-
Notifications
You must be signed in to change notification settings - Fork 17
/
GodStra.py
176 lines (155 loc) · 6.61 KB
/
GodStra.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
# GodStra Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
# {
# "method": "AgeFilter",
# "min_days_listed": 30
# },
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json
# --- Do not remove these libs ---
import logging
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
# import talib.abstract as ta
from ta import add_all_ta_features
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np
class GodStra(IStrategy):
# 5/66: 9 trades. 8/0/1 Wins/Draws/Losses. Avg profit 21.83%. Median profit 35.52%. Total profit 1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
# +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
# |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
# | * Best | 1/500 | 11 | 2 1 8 | 5.22% | 280.74230393 USDT (57.40%) | 2,421.8 m | -2.85206 |
# | * Best | 2/500 | 10 | 7 0 3 | 18.76% | 983.46414442 USDT (187.58%) | 360.0 m | -4.32665 |
# | * Best | 5/500 | 9 | 8 0 1 | 21.83% | 1,060.11476586 USDT (196.50%) | 3,440.0 m | -7.0696 |
# Buy hyperspace params:
buy_params = {
'buy-cross-0': 'volatility_kcc',
'buy-indicator-0': 'trend_ichimoku_base',
'buy-int-0': 42,
'buy-oper-0': '<R',
'buy-real-0': 0.06295
}
# Sell hyperspace params:
sell_params = {
'sell-cross-0': 'volume_mfi',
'sell-indicator-0': 'trend_kst_diff',
'sell-int-0': 98,
'sell-oper-0': '=R',
'sell-real-0': 0.8779
}
# ROI table:
minimal_roi = {
"0": 0.3556,
"4818": 0.21275,
"6395": 0.09024,
"22372": 0
}
# Stoploss:
stoploss = -0.34549
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.22673
trailing_stop_positive_offset = 0.2684
trailing_only_offset_is_reached = True
# Buy hypers
timeframe = '12h'
print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
def dna_size(self, dct: dict):
def int_from_str(st: str):
str_int = ''.join([d for d in st if d.isdigit()])
if str_int:
return int(str_int)
return -1 # in case if the parameter somehow doesn't have index
return len({int_from_str(digit) for digit in dct.keys()})
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add all ta features
dataframe = dropna(dataframe)
dataframe = add_all_ta_features(
dataframe, open="open", high="high", low="low", close="close", volume="volume",
fillna=True)
# dataframe.to_csv("df.csv", index=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
# /5: Cuz We have 5 Group of variables inside buy_param
for i in range(self.dna_size(self.buy_params)):
OPR = self.buy_params[f'buy-oper-{i}']
IND = self.buy_params[f'buy-indicator-{i}']
CRS = self.buy_params[f'buy-cross-{i}']
INT = self.buy_params[f'buy-int-{i}']
REAL = self.buy_params[f'buy-real-{i}']
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
if OPR == ">":
conditions.append(DFIND > DFCRS)
elif OPR == "=":
conditions.append(np.isclose(DFIND, DFCRS))
elif OPR == "<":
conditions.append(DFIND < DFCRS)
elif OPR == "CA":
conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
elif OPR == "CB":
conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
elif OPR == ">I":
conditions.append(DFIND > INT)
elif OPR == "=I":
conditions.append(DFIND == INT)
elif OPR == "<I":
conditions.append(DFIND < INT)
elif OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
print(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 = list()
for i in range(self.dna_size(self.sell_params)):
OPR = self.sell_params[f'sell-oper-{i}']
IND = self.sell_params[f'sell-indicator-{i}']
CRS = self.sell_params[f'sell-cross-{i}']
INT = self.sell_params[f'sell-int-{i}']
REAL = self.sell_params[f'sell-real-{i}']
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
if OPR == ">":
conditions.append(DFIND > DFCRS)
elif OPR == "=":
conditions.append(np.isclose(DFIND, DFCRS))
elif OPR == "<":
conditions.append(DFIND < DFCRS)
elif OPR == "CA":
conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
elif OPR == "CB":
conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
elif OPR == ">I":
conditions.append(DFIND > INT)
elif OPR == "=I":
conditions.append(DFIND == INT)
elif OPR == "<I":
conditions.append(DFIND < INT)
elif OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe