# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/thetank4TV.py # directory_url: https://github.com/jaredrsommer/freqtradestrategies/blob/main// # User: jaredrsommer # Repository: freqtradestrategies # -------------------- import logging import numpy as np import pandas as pd from technical import qtpylib, pivots_points from pandas import DataFrame from datetime import datetime, timezone from typing import Optional from functools import reduce import talib.abstract as ta import pandas_ta as pta from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair) import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade logger = logging.getLogger(__name__) class Github_jaredrsommer_freqtradestrategies__thetank4TV__20260518_195114(IStrategy): ### Strategy parameters ### exit_profit_only = True ### No selling at a loss use_custom_stoploss = True trailing_stop = True position_adjustment_enable = True ignore_roi_if_entry_signal = True use_exit_signal = True stoploss = -0.09 startup_candle_count: int = 30 timeframe = '15m' # DCA Parameters position_adjustment_enable = True max_entry_position_adjustment = 0 max_dca_multiplier = 1 minimal_roi = { "12000": 0.01, "2400": 0.10, "300": 0.15, "180": 0.30, "120":0.40, "60": 0.45, "0": 0.50 } ### Hyperoptable parameters ### # entry optizimation max_epa = CategoricalParameter([0, 1], default=0, space="buy", optimize=True) # protections cooldown_lookback = IntParameter(4, 48, default=16, space="protection", optimize=True) stop_duration = IntParameter(5, 96, default=5, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) # indicators wavelength = IntParameter(low=3, high=10, default=8, space='buy', optimize=True) crosslength = IntParameter(low=3, high=10, default=3, space='sell', optimize=True) filterlength = IntParameter(low=15, high=35, default=25, space='buy', optimize=True) # trading buy_rsi = IntParameter(low=20, high=35, default=25, space='buy', optimize=True, load=True) buy_rsi_bear = IntParameter(low=40, high=55, default=45, space='buy', optimize=True, load=True) buy_rsi_bull = IntParameter(low=40, high=75, default=65, space='buy', optimize=True, load=True) buy_wt_bear = IntParameter(low=20, high=55, default=45, space='buy', optimize=True, load=True) buy_wt_bull = IntParameter(low=30, high=75, default=65, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=80, default=55, space='sell', optimize=True, load=True) # dca level optimization dca1 = DecimalParameter(low=0.03, high=0.08, decimals=2, default=0.05, space='buy', optimize=True, load=True) # dca2 = DecimalParameter(low=0.08, high=0.15, decimals=2, default=0.10, space='buy', optimize=True, load=True) # dca3 = DecimalParameter(low=0.15, high=0.25, decimals=2, default=0.15, space='buy', optimize=True, load=True) #trailing stop loss optimiziation tsl_target5 = DecimalParameter(low=0.2, high=0.4, decimals=1, default=0.3, space='sell', optimize=True, load=True) ts5 = DecimalParameter(low=0.04, high=0.06, default=0.05, space='sell', optimize=True, load=True) tsl_target4 = DecimalParameter(low=0.18, high=0.3, default=0.2, space='sell', optimize=True, load=True) ts4 = DecimalParameter(low=0.03, high=0.05, default=0.045, space='sell', optimize=True, load=True) tsl_target3 = DecimalParameter(low=0.10, high=0.15, default=0.15, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.07, high=0.12, default=0.1, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.05, high=0.06, default=0.07, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.02, high=0.05, default=0.03, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.008, high=0.015, default=0.013, space='sell', optimize=True, load=True) ### protections ### @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": 1, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False }) return prot ### Dollar Cost Averaging ### # This is called when placing the initial order (opening trade) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: # if self.max_epa.value == 0: # self.max_dca_multiplier = 1 # elif self.max_epa.value == 1: # self.max_dca_multiplier = 2 # elif self.max_epa.value == 2: # self.max_dca_multiplier = 3 # else: # self.max_dca_multiplier = 4 # We need to leave most of the funds for possible further DCA orders # This also applies to fixed stakes return proposed_stake / self.max_dca_multiplier def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased or decreased. This means extra buy or sell orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns None :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). :param current_entry_rate: Current rate using entry pricing. :param current_exit_rate: Current rate using exit pricing. :param current_entry_profit: Current profit using entry pricing. :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: Stake amount to adjust your trade, Positive values to increase position, Negative values to decrease position. Return None for no action. """ if current_profit > 0.10 and trade.nr_of_successful_exits == 0: # Take half of the profit at +5% return -(trade.stake_amount / 2) if current_profit > -(self.dca1.value) and trade.nr_of_successful_entries == 1: return None # if current_profit > -(self.dca2.value) and trade.nr_of_successful_entries == 2: # return None # if current_profit > -(self.dca3.value) and trade.nr_of_successful_entries == 3: # return None # Obtain pair dataframe (just to show how to access it) dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries # Allow up to 3 additional increasingly larger buys (4 in total) # Initial buy is 1x # If that falls to -2.5% profit, we buy more, # If that falls down to -5% again, we buy 1.5x more # If that falls once again down to -5%, we buy more # Total stake for this trade would be 1 + 1.5 + 2 + 2.5 = 7x of the initial allowed stake. # That is why max_dca_multiplier is 7 # Hope you have a deep wallet! try: # This returns first order stake size stake_amount = filled_entries[0].cost # This then calculates current safety order size if count_of_entries == 1: stake_amount = stake_amount * 1 elif count_of_entries == 2: stake_amount = stake_amount * 1 elif count_of_entries == 3: stake_amount = stake_amount * 1 else: stake_amount = stake_amount return stake_amount except Exception as exception: return None return None ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: for stop5 in self.tsl_target5.range: if (current_profit > stop5): for stop5a in self.ts5.range: return stop5a for stop4 in self.tsl_target4.range: if (current_profit > stop4): for stop4a in self.ts4.range: return stop4a for stop3 in self.tsl_target3.range: if (current_profit > stop3): for stop3a in self.ts3.range: return stop3a for stop2 in self.tsl_target2.range: if (current_profit > stop2): for stop2a in self.ts2.range: return stop2a for stop1 in self.tsl_target1.range: if (current_profit > stop1): for stop1a in self.ts1.range: return stop1a for stop0 in self.tsl_target0.range: if (current_profit > stop0): for stop0a in self.ts0.range: return stop0a return self.stoploss ### NORMAL INDICATORS ### def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Pivot Points pivots = pivots_points.pivots_points(dataframe) dataframe['pivot'] = pivots['pivot'] dataframe['s1'] = pivots['s1'] dataframe['r1'] = pivots['r1'] dataframe['s2'] = pivots['s2'] dataframe['r2'] = pivots['r2'] dataframe['s3'] = pivots['s3'] dataframe['r3'] = pivots['r3'] dataframe['r3-dif'] = (dataframe['r3'] - dataframe['r2']) / 4 dataframe['r2.25'] = dataframe['r2'] + dataframe['r3-dif'] dataframe['r2.50'] = dataframe['r2'] + (dataframe['r3-dif'] * 2) dataframe['r2.75'] = dataframe['r2'] + (dataframe['r3-dif'] * 3) # Filter ZEMA for length in self.filterlength.range: dataframe[f'ema_1{length}'] = ta.EMA(dataframe['close'], timeperiod=length) dataframe[f'ema_2{length}'] = ta.EMA(dataframe[f'ema_1{length}'], timeperiod=length) dataframe[f'ema_dif{length}'] = dataframe[f'ema_1{length}'] - dataframe[f'ema_2{length}'] dataframe[f'zema_{length}'] = dataframe[f'ema_1{length}'] + dataframe[f'ema_dif{length}'] # RSI dataframe['rsi'] = ta.RSI(dataframe) dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=7) # WaveTrend using OHLC4 or HA close - 3/21 ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"])) for wave in self.wavelength.range: dataframe[f'esa{wave}'] = ta.EMA(ap, timeperiod = wave) dataframe[f'd{wave}'] = ta.EMA(abs(ap - dataframe[f'esa{wave}']), timeperiod = wave) dataframe[f'wave_ci{wave}'] = (ap-dataframe[f'esa{wave}']) / (0.015 * dataframe[f'd{wave}']) dataframe[f'wave_t1{wave}'] = ta.EMA(dataframe[f'wave_ci{wave}'], timeperiod = 21) for cross in self.crosslength.range: dataframe[f'wave_t2{cross}_{wave}'] = ta.SMA(dataframe[f'wave_t1{wave}'], timeperiod = cross) # SMA dataframe['200_SMA'] = ta.SMA(dataframe["close"], timeperiod = 200) dataframe['30_SMA'] = ta.SMA(dataframe["close"], timeperiod = 30) dataframe['8_SMA'] = ta.SMA(dataframe["close"], timeperiod = 8) # TTM Squeeze ttm_Squeeze = pta.squeeze(high = dataframe['high'], low = dataframe['low'], close = dataframe["close"], lazybear = True) dataframe['ttm_Squeeze'] = ttm_Squeeze['SQZ_20_2.0_20_1.5_LB'] dataframe['ttm_ema'] = ta.EMA(dataframe['ttm_Squeeze'], timeperiod = 4) dataframe['squeeze_ON'] = ttm_Squeeze['SQZ_ON'] dataframe['squeeze_OFF'] = ttm_Squeeze['SQZ_OFF'] dataframe['NO_squeeze'] = ttm_Squeeze['SQZ_NO'] # Calculate the percentage change between the high and open prices for each 5-minute candle dataframe['perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100 # Create a custom indicator that checks if any of the past 100 5-minute candles' high price is 3% or more above the open price dataframe['candle_3perc_100'] = dataframe['perc_change'].rolling(200).apply(lambda x: np.where(x >= 3, 1, 0).sum()).shift() # Create a custom indicator that checks if the price has gone up 10% or more over the last hundred candles dataframe['candle_10perc_100'] = dataframe['close'].pct_change(periods=50).shift() # Calculate the percentage of the current candle's range where the close price is dataframe['close_percentage'] = (dataframe['close'] - dataframe['low']) / (dataframe['high'] - dataframe['low']) dataframe['body_size'] = abs(dataframe['open'] - dataframe['close']) dataframe['range_size'] = dataframe['high'] - dataframe['low'] dataframe['body_range_ratio'] = dataframe['body_size'] / dataframe['range_size'] dataframe['upper_wick_size'] = dataframe['high'] - dataframe[['open', 'close']].max(axis=1) dataframe['upper_wick_range_ratio'] = dataframe['upper_wick_size'] / dataframe['range_size'] lookback_period = 10 dataframe['max_high'] = dataframe['high'].rolling(lookback_period).max() dataframe['min_low'] = dataframe['low'].rolling(lookback_period).min() dataframe['close_position'] = (dataframe['close'] - dataframe['min_low']) / (dataframe['max_high'] - dataframe['min_low']) dataframe['current_candle_perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100 # if self.dp.runmode.value in ('live', 'dry_run'): # ticker = self.dp.ticker(metadata['pair']) # dataframe['last_price'] = ticker['last'] # dataframe['volume24h'] = ticker['quoteVolume'] # dataframe['vwap'] = ticker['vwap'] # # Trading pair: {'symbol': 'KAVA/USDT', 'timestamp': 1675451904532, 'datetime': '2023-02-03T19:18:24.532Z', # 'high': 1.0652, 'low': 1.0031, 'bid': 1.0382, 'bidVolume': None, 'ask': 1.0395, 'askVolume': None, # 'vwap': 1.035200204815235, 'open': 1.0558, 'close': 1.0403, 'last': 1.0403, 'previousClose': None, 'change': -0.0155, # 'percentage': -1.46, 'average': 1.04130553, 'baseVolume': 457210.7641, 'quoteVolume': 473304.67664005, # 'info': # {'time': 1675451904532, 'symbol': 'KAVA-USDT', 'buy': '1.0382', 'sell': '1.0395', 'changeRate': '-0.0146', 'changePrice': '-0.0155', 'high': '1.0652', 'low': '1.0031', 'vol': '457210.7641', 'volValue': '473304.67664005', 'last': '1.0403', 'averagePrice': '1.04130553', 'takerFeeRate': '0.001', 'makerFeeRate': '0.001', 'takerCoefficient': '1', 'makerCoefficient': '1'}}, self.max_epa.value: 1, self.max_dca_multiplier: 2 # if (dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1] # and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2] # and dataframe['200_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-2]).all(): # self.max_epa.value = 1 # elif (dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1] # and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all()): # self.max_epa.value = 1 # elif (dataframe['30_SMA'].iloc[-1] < dataframe['200_SMA'].iloc[-1] # and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all()): # self.max_epa.value = 2 # else: # self.max_epa.value = 2 # # print(f"Trading Pair: {dataframe.name}") # print(f"self.max_epa.value: {self.max_epa.value}") # print(f"self.max_dca_multiplier: {self.max_dca_multiplier}") # print(f"Trading pair: {ticker['symbol']}, max_epa: {self.max_epa.value}, dca_multiplier: {self.max_dca_multiplier}") return dataframe ### ENTRY CONDITIONS ### def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: # df.loc[ # ( # # Signal: RSI crosses above 30 # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['r2']) & # (df['squeeze_ON'] == 1)& # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['200_SMA'] < df['200_SMA'].shift(1)) & # (df['rsi'] < self.buy_rsi_bear.value) & # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)& # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (qtpylib.crossed_above(df[f'wave_t1{self.wavelength.value}'], df[f'wave_t2{self.crosslength.value}_{self.wavelength.value}'])) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WT - bear') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['r2']) & # (df['squeeze_ON'] == 1)& # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['rsi'] < self.buy_rsi_bear.value) & # (df['200_SMA'] < df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)& # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WTT - bear') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['r2']) & # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['rsi'] < self.buy_rsi_bear.value) & # (df['200_SMA'] < df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)& # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'WT transition - bear') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['r2']) & # (df['200_SMA'] < df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value) & # (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'WT - bear') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['r2']) & # (df['squeeze_ON'] == 1)& # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['rsi'] < self.buy_rsi_bull.value) & # (df['200_SMA'] > df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (qtpylib.crossed_above(df[f'wave_t1{self.wavelength.value}'], df[f'wave_t2{self.crosslength.value}_{self.wavelength.value}'])) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WT - bull') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['s1']) & # (df['squeeze_ON'] == 1)& # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['rsi'] < self.buy_rsi_bull.value) & # (df['200_SMA'] > df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) & # (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # # Check if no candle high price rose more than 5% in the past 50 candles # (df['candle_3perc_100'] == 0) & # # Check if the price has not gone up 10% or more over the last hundred candles # (df['candle_10perc_100'] < 0.5) & # # Check if the high price of the current candle is not 3% or more above the open price # (df['current_candle_perc_change'] < 0.75) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WTT - bull') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['close'] < df['s1']) & # (df['rsi_ma'] > df['rsi_ma'].shift(1)) & # (df['rsi'] < self.buy_rsi_bull.value) & # (df['200_SMA'] > df['200_SMA'].shift(1)) & # (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising # (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) & # (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # # Check if no candle high price rose more than 5% in the past 50 candles # (df['candle_3perc_100'] == 0) & # # Check if the price has not gone up 10% or more over the last hundred candles # (df['candle_10perc_100'] < 0.5) & # # Check if the high price of the current candle is not 3% or more above the open price # (df['current_candle_perc_change'] < 0.75) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'WT transition - bull') df.loc[ ( (df['close'] > df[f'zema_{self.filterlength.value}'])& (df['close'] < df['s1']) & (df['200_SMA'] > df['200_SMA'].shift(1)) & (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) & (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Check if no candle high price rose more than 5% in the past 50 candles (df['candle_3perc_100'] == 0) & # Check if the price has not gone up 10% or more over the last hundred candles (df['candle_10perc_100'] < 0.5) & # Check if the high price of the current candle is not 3% or more above the open price (df['current_candle_perc_change'] < 0.75) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'WT - bull') # df.loc[ # ( # (df['close'] > df[f'zema_{self.filterlength.value}'])& # (df['rsi'] > self.buy_rsi.value) & # (df['rsi'] < self.buy_rsi_bear.value) & # (df['200_SMA'] < df['200_SMA'].shift(1)) & # (qtpylib.crossed_above(df['rsi'], df['rsi_ma'])) & # # Check if no candle high price rose more than 5% in the past 50 candles # (df['candle_3perc_100'] == 0) & # # Check if the price has not gone up 10% or more over the last hundred candles # (df['candle_10perc_100'] < 0.5) & # # Check if the high price of the current candle is not 3% or more above the open price # (df['current_candle_perc_change'] < 0.75) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'RSI-XO bear') # df.loc[ # ( # (df['rsi'] > self.buy_rsi.value) & # (df['rsi'] < self.buy_rsi_bull.value) & # (df['30_SMA'] > df['200_SMA']) & # (df['200_SMA'] > df['200_SMA'].shift(1)) & # (qtpylib.crossed_above(df['rsi'], df['rsi_ma'])) & # # Check if no candle high price rose more than 5% in the past 50 candles # (df['candle_3perc_100'] == 0) & # # Check if the price has not gone up 10% or more over the last hundred candles # (df['candle_10perc_100'] < 0.5) & # # Check if the high price of the current candle is not 3% or more above the open price # (df['current_candle_perc_change'] < 0.75) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'RSI-XO bull') return df ### EXIT CONDITIONS ### def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( (qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r3'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'R3 - XO') df.loc[ ( (qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.75'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'R2.75 - XO') df.loc[ ( (qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.50'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'R2.5 - XO') df.loc[ ( (qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.25'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'R2.25 - XO') df.loc[ ( (qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'R2 - XO') return df