BBRSILeveraged

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from freqtrade.persistence import Trade
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from functools import reduce

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib
import re


class BBRSILeveraged(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_entry_trend, populate_exit_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 = 3

    # Optimal timeframe for the strategy.
    timeframe = '1h'

    # Can this strategy go short?
    can_short: bool = False

    # Strategy parameters
    # Buy hyperspace params:
    # buy_params = {
    #     "buy_above_ema_window": 13,
    #     "buy_bb_std": 2.0,
    #     "buy_bb_window": 17,
    #     "buy_ema": 200,  # value loaded from strategy
    # }
    #
    # # Sell hyperspace params:
    # sell_params = {
    #     "sell_rr_ratio": 1.5,
    #     "sell_rsi_bearish": 35,
    #     "sell_rsi_bullish": 65,
    #     "sell_unclog": 6,
    # }

    buy_ema = IntParameter(150, 200, default=200, space="buy", optimize=False)
    buy_above_ema_window = IntParameter(5, 15, default=13, space="buy",
                                        optimize=True)
    buy_bb_window = IntParameter(14, 21, default=17, space="buy", optimize=True)
    buy_bb_std = DecimalParameter(2.0, 3.0, default=2.0, decimals=1,
                                  optimize=True)
    sell_rsi_bullish = IntParameter(65, 90, default=65, space="sell",
                                    optimize=True)
    sell_rsi_bearish = IntParameter(10, 35, default=35, space="sell",
                                    optimize=True)
    sell_unclog = IntParameter(5, 14, default=6, space="sell", optimize=True)
    sell_rr_ratio = DecimalParameter(1.5, 3.0, default=1.5, decimals=1,
                                     space="sell", optimize=True)

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.10

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": sell_rr_ratio.value * abs(stoploss)
    }

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

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden 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 = int(
        max(
            buy_ema.value,
            buy_above_ema_window.value,
            buy_bb_window.value
        )
    )

    # Optional order type mapping.
    order_types = {
        'entry': 'limit',
        'exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'entry': 'GTC',
        'exit': 'GTC'
    }
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                # f'bb_lowerband_{self.timeframe}': {},
                # f'bb_upperband_{self.timeframe}': {},
                # f'ema{self.buy_ema.value}_{self.timeframe}': {}
            },
            'subplots': {
                # Subplots - each dict defines one additional plot
                "RSI_INF": {
                    f'rsi_{self.timeframe}': {}
                }
            }
        }

    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"),
                            ]
        """
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()

        # Get stake currency
        stake_currency = self.config['stake_currency']

        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [
            (
                f"{re.findall(f'.*[^(UP|DOWN)/{stake_currency}]', pair)[0]}/{stake_currency}",
                self.timeframe
            ) for pair in pairs
        ]
        return informative_pairs

    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
        """
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return dataframe

        # Getting the base currency and quote currency
        quote_currency = self.config['stake_currency']
        base_currency = re.findall(f'.*[^(UP|DOWN)/{quote_currency}]', metadata['pair'])[0]

        # Get the informative pairs
        informative = self.dp.get_pair_dataframe(pair=f"{base_currency}/{quote_currency}",
                                                 timeframe=self.timeframe)

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

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(informative),
            window=self.buy_bb_window.value,
            stds=self.buy_bb_std.value)
        informative['bb_lowerband'] = bollinger['lower']
        informative['bb_upperband'] = bollinger['upper']

        # # EMA - Exponential Moving Average
        informative[f'ema{self.buy_ema.value}'] = ta.EMA(informative, timeperiod=self.buy_ema.value)

        # Use the helper function merge_informative_pair to safely merge the
        # pairs. Automatically renames the columns and merges a shorter
        # timeframe dataframe and a longer timeframe informative pair.
        # Use 'ffill' to have the informative pair value available in every row
        # throughout the day. Without this, comparisons between columns of the
        # original and the informative pair would only work once per day.
        dataframe = merge_informative_pair(
            dataframe,
            informative,
            self.timeframe,
            self.timeframe,
            ffill=True
        )

        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
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry columns populated
        """
        # Getting the base currency and quote currency
        quote_currency = self.config['stake_currency']
        base_currency = re.findall(f'.*[^(UP|DOWN)/{quote_currency}]', metadata['pair'])[0]

        # ======================================================================
        # BULLISH CONDITIONS
        conditions_bullish = []
        if metadata['pair'] == f"{base_currency}UP/{quote_currency}":
            # Bullish 1: All candles within the specified window need to open and
            # close above the EMA line
            for i in range(self.buy_above_ema_window.value-1, -1, -1):
                conditions_bullish.append(
                    dataframe[[f'open_{self.timeframe}', f'close_{self.timeframe}']].shift(i).min(axis=1) >=
                    dataframe[f'ema{self.buy_ema.value}_{self.timeframe}'].shift(i)
                )

            # Bullish 2: The trigger candle must cross below the lower BB
            conditions_bullish.append(
                qtpylib.crossed_below(dataframe[f'close_{self.timeframe}'], dataframe[f'bb_lowerband_{self.timeframe}'])
            )

            # Bullish 3: Volume is greater than 0
            conditions_bullish.append((dataframe['volume'] > 0))

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

        # ======================================================================
        # BEARISH CONDITIONS
        conditions_bearish = []
        if metadata['pair'] == f"{base_currency}DOWN/{quote_currency}":
            # Bearish 1: All candles within the specified window need to open and
            # close below the EMA line
            for i in range(self.buy_above_ema_window.value - 1, -1, -1):
                conditions_bearish.append(
                    dataframe[[f'open_{self.timeframe}',
                               f'close_{self.timeframe}']].shift(i).max(axis=1) <=
                    dataframe[f'ema{self.buy_ema.value}_{self.timeframe}'].shift(i)
                )

            # Bearish 2: The trigger candle must cross above the upper BB
            conditions_bearish.append(
                qtpylib.crossed_above(dataframe[f'close_{self.timeframe}'],
                                      dataframe[f'bb_upperband_{self.timeframe}'])
            )

            # Bearish 3: Volume is greater than 0
            conditions_bearish.append((dataframe['volume'] > 0))

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

        # print(f"Dataframe for pair {metadata['pair']}:\n{dataframe.head()}")

        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
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with exit columns populated
        """
        # Getting the base currency and quote currency
        quote_currency = self.config['stake_currency']
        base_currency = re.findall(f'.*[^(UP|DOWN)/{quote_currency}]',
                                   metadata['pair'])[0]
        # Exit bullish
        if metadata['pair'] == f"{base_currency}UP/{quote_currency}":
            dataframe.loc[
                (
                    # Condition 1: RSI crosses above sell_rsi value
                    (qtpylib.crossed_above(dataframe[f'rsi_{self.timeframe}'], self.sell_rsi_bullish.value)) &
                    # Condition 2: Volume is greater than 0
                    (dataframe['volume'] > 0)  # Make sure Volume is not 0
                ),
                'exit_long'] = 1

        if metadata['pair'] == f"{base_currency}DOWN/{quote_currency}":
            dataframe.loc[
                (
                    # Condition 1: RSI crosses above sell_rsi value
                    (qtpylib.crossed_below(dataframe[f'rsi_{self.timeframe}'], self.sell_rsi_bearish.value)) &
                    # Condition 2: Volume is greater than 0
                    (dataframe['volume'] > 0)  # Make sure Volume is not 0
                ),
                'exit_long'] = 1

        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        # Sell any positions if they are held for more than a specific number of
        # days
        if (
                ((current_time - trade.open_date_utc).days >= self.sell_unclog.value)
        ):
            return 'unclog'