from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
from cachetools import TTLCache
## I hope you know what these are already
from pandas import DataFrame
import numpy as np
## Indicator libs
import talib.abstract as ta
from finta import TA as fta
## FT stuffs
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 CryptoFrogHO3A2(IStrategy):
    INTERFACE_VERSION = 3
    # Sell hyperspace params:
    exit_params = {'cstp_bail_how': 'roc', 'cstp_bail_roc': -0.01, 'cstp_bail_time': 1310, 'cstp_threshold': 0.0, 'droi_pullback': True, 'droi_pullback_amount': 0.009, 'droi_pullback_respect_table': False, 'droi_trend_type': 'rmi'}
    # ROI table - this strat REALLY benefits from roi and trailing hyperopt:
    minimal_roi = {'0': 0.051, '10': 0.02, '24': 0.01, '64': 0}
    # Stoploss:
    stoploss = -0.239
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.221
    trailing_stop_positive_offset = 0.3
    trailing_only_offset_is_reached = True
    use_custom_stoploss = True
    # Linear Decay Parameters
    # minutes to reach end, I find it works well to match this to the final ROI value - default 1080
    # minutes to wait before decay starts
    # -0.32118, # -0.07163,     # starting value: should be the same or smaller than initial stoploss - default -0.30
    # ending value - default -0.03
    # Profit and TA  
    # diff between current and minimum profit to move stoploss up to min profit point
    # how far negative should current profit be before we consider moving it up based on cur/min or roc
    # value for roc to use for dynamic bailout
    # rmi-slow value to pause stoploss decay
    # set the stoploss to the atr offset below current price, or immediate
    # Positive Trailing
    # enable trailing once positive  
    # trail after how far positive
    # how far behind to place the trail
    custom_stop = {'decay-time': 166, 'decay-delay': 0, 'decay-start': -0.085, 'decay-end': -0.02, 'cur-min-diff': 0.03, 'cur-threshold': -0.02, 'roc-bail': -0.03, 'rmi-trend': 50, 'bail-how': 'immediate', 'pos-trail': True, 'pos-threshold': 0.005, 'pos-trail-dist': 0.015}
    # Dynamic ROI
    droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='exit', optimize=True)
    droi_pullback = CategoricalParameter([True, False], default=True, space='exit', optimize=True)
    droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='exit')
    droi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='exit', optimize=True)
    # Custom Stoploss
    cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space='exit')
    cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default='roc', space='exit', optimize=True)
    cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space='exit')
    cstp_bail_time = IntParameter(720, 1440, default=720, space='exit')
    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 = False
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    use_dynamic_roi = True
    timeframe = '5m'
    informative_timeframe = '1h'
    # Optional order type mapping
    order_types = {'entry': 'limit', 'exit': '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
    ## smoothed Heiken Ashi

    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']}
    ## detect BB width expansion to indicate possible volatility

    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
    ## do_indicator style a la Obelisk strategies

    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
    ## stolen from Obelisk's Ichi strat code and backtest blog post, and Solipsis4

    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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ## close ALWAYS needs to be lower than the heiken low at 5m
        ## Hansen's HA EMA at informative timeframe
        ## potential uptick incoming so entry
        # this tries to find extra entrys in undersold regions
        # find smaller temporary dips in sideways
        ## if nothing else is making a entry signal
        ## just throw in any old SQZMI shit based fastd
        ## this needs work!
        ## volume sanity checks
        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)), 'entry'] = 1
        return dataframe
    ## more going on here

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ## close ALWAYS needs to be higher than the heiken high at 5m
        ## Hansen's HA EMA at informative timeframe
        ## try to find oversold regions with a corresponding BB expansion
        ## volume sanity checks
        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)), 'exit'] = 1
        return dataframe
    '\n    Everything from here completely stolen from the godly work of @werkkrew\n    \n    Custom Stoploss \n    '

    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]
        else:
            # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
            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
    '\n    Freqtrade ROI Overload for dynamic ROI functionality\n    '

    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]
            else:
                # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
                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 exit 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)
    # Change here to allow loading of the dynamic_roi settings

    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
    # Get the current price from the exchange (or local cache)

    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
    '\n    Stripped down version from Schism, meant only to update the price data a bit\n    more frequently than the default instead of getting all sorts of trade information\n    '

    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
    # nested hyperopt class

    class HyperOpt:
        # defining as dummy, so that no error is thrown about missing
        # exit indicator space when hyperopting for all spaces

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

def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    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