# source: https://raw.githubusercontent.com/webclinic017/Freq-trade-stra/0cd2aca7e34cf1f843ab430633acc66fb1cf55b7/ROCnRoll.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
# --------------------------------
# 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
from freqtrade.strategy.hyper import IntParameter, DecimalParameter

import logging
logger = logging.getLogger(__name__)
###Hyperopt this for ROI and Stoploss
### 
class github_webclinic017_Freq_trade_stra__ROCnRoll__20210826_040026(IStrategy):
    
    #1993/2000:    200 trades. 123/74/3 Wins/Draws/Losses. Avg profit   0.81%. Median profit   0.75%.
    #Total profit  705.30456055 USD (  70.53%). Avg duration 10:12:00 min. Objective: -705.30456  
    

     # ROI table:
    minimal_roi = {
        "0": 0.132,
        "32": 0.057,
        "80": 0.011,
        "195": 0
    }

    # Stoploss:
    stoploss = -0.309


    buy_roc = DecimalParameter(-6.00, 6.00, default=-0.675, space="buy") 
    sell_mfi = IntParameter(75, 99, default=82, space="sell") 

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.011
    trailing_stop_positive_offset = 0.085
    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['mfi'] = ta.MFI(dataframe, timeperiod=20) #guard
        dataframe['roc'] = ta.ROC(dataframe, timeperiod=20) #trigger
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=20) #trigger
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
              
        dataframe.loc[
            (
                ((dataframe["roc"] <= self.buy_roc.value)) &
                ((dataframe["roc"]) <= (dataframe["roc"].shift(1))) &
                ((dataframe["roc"]) <= (dataframe["roc"].shift(2))) &
                ((dataframe["adx"] >= 25)) & #guard
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        dataframe.loc[
            (
                #((dataframe["roc"]) >= (dataframe["roc"].shift(1))) &
                ((dataframe["mfi"] >= self.sell_mfi.value)) & 
                ((dataframe["adx"] >= 25)) &
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'sell'] = 1
        return dataframe