# source: https://raw.githubusercontent.com/petitoff/freqtrade/08c707001bea7ae6fc108f02f2a3200349f19442/user_data/strategies/bbrsi.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

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


# from freqtrade.indicator_helpers import fishers_inverse


# This class is a sample. Feel free to customize it.
class github_petitoff_freqtrade__bbrsi__20220104_171918(IStrategy):
    __test__ = False  # pytest expects to find tests here because of the name
    """
    This is a test strategy to inspire you.
    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
    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 prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
    populate_sell_trend, hyperopt_space, buy_strategy_generator
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 10
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.99

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = None  # Disabled / not configured

    # Optimal ticker interval for the strategy
    timeframe = '5m'

    # run "populate_indicators" only for new candle
    ta_on_candle = False

    # 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'
    }

    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: 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 Indicator
        # ------------------------------------

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

        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
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['rsi'] < 40) &
                    (dataframe['close'] < dataframe['bb_lowerband'])
            ),
            '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
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['close'] > dataframe['bb_upperband'])
            ),
            'sell'] = 1
        return dataframe