(_bb_smooth_length):
            dataframe['rsi_lb_smoothed'] += dataframe['rsi_lb'].shift(i) / _bb_smooth_length
        # auc = ema( max( src - src[1], 0 ), ep )
        # adc = ema( max( src[1] - src, 0 ), ep )
        # x1 = (length - 1) * ( adc * obLevel / (100-obLevel) - auc)
        # ub = iff( x1 >= 0, src + x1, src + x1 * (100-obLevel)/obLevel )
        # x2 = (length - 1) * ( adc * osLevel / (100-osLevel) - auc)
        # lb = iff( x2 >= 0, src + x2, src + x2 * (100-osLevel)/osLevel )
        dataframe['bb_minus_rsi_percent'] = dataframe['bb_percent'] - dataframe['rsi_percent']
        dataframe['ub_bb_over_rsi'] = dataframe['bb_ub'] > dataframe['rsi_ub']
        dataframe['lb_bb_under_rsi'] = dataframe['bb_lb'] < dataframe['rsi_lb']
        dataframe['ub_bb_over_rsi_trend'] = True
        dataframe['lb_bb_under_rsi_trend'] = True
        # Verify RSI positivity or negativity over trend
        # dataframe['bb_above_rsi_count'] = True
        # dataframe['bb_below_rsi_count'] = True
        for i in range(_trend_length):
            # dataframe['bb_above_rsi_count'] = (dataframe['bb_minus_rsi_percent'].shift(i) > 0) & dataframe['bb_above_rsi_count']
            # dataframe['bb_below_rsi_count'] = (dataframe['bb_minus_rsi_percent'].shift(i) < 0) & dataframe['bb_below_rsi_count']
            dataframe['ub_bb_over_rsi_trend'] = dataframe['ub_bb_over_rsi'].shift(i) & dataframe['ub_bb_over_rsi_trend']
            dataframe['lb_bb_under_rsi_trend'] = dataframe['lb_bb_under_rsi'].shift(i) & dataframe['lb_bb_under_rsi_trend']
        dataframe['ema14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['ema2'] = ta.EMA(dataframe, timeperiod=2)
        ## old method
        # bb_above_rsi = False
        # bb_below_rsi = False
        # dataframe['bb_above_rsi_count'] = 0
        # dataframe['bb_below_rsi_count'] = 0
        # for i in range(_trend_length):
        #     if np.where(dataframe['bb_percent'].shift(i) < dataframe['rsi_percent'].shift(i)):
        #         dataframe['bb_below_rsi_count'] += 1
        #     if np.where(dataframe['bb_percent'].shift(i) > dataframe['rsi_percent'].shift(i)):
        #         dataframe['bb_above_rsi_count'] += 1
        # if bb_above_rsi == True & bb_below_rsi == False:
        # dataframe['bb_above_rsi'] = 1
        # if bb_above_rsi == False & bb_below_rsi == True:
        #     dataframe['bb_below_rsi'] = 1
        # print(type(dataframe['bb_below_rsi']))
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry column
        """
        # (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
        # (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
        # (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
        # (dataframe['volume'] > 0)  # Make sure Volume is not 0
        # qtpylib.crossed_above(dataframe['bb_lb'], dataframe['rsi_lb']) &
        # if rsi trend has been above or low for a certain amount of time
        # # if close is higher than rsi lower bound
        # (dataframe['close'] > dataframe['rsi_lb']) &
        # (dataframe['close'].shift(1) < dataframe['rsi_lb'].shift(1)) &
        # if bb lower bound is less than rsi lower bound
        # # if bb lb is higher than it was before
        # (dataframe['bb_lb'] > dataframe['bb_lb'].shift(1)) &
        # (dataframe['bb_lb'].shift(1) < dataframe['bb_lb'].shift(2)) &
        # if ema2 is rising
        # # dataframes are less than 5 percent, though this shouldn't matter due to ema2 comparison
        # (dataframe['bb_percent'] < 0.5) & 
        # (dataframe['rsi_percent'] < 0.5)
        dataframe.loc[dataframe['lb_bb_under_rsi_trend'] & (dataframe['bb_lb'] < dataframe['rsi_lb']) & (dataframe['bb_lb'].shift(1) < dataframe['rsi_lb'].shift(1)) & (dataframe['ema2'] > dataframe['ema2'].shift(1)), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry column
        """
        # (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
        # (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
        # (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
        # (dataframe['volume'] > 0)  # Make sure Volume is not 0
        # qtpylib.crossed_below(dataframe['bb_percent'], dataframe['rsi_percent']) &
        # qtpylib.crossed_below(dataframe['bb_ub'], dataframe['rsi_ub']) &
        # (dataframe['close'] < dataframe['rsi_ub']) &
        # (dataframe['close'].shift(1) > dataframe['rsi_ub'].shift(1)) &
        # (dataframe['bb_ub'] > dataframe['rsi_ub']) &
        # (dataframe['bb_ub'].shift(1) > dataframe['rsi_ub'].shift(1)) 
        # (dataframe['bb_percent'] > 0.5) & 
        # (dataframe['rsi_percent'] > 0.5) &
        # (dataframe['ub_bb_over_rsi_trend'].shift(1))
        # (qtpylib.crossed_above(dataframe['rsi'], 50))  # Signal: RSI crosses above 70
        # (qtpylib.crossed_above(dataframe['close'], dataframe['ema14']))  # Signal: RSI crosses above 70
        # if ema2 is falling
        dataframe.loc[dataframe['ema2'] < dataframe['ema2'].shift(1), 'exit'] = 1
        return dataframe