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NostalgiaForInfinityV3.py
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NostalgiaForInfinityV3.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
from pandas import DataFrame
from functools import reduce
from freqtrade.persistence import Trade
###########################################################################################################
## NostalgiaForInfinityV3 by iterativ ##
## ##
## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 20 to 80 pairs. Volume pairlist works well. ##
## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ##
## Ensure that you don't override any variables in you config.json. Especially ##
## the timeframe (must be 5m). ##
## ##
###########################################################################################################
## DONATIONS ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## ##
###########################################################################################################
class NostalgiaForInfinityV3(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 10,
}
stoploss = -0.99 # effectively disabled.
timeframe = '5m'
inf_1h = '1h'
custom_info = {}
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = False
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.05
trailing_stop_positive_offset = 0.15
# Custom stoploss
use_custom_stoploss = False
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 400
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
#############################################################
buy_params = {
#############
# Enable/Disable conditions
"buy_condition_1_enable": True,
"buy_condition_2_enable": True,
"buy_condition_3_enable": True,
"buy_condition_4_enable": True,
"buy_condition_5_enable": True,
"buy_condition_6_enable": True,
"buy_condition_7_enable": True,
"buy_condition_8_enable": True,
"buy_condition_9_enable": True,
"buy_condition_10_enable": True,
}
sell_params = {
#############
# Enable/Disable conditions
"sell_condition_1_enable": True,
"sell_condition_2_enable": True,
"sell_condition_3_enable": True,
"sell_condition_4_enable": True,
"sell_condition_5_enable": True,
"sell_condition_6_enable": True,
"sell_condition_7_enable": True,
"sell_condition_8_enable": True,
}
############################################################################
# Buy
buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.03, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_1 = DecimalParameter(0.001, 0.2, default=0.12, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_2 = DecimalParameter(0.05, 0.4, default=0.3, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_3 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)
buy_volume_1 = DecimalParameter(1.0, 30.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
buy_min_inc_1 = DecimalParameter(0.005, 0.05, default=0.029, space='buy', decimals=3, optimize=False, load=True)
buy_rsi_1h_min_1 = DecimalParameter(40.0, 70.0, default=45.25, space='buy', decimals=2, optimize=False, load=True)
buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=85.06, space='buy', decimals=2, optimize=False, load=True)
buy_rsi_1 = DecimalParameter(30.0, 40.0, default=36.64, space='buy', decimals=2, optimize=False, load=True)
buy_mfi_1 = DecimalParameter(36.0, 65.0, default=45.25, space='buy', decimals=2, optimize=False, load=True)
buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.96, space='buy', decimals=2, optimize=False, load=True)
buy_ema_relative_2 = DecimalParameter(0.005, 0.08, default=0.006, space='buy', decimals=3, optimize=False, load=True)
buy_rsi_1h_min_2 = DecimalParameter(40.0, 70.0, default=63.91, space='buy', decimals=2, optimize=False, load=True)
buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=89.94, space='buy', decimals=2, optimize=False, load=True)
buy_rsi_1h_diff_2 = DecimalParameter(35.0, 55.0, default=38.69, space='buy', decimals=2, optimize=False, load=True)
buy_mfi_2 = DecimalParameter(36.0, 65.0, default=53.89, space='buy', decimals=2, optimize=False, load=True)
buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True)
buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True)
buy_bb40_tail_bbdelta = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True)
buy_bb20_close_bblowerband = DecimalParameter(0.7, 1.1, default=0.98, space='buy', optimize=False, load=True)
buy_bb20_volume = IntParameter(18, 35, default=24, space='buy', optimize=False, load=True)
buy_volume_5 = DecimalParameter(1.0, 10.0, default=4.12, space='buy', decimals=2, optimize=False, load=True)
buy_ema_open_mult_5 = DecimalParameter(0.01, 0.04, default=0.019, space='buy', decimals=3, optimize=False, load=True)
buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.48, space='buy', decimals=2, optimize=False, load=True)
buy_ema_open_mult_6 = DecimalParameter(0.025, 0.05, default=0.033, space='buy', decimals=3, optimize=False, load=True)
buy_volume_7 = DecimalParameter(1.0, 10.0, default=7.04, space='buy', decimals=2, optimize=False, load=True)
buy_ema_open_mult_7 = DecimalParameter(0.015, 0.03, default=0.02, space='buy', decimals=3, optimize=False, load=True)
buy_rsi_7 = DecimalParameter(24.0, 50.0, default=41.09, space='buy', decimals=2, optimize=False, load=True)
buy_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=False, load=True)
buy_volume_9 = DecimalParameter(1.0, 30.0, default=17.0, space='buy', decimals=1, optimize=False, load=True)
buy_bb_offset_9 = DecimalParameter(0.97, 1.05, default=0.98, space='buy', decimals=3, optimize=False, load=True)
buy_volume_10 = DecimalParameter(1.0, 26.0, default=9.6, space='buy', decimals=1, optimize=False, load=True)
buy_bb_offset_10 = DecimalParameter(0.97, 1.05, default=0.994, space='buy', decimals=3, optimize=False, load=True)
buy_rsi_1h_10 = DecimalParameter(15.0, 40.0, default=30.2, space='buy', decimals=1, optimize=False, load=True)
# Sell
sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True)
sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True)
sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True)
sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True)
sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.382, space='sell', optimize=False, load=True)
sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=87.708, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True)
sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_profit_1 = DecimalParameter(0.01, 0.20, default=0.01, space='sell', decimals=2, optimize=False, load=True)
sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.65, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_2 = DecimalParameter(0.01, 0.20, default=0.05, space='sell', decimals=2, optimize=False, load=True)
sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.37, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_3 = DecimalParameter(0.15, 0.30, default=0.25, space='sell', decimals=2, optimize=False, load=True)
sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=51.87, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_4 = DecimalParameter(0.3, 0.7, default=0.45, space='sell', decimals=2, optimize=False, load=True)
sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.35, space='sell', decimals=2, optimize=False, load=True)
sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.025, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_profit_3 = DecimalParameter(0.05, 0.3, default=0.07, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.166, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.38, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.154, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.035, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.045, space='sell', decimals=3, optimize=False, load=True)
############################################################################
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, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if (last_candle is not None):
if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value):
return 'target_profit_4'
elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
return 'target_profit_3'
elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
return 'target_profit_2'
elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
return 'target_profit_1'
elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['close'] < last_candle['ema_200']):
return 'target_profit_u_1'
elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['sma_200_dec']):
return 'target_profit_u_2'
elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)):
return 'target_profit_t_1'
elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)):
return 'target_profit_t_2'
return None
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# SMA
informative_1h['sma_50'] = ta.SMA(informative_1h, timeperiod=50)
informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# BB
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
informative_1h['bb_lowerband'] = bollinger['lower']
informative_1h['bb_middleband'] = bollinger['mid']
informative_1h['bb_upperband'] = bollinger['upper']
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# BB 40
bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['lower'] = bb_40['lower']
dataframe['mid'] = bb_40['mid']
dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# BB 20
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['volume_mean_30'] = dataframe['volume'].rolling(window=30).mean()
# EMA
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# SMA
dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
# MFI
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Alligator
dataframe['lips'] = ta.SMA(dataframe, timeperiod=5)
dataframe['smma_lips'] = dataframe['lips'].rolling(3).mean()
dataframe['teeth'] = ta.SMA(dataframe, timeperiod=8)
dataframe['smma_teeth'] = dataframe['teeth'].rolling(5).mean()
dataframe['jaw'] = ta.SMA(dataframe, timeperiod=13)
dataframe['smma_jaw'] = dataframe['jaw'].rolling(8).mean()
# Volume
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
# If don't exceed the dip limits
dataframe['safe_dips'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_0.value) &
(((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) &
(((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) &
(((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value))
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
self.buy_condition_1_enable.value &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_1.value > dataframe['volume']) &
(((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) &
(dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) &
(dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) &
(dataframe['rsi'] < self.buy_rsi_1.value) &
(dataframe['mfi'] < self.buy_mfi_1.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_2_enable.value &
(dataframe['close'] < dataframe['sma_5']) &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50'] > dataframe['ema_100']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) &
(((dataframe['close'] - dataframe['ema_200']) / dataframe['ema_200']) < self.buy_ema_relative_2.value) &
(dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) &
(dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) &
(dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) &
(dataframe['mfi'] < self.buy_mfi_2.value) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_3_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_100'] > dataframe['ema_200']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['safe_dips']) &
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close.value) &
dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close.value) &
dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta.value) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift()) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_4_enable.value &
(dataframe['close'] > dataframe['ema_100']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50'] > dataframe['ema_100']) &
(dataframe['ema_15_1h'] > dataframe['ema_50_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) &
(((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) &
(((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) &
(dataframe['close'] < dataframe['ema_50']) &
(dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume.value))
)
)
conditions.append(
(
self.buy_condition_5_enable.value &
#(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_100_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_6_enable.value &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_6.value > dataframe['volume']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_7_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_7.value > dataframe['volume']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['rsi'] < self.buy_rsi_7.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_8_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['close'] > dataframe['open']) &
(dataframe['close'] > dataframe['smma_lips']) &
(dataframe['smma_lips'] > dataframe['smma_teeth']) &
(dataframe['smma_teeth'] > dataframe['smma_jaw']) &
(dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) &
(dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) &
(dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) &
(dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) &
(dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) &
(dataframe['rsi'] < self.buy_rsi_8.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_9_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) &
(dataframe['close'] < dataframe['ema_50']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_10_enable.value &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &
(dataframe['safe_dips']) &
(dataframe['volume_mean_4'] * self.buy_volume_10.value > dataframe['volume']) &
(dataframe['close'] < dataframe['ema_50']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) &
(dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
self.sell_condition_1_enable.value &
(dataframe['rsi'] > self.sell_rsi_bb_1.value) &
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) &
(dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) &
(dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_2_enable.value &
(dataframe['rsi'] > self.sell_rsi_bb_2.value) &
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_3_enable.value &
(dataframe['rsi'] > self.sell_rsi_main_3.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_4_enable.value &
(dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) &
(dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_5_enable.value &
(dataframe['close'] < dataframe['ema_200']) &
(((dataframe['ema_200'] - dataframe['close']) / dataframe['close']) < self.sell_ema_relative_5.value) &
(dataframe['rsi'] > dataframe['rsi_1h'] + self.sell_rsi_diff_5.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_6_enable.value &
(dataframe['close'] < dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_50']) &
(dataframe['rsi'] > self.sell_rsi_under_6.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_7_enable.value &
(dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) &
(qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26'])) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_8_enable.value &
(dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
] = 1
return dataframe