/
chandem.py
128 lines (102 loc) · 4.31 KB
/
chandem.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 2 13:50:49 2020
@author: alex
"""
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Chandem(IStrategy):
"""
Sample strategy implementing Informative Pairs - compares stake_currency with USDT.
Not performing very well - but should serve as an example how to use a referential pair against USDT.
author@: xmatthias
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 freqtrade -s InformativeSample
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.28396,
"974": 0.09268,
"1740": 0.06554,
"3087": 0
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.28031
# Optimal timeframe for the strategy
timeframe = '5m'
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01013
trailing_stop_positive_offset = 0.10858
trailing_only_offset_is_reached = True
# run "populate_indicators" only for new candle
ta_on_candle = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [(f"{self.config['stake_currency']}/USDT", self.timeframe)]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
dataframe['CMO'] = ta.CMO(dataframe, timeperiod = 50)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=25, stds=3.5)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
bollingerTA = ta.BBANDS(dataframe, timeperiod=25, nbdevup=3.2, nbdevdn=3.2, matype=0)
dataframe['bb_lowerbandTA'] = bollingerTA['lowerband']
dataframe['bb_middlebandTA'] = bollingerTA['middleband']
dataframe['bb_upperbandTA'] = bollingerTA['upperband']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe["CMO"].shift(1), 0)) &
(dataframe["CMO"]>=0)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'],dataframe['bb_upperband']))
),
'sell'] = 1
return dataframe