# source: https://raw.githubusercontent.com/Mastaaa1987/freqtrade-strategy/bfe7f2274299f758d487034249b32f6a553b4421/user_data/strategies/Scalping.py
import math, os, json, sys, time
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib

class Github_Mastaaa1987_freqtrade_strategy__Scalping__20241103_060507(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'

    # Trading Parameter
    # minimal_roi = {
    #     "60": 0.01,
    #     "30": 0.02,
    #     "0": 0.04
    # }
    stoploss = -0.05
    trailing_stop = False
    process_only_new_candles = True
    use_exit_signal = True
    use_custom_stoploss=False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    startup_candle_count = 200

    # Plot Konfiguration
    @property
    def plot_config(self):
        return {
            "main_plot": {
                "ema21": { "color": "#bfa721" },
                "ema50": { "color": "#9dfd0f" },
                "ema200": { "color": "#543ad1" },
                "tp1": {
                    "color": "#81ff70",
                    "fill_to": "entry"
                },
                "tp2": {
                    "color": "#27ff0a",
                    "fill_to": "tp1"
                },
                "entry": {
                    "color": "#ffffff"
                },
                "sl1": {
                    "color": "#d86969",
                    "fill_to": "entry"
                },
                "sl2": {
                    "color": "#ff0000",
                    "fill_to": "sl1"
                }
            },
            "subplots": {
                "stoch": {
                    "slowd": { "color": "#eeff00" },
                    "slowk": { "color": "#ff0000" }
                },
                "rsi": {
                    "rsi": { "color": "#00ff04" },
                    "rsi_mid": { "color": "#ffffff" }
                },
                "macd": {
                    "macd": { "color": "#00fbff" },
                    "macdsignal": { "color": "#ff0000" },
                    "macdhist": { "color": "#6b98b0", "type": "bar"}
                },
                "cond": {
                    "profits": { "color": "#ffffff" },
                    "profit": { "color": "#ffffff" },
                    "tag": { "color": "#de27f8" },
                    "change": { "color": "#14896f" },
                    "sc": { "color": "#7fd9da" },
                    "rc": { "color": "#4fa25f" },
                    "mc": { "color": "#ff0000" }
                }
            }
        }

    def calc(self, dataframe: DataFrame, pair):
        if self.dp.runmode.value in ('live', 'dry_run'):
            if(self.timeframe == '1h'):
                df = dataframe.copy()
            else:
                df = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
                if(int(df.loc[len(df)-1]['date'].strftime("%M")) != 55):
                    data = {'date': dataframe.loc[len(dataframe)-1]['date'], 'open': df.loc[len(df)-1]['close'], 'high': 0, 'low': 0, 'close': dataframe.loc[len(dataframe)-1]['close'], 'volume': 0}
                    df = df._append(data, ignore_index = True)
        else:
            df = dataframe.copy()
        df["change"] = (100 / df['open'] * df['close'] - 100)

        df['vol_ma'] = df['volume'].rolling(window=30).mean()
        df['vc'] = 0
        df.loc[((df['open'] < df['close']) & (df['volume'] > df['vol_ma'])), 'vc'] = 1

        df["rsi"] = ta.RSI(df)
        df["rsi_ma"] = ta.SMA(df['rsi'], timeperiod=14)
        df["rsi_mid"] = 50
        df['rc'] = 0
        df.loc[
            (
                ((df['rsi'].shift(1) < 50) & (df['rsi'] > 50)) |
                ((df['rsi'].shift(2) < 50) & (df['rsi'].shift(1) > 50)) |
                ((df['rsi'].shift(3) < 50) & (df['rsi'].shift(2) > 50))
            ), 'rc'] = 1

        df["min"] = ta.MIN(df['open'], timeperiod=10)
        stoch = ta.STOCH(df)
        df["slowd"] = stoch["slowd"]
        df["slowk"] = stoch["slowk"]
        #df = self.calc_stoch(df, metadata['pair'])

        df["ema21"] = ta.EMA(df, timeperiod=21)
        df["ema50"] = ta.EMA(df, timeperiod=50)
        df["ema200"] = ta.EMA(df, timeperiod=200)
        df["ma13p"] = (100 / df["ema200"] * df["ema21"] - 100)
        df["ma23p"] = (100 / df["ema200"] * df["ema50"] - 100)

        df["cci"] = ta.CCI(dataframe)
        df["cci_mid"] = 0

        macd = ta.MACD(df)
        df["macd"] = macd["macd"]
        df["macdsignal"] = macd["macdsignal"]
        df["macdhist"] = macd["macdhist"]
        df["mc"] = 0
        df.loc[(
                ((df['macd'].shift(1) < df['macdsignal'].shift(1)) & (df['macd'] > df['macdsignal'])) |
                ((df['macd'].shift(2) < df['macdsignal'].shift(2)) & (df['macd'].shift(1) > df['macdsignal'].shift(1))) |
                ((df['macd'].shift(3) < df['macdsignal'].shift(3)) & (df['macd'].shift(2) > df['macdsignal'].shift(2)))
                ), 'mc'] = 1
        v = {'enter': 0, 'ENTER': [], 'exit': 0, 'EXIT': [], 'trade': 0, 'TRADE': [], 'profit': 0, 'PROFIT': [], 'profits': 0, 'PROFITS': [], 'aw': 0, 'AW': [], 'al': 0, 'AL': [], 'wc': 0, 'WC': [], 'lc': 0, 'LC': [], 'sc': 0, 'SC': [], 'sl1': None, 'SL1': [], 'sl2': None, 'SL2': [], 'tp1': None, 'TP1': [], 'tp2': None, 'TP2': [], 'op1': None, 'OP1': [], 'op2': None, 'OP2': [], 'entry': None, 'ENTRY': [], 'count': 0, 'COUNT': [], 'wait': 0, 'WAIT': [], 'tagg': '', 'TAGG': []}
        if self.dp.runmode.value in ('live', 'dry_run'):
            if(self.timeframe != '1h'):
                if(len(df) == 999): z = 1
                else: z = 0
            else: z = 0
        else: z = 0
        start = None
        for i in range(z, len(df)):
            v['enter'], v['exit'] = 0, 0
            if(df.loc[i]['slowd'] > 80) and (df.loc[i]['slowk'] > 80): v['sc'] = 1
            elif(df.loc[i]['slowd'] < 20) and (df.loc[i]['slowk'] < 20): v['sc'] = -1
            if(v['trade'] == 0):
                if(df.loc[i]['rc'] == 1) and (df.loc[i]['mc'] == 1) and (v['sc'] == -1) and (df.loc[i]['close'] > df.loc[i]['open']):
                    if(i > 0):
                        if(df.loc[i]['close'] > df.loc[i]['ema200']) and (df.loc[i]['ema50'] > df.loc[i]['ema200']) and (df.loc[i]['ema200'] > df.loc[i-1]['ema200']):
                            if(df.loc[i]['ma13p'] > df.loc[i-1]['ma13p']) and (df.loc[i]['ma23p'] > df.loc[i-1]['ma23p']):
                                op = df.loc[i]['open']
                                op2 = df.loc[i]['min']
                                # for x in range(1, i):
                                #     if(df.loc[i-x]['open'] < df.loc[i-x]['close']): op2 = df.loc[i-x]['open']
                                #     else: break
                                v['op1'] = (df.loc[i]['close'] - op)
                                v['op2'] = (df.loc[i]['close'] - op2)
                                v['sl1'] = (df.loc[i]['close'] - v['op1'])
                                v['sl2'] = (df.loc[i]['close'] - v['op2'])
                                v['tp1'] = (df.loc[i]['close'] + v['op1'] * 1.5)
                                v['tp2'] = (df.loc[i]['close'] + v['op2'] * 1.5)
                                v['entry'] = df.loc[i]['close']
                                start = df.loc[i]['date'].strftime("%H-%d-%m")

                                v['enter'], v['trade'], v['count'], v['profit'], v['tagg'] = 1, 1, 0, 0, ''
            elif(v['trade'] == 1):
                exit = 0
                v['count'] = (v['count'] + 1)
                v['profit'] = (100 / v['entry'] * df.loc[i]['close'] - 100) - 0.3
                if(v['wait'] == 1):
                    if(df.loc[i]['close'] < df.loc[i]['open']):
                        exit = 1
                        v['tagg'] = 'exit: wait=1'
                elif(df.loc[i]['close'] > v['tp2']) or (df.loc[i]['close'] < v['sl2']):
                    exit = 1
                    if(df.loc[i]['close'] > v['tp2']):
                        v['tagg'] = 'exit: close>tp2'
                    elif(df.loc[i]['close'] < v['sl2']):
                        v['tagg'] = 'exit: close<sl2'
                elif(v['count'] > 2) and (v['count'] < 7):
                    if(df.loc[i]['close'] > v['tp1']):
                        if(v['wait'] == 0):
                            exit = 1
                            v['tagg'] = 'exit: close>tp1'
                    elif(df.loc[i]['close'] > v['entry']):
                        if(v['profit'] > 1):
                            t = True
                            for x in range(1, v['count']):
                                if(df.loc[i-x]['close'] < df.loc[i-x]['open']):
                                    t = False
                                    break
                            if(t):
                                v['wait'] = 1
                                v['tagg'] = 'wait=1'
                            if(v['wait'] == 0):
                                exit = 1
                                v['tagg'] = 'exit: win>1'
                elif(v['count'] >= 7):
                    if(v['profit'] > 2):
                        exit = 1
                        v['tagg'] = 'exit: win>2,count>=7'
                    elif(df.loc[i]['close'] > v['tp1']):
                        exit = 1
                        v['tagg'] = 'exit: close>tp1,count>=7'
                    elif(v['count'] > 20) and (df.loc[i]['close'] > v['entry']):
                        if(v['profit'] > 0):
                            exit = 1
                            v['tagg'] = 'exit: win>0,count>20'
                if(exit == 0):
                    cw = (100 / v['entry'] * df.loc[i]['close'] - 100)
                    ow = (100 / v['entry'] * df.loc[i]['open'] - 100)
                    if(ow < -5) and (cw < -5):
                        exit = 1
                        v['tagg'] = 'exit: open+close<-5'
                if(exit == 1):
                    if(v['profit'] > 0):
                        v['aw'] = (v['aw'] + v['profit'])
                        v['wc'] = (v['wc'] + 1)
                    elif(v['profit'] < 0):
                        v['al'] = (v['al'] + v['profit'])
                        v['lc'] = (v['lc'] + 1)
                    start = None
                    #end = df.loc[i]['date'].strftime("%H-%d-%m")
                    #print(pair, start, end, v['profit'])
                    v['profits'] = (v['profits'] + v['profit'])
                    v['exit'], v['trade'], v['sl1'], v['sl2'], v['tp1'], v['tp2'], v['op1'], v['op2'], v['entry'], v['wait'], v['count'] = 1, 0, None, None, None, None, None, None, None, 0, 0
            v['PROFIT'].append(v['profit']), v['AW'].append(v['aw']), v['AL'].append(v['al']), v['WC'].append(v['wc']), v['LC'].append(v['lc']), v['TRADE'].append(v['trade']), v['COUNT'].append(v['count']), v['WAIT'].append(v['wait']), v['ENTRY'].append(v['entry']), v['SL1'].append(v['sl1']), v['SL2'].append(v['sl2']), v['OP1'].append(v['op1']), v['OP2'].append(v['op2']), v['TP1'].append(v['tp1']), v['TP2'].append(v['tp2']), v['ENTER'].append(v['enter']), v['EXIT'].append(v['exit']), v['PROFITS'].append(v['profits']), v['SC'].append(v['sc']), v['TAGG'].append(v['tagg'])
        df['aw'], df['al'], df['wc'], df['lc'], df['trade'], df['count'], df['wait'], df['enter'], df['exit'], df['profits'], df['profit'], df['sc'], df['tag'], df['sl1'], df['sl2'], df['tp1'], df['tp2'], df['op1'], df['op2'], df['entry'] = v['AW'], v['AL'], v['WC'], v['LC'], v['TRADE'], v['COUNT'], v['WAIT'], v['ENTER'], v['EXIT'], v['PROFITS'], v['PROFIT'], v['SC'], v['TAGG'], v['SL1'], v['SL2'], v['TP1'], v['TP2'], v['OP1'], v['OP2'], v['ENTRY']
        if self.dp.runmode.value in ('live', 'dry_run'):
            if(self.timeframe != '1h'):
                dataframe = merge_informative_pair(dataframe, df, self.timeframe, '1h', ffill=True)

        if(start):
            print(pair, start)
        print(v['profits'], v['wc'], v['aw'], v['lc'], v['al'], pair)
        if self.dp.runmode.value in ('live', 'dry_run'):
            if(self.timeframe != '1h'):
                return dataframe
            else:
                return df
        else:
            return df

    def calc2(self, dataframe: DataFrame, pair):
        df = dataframe.copy()

        df["change"] = (100 / df['open'] * df['close'] - 100)

        df['vol_ma'] = df['volume'].rolling(window=30).mean()
        df['vc'] = 0
        df.loc[((df['open'] < df['close']) & (df['volume'] > df['vol_ma'])), 'vc'] = 1

        df["rsi"] = ta.RSI(df)
        df["rsi_ma"] = ta.SMA(df['rsi'], timeperiod=14)
        df["rsi_mid"] = 50
        df['rc'] = 0
        df.loc[
            (
                ((df['rsi'].shift(1) < 50) & (df['rsi'] > 50)) |
                ((df['rsi'].shift(2) < 50) & (df['rsi'].shift(1) > 50)) |
                ((df['rsi'].shift(3) < 50) & (df['rsi'].shift(2) > 50))
            ), 'rc'] = 1

        df["min"] = ta.MIN(df['open'], timeperiod=10)
        stoch = ta.STOCH(df)
        df["slowd"] = stoch["slowd"]
        df["slowk"] = stoch["slowk"]
        #df = self.calc_stoch(df, metadata['pair'])

        df["ema21"] = ta.EMA(df, timeperiod=21)
        df["ema50"] = ta.EMA(df, timeperiod=50)
        df["ema200"] = ta.EMA(df, timeperiod=200)

        df["cci"] = ta.CCI(dataframe)
        df["cci_mid"] = 0

        macd = ta.MACD(df)
        df["macd"] = macd["macd"]
        df["macdsignal"] = macd["macdsignal"]
        df["macdhist"] = macd["macdhist"]
        df["mc"] = 0
        df.loc[(
                ((df['macd'].shift(1) < df['macdsignal'].shift(1)) & (df['macd'] > df['macdsignal'])) |
                ((df['macd'].shift(2) < df['macdsignal'].shift(2)) & (df['macd'].shift(1) > df['macdsignal'].shift(1))) |
                ((df['macd'].shift(3) < df['macdsignal'].shift(3)) & (df['macd'].shift(2) > df['macdsignal'].shift(2)))
                ), 'mc'] = 1
        df.loc[(
                ((df['macdhist'].shift(1) < 0) & (df['macdhist'] > 0)) |
                ((df['macdhist'].shift(2) < 0) & (df['macdhist'].shift(1) > 0)) |
                ((df['macdhist'].shift(3) < 0) & (df['macdhist'].shift(2) > 0))
                ), 'hc'] = 1
        v = {'enter': 0, 'ENTER': [], 'exit': 0, 'EXIT': [], 'trade': 0, 'TRADE': [], 'profit': 0, 'PROFIT': [], 'profits': 0, 'PROFITS': [], 'aw': 0, 'AW': [], 'al': 0, 'AL': [], 'wc': 0, 'WC': [], 'lc': 0, 'LC': [], 'sc': 0, 'SC': [], 'sl1': None, 'SL1': [], 'sl2': None, 'SL2': [], 'tp1': None, 'TP1': [], 'tp2': None, 'TP2': [], 'op1': None, 'OP1': [], 'op2': None, 'OP2': [], 'entry': None, 'ENTRY': [], 'count': 0, 'COUNT': [], 'wait': 0, 'WAIT': [], 'tagg': '', 'TAGG': []}
        z = 0
        start = None
        for i in range(z, len(df)):
            v['enter'], v['exit'] = 0, 0
            if(df.loc[i]['slowd'] > 80) and (df.loc[i]['slowk'] > 80): v['sc'] = 1
            elif(df.loc[i]['slowd'] < 20) and (df.loc[i]['slowk'] < 20): v['sc'] = -1
            if(v['trade'] == 0):
                if(df.loc[i]['rc'] == 1) and (df.loc[i]['mc'] == 1) and (v['sc'] == -1) and (df.loc[i]['close'] > df.loc[i]['open']):
                    if(i > 0):
                        if(df.loc[i]['close'] > df.loc[i]['ema200']) and (df.loc[i]['ema50'] > df.loc[i]['ema200']) and (df.loc[i]['ema200'] > df.loc[i-1]['ema200']):
                            op = df.loc[i]['open']
                            op2 = df.loc[i]['min']
                            # for x in range(1, i):
                            #     if(df.loc[i-x]['open'] < df.loc[i-x]['close']): op2 = df.loc[i-x]['open']
                            #     else: break
                            v['op1'] = (df.loc[i]['close'] - op)
                            v['op2'] = (df.loc[i]['close'] - op2)
                            v['sl1'] = (df.loc[i]['close'] - v['op1'])
                            v['sl2'] = (df.loc[i]['close'] - v['op2'])
                            v['tp1'] = (df.loc[i]['close'] + v['op1'] * 1.5)
                            v['tp2'] = (df.loc[i]['close'] + v['op2'] * 1.5)
                            v['entry'] = df.loc[i]['close']
                            start = df.loc[i]['date'].strftime("%H-%d-%m")

                            v['enter'], v['trade'], v['count'], v['profit'], v['tagg'] = 1, 1, 0, 0, ''
            elif(v['trade'] == 1):
                exit = 0
                v['count'] = (v['count'] + 1)
                v['profit'] = (100 / v['entry'] * df.loc[i]['close'] - 100) - 0.3
                if(v['wait'] == 1):
                    if(df.loc[i]['close'] < df.loc[i]['open']):
                        exit = 1
                        v['tagg'] = 'exit: wait=1'
                elif(df.loc[i]['close'] > v['tp2']) or (df.loc[i]['close'] < v['sl2']):
                    exit = 1
                    if(df.loc[i]['close'] > v['tp2']):
                        v['tagg'] = 'exit: close>tp2'
                    elif(df.loc[i]['close'] < v['sl2']):
                        v['tagg'] = 'exit: close<sl2'
                elif(v['count'] > 2) and (v['count'] < 7):
                    if(df.loc[i]['close'] > v['tp1']):
                        if(v['wait'] == 0):
                            exit = 1
                            v['tagg'] = 'exit: close>tp1'
                    elif(df.loc[i]['close'] > v['entry']):
                        if(v['profit'] > 2):
                            t = True
                            for x in range(1, v['count']):
                                if(df.loc[i-x]['close'] < df.loc[i-x]['open']):
                                    t = False
                                    break
                            if(t):
                                v['wait'] = 1
                                v['tagg'] = 'wait=1'
                            if(v['wait'] == 0):
                                exit = 1
                                v['tagg'] = 'exit: win>2'
                elif(v['count'] >= 7):
                    if(v['profit'] > 2):
                        exit = 1
                        v['tagg'] = 'exit: win>2,count>=7'
                    elif(df.loc[i]['close'] > v['tp1']):
                        exit = 1
                        v['tagg'] = 'exit: close>tp1,count>=7'
                    elif(v['count'] > 20) and (df.loc[i]['close'] > v['entry']):
                        if(v['profit'] > 0):
                            exit = 1
                            v['tagg'] = 'exit: win>0,count>20'
                if(exit == 0):
                    cw = (100 / v['entry'] * df.loc[i]['close'] - 100)
                    ow = (100 / v['entry'] * df.loc[i]['open'] - 100)
                    if(ow < -5) and (cw < -5):
                        exit = 1
                        v['tagg'] = 'exit: open+close<-5'
                if(exit == 1):
                    if(v['profit'] > 0):
                        v['aw'] = (v['aw'] + v['profit'])
                        v['wc'] = (v['wc'] + 1)
                    elif(v['profit'] < 0):
                        v['al'] = (v['al'] + v['profit'])
                        v['lc'] = (v['lc'] + 1)
                    start = None
                    #end = df.loc[i]['date'].strftime("%H-%d-%m")
                    #print(pair, start, end, v['profit'])
                    v['profits'] = (v['profits'] + v['profit'])
                    v['exit'], v['trade'], v['sl1'], v['sl2'], v['tp1'], v['tp2'], v['op1'], v['op2'], v['entry'], v['wait'], v['count'] = 1, 0, None, None, None, None, None, None, None, 0, 0
            v['PROFIT'].append(v['profit']), v['AW'].append(v['aw']), v['AL'].append(v['al']), v['WC'].append(v['wc']), v['LC'].append(v['lc']), v['TRADE'].append(v['trade']), v['COUNT'].append(v['count']), v['WAIT'].append(v['wait']), v['ENTRY'].append(v['entry']), v['SL1'].append(v['sl1']), v['SL2'].append(v['sl2']), v['OP1'].append(v['op1']), v['OP2'].append(v['op2']), v['TP1'].append(v['tp1']), v['TP2'].append(v['tp2']), v['ENTER'].append(v['enter']), v['EXIT'].append(v['exit']), v['PROFITS'].append(v['profits']), v['SC'].append(v['sc']), v['TAGG'].append(v['tagg'])
        df['aw'], df['al'], df['wc'], df['lc'], df['trade'], df['count'], df['wait'], df['enter'], df['exit'], df['profits'], df['profit'], df['sc'], df['tag'], df['sl1'], df['sl2'], df['tp1'], df['tp2'], df['op1'], df['op2'], df['entry'] = v['AW'], v['AL'], v['WC'], v['LC'], v['TRADE'], v['COUNT'], v['WAIT'], v['ENTER'], v['EXIT'], v['PROFITS'], v['PROFIT'], v['SC'], v['TAGG'], v['SL1'], v['SL2'], v['TP1'], v['TP2'], v['OP1'], v['OP2'], v['ENTRY']

        if(start):
            print(pair, start)
        print(v['profits'], v['wc'], v['aw'], v['lc'], v['al'], pair)

        return df

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        #informative_pairs = [(pair, '1d') for pair in pairs]
        if(self.timeframe == '1h'):
            informative_pairs = [(pair, '1h') for pair in pairs]
        else:
            informative_pairs = [(pair, self.timeframe) for pair in pairs]
            informative_pairs += [(pair, '1h') for pair in pairs]
        return informative_pairs

    # Indikatoren berechnen
    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        if self.dp.runmode.value in ('live', 'dry_run'):
            if not self.dp:
                return dataframe
            # Some Stuff ...
            pairs = self.dp.current_whitelist()
            p1 = pairs[len(pairs)-1]
            p0 = pairs[0]
            if(metadata["pair"] == p0):
                rp = os.path.normpath(os.path.dirname(os.path.abspath(__file__))+'/../')
                file = os.path.join(rp, '.ts')
                if os.path.exists(file+".json"):
                    os.remove(file+".json")
                t = int(time.time())
                data = {"value": t}
                with open(file+".json", "w") as k:
                    json.dump(data, k, indent=4)

        # Strategy Position ...
        dataframe["enter"] = 0
        dataframe["exit"] = 0
        # if(self.timeframe == '1h'):
        #     dataframe["enter_1h"] = 0
        #     dataframe["exit_1h"] = 0

        if(self.timeframe == '1h'):
            dataframe = self.calc(dataframe, metadata['pair'])
        if(self.timeframe == '5m'):
            dataframe = self.calc2(dataframe, metadata['pair'])

        # Other Stuff ...
        if self.dp.runmode.value in ('live', 'dry_run'):
            if(metadata["pair"] == p1):
                rp = os.path.normpath(os.path.dirname(os.path.abspath(__file__))+'/../')
                file = os.path.join(rp, '.ts')
                if os.path.exists(file+".json"):
                    with open(file+".json") as k:
                        r = json.load(k)
                    value = r.get('value')
                    t = int(time.time())
                    print('Loading took %s seconds ...' % str(t-value))

        return dataframe

    # Kaufbedingung (Long Entry)
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['enter'] == 1) #|
                #(dataframe['enter_1h'] == 1)
            ),
            'enter_long'] = 1
        return dataframe

    # Verkaufsbedingung (Exit)
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['exit'] == 1) #|
                #(dataframe['exit_1h'] == 1)
            ),
            'exit_long'] = 1
        return dataframe

    # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
    #                     current_rate: float, current_profit: float, after_fill: bool,
    #                     **kwargs) -> Optional[float]:
    #
    #     dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
    #     last_candle = dataframe.iloc[-1].squeeze()
    #
    #     # Use parabolic sar as absolute stoploss price
    #     sp1 = last_candle['sl1']
    #     sp2 = last_candle['sl2']
    #     c = last_candle['c']
    #     print(sp1, current_rate, c)
    #
    #     # Convert absolute price to percentage relative to current_rate
    #     # if stoploss_price < current_rate:
    #     #     return stoploss_from_absolute(stoploss_price, current_rate, is_short=trade.is_short)
    #
    #     # return maximum stoploss value, keeping current stoploss price unchanged
    #     return None