# source: https://raw.githubusercontent.com/Audris-M/Data-science-trading-project/345d79ab4a74920da44baaaf2e01390cbe20cd64/crypto_crawler/strategies/RSI_STOCH.py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta


class github_Audris_M_Data_science_trading_project__RSI_STOCH__20230110_114314(IStrategy):

    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy
    minimal_roi = {
        "40": 0.0,
        "30": 0.01,
        "20": 0.02,
        "0": 0.04
    }

    # Optimal stoploss designed for the strategy
    stoploss = -0.10

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

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

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # stoch
        dataframe['L14'] = dataframe['Low'].rolling(window=14)
        dataframe['H14'] = dataframe["high"].rolling(window=14)
        dataframe['%K'] = 100 * ((dataframe['Close'] - dataframe['L14']) / dataframe['H14'] -dataframe['L14'] ) )
        dataframe['%D'] = dataframe['%K'].rolling(window=3).mean()

		# RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        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_fast'] < 35) &
            dataframe['%K'] == dataframe['%D'] &
            dataframe['%D'] > 30

            ),
            '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['rsi'] > 50) &
            dataframe['%K'] == dataframe['%D'] &
            dataframe['%D'] < 50

            ),
            'sell'] = 1
        return dataframe