# source: https://raw.githubusercontent.com/werkkrew/freqtrade-strategies/a956433f2c0787ca0723a1cd226fa7f7d347c06b/strategies/archived/Hacklemore3.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from pandas import DataFrame, Series
from functools import reduce
from datetime import datetime
from freqtrade.persistence import Trade
from technical.indicators import RMI

class github_werkkrew_freqtrade_strategies__Hacklemore3__20210604_160318(IStrategy):

    """
    PASTE OUTPUT FROM HYPEROPT HERE
    """
    # ROI table:
    minimal_roi = {
        "0": 0.15,
        "5": 0.015
    }

    # Stoploss:
    stoploss = -0.99

    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    """
    END HYPEROPT
    """
    
    timeframe = '5m'

    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = True
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=24).mean()
        dataframe['rmi'] = RMI(dataframe)
        dataframe['sar'] = ta.SAR(dataframe)
        dataframe['max'] = dataframe['high'].rolling(12).max()      
        dataframe['min'] = dataframe['low'].rolling(12).min()       
        dataframe['upper'] = np.where(dataframe['max'] > dataframe['max'].shift(),1,0)      
        dataframe['lower'] = np.where(dataframe['min'] < dataframe['min'].shift(),1,0)      
        dataframe['up_trend'] = np.where(dataframe['upper'].rolling(3, min_periods=1).sum() != 0,1,0)      
        dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(3, min_periods=1).sum() != 0,1,0)

        return dataframe

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

        conditions = []

        active_trade = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True),]).all()

        # Normal buy triggers that apply to new trades we want to enter
        if not active_trade:
            conditions.append(
                (dataframe['up_trend'] == 1) &
                (dataframe['rmi'] > 55) &
                (dataframe['rmi'] >= dataframe['rmi'].rolling(3).mean()) &
                (dataframe['close'] > dataframe['close'].shift()) &
                (dataframe['close'].shift() > dataframe['close'].shift(2)) &
                (dataframe['sar'] < dataframe['close']) &
                (dataframe['sar'].shift() < dataframe['close'].shift()) &
                (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 30))
            )
        # Persist a buy signal for existing trades to make use of ignore_roi_if_buy_signal = True
        # when this buy signal is not present a sell will happen according to ROI table
        else:
            conditions.append(dataframe['rmi'] >= 75) 

        conditions.append(dataframe['volume'].gt(0))

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

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    
        conditions = []

        active_trade = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True),]).all()
        
        # if we are in an active trade for this pair consider various things in our sell signal
        if active_trade:
            ob = self.dp.orderbook(metadata['pair'], 1)
            current_price = ob['asks'][0][0]
            current_profit = active_trade[0].calc_profit_ratio(rate=current_price)
            max_price = active_trade[0].max_rate
            # if we are at a loss, consider what the trend looks like in the sell
            if current_profit < 0:
                conditions.append(
                    (dataframe['dn_trend'] == 1) &
                    (dataframe['rmi'] < 50) &
                    (dataframe['volume'].gt(0))
                    # custom sell-reason: dynamic-stop-loss
                )
            # if we are in a profit, produce a sort of dynamic trailing stoploss
            else: 
                conditions.append(
                    (current_price > (max_price * 0.8)) &
                    (dataframe['close'] < dataframe['close'].shift()) &
                    (dataframe['high'] < dataframe['high'].shift())
                    # custom sell-reason: dynamic-trailing-stop
                ) 
        else:
            # impossible condition needed for some reason?
            conditions.append(dataframe['volume'].lt(0))
                           
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'sell'] = 1
        
        return dataframe

    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['bids'][0][0]
        # Cancel buy order if price is more than 1% above the order.
        if current_price > order['price'] * 1.01:
            return True
        return False

    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel sell order if price is more than 1% below the order.
        if current_price < order['price'] * 0.99:
            return True
        return False

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel buy order if price is more than 1% above the order.
        if current_price > rate * 1.01:
            return False
        return True

    """
    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
        _, roi = self.min_roi_reached_entry(0)

        if roi is None:
           if Trade.max_rate >= Trade.rate * 0.8 and Trade.rate > Trade.open_rate: 
                return False
            if Trade.max_rate < Trade.rate * 0.8 and Trade.rate < Trade.open_rate: 
                return False
            if Trade.max_rate < Trade.rate * 0.8 and Trade.rate > Trade.open_rate: 
                return current_profit > roi
        return False
    """