StarRise

# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, informative
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas_ta as pta
import talib.abstract as ta
import numpy as np
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
import math
import logging
logger = logging.getLogger(__name__)
# --------------------------------

def top_percent_change(dataframe: DataFrame, length: int) -> float:
    """
        Percentage change of the current close from the range maximum Open price

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
    if length == 0:
        return (dataframe['open'] - dataframe['close']) / dataframe['close']
    else:
        return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']
# Williams %R

def williams_r(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

class StarRise(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    Designed to use with StarRise DCA settings\n\n    TTP: 1.1%(0.2%), BO: 38.0 USDT, SO: 38.0 USDT, OS: 1.2, SS: 1.13, MAD: 2, SOS: 1.6, MSTC: 11\n\n\n    2021/12 Crash\n        ========================================================== BUY TAG STATS ===========================================================\n    |   TAG |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n    |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n    | TOTAL |    412 |           1.14 |         469.43 |          1157.492 |           0.45 |        5:04:00 |   412     0     0   100 |\n\n    2021/05 Crash\n        ========================================================== BUY TAG STATS ===========================================================\n    |   TAG |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n    |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n    | TOTAL |    197 |           1.25 |         245.79 |           631.840 |           0.25 |        4:22:00 |   197     0     0   100 |\n\n    2021/09 - 2021/11 Bull\n        ========================================================== BUY TAG STATS ===========================================================\n    |   TAG |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n    |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n    | TOTAL |    327 |           1.30 |         424.98 |           961.187 |           0.37 |        3:26:00 |   326     0     1  99.7 |\n\n    '
    # Minimal ROI designed for the strategy.
    minimal_roi = {'0': 0.092, '29': 0.042, '85': 0.03, '128': 0.005}
    # Sell hyperspace params:
    # 1.1% TTP
    sell_params = {'pHSL': -0.998, 'pPF_1': 0.011, 'pPF_2': 0.065, 'pSL_1': 0.011, 'pSL_2': 0.062}
    # Max Deviation -0.349
    stoploss = -0.998
    # Custom stoploss
    use_custom_stoploss = True
    process_only_new_candles = True
    startup_candle_count = 168
    # Optimal timeframe for the strategy
    timeframe = '5m'
    # hard stoploss profit
    pHSL = DecimalParameter(-0.5, -0.04, default=-0.08, decimals=3, space='sell', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='sell', load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='sell', load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='sell', load=True)
    use_exit_signal = True
    exit_profit_only = True
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99
        return stoploss_from_open(sl_profit, current_profit)

    @informative('1h')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # CTI
        dataframe['cti_40'] = pta.cti(dataframe['close'], length=40)
        # %R
        dataframe['r_96'] = williams_r(dataframe, period=96)
        dataframe['r_480'] = williams_r(dataframe, period=480)
        # 1h mama > fama for general trend check
        dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
        dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5, 0.05)
        dataframe['mama_diff'] = (dataframe['mama'] - dataframe['fama']) / dataframe['hl2']
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
        # Bollinger bands
        bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=17, stds=1)
        dataframe['bb_lowerband'] = bollinger1['lower']
        dataframe['bb_middleband'] = bollinger1['mid']
        dataframe['bb_upperband'] = bollinger1['upper']
        # Close delta
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        # Dip Protection
        dataframe['tpct_change_0'] = top_percent_change(dataframe, 0)
        dataframe['tpct_change_1'] = top_percent_change(dataframe, 1)
        dataframe['tpct_change_2'] = top_percent_change(dataframe, 2)
        dataframe['tpct_change_4'] = top_percent_change(dataframe, 4)
        dataframe['tpct_change_5'] = top_percent_change(dataframe, 5)
        dataframe['tpct_change_9'] = top_percent_change(dataframe, 9)
        # SMA
        dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50)
        dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200)
        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        # ADX
        dataframe['adx'] = ta.ADX(dataframe)
        # %R
        dataframe['r_14'] = williams_r(dataframe, period=14)
        dataframe['r_96'] = williams_r(dataframe, period=96)
        # MAMA / FAMA
        dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
        dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5, 0.05)
        dataframe['mama_diff'] = (dataframe['mama'] - dataframe['fama']) / dataframe['hl2']
        # CRSI (3, 2, 100)
        crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
        crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
        dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # is_crash_1 = (
        #         (dataframe['tpct_change_1'] < 0.08) &
        #         (dataframe['tpct_change_2'] < 0.08) &
        #         (dataframe['tpct_change_4'] < 0.10)
        #     )
        # dataframe.loc[is_crash_1, 'enter_long'] = 1
        # dataframe.loc[is_crash_1, 'enter_tag'] = 'is crash'
        # Dip check
        # Bull confirm
        # Overpump check
        is_dip = (dataframe['close'] < dataframe['mama']) & (dataframe['r_14'] < -30) & (dataframe['cti'] < 3.0) & (dataframe['adx'] > 26) & (dataframe['mama_diff_1h'] > 0.003) & (dataframe['mama'] > dataframe['fama']) & (dataframe['sma_50'] > dataframe['sma_200'] * 1.01) & (dataframe['mama_1h'] > dataframe['fama_1h'] * 1.01) & (dataframe['rsi_84'] < 55) & (dataframe['rsi_112'] < 55) & (dataframe['cti_40_1h'] < 0.73) & (dataframe['r_96_1h'] < -6) & (dataframe['mama_diff_1h'] < 0.027) & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.03)
        dataframe.loc[is_dip, 'enter_long'] = 1
        dataframe.loc[is_dip, 'enter_tag'] = 'is dip'
        return dataframe


    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        is_bb = (dataframe['close'] > dataframe['bb_upperband'] * 0.999) & (dataframe['rsi'] > 76)
        dataframe.loc[is_bb, 'exit_long'] = 1
        dataframe.loc[is_bb, 'exit_tag'] = 'BB upper'
        return dataframe