# source: https://raw.githubusercontent.com/webclinic017/Freq-trade-stra/0cd2aca7e34cf1f843ab430633acc66fb1cf55b7/RMB.py
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.hyper import IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime
from freqtrade.persistence import Trade

import logging
logger = logging.getLogger(__name__)

class github_webclinic017_Freq_trade_stra__RMB__20210826_040026(IStrategy):
    INTERFACE_VERSION = 2
         
      
    # ROI table:
    minimal_roi = {
        #"0": 0.01,
        #"30": 0.01,
        #"60": 0.005,
        #"120": 0.00
        "0": 10
    }

    # Stoploss:
    stoploss = -0.99

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.343
    trailing_stop_positive_offset = 0.433
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy 
    timeframe = '5m'
    
    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = True
    sell_profit_offset = 0.0
    ignore_roi_if_buy_signal = False
    
    # Optional order type mapping
    order_types = {
        'buy': 'market',
        'sell': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # run "populate_indicators" only for new candle
    process_only_new_candles = False
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=14, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
                
        dataframe.loc[
            (
                (dataframe["rsi"] <= 30) & 
                (dataframe["mfi"] <= 20) & 
                (dataframe["low"] < dataframe["bb_lowerband"]) & #trigger
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'buy'] = 1
        return dataframe

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

        dataframe.loc[
            (    
                (dataframe["rsi"] >= 70) &
                (dataframe["mfi"] >= 80) &
                (dataframe["high"] > dataframe["bb_upperband"]) & #Trigger
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'sell'] = 1
        return dataframe