# source: https://raw.githubusercontent.com/enricogolfieri/yuccatrader/b4877c2592d67333df5c99662e962190c636f3b4/user_data/strategies/RSIStrategy.py
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from functools import reduce
from pandas import DataFrame

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,IStrategy, IntParameter)

# --- Add your lib to import here ---
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

# --- Generic strategy settings ---

class github_enricogolfieri_yuccatrader__RSIStrategy__20220818_151950(IStrategy):
    INTERFACE_VERSION = 2
    
    # Determine timeframe and # of candles before strategysignals becomes valid
    timeframe = '4h'
    startup_candle_count: int = 25

    # Determine roi take profit and stop loss points
    minimal_roi = {
        "60":  0.01,
        "30":  0.03,
        "20":  0.04,
        "0":  0.05
    }

    stoploss = -0.10
    trailing_stop = False
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.0
    ignore_roi_if_buy_signal = False

# --- Define spaces for the indicators ---

    buy_rsi = IntParameter(25, 35, default=30, space="buy")
    sell_rsi = IntParameter(60, 80, default=70, space="sell")
    rsi_period = IntParameter(7, 21, default=14, space="sell")

# --- Used indicators of strategy code ----
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['RSI'] = ta.RSI(dataframe, timeperiod=self.rsi_period.value)

        return dataframe

# --- Buy settings ---
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
       conditions = []
       conditions.append((dataframe['RSI'] < self.buy_rsi.value ))

       if conditions:
           dataframe.loc[
               reduce(lambda x, y: x & y, conditions),
               'buy'] = 1


       return dataframe

# --- Sell settings ---
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
       conditions = []
       conditions.append((dataframe['RSI'] > self.sell_rsi.value ))

       if conditions:
           dataframe.loc[
               reduce(lambda x, y: x & y, conditions),
               'sell'] = 1

       return dataframe