# source: https://raw.githubusercontent.com/mupol313/hossain/fbb130e963268749c3c05cccea4f5e3b05054836/user_data/strategies/strats/CryptoFrogHO3A4.py
from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
from cachetools import TTLCache
from pandas import DataFrame
import numpy as np
import talib.abstract as ta
from finta import TA as fta
from freqtrade.strategy import IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from skopt.space import Dimension

class Github_mupol313_hossain__CryptoFrogHO3A4__20240622_082213(IStrategy):
    sell_params = {
        "cstp_bail_how": "roc",
        "cstp_bail_roc": -0.01,
        "cstp_bail_time": 1124,
        "cstp_threshold": -0.001,
        "droi_pullback": False,
        "droi_pullback_amount": 0.005,
        "droi_pullback_respect_table": True,
        "droi_trend_type": "rmi",
    }

    minimal_roi = {
        "0": 0.219,
        "14": 0.021,
        "24": 0.01,
        "49": 0
    }

    trailing_stop = True
    trailing_stop_positive = 0.324
    trailing_stop_positive_offset = 0.42
    trailing_only_offset_is_reached = True

    stoploss = -0.299

    use_custom_stoploss = True
    custom_stop = {
        # Linear Decay Parameters
        'decay-time': 49,       # minutes to reach end, I find it works well to match this to the final ROI value - default 1080
        'decay-delay': 0,         # minutes to wait before decay starts
        'decay-start': -0.299, # -0.32118, # -0.07163,     # starting value: should be the same or smaller than initial stoploss - default -0.30
        'decay-end': -0.02,       # ending value - default -0.03
        # Profit and TA  
        'cur-min-diff': 0.03,     # diff between current and minimum profit to move stoploss up to min profit point
        'cur-threshold': -0.02,   # how far negative should current profit be before we consider moving it up based on cur/min or roc
        'roc-bail': -0.03,        # value for roc to use for dynamic bailout
        'rmi-trend': 50,          # rmi-slow value to pause stoploss decay
        'bail-how': 'immediate',  # set the stoploss to the atr offset below current price, or immediate
        # Positive Trailing
        'pos-trail': True,        # enable trailing once positive  
        'pos-threshold': 0.005,   # trail after how far positive
        'pos-trail-dist': 0.015   # how far behind to place the trail
    }

    # Dynamic ROI
    droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='sell', optimize=True)
    droi_pullback = CategoricalParameter([True, False], default=True, space='sell', optimize=True)
    droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='sell')
    droi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='sell', optimize=True)    
    
    # Custom Stoploss
    cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space='sell')
    cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default='roc', space='sell', optimize=True)
    cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell')
    cstp_bail_time = IntParameter(720, 1440, default=720, space='sell')    
    
    stoploss = custom_stop['decay-start']    

    custom_trade_info = {}
    custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # 5 minutes
        
    # run "populate_indicators" only for new candle
    process_only_new_candles = True

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = True

    use_dynamic_roi = True    
    
    timeframe = '5m'
    informative_timeframe = '1h'

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }
    
    plot_config = {
        'main_plot': {
            'Smooth_HA_H': {'color': 'orange'},
            'Smooth_HA_L': {'color': 'yellow'},
        },
        'subplots': {
            "StochRSI": {
                'srsi_k': {'color': 'blue'},
                'srsi_d': {'color': 'red'},
            },
            "MFI": {
                'mfi': {'color': 'green'},
            },
            "BBEXP": {
                'bbw_expansion': {'color': 'orange'},
            },
            "FAST": {
                'fastd': {'color': 'red'},
                'fastk': {'color': 'blue'},
            },
            "SQZMI": {
                'sqzmi': {'color': 'lightgreen'},
            },
            "VFI": {
                'vfi': {'color': 'lightblue'},
            },
            "DMI": {
                'dmi_plus': {'color': 'orange'},
                'dmi_minus': {'color': 'yellow'},
            },
            "EMACO": {
                'emac_1h': {'color': 'red'},
                'emao_1h': {'color': 'blue'},
            },
        }
    }


    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        #pairs.append("BTC/USDT")
        #pairs.append("ETH/USDT")
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def HA(self, dataframe, smoothing=None):
        df = dataframe.copy()

        df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4

        df.reset_index(inplace=True)

        ha_open = [ (df['open'][0] + df['close'][0]) / 2 ]
        [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ]
        df['HA_Open'] = ha_open

        df.set_index('index', inplace=True)

        df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1)
        df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1)

        if smoothing is not None:
            sml = abs(int(smoothing))
            if sml > 0:
                df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml)
                df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml)
                df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml)
                df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml)
                
        return df
    
    def hansen_HA(self, informative_df, period=6):
        dataframe = informative_df.copy()
        
        dataframe['hhclose']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
        dataframe['hhopen']= ((dataframe['open'].shift(2) + dataframe['close'].shift(2))/ 2) #it is not the same as real heikin ashi since I found that this is better.
        dataframe['hhhigh']=dataframe[['open','close','high']].max(axis=1)
        dataframe['hhlow']=dataframe[['open','close','low']].min(axis=1)

        dataframe['emac'] = ta.SMA(dataframe['hhclose'], timeperiod=period) #to smooth out the data and thus less noise.
        dataframe['emao'] = ta.SMA(dataframe['hhopen'], timeperiod=period)
        
        return {'emac': dataframe['emac'], 'emao': dataframe['emao']}
    
    def bbw_expansion(self, bbw_rolling, mult=1.1):
        bbw = list(bbw_rolling)

        m = 0.0
        for i in range(len(bbw)-1):
            if bbw[i] > m:
                m = bbw[i]

        if (bbw[-1] > (m * mult)):
            return 1
        return 0

    def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Stoch fast - mainly due to 5m timeframes
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']        
        
        #StochRSI for double checking things
        period = 14
        smoothD = 3
        SmoothK = 3
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        stochrsi  = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min())
        dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
        dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean()

        # Bollinger Bands because obviously
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        
        # SAR Parabol - probably don't need this
        dataframe['sar'] = ta.SAR(dataframe)
        
        ## confirm wideboi variance signal with bbw expansion
        dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"])
        dataframe['bbw_expansion'] = dataframe['bb_width'].rolling(window=4).apply(self.bbw_expansion)

        # confirm entry and exit on smoothed HA
        dataframe = self.HA(dataframe, 4)

        # thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative
        # https://github.com/hansen1015/freqtrade_strategy
        hansencalc = self.hansen_HA(dataframe, 6)
        dataframe['emac'] = hansencalc['emac']
        dataframe['emao'] = hansencalc['emao']
        
        # money flow index (MFI) for in/outflow of money, like RSI adjusted for vol
        dataframe['mfi'] = fta.MFI(dataframe)
        
        ## sqzmi to detect quiet periods
        dataframe['sqzmi'] = fta.SQZMI(dataframe) #, MA=hansencalc['emac'])
        
        # Volume Flow Indicator (MFI) for volume based on the direction of price movement
        dataframe['vfi'] = fta.VFI(dataframe, period=14)
        
        dmi = fta.DMI(dataframe, period=14)
        dataframe['dmi_plus'] = dmi['DI+']
        dataframe['dmi_minus'] = dmi['DI-']
        dataframe['adx'] = fta.ADX(dataframe, period=14)
        
        ## for stoploss - all from Solipsis4
        ## simple ATR and ROC for stoploss
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)        
        dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')        
        dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(),1,0)      
        dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3,1,0) 
        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0)
        dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0)        
        
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Populate/update the trade data if there is any, set trades to false if not live/dry
        self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
        
        if self.config['runmode'].value in ('backtest', 'hyperopt'):
            assert (timeframe_to_minutes(self.timeframe) <= 30), "Backtest this strategy in 5m or 1m timeframe."

        if self.timeframe == self.informative_timeframe:
            dataframe = self.do_indicators(dataframe, metadata)
        else:
            if not self.dp:
                return dataframe

            informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)

            informative = self.do_indicators(informative.copy(), metadata)
            
            dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
            
            skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume', 'emac', 'emao']]
            dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True)

        # Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
        if self.dp.runmode.value in ('backtest', 'hyperopt'):
            self.custom_trade_info[metadata['pair']]['roc'] = dataframe[['date', 'roc']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['sroc'] = dataframe[['date', 'sroc']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[['date', 'ssl-dir']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['candle-up-trend'] = dataframe[['date', 'candle-up-trend']].copy().set_index('date')            
            
        return dataframe

    ## cryptofrog signals
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (dataframe['close'] < dataframe['Smooth_HA_L'])
                    &
                    (dataframe['emac_1h'] < dataframe['emao_1h'])
                )
                &
                (
                    (
                        (dataframe['bbw_expansion'] == 1) & (dataframe['sqzmi'] == False)
                        &
                        (
                            (dataframe['mfi'] < 20)
                            |
                            (dataframe['dmi_minus'] > 30)
                        )
                    )
                    |
                    (
                        (dataframe['close'] < dataframe['sar'])
                        &
                        ((dataframe['srsi_d'] >= dataframe['srsi_k']) & (dataframe['srsi_d'] < 30))
                        &
                        ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 23))
                        &
                        (dataframe['mfi'] < 30)
                    )
                    |
                    (
                        (
                            ((dataframe['dmi_minus'] > 30) & qtpylib.crossed_above(dataframe['dmi_minus'], dataframe['dmi_plus']))
                            &
                            (dataframe['close'] < dataframe['bb_lowerband'])
                        )
                        |
                        (
                            (dataframe['sqzmi'] == True)
                            &
                            ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 20))
                        )
                    )
                    &
                    (dataframe['vfi'] < 0.0)                    
                    &
                    (dataframe['volume'] > 0)                    
                )
            ),
            'buy'] = 1

        return dataframe
    
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (dataframe['close'] > dataframe['Smooth_HA_H'])
                    &
                    (dataframe['emac_1h'] > dataframe['emao_1h'])
                )
                &
                (
                    (
                        (dataframe['bbw_expansion'] == 1)
                        &
                        (
                            (dataframe['mfi'] > 80)
                            |
                            (dataframe['dmi_plus'] > 30)
                        )
                    )
                    &
                    (dataframe['vfi'] > 0.0)
                    &
                    (dataframe['volume'] > 0)                    
                )
            ),
            'sell'] = 1
        return dataframe


    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)

        if self.config['runmode'].value in ('live', 'dry_run'):
            dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
            sroc = dataframe['sroc'].iat[-1]
        # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
        else:
            sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc']

        if current_profit < self.cstp_threshold.value:
            if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any':
                # Dynamic bailout based on rate of change
                if (sroc/100) <= self.cstp_bail_roc.value:
                    return 0.001
            if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any':
                # Dynamic bailout based on time
                if trade_dur > self.cstp_bail_time.value:
                    return 0.001
                   
        return 1


    def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:

        minimal_roi = self.minimal_roi
        _, table_roi = self.min_roi_reached_entry(trade_dur)

        # see if we have the data we need to do this, otherwise fall back to the standard table
        if self.custom_trade_info and trade and trade.pair in self.custom_trade_info:
            if self.config['runmode'].value in ('live', 'dry_run'):
                dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe)
                rmi_trend = dataframe['rmi-up-trend'].iat[-1]
                candle_trend = dataframe['candle-up-trend'].iat[-1]
                ssl_dir = dataframe['ssl-dir'].iat[-1]
            # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
            else:
                rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend'].loc[current_time]['rmi-up-trend']
                candle_trend = self.custom_trade_info[trade.pair]['candle-up-trend'].loc[current_time]['candle-up-trend']
                ssl_dir = self.custom_trade_info[trade.pair]['ssl-dir'].loc[current_time]['ssl-dir']

            min_roi = table_roi
            max_profit = trade.calc_profit_ratio(trade.max_rate)
            pullback_value = (max_profit - self.droi_pullback_amount.value)
            in_trend = False

            if self.droi_trend_type.value == 'rmi' or self.droi_trend_type.value == 'any':
                if rmi_trend == 1:
                    in_trend = True
            if self.droi_trend_type.value == 'ssl' or self.droi_trend_type.value == 'any':
                if ssl_dir == 'up':
                    in_trend = True
            if self.droi_trend_type.value == 'candle' or self.droi_trend_type.value == 'any':
                if candle_trend == 1:
                    in_trend = True

            # Force the ROI value high if in trend
            if (in_trend == True):
                min_roi = 100
                # If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend
                if self.droi_pullback.value == True and (current_profit < pullback_value):
                    if self.droi_pullback_respect_table.value == True:
                        min_roi = table_roi
                    else:
                        min_roi = current_profit / 2

        else:
            min_roi = table_roi

        return trade_dur, min_roi

    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:  
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)

        if self.use_dynamic_roi:
            _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur)
        else:
            _, roi = self.min_roi_reached_entry(trade_dur)
        if roi is None:
            return False
        else:
            return current_profit > roi    
    
    def get_current_price(self, pair: str, refresh: bool) -> float:
        if not refresh:
            rate = self.custom_current_price_cache.get(pair)
            # Check if cache has been invalidated
            if rate:
                return rate

        ask_strategy = self.config.get('ask_strategy', {})
        if ask_strategy.get('use_order_book', False):
            ob = self.dp.orderbook(pair, 1)
            rate = ob[f"{ask_strategy['price_side']}s"][0][0]
        else:
            ticker = self.dp.ticker(pair)
            rate = ticker['last']

        self.custom_current_price_cache[pair] = rate
        return rate    
    
    def populate_trades(self, pair: str) -> dict:
        # Initialize the trades dict if it doesn't exist, persist it otherwise
        if not pair in self.custom_trade_info:
            self.custom_trade_info[pair] = {}

        # init the temp dicts and set the trade stuff to false
        trade_data = {}
        trade_data['active_trade'] = False

        # active trade stuff only works in live and dry, not backtest
        if self.config['runmode'].value in ('live', 'dry_run'):
            
            # find out if we have an open trade for this pair
            active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all()

            # if so, get some information
            if active_trade:
                # get current price and update the min/max rate
                current_rate = self.get_current_price(pair, True)
                active_trade[0].adjust_min_max_rates(current_rate)

        return trade_data

    class HyperOpt:

        @staticmethod
        def indicator_space() -> List[Dimension]:
            return []


def RMI(dataframe, *, length=20, mom=5):

    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]

def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])

    return df['sslDown'], df['sslUp']

def SROC(dataframe, roclen=21, emalen=13, smooth=21):
    df = dataframe.copy()

    roc = ta.ROC(df, timeperiod=roclen)
    ema = ta.EMA(df, timeperiod=emalen)
    sroc = ta.ROC(ema, timeperiod=smooth)

    return sroc