FBB_DWT

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)


"""
####################################################################################
FBB_DWT - use a Discreet Wavelet Transform to estimate future price movements,
          and Fisher/Williams/Bollinger buy/sell signals
          The DWT is good at detecting swings, while the FBB checks are to try and keep
          trades within oversold/overbought regions

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


class FBB_DWT(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_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = True

    # Required
    startup_candle_count: int = 128
    process_only_new_candles = True

    custom_trade_info = {}

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

    # Strategy Specific Variable Storage

    # Hyperopt Variables

    # FBB_ hyperparams
    buy_bb_gain = 0.07
    buy_dwt_diff = 0.036
    buy_fisher_wr = -0.84
    buy_force_fisher_wr = -0.96

    # DWT  hyperparams

    # buy_dwt_window = IntParameter(8 164 default=64 space='buy' load=True optimize=True)
    # buy_dwt_lookahead = IntParameter(0 64 default=0 space='buy' load=True optimize=True)

    dwt_window = 128
    dwt_lookahead = 0

    csell_endtrend_respect_roi = True
    csell_pullback = False
    csell_pullback_amount = 0.03
    csell_pullback_respect_roi = False
    csell_roi_end = 0.0
    csell_roi_start = 0.03
    csell_roi_time = 1075
    csell_roi_type = "decay"
    csell_trend_type = "candle"
    cstop_bail_how = "none"
    cstop_bail_roc = -4.526
    cstop_bail_time = 1246
    cstop_bail_time_trend = False
    cstop_loss_threshold = -0.014
    cstop_max_stoploss = -0.102
    sell_bb_gain = 1.26
    sell_dwt_diff = -0.028
    sell_fisher_wr = 0.81
    sell_force_fisher_wr = 0.98

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

    """
    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

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

        # 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_predict'] = ta.LINEARREG(dataframe[f"dwt_predict_{self.inf_timeframe}"], timeperiod=12)
        dataframe['dwt_predict'] = dataframe[f"dwt_predict_{self.inf_timeframe}"]
        # dataframe['dwt_model'] = dataframe[f"dwt_model_{self.inf_timeframe}"]
        # dataframe['dwt_predict_diff'] = (dataframe['dwt_predict'] - dataframe['dwt_model']) / dataframe['dwt_model']
        dataframe['dwt_predict_diff'] = (
            dataframe['dwt_predict'] - dataframe['close']) / dataframe['close']

        # 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

        # 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

        # RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/
        dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5)

        # 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, Peaks and Crosses
        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['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'] = np.where(dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0)
        dataframe['rmi-dn-count'] = dataframe['rmi-dn'].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)

        # # de-trend the data
        # n = data.size
        # t = np.arange(0, n)
        # p = np.polyfit(t, data, 1)  # find linear trend in data
        # x_notrend = data - p[0] * t  # detrended data
        w_mean = data.mean()
        w_std = data.std()
        x_notrend = (data - w_mean) / w_std

        coeff = pywt.wavedec(x_notrend, 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
        restored_sig = pywt.waverec(coeff, wavelet, mode=wmode)

        # re-trend the data
        # model = restored_sig + p[0] * t
        model = (restored_sig * w_std) + w_mean

        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))
        model = self.dwtModel(a)
        length = len(model)
        return model[length - 1]

    def predict(self, a: np.ndarray) -> np.float:
        # must return scalar, so just calculate prediction and take last value
        # npredict = self.buy_dwt_lookahead
        npredict = self.dwt_lookahead

        y = self.dwtModel(np.array(a))
        length = len(y)
        if npredict == 0:
            predict = y[length - 1]
        else:
            x = np.arange(length)
            f = scipy.interpolate.UnivariateSpline(x, y, k=3)

            predict = f(length - 1 + npredict)

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

    """
    Buy Signal
    """

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'buy_tag'] = ''

        # conditions.append(dataframe['volume'] > 0)

        # DWT triggers
        dwt_cond = (
                qtpylib.crossed_above(dataframe['dwt_predict_diff'], self.buy_dwt_diff)
        )

        conditions.append(dwt_cond)

        # DWTs will spike on big gains, so try to constrain
        spike_cond = (
                dataframe['dwt_predict_diff'] < 2.0 * self.buy_dwt_diff
        )
        conditions.append(spike_cond)

        # set buy tags
        dataframe.loc[dwt_cond, 'buy_tag'] += 'dwt_buy '

        # FBB_ triggers
        fbb_cond = (
                (dataframe['fisher_wr'] <= self.buy_fisher_wr) &
                (dataframe['bb_gain'] >= self.buy_bb_gain)
        )

        strong_buy_cond = (
                (
                        (dataframe['bb_gain'] >= 1.5 * self.buy_bb_gain) |
                        (dataframe['fisher_wr'] < self.buy_force_fisher_wr)
                ) &
                (
                    (dataframe['bb_gain'] > 0.02)  # make sure there is some potential gain
                )
        )
        conditions.append(fbb_cond | strong_buy_cond)
        dataframe.loc[fbb_cond, 'buy_tag'] += 'fbb_buy '
        dataframe.loc[strong_buy_cond, 'buy_tag'] += 'strong '

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

        return dataframe

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

    """
    Sell Signal
    """

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'exit_tag'] = ''

        # FFT triggers
        dwt_cond = (
                qtpylib.crossed_below(dataframe['dwt_predict_diff'], self.sell_dwt_diff)
        )

        conditions.append(dwt_cond)

        # DWTs will spike on big gains, so try to constrain
        spike_cond = (
                dataframe['dwt_predict_diff'] > 2.0 * self.sell_dwt_diff
        )
        conditions.append(spike_cond)

        # FBB_ triggers
        fbb_cond = (
                (dataframe['fisher_wr'] > self.sell_fisher_wr) &
                (dataframe['close'] >= (dataframe['bb_upperband'] * self.sell_bb_gain))
        )

        strong_sell_cond = (
            qtpylib.crossed_above(dataframe['fisher_wr'], self.sell_force_fisher_wr)  # &
            # (dataframe['close'] > dataframe['bb_upperband'] * self.sell_bb_gain)
        )

        conditions.append(fbb_cond | strong_sell_cond)

        # set exit tags
        dataframe.loc[fbb_cond, 'exit_tag'] += 'fbb_sell '
        dataframe.loc[strong_sell_cond, 'exit_tag'] += 'strong_sell '

        # set sell tags
        dataframe.loc[dwt_cond, 'exit_tag'] += 'dwt_sell '

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

        return dataframe

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

    """
    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 sell 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 Sell
    """

    def custom_sell(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.csell_pullback_amount))
        in_trend = False

        # Determine our current ROI point based on the defined type
        if self.csell_roi_type == 'static':
            min_roi = self.csell_roi_start
        elif self.csell_roi_type == 'decay':
            min_roi = cta.linear_decay(self.csell_roi_start, self.csell_roi_end, 0,
                                       self.csell_roi_time, trade_dur)
        elif self.csell_roi_type == 'step':
            if trade_dur < self.csell_roi_time:
                min_roi = self.csell_roi_start
            else:
                min_roi = self.csell_roi_end

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

        # Don't sell 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 sell message later
            self.custom_trade_info[trade.pair]['had-trend'] = True
            # If pullback is enabled and profit has pulled back allow a sell, maybe
            if self.csell_pullback == True and (current_profit <= pullback_value):
                if self.csell_pullback_respect_roi == True and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif self.csell_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.csell_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