# source: https://raw.githubusercontent.com/docugs/strategies-1/f7848da5f053d89991b322838803a24fb5331ace/kucoin/FBB_DWT_short.py
import numpy as np
import scipy.fft
from scipy.fft import rfft, irfft
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import arrow

from freqtrade.strategy import (IStrategy, merge_informative_pair, stoploss_from_open,
                                IntParameter, DecimalParameter, CategoricalParameter)

from typing import Dict, List, Optional, Tuple, Union
from pandas import DataFrame, Series
from functools import reduce
from datetime import datetime, timedelta
from freqtrade.persistence import Trade

# Get rid of pandas warnings during backtesting
import pandas as pd

pd.options.mode.chained_assignment = None  # default='warn'

# Strategy specific imports, files must reside in same folder as strategy
import sys
from pathlib import Path

sys.path.append(str(Path(__file__).parent))

import logging
import warnings

log = logging.getLogger(__name__)
# log.setLevel(logging.DEBUG)
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

import custom_indicators as cta

import pywt
import scipy

"""
####################################################################################
github_docugs_strategies_1__FBB_DWT_short__20230228_192125 - use a Discreet Wavelet Transform (DWT) to estimate future price movements
            This version enters short positions (not long)

####################################################################################
"""


class github_docugs_strategies_1__FBB_DWT_short__20230228_192125(IStrategy):
    # Do *not* hyperopt for the roi and stoploss spaces

    # ROI table:
    minimal_roi = {
        "0": 0.1
    }

    # Stoploss:
    stoploss = -0.1

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = None
    trailing_stop_positive_offset = 0.0
    trailing_only_offset_is_reached = False

    timeframe = '5m'
    inf_timeframe = '15m'

    use_custom_stoploss = True

    # Recommended
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Required
    startup_candle_count: int = 128  # must be power of 2

    process_only_new_candles = True

    trading_mode = "futures"
    margin_mode = "isolated"
    can_short = True

    custom_trade_info = {}

    ###################################

    # Strategy Specific Variable Storage

    ## Hyperopt Variables

    # FBB_ hyperparams
    exit_short_bb_gain = DecimalParameter(0.01, 0.50, decimals=2, default=0.09, space='buy', load=True, optimize=True)
    exit_short_fisher_wr = DecimalParameter(-0.99, -0.75, decimals=2, default=-0.75, space='buy', load=True, optimize=True)
    exit_short_force_fisher_wr = DecimalParameter(-0.99, -0.85, decimals=2, default=-0.99, space='buy', load=True, optimize=True)

    entry_short_bb_gain = DecimalParameter(0.7, 1.5, decimals=2, default=0.8, space='sell', load=True, optimize=True)
    entry_short_fisher_wr = DecimalParameter(0.75, 0.99, decimals=2, default=0.9, space='sell', load=True, optimize=True)
    entry_short_force_fisher_wr = DecimalParameter(0.85, 0.99, decimals=2, default=0.99, space='sell', load=True, optimize=True)
    
    dwt_window = startup_candle_count

    # DWT  hyperparams
    entry_short_dwt_diff = DecimalParameter(-5.0, 0.0, decimals=1, default=-2.0, space='buy', load=True, optimize=True)
    exit_short_dwt_diff = DecimalParameter(0.0, 5.0, decimals=1, default=-2.0, space='sell', load=True, optimize=True)

    entry_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'macd', 'none'], default='none', space='buy',
                                            load=True, optimize=True)

    # Custom exit Profit (formerly Dynamic ROI)
    cexit_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True,
                                          optimize=True)
    cexit_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=True)
    cexit_roi_start = DecimalParameter(0.01, 0.05, default=0.01, space='sell', load=True, optimize=True)
    cexit_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=True)
    cexit_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell',
                                            load=True, optimize=True)
    cexit_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True)
    cexit_pullback_amount = DecimalParameter(0.005, 0.03, default=0.01, space='sell', load=True, optimize=True)
    cexit_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=True)
    cexit_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=True)

    # Custom Stoploss
    cstop_loss_threshold = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell', load=True, optimize=True)
    cstop_bail_how = CategoricalParameter(['roc', 'time', 'any', 'none'], default='none', space='sell', load=True,
                                          optimize=True)
    cstop_bail_roc = DecimalParameter(-5.0, -1.0, default=-3.0, space='sell', load=True, optimize=True)
    cstop_bail_time = IntParameter(60, 1440, default=720, space='sell', load=True, optimize=True)
    cstop_bail_time_trend = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True)
    cstop_max_stoploss = DecimalParameter(-0.30, -0.01, default=-0.10, space='sell', load=True, optimize=True)

    ###################################

    """
    Informative Pair Definitions
    """

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.inf_timeframe) for pair in pairs]
        return informative_pairs

    ###################################

    """
    Indicator Definitions
    """

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

        # Base pair informative timeframe indicators
        curr_pair = metadata['pair']
        informative = self.dp.get_pair_dataframe(pair=curr_pair, timeframe=self.inf_timeframe)

        # DWT

        informative['dwt_model'] = informative['close'].rolling(window=self.dwt_window).apply(self.model)
        # informative['dwt_predict'] = informative['dwt_model'].rolling(window=self.dwt_window).apply(self.predict)
        # informative['stddev'] = informative['close'].rolling(window=self.dwt_window).std()

        # merge into normal timeframe
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)

        # calculate predictive indicators in shorter timeframe (not informative)

        dataframe['dwt_model'] = dataframe[f"dwt_model_{self.inf_timeframe}"]
        # dataframe['stddev'] = dataframe[f"stddev_{self.inf_timeframe}"]
        dataframe['dwt_model_diff'] = 100.0 * (dataframe['dwt_model'] - dataframe['close']) / dataframe['close']
        # dataframe['dwt_model_diff2'] = (dataframe['dwt_model'] - dataframe['close']) / dataframe['stddev']
        # dataframe['dwt_predict'] = dataframe[f"dwt_predict_{self.inf_timeframe}"]
        # dataframe['dwt_predict_diff'] = 100.0 * (dataframe['dwt_predict'] - dataframe['dwt_model']) / dataframe['dwt_model']

        # FisherBB

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"])

        # Williams %R
        dataframe['wr'] = 0.02 * (self.williams_r(dataframe, period=14) + 50.0)

        # Combined Fisher RSI and Williams %R
        dataframe['fisher_wr'] = (dataframe['wr'] + dataframe['fisher_rsi']) / 2.0

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # Custom Stoploss

        if not metadata['pair'] in self.custom_trade_info:
            self.custom_trade_info[metadata['pair']] = {}
            if not 'had-trend' in self.custom_trade_info[metadata["pair"]]:
                self.custom_trade_info[metadata['pair']]['had-trend'] = False

        # MA Streak: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
        # dataframe['mastreak'] = cta.mastreak(dataframe, period=4)

        # Trends

        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0)
        dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0)
        dataframe['candle-dn-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() <= 2, 1, 0)

        # RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/
        dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5)
        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['rmi-dn-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() <= 2, 1, 0)

        # dataframe['rmi-dn'] = np.where(dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0)
        # dataframe['rmi-dn-count'] = dataframe['rmi-dn'].rolling(8).sum()
        #
        # dataframe['rmi-up'] = np.where(dataframe['rmi'] > dataframe['rmi'].shift(), 1, 0)
        # dataframe['rmi-up-count'] = dataframe['rmi-up'].rolling(8).sum()

        # Indicators used only for ROI and Custom Stoploss
        ssldown, sslup = cta.SSLChannels_ATR(dataframe, length=21)
        dataframe['sroc'] = cta.SROC(dataframe, roclen=21, emalen=13, smooth=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')

        return dataframe

    ###################################

    def madev(self, d, axis=None):
        """ Mean absolute deviation of a signal """
        return np.mean(np.absolute(d - np.mean(d, axis)), axis)

    def dwtModel(self, data):

        # the choice of wavelet makes a big difference
        # for an overview, check out: https://www.kaggle.com/theoviel/denoising-with-direct-wavelet-transform
        # wavelet = 'db1'
        # wavelet = 'bior1.1'
        wavelet = 'haar'  # deals well with harsh transitions
        level = 1
        wmode = "smooth"
        length = len(data)

        coeff = pywt.wavedec(data, wavelet, mode=wmode)

        # remove higher harmonics
        sigma = (1 / 0.6745) * self.madev(coeff[-level])
        uthresh = sigma * np.sqrt(2 * np.log(length))
        coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])

        # inverse transform
        model = pywt.waverec(coeff, wavelet, mode=wmode)

        return model

    def model(self, a: np.ndarray) -> float:
        # must return scalar, so just calculate prediction and take last value
        # model = self.dwtModel(np.array(a))

        # de-trend the data
        w_mean = a.mean()
        w_std = a.std()
        x_notrend = (a - w_mean) / w_std

        # get DWT model of data
        restored_sig = self.dwtModel(x_notrend)

        # re-trend
        model = (restored_sig * w_std) + w_mean

        length = len(model)
        return model[length - 1]

    def scaledModel(self, a: np.ndarray) -> float:
        # must return scalar, so just calculate prediction and take last value
        # model = self.dwtModel(np.array(a))

        # de-trend the data
        w_mean = a.mean()
        w_std = a.std()
        x_notrend = (a - w_mean) / w_std

        # get DWT model of data
        model = self.dwtModel(x_notrend)

        length = len(model)
        return model[length - 1]

    def scaledData(self, a: np.ndarray) -> float:

        # scale the data
        standardized = a.copy()
        w_mean = np.mean(standardized)
        w_std = np.std(standardized)
        scaled = (standardized - w_mean) / w_std
        # scaled.fillna(0, inplace=True)

        length = len(scaled)
        return scaled.ravel()[length - 1]

    def predict(self, a: np.ndarray) -> float:
        return np.dot(a, self.w)

        # predicts the next value using polynomial extrapolation

        # a.fillna(0)

        # fit the supplied data
        # Note: extrapolation is notoriously fickle. Be careful
        length = len(a)
        x = np.arange(length)
        f = scipy.interpolate.UnivariateSpline(x, a, k=5)

        # predict 1 step ahead
        predict = f(length)

        return predict

    # Williams %R
    def williams_r(self, dataframe: DataFrame, period: int = 14) -> Series:
        """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
            of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
            Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
            of its recent trading range.
            The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
        """

        highest_high = dataframe["high"].rolling(center=False, window=period).max()
        lowest_low = dataframe["low"].rolling(center=False, window=period).min()

        WR = Series(
            (highest_high - dataframe["close"]) / (highest_high - lowest_low),
            name=f"{period} Williams %R",
        )

        return WR * -100

    ###################################

    """
    Short Entry Signal
    """

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        short_conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        # checks for long/short conditions
        if (self.entry_trend_type.value != 'none'):

            # short if uptrend, long if downtrend (contrarian)
            if (self.entry_trend_type.value != 'rmi'):
                short_cond = (dataframe['rmi-up-trend'] == 1)
            elif (self.entry_trend_type.value != 'ssl'):
                short_cond = (dataframe['ssl-dir'] == 'up')
            elif (self.entry_trend_type.value != 'candle'):
                short_cond = (dataframe['candle-up-trend'] == 1)
            elif (self.entry_trend_type.value != 'macd'):
                short_cond = (dataframe['macdhist'] > 0.0)

            short_conditions.append(short_cond)

        # Short Processing

        # DWT triggers
        short_dwt_cond = (
            qtpylib.crossed_below(dataframe['dwt_model_diff'], self.entry_short_dwt_diff.value)
        )

        # DWTs will spike on big gains, so try to constrain
        short_spike_cond = (
                dataframe['dwt_model_diff'] > 2.0 * self.entry_short_dwt_diff.value
        )

        short_conditions.append(short_dwt_cond)
        short_conditions.append(short_spike_cond)

        # set entry tags
        dataframe.loc[short_dwt_cond, 'enter_tag'] += 'short_dwt_entry '

        # FBB_ triggers (convert FBB buy triggers to FBB short)
        short_fbb_cond = (
                (dataframe['fisher_wr'] >= self.entry_short_fisher_wr.value) &
                (dataframe['bb_gain'] <= self.entry_short_bb_gain.value)
        )

        strong_short_cond = (
                (
                        (dataframe['bb_gain'] <= 1.5 * self.entry_short_bb_gain.value) |
                        (dataframe['fisher_wr'] > self.entry_short_force_fisher_wr.value)
                ) &
                (
                    (dataframe['bb_gain'] < 0.02)  # make sure there is some potential gain
                )
        )
        short_conditions.append(short_fbb_cond | strong_short_cond)
        dataframe.loc[short_fbb_cond, 'short_tag'] += 'fbb_short '
        dataframe.loc[strong_short_cond, 'short_tag'] += 'strong '

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

        # apparently, have to set something in 'enter_long' column
        dataframe.loc[(dataframe['close'].notnull() ), 'enter_long'] = 0

        return dataframe

    ###################################

    """
    Exit Signal
    """

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        short_conditions = []
        dataframe.loc[:, 'exit_tag'] = ''
        curr_pair = metadata['pair']

        if self.config['runmode'].value == 'hyperopt':
            if 'sell' in self.config:
                for params in self.config['sell'].values():
                    if 'sell_exit' in params:
                        for exit_condition in params['sell_exit']:
                            if 'sell_exit_trend' in exit_condition:
                                short_conditions.append(exit_condition['sell_exit_trend'])
        
        # Short Processing

        # DWT triggers
        short_dwt_cond = (
            qtpylib.crossed_above(dataframe['dwt_model_diff'], self.exit_short_dwt_diff.value)
        )

        # DWTs will spike on big gains, so try to constrain
        short_spike_cond = (
                dataframe['dwt_model_diff'] < 2.0 * self.exit_short_dwt_diff.value
        )
        
        # conditions.append(short_cond)
        short_conditions.append(short_dwt_cond)
        short_conditions.append(short_spike_cond)

        # set exit tags
        dataframe.loc[short_dwt_cond, 'exit_tag'] += 'short_dwt_exit '
        
        # FBB triggers (convert FBB buy triggers to FBB short)
        short_fbb_cond = (
                (dataframe['fisher_wr'] <= self.exit_short_fisher_wr.value)&
                (dataframe['bb_gain'] >= self.exit_short_bb_gain.value))
        
        short_conditions.append(short_fbb_cond)
        
        dataframe.loc[short_fbb_cond, 'exit_tag'] = 'short_fbb_exit'
 
        if short_conditions:
            dataframe.loc[reduce(lambda x, y: x & y, short_conditions), 'exit_short'] = 1

        # apparently, have to set something in 'enter_long' column
        dataframe.loc[(dataframe['close'].notnull() ), 'exit_long'] = 0

        return dataframe

    ###################################

    # the custom stoploss/exit logic is adapted from Solipsis by werkkrew (https://github.com/werkkrew/freqtrade-strategies)

    """
    Custom Stoploss
    """

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float,
                        current_profit: float, **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        in_trend = self.custom_trade_info[trade.pair]['had-trend']

        # limit stoploss
        if current_profit < self.cstop_max_stoploss.value:
            return 0.01

        # Determine how we exit when we are in a loss
        if current_profit < self.cstop_loss_threshold.value:
            if self.cstop_bail_how.value == 'roc' or self.cstop_bail_how.value == 'any':
                # Dynamic bailout based on rate of change
                if self.last_candle['sroc'] <= self.cstop_bail_roc.value:
                    return 0.01
            if self.cstop_bail_how.value == 'time' or self.cstop_bail_how.value == 'any':
                # Dynamic bailout based on time, unless time_trend is true and there is a potential reversal
                if trade_dur > self.cstop_bail_time.value:
                    if self.cstop_bail_time_trend.value == True and in_trend == True:
                        return 1
                    else:
                        return 0.01
        return 1

    ###################################

    """
    Custom Exit
    """

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        max_profit = max(0, trade.calc_profit_ratio(trade.max_rate))
        pullback_value = max(0, (max_profit - self.cexit_pullback_amount.value))
        in_trend = False

        # Determine our current ROI point based on the defined type
        if self.cexit_roi_type.value == 'static':
            min_roi = self.cexit_roi_start.value
        elif self.cexit_roi_type.value == 'decay':
            min_roi = cta.linear_decay(self.cexit_roi_start.value, self.cexit_roi_end.value, 0,
                                       self.cexit_roi_time.value, trade_dur)
        elif self.cexit_roi_type.value == 'step':
            if trade_dur < self.cexit_roi_time.value:
                min_roi = self.cexit_roi_start.value
            else:
                min_roi = self.cexit_roi_end.value

        # Determine if there is a trend
        if self.cexit_trend_type.value == 'rmi' or self.cexit_trend_type.value == 'any':
            if last_candle['rmi-dn-trend'] == 1:
                in_trend = True
        if self.cexit_trend_type.value == 'ssl' or self.cexit_trend_type.value == 'any':
            if last_candle['ssl-dir'] == 'down':
                in_trend = True
        if self.cexit_trend_type.value == 'candle' or self.cexit_trend_type.value == 'any':
            if last_candle['candle-dn-trend'] == 1:
                in_trend = True

        # Don't exit if we are in a trend unless the pullback threshold is met
        if in_trend == True and current_profit > 0:
            # Record that we were in a trend for this trade/pair for a more useful exit message later
            self.custom_trade_info[trade.pair]['had-trend'] = True
            # If pullback is enabled and profit has pulled back allow a exit, maybe
            if self.cexit_pullback.value == True and (current_profit <= pullback_value):
                if self.cexit_pullback_respect_roi.value == True and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif self.cexit_pullback_respect_roi.value == False:
                    if current_profit > min_roi:
                        return 'intrend_pullback_roi'
                    else:
                        return 'intrend_pullback_noroi'
            # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold
            return None
        # If we are not in a trend, just use the roi value
        elif in_trend == False:
            if self.custom_trade_info[trade.pair]['had-trend']:
                if current_profit > min_roi:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_roi'
                elif self.cexit_endtrend_respect_roi.value == False:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_noroi'
            elif current_profit > min_roi:
                return 'notrend_roi'
        else:
            return None