# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from datetime import timedelta, datetime, timezone
#from freqtrade.strategy.strategy_helper import  merge_informative_pair
from typing import Dict, List
import numpy as np
# --------------------------------
# 11-Aug-20  - seems to be good making a few trades  5 days 33 wins 7 losses AVE 0.41% tot ROI 17.14%

class cryptohassle(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Sp0ngeB0bUK\n    Title:  Crypto Hassle \n    Version: 0.1\n\n    Heikin Ashi Candles - SSL Channel, Momentum cross supported by MACD\n    \n    '
    #"192": -1
    minimal_roi = {'0': 0.5}
    # Stoploss:
    stoploss = -0.2
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.07
    trailing_only_offset_is_reached = True
    # Optimal ticker interval for the strategy
    timeframe = '1h'
    # Optional order type mapping.
    order_types = {'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': True}
    # Configuration for main plot indicators.
    # Specifies `ema10` to be red, and `ema50` to be a shade of gray
    # Additional subplot RSI
    plot_config = {'main_plot': {'ha_ema9': {'color': 'green'}, 'ha_ema20': {'color': 'red'}}, 'subplots': {'ADX': {'ha_adx': {'color': 'blue'}}}}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # # Heikin Ashi Strategy
        heikinashi = qtpylib.heikinashi(dataframe)
        # Heikinashi EMA
        #dataframe['ha_ema9'] = ta.EMA(heikinashi, timeperiod=9)
        #dataframe['ha_ema20'] = ta.EMA(heikinashi, timeperiod=20)
        # Heikinashi ADX
        #dataframe['ha_adx'] = ta.ADX(heikinashi)
        # HeikinAshi EMA cross to ADX cross rolling delta
        #dataframe['ha_adx_cross'] = qtpylib.crossed_above(dataframe['ha_adx'],25)
        # HeikinAhi EMA9 crossed above EMA20
        #dataframe['ha_ema_cross_above'] = qtpylib.crossed_above(dataframe['ha_ema9'],dataframe['ha_ema20'])
        # HeikinAhi EMA9 crossed below EMA20
        #dataframe['ha_ema_cross_below'] = qtpylib.crossed_below(dataframe['ha_ema9'],dataframe['ha_ema20'])
        # Heikin Ashi Momentum
        #Momentum
        dataframe['ha_mom'] = ta.MOM(heikinashi, timeperiod=14)
        dataframe['ha_mom_cross_above'] = qtpylib.crossed_above(dataframe['ha_mom'], 0)
        # Heikin Ashi Candles
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']
        # Heikin Ashi MACD
        macd = ta.MACD(heikinashi)
        dataframe['ha_macd'] = macd['macd']
        dataframe['ha_macdsignal'] = macd['macdsignal']
        dataframe['ha_macdhist'] = macd['macdhist']
        dataframe['ha_macd_cross_above'] = qtpylib.crossed_above(dataframe['ha_macd'], dataframe['ha_macdsignal'])
        # Heikin Ashi SSl Channels

        def SSLChannels(dataframe, length=10, mode='sma'):
            """
            Source: https://www.tradingview.com/script/xzIoaIJC-SSL-channel/
            Author: xmatthias
            Pinescript Author: ErwinBeckers
            SSL Channels.
            Average over highs and lows form a channel - lines "flip" when close crosses either of the 2 lines.
            Trading ideas:
                * Channel cross
                * as confirmation based on up > down for long
                MC - MODIFIED FOR HA CANDLES
            """
            if mode not in 'sma':
                raise ValueError(f'Mode {mode} not supported yet')
            df = dataframe.copy()
            if mode == 'sma':
                df['smaHigh'] = df['ha_high'].rolling(length).mean()
                df['smaLow'] = df['ha_low'].rolling(length).mean()
            df['hlv'] = np.where(df['ha_close'] > df['smaHigh'], 1, np.where(df['ha_close'] < df['smaLow'], -1, np.NAN))
            df['hlv'] = df['hlv'].ffill()
            df['ha_sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
            df['ha_sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
            return (df['ha_sslDown'], df['ha_sslUp'])
        ssl = SSLChannels(dataframe, 10)
        dataframe['ha_sslDown'] = ssl[0]
        dataframe['ha_sslUp'] = ssl[1]
        dataframe['ha_ssl_cross_above'] = qtpylib.crossed_above(dataframe['ha_sslUp'], dataframe['ha_sslDown'])
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Heikin Ashi SSL Channels
        # Momentum
        # Heikin Ashi MacD
        # Volume
        dataframe.loc[(dataframe['ha_ssl_cross_above'].rolling(5).apply(lambda x: x.any(), raw=False) == 1) & (dataframe['ha_mom_cross_above'].rolling(5).apply(lambda x: x.any(), raw=False) == 1) & (dataframe['ha_macd_cross_above'].rolling(5).apply(lambda x: x.any(), raw=False) == 1) & (dataframe['volume'] > 1000), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[qtpylib.crossed_below(dataframe['ha_sslUp'], dataframe['ha_sslDown']) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe