forked from phuchust/freqtrade_strategy
-
Notifications
You must be signed in to change notification settings - Fork 0
/
CombinedBinHAndClucV8XH.py
477 lines (398 loc) · 23.2 KB
/
CombinedBinHAndClucV8XH.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade
from pandas import DataFrame
from datetime import datetime, timedelta
from functools import reduce
###########################################################################################################
## Based on CombinedBinHAndClucV8 by iterativ
## (Few improvements on this version by themoz) ##
## ##
## Freqtrade https://github.com/freqtrade/freqtrade ##
## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ##
## V8 by iterativ. ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 20 to 60 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) & sell_profit_only (must be true). ##
## ##
###########################################################################################################
## DONATIONS ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## ##
###########################################################################################################
# SSL Channels
def SSLChannels(dataframe, length=7):
df = dataframe.copy()
df['ATR'] = ta.ATR(df, timeperiod=14)
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1,
np.where(df['close'] < df['smaLow'], -1, np.NAN))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
return df['sslDown'], df['sslUp']
class CombinedBinHAndClucV8XH(IStrategy):
INTERFACE_VERSION = 2
# Buy hyperspace params:
buy_params = {
"buy_bb20_close_bblowerband": 0.917,
"buy_bb20_volume": 32,
"buy_bb40_bbdelta_close": 0.039,
"buy_bb40_closedelta_close": 0.02,
"buy_bb40_tail_bbdelta": 0.239,
"buy_mfi": 37.77,
"buy_min_inc": 0.01,
"buy_rsi": 35.74,
"buy_rsi_1h": 66.97,
"buy_rsi_diff": 49.29,
"buy_dip_threshold_0": 0.015, # value loaded from strategy
"buy_dip_threshold_1": 0.12, # value loaded from strategy
"buy_dip_threshold_2": 0.28, # value loaded from strategy
"buy_dip_threshold_3": 0.36, # value loaded from strategy
"buy_ema_open_mult_1": 0.02, # value loaded from strategy
"buy_volume_1": 2.0, # value loaded from strategy
}
# Sell hyperspace params:
sell_params = {
"sell_rsi_main": 72.19,
"sell_rsi_parachute": 39.84,
"sell_custom_roi_profit_1": 0.01, # value loaded from strategy
"sell_custom_roi_profit_2": 0.04, # value loaded from strategy
"sell_custom_roi_profit_3": 0.08, # value loaded from strategy
"sell_custom_roi_profit_4": 0.14, # value loaded from strategy
"sell_custom_roi_profit_5": 0.04, # value loaded from strategy
"sell_custom_roi_rsi_1": 50, # value loaded from strategy
"sell_custom_roi_rsi_2": 50, # value loaded from strategy
"sell_custom_roi_rsi_3": 56, # value loaded from strategy
"sell_custom_roi_rsi_4": 58, # value loaded from strategy
"sell_custom_stoploss_1": -0.05, # value loaded from strategy
"sell_trail_down_1": 0.03, # value loaded from strategy
"sell_trail_down_2": 0.015, # value loaded from strategy
"sell_trail_profit_max_1": 0.4, # value loaded from strategy
"sell_trail_profit_max_2": 0.1, # value loaded from strategy
"sell_trail_profit_min_1": 0.1, # value loaded from strategy
"sell_trail_profit_min_2": 0.02, # value loaded from strategy
}
minimal_roi = {
"0": 10
}
stoploss = -0.99 # effectively disabled.
timeframe = '5m'
inf_1h = '1h' # informative tf
# Sell signal
use_sell_signal = True
sell_profit_only = True
# it doesn't meant anything, just to guarantee there is a minimal profit.
sell_profit_offset = 0.001
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = False
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.03
# Custom stoploss
use_custom_stoploss = True
# 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 = 200
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Buy Hyperopt params
buy_dip_threshold_0 = DecimalParameter(
0.001, 0.1, default=0.015, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_1 = DecimalParameter(
0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=False, load=True)
buy_dip_threshold_2 = DecimalParameter(
0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=False, load=True)
buy_dip_threshold_3 = DecimalParameter(
0.25, 0.44, default=0.36, space='buy', decimals=2, optimize=False, load=True)
buy_bb40_bbdelta_close = DecimalParameter(
0.005, 0.04, default=0.031, space='buy', optimize=True, load=True)
buy_bb40_closedelta_close = DecimalParameter(
0.01, 0.03, default=0.021, space='buy', optimize=True, load=True)
buy_bb40_tail_bbdelta = DecimalParameter(
0.2, 0.4, default=0.264, space='buy', optimize=True, load=True)
buy_bb20_close_bblowerband = DecimalParameter(
0.8, 1.1, default=0.992, space='buy', optimize=True, load=True)
buy_bb20_volume = IntParameter(
18, 36, default=29, space='buy', optimize=True, load=True)
buy_rsi_diff = DecimalParameter(
34.0, 60.0, default=50.48, space='buy', decimals=2, optimize=True, load=True)
buy_min_inc = DecimalParameter(
0.005, 0.05, default=0.01, space='buy', decimals=2, optimize=True, load=True)
buy_rsi_1h = DecimalParameter(
40.0, 70.0, default=67.0, space='buy', decimals=2, optimize=True, load=True)
buy_rsi = DecimalParameter(
30.0, 40.0, default=38.5, space='buy', decimals=2, optimize=True, load=True)
buy_mfi = DecimalParameter(
36.0, 65.0, default=36.0, space='buy', decimals=2, optimize=True, load=True)
buy_volume_1 = DecimalParameter(
1.0, 10.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
buy_ema_open_mult_1 = DecimalParameter(
0.01, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True)
# Sell Hyperopt params
sell_custom_roi_profit_1 = DecimalParameter(
0.01, 0.03, default=0.01, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_rsi_1 = DecimalParameter(
40.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_profit_2 = DecimalParameter(
0.01, 0.20, default=0.04, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_rsi_2 = DecimalParameter(
42.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_profit_3 = DecimalParameter(
0.15, 0.30, default=0.08, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_rsi_3 = DecimalParameter(
44.0, 58.0, default=56, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_profit_4 = DecimalParameter(
0.3, 0.7, default=0.14, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_rsi_4 = DecimalParameter(
44.0, 60.0, default=58, space='sell', decimals=2, optimize=False, load=True)
sell_custom_roi_profit_5 = DecimalParameter(
0.01, 0.1, default=0.04, space='sell', decimals=2, optimize=False, load=True)
sell_trail_profit_min_1 = DecimalParameter(
0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_1 = DecimalParameter(
0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_1 = DecimalParameter(
0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_2 = DecimalParameter(
0.01, 0.1, default=0.02, 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=0.015, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_1 = DecimalParameter(
-0.15, -0.03, default=-0.05, space='sell', decimals=2, optimize=False, load=True)
sell_rsi_main = DecimalParameter(
72.0, 90.0, default=80, space='sell', decimals=2, optimize=True, load=True)
#X version additional RSI value
sell_rsi_parachute = DecimalParameter(
30.0, 55.0, default=40, space='sell', decimals=2, optimize=True, load=True)
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, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Manage losing trades and open room for better ones.
if (current_profit < 0) & (current_time - timedelta(minutes=280) > trade.open_date_utc):
return 0.01
elif (current_profit < self.sell_custom_stoploss_1.value):
if (last_candle is not None):
if (last_candle['sma_200_dec']) & (last_candle['sma_200_dec_1h']):
return 0.01
# X version:
# try eventually to catch a minimal pullback before it is too late
elif (0 >= current_profit >= self.sell_custom_stoploss_1.value):
if (last_candle is not None):
if ((last_candle['sma_200_dec']) &
(last_candle['close'] > last_candle['bb_middleband']) &
(last_candle['rsi'] > self.sell_rsi_parachute.value)):
return 0.01
return 0.99
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_roi_profit_4.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_4.value):
return 'roi_target_4'
elif (current_profit > self.sell_custom_roi_profit_3.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_3.value):
return 'roi_target_3'
elif (current_profit > self.sell_custom_roi_profit_2.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_2.value):
return 'roi_target_2'
elif (current_profit > self.sell_custom_roi_profit_1.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_1.value):
return 'roi_target_1'
elif (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & (last_candle['sma_200_dec']):
return 'roi_target_5'
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 'trail_target_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 'trail_target_2'
return None
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.inf_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_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_200'] = ta.SMA(informative_1h, timeperiod=200)
informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(
20)
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# SSL Channels
ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
informative_1h['ssl_down'] = ssl_down_1h
informative_1h['ssl_up'] = ssl_up_1h
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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()
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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = 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_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)
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(
(
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50'] > dataframe['ema_200']) &
(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['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(
(
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_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['close'] < dataframe['ema_slow']) &
(dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(
1) * self.buy_bb20_volume.value))
)
)
conditions.append(
(
(dataframe['close'] < dataframe['sma_5']) &
(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) &
(dataframe['ema_50'] > dataframe['ema_200']) &
(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['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_diff.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) &
(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(16)) &
(((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['open'].rolling(24).min() - dataframe['close']) / dataframe['close']) > self.buy_min_inc.value) &
(dataframe['rsi_1h'] > self.buy_rsi_1h.value) &
(dataframe['rsi'] < self.buy_rsi.value) &
(dataframe['mfi'] < self.buy_mfi.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
(dataframe['close'] > dataframe['ema_100_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_100_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['volume'].rolling(4).mean() * self.buy_volume_1.value > dataframe['volume']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(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(
(
(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['volume'] > 0)
)
)
conditions.append(
(
(dataframe['rsi'] > self.sell_rsi_main.value) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
] = 1
return dataframe