DWT_short

import scipy
import pywt
import custom_indicators as cta
import warnings
import logging
from pathlib import Path
import sys
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

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


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


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

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


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

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

    # Stoploss:
    stoploss = -0.10

    # 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

    dwt_window = startup_candle_count

    # DWT  hyperparams
    entry_short_dwt_diff = -0.4
    entry_trend_type = "candle"
    cexit_endtrend_respect_roi = True
    cexit_pullback = False
    cexit_pullback_amount = 0.026
    cexit_pullback_respect_roi = True
    cexit_roi_end = 0.006
    cexit_roi_start = 0.028
    cexit_roi_time: 1036
    cexit_roi_type = "static"
    cexit_trend_type = "none"
    cstop_bail_how = "none"
    cstop_bail_roc = -4.07
    cstop_bail_time = 1396
    cstop_bail_time_trend = True
    cstop_loss_threshold = -0.032
    cstop_max_stoploss = -0.089
    exit_short_dwt_diff = 3.4

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

    """
    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']

        # 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) -> np.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) -> np.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) -> np.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) -> np.float:

        # 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

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

    """
    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 != 'none'):

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

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

        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 '

        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'] = ''

        # Short Processing

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

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

        # conditions.append(long_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 '

        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:
            return 0.01

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

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

        # Determine if there is a trend
        if self.cexit_trend_type == 'rmi' or self.cexit_trend_type == 'any':
            if last_candle['rmi-dn-trend'] == 1:
                in_trend = True
        if self.cexit_trend_type == 'ssl' or self.cexit_trend_type == 'any':
            if last_candle['ssl-dir'] == 'down':
                in_trend = True
        if self.cexit_trend_type == 'candle' or self.cexit_trend_type == '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 == True and (current_profit <= pullback_value):
                if self.cexit_pullback_respect_roi == True and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif self.cexit_pullback_respect_roi == 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 == False:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_noroi'
            elif current_profit > min_roi:
                return 'notrend_roi'
        else:
            return None