# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
from freqtrade.data.converter import parse_ticker_dataframe
# --------------------------------

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class InformativeSample(IStrategy):
    """
    Sample strategy implementing Informative Pairs - compares ETH/BTC with USDT.
    Not performing very well - but should serve as an example to use a referential pair against USD.
    author@: xmatthias
    github@: https://github.com/freqtrade/freqtrade-strategies

    How to use it?
    > python3 freqtrade -s InformativeSample
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "60":  0.01,
        "30":  0.03,
        "20":  0.04,
        "0":  0.05
    }

    # 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'

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02

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

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

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

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

    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 [(f"{self.config['stake_currency']}/USDT", self.ticker_interval)]

    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.
        """

        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
        if self.dp:
            if self.dp.runmode in('live', 'dry_run'):
                # Compare stake-currency with USDT - using the defined ticker-interval
                if (f"{self.stake_currency}/USDT", self.ticker_interval) in self.dp.available_pairs:
                    data = self.dp.ohlcv(pair='ETH/BTC',
                                         ticker_interval=self.ticker_interval)
            else:
                # Get historic ohlcv data (cached on disk).
                # data = parse_ticker_dataframe(self.dp.historic_ohlcv(pair='ETH/BTC',
                #                                  ticker_interval=self.ticker_interval), "5m")
                data = self.dp.historic_ohlcv(pair=f"{self.stake_currency}/USDT",
                                 ticker_interval=self.ticker_interval)
            if len(data) == 0:
                logger.warning(f"No data found for {self.stake_currency}/USDT")
            # Combine the 2 dataframes using close
            # this will result in a column named closeETH or closeBTC - depnding on stake_currency.
            dataframe = dataframe.merge(data[["date", "close"]], on="date", how="left", suffixes=("", self.config['stake_currency']))

            # Calculate SMA20 on stakecurrency. Resulting column = smaETH20
            dataframe[f"sma{self.config['stake_currency']}20"] = dataframe[f'close{self.stake_currency}'].rolling(20).mean()

        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
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['ema20'] > dataframe['ema50']) &
                # stake/USDT above sma(stake/USDT, 20)
                (dataframe[f'close{self.stake_currency}'] > dataframe[f'sma{self.stake_currency}20'])
            ),
            '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
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['ema20'] < dataframe['ema50']) &
                # stake/USDT below sma(stake/USDT, 20)
                (dataframe[f'close{self.stake_currency}'] < dataframe[f'sma{self.stake_currency}20'])
            ),
            'sell'] = 1
        return dataframe