import talib.abstract as ta
import pandas
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy.interface import IStrategy
pandas.set_option("display.precision",8)


class bbrsi_941(IStrategy):
    """
    Default Strategy provided by freqtrade bot.
    You can override it with your own strategy
    """

    minimal_roi = {
        "0": 0.131,
        "109": 0.08,
        "226": 0.031,
        "522": 0
    }

    stoploss = -0.348
    trailing_stop = True
    trailing_stop_positive = 0.293
    trailing_stop_positive_offset = 0.362
    trailing_only_offset_is_reached = True

    timeframe = '15m'

    order_types = {
        "buy": "limit",
        "sell": "limit",
        "emergencysell": "market",
        "forcebuy": "market",
        "forcesell": "market",
        "stoploss": "market",
        "stoploss_on_exchange": True,
        "stoploss_on_exchange_interval": 60,
        "stoploss_on_exchange_limit_ratio": 0.99,
    }

    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
        """

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

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        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'] < 74) &
                (dataframe['close'] < dataframe['bb_middleband'])

            ),
            '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