# --- 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


# This class is a sample. Feel free to customize it.
class TestStrategyLegacy(IStrategy):
    """
    This is a test strategy using the legacy function headers, which will be
    removed in a future update.
    Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
    for a uptodate version of this template.
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "40": 0.0,
        "30": 0.01,
        "20": 0.02,
        "0": 0.04
    }

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

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

    def populate_indicators(self, dataframe: DataFrame) -> 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.
        """

        # Momentum Indicator
        # ------------------------------------

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)

        # TEMA - Triple Exponential Moving Average
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['adx'] > 30) &
                (dataframe['tema'] > dataframe['tema'].shift(1)) &
                (dataframe['volume'] > 0)
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['adx'] > 70) &
                (dataframe['tema'] < dataframe['tema'].shift(1)) &
                (dataframe['volume'] > 0)
            ),
            'sell'] = 1
        return dataframe