# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from technical.indicators import accumulation_distribution
from technical.util import resample_to_interval, resampled_merge
# --------------------------------

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


# This class is a sample. Feel free to customize it.
class ichis(IStrategy):


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

        "0": 1
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.1

    # trailing stoploss
    trailing_stop = True
    trailing_stop_positive = -0.15
    trailing_stop_positive_offset = 0.20 # Disabled / not configured
    trailing_only_offset_is_reached = True

    # Optimal ticker interval for the strategy
    ticker_interval = '15m'

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

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    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:

        ichi=ichimoku(dataframe)
        dataframe['tenkan']=ichi['tenkan_sen']
        dataframe['kijun']=ichi['kijun_sen']
        dataframe['senkou_a']=ichi['senkou_span_a']
        dataframe['senkou_b']=ichi['senkou_span_b']
        dataframe['cloud_green']=ichi['cloud_green']
        dataframe['cloud_red']=ichi['cloud_red']

        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 populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """

        dataframe.loc[
            (
                (dataframe['tenkan'].shift(1)<dataframe['kijun'].shift(1)) &
                (dataframe['tenkan']>dataframe['kijun']) &
                (dataframe['cloud_red']==True) &

            ),
            '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 populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (

            ),
            'sell'] = 1

        return dataframe