-
Notifications
You must be signed in to change notification settings - Fork 17
/
CombinedBinHAndClucV5.py
134 lines (115 loc) · 6.64 KB
/
CombinedBinHAndClucV5.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
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
###########################################################################################################
## CombinedBinHAndClucV5 by iterativ ##
## ##
## Fretrade https://github.com/freqtrade/freqtrade ##
## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ##
## V5 by iterativ. ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 20 to 40 pairs. Volume pairlist works well. ##
## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
## 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 ##
## ##
###########################################################################################################
class CombinedBinHAndClucV5(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.018
}
stoploss = -0.99 # effectively disabled.
timeframe = '5m'
# Sell signal
use_sell_signal = True
sell_profit_only = True
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 = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.025
# Custom stoploss
use_custom_stoploss = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 50
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Manage losing trades and open room for better ones.
if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc):
return 0.01
return 0.99
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
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()
# strategy ClucMay72018
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()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
( # strategy BinHV45
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift()) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
|
( # strategy ClucMay72018
(dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
( # Improves the profit slightly.
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
,
'sell'
] = 1
return dataframe