# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame, Series
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import IntParameter, DecimalParameter
# --------------------------------
# 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 TRSI(IStrategy):
    
    #1878/2000:    214 trades. 166/0/48 Wins/Draws/Losses. Avg profit   1.10%. Median profit   1.48%.
    #Total profit  1156.73756599 USD ( 115.67%). Avg duration 7:57:00 min. Objective: -74.91479
    # Sharpe 1month
    
    INTERFACE_VERSION = 2
    buy_rsi1 = IntParameter(5, 30, default=7, space="buy")
    buy_rsi2 = IntParameter(15, 35, default=34, space="buy")
    buy_cmf = DecimalParameter(-0.5, -0.1, default=-0.162, space="buy")
    sell_rsi1 = IntParameter(70, 95, default=82, space="sell")
    sell_rsi2 = IntParameter(65, 85, default=68, space="sell") 
    sell_cmf = DecimalParameter(0.1, 0.5, default=0.173, space="sell")
    # ROI table:
    minimal_roi = {
        #"0": 0.051,
        #"26": 0.035,
        #"64": 0.014,
        #"173": 0.00
        "0": 10
    }

    # Stoploss:
    stoploss = -0.235

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.011
    trailing_stop_positive_offset = 0.033
    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 = False
    sell_profit_offset = 0.0
    ignore_roi_if_buy_signal = False
    
    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # run "populate_indicators" only for new candle
    process_only_new_candles = True
    startup_candle_count = 20 #time to wait before valid signals
    
    def chaikin_mf(self, dataframe, periods=20):
        close = dataframe['close']
        low = dataframe['low']
        high = dataframe['high']
        volume = dataframe['volume']

        mfv = ((close - low) - (high - close)) / (high - low)
        mfv = mfv.fillna(0.0)  # float division by zero
        mfv *= volume
        cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()

        return Series(cmf, name='cmf')
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        #RSI
        dataframe['rsi1'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi2'] = ta.RSI(dataframe, timeperiod=14)
        #CMF
        dataframe['cmf'] = self.chaikin_mf(dataframe) #guard
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
                
        dataframe.loc[
            (
                (dataframe["rsi1"] <= self.buy_rsi1.value) & #Trigger
                (dataframe["rsi2"] <= self.buy_rsi2.value) & #Trigger
                (dataframe["cmf"] <= self.buy_cmf.value) & #Guard
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'buy'] = 1
        return dataframe

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

        dataframe.loc[
            (    
                (dataframe["rsi1"] >= self.sell_rsi1.value) & #Trigger
                (dataframe["rsi2"] >= self.sell_rsi2.value) & #Trigger
                (dataframe["cmf"] >= self.sell_cmf.value) & #Guard
                (dataframe['volume'] > 0) # volume above zero
            )
        ,'sell'] = 1
        return dataframe