-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_general.py
193 lines (151 loc) · 7.38 KB
/
test_general.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
import os
import pytest
import numpy as np
import pandas as pd
from labquant import LabQuant
current_dir = os.path.dirname(__file__)
csv_path = os.path.join(current_dir, "ohlcv_data.csv")
df = pd.read_csv(csv_path)
# Main Strategy
def strategy(args):
df = args[0].copy()
amount = args[1]
df["SMA1"] = df.c.rolling(args[2]).mean()
df["SMA2"] = df.c.rolling(args[3]).mean()
df["pred"] = np.where(df.SMA1 > df.SMA2, amount, -amount)
df["true"] = np.where(df.SMA1 > df.SMA2, amount, -amount)
df["SMA1_PLT1_BLUE"] = df.SMA1.values
df["PRED_PLT2_GREEN"] = df.SMA1.values
df["SMA2_PLT3_RED"] = df.SMA2.values
return df
amount = 1
str_params = [df, amount, 44, 120]
data = strategy(str_params)
@pytest.fixture(scope="class")
def lab_inst():
# Instance creation
lab_instance = LabQuant(data, seed=1, show_roi=False)
lab_instance.test_ = True
lab_instance.start(
opers_fee=1,
stop_rate=2,
gain_rate=2
)
# Returns the instance to the test
yield lab_instance
# Del instance
del lab_instance
class TestLab1:
def test_price(self, lab_inst):
assert sum(lab_inst.df_1.c) == 110643379.0
def test_strategy_returns_pred(self, lab_inst):
assert round(sum(lab_inst.strategy_returns_pred), 4) == -0.3209
def test_strategy_returns_true(self, lab_inst):
assert round(sum(lab_inst.strategy_returns_true), 4) == 0.0091
def test_positions(self, lab_inst):
assert sum(lab_inst.df_1.positions_pred[lab_inst.df_1.positions_pred > 0]) == 118.0
assert sum(lab_inst.df_1.positions_pred[lab_inst.df_1.positions_pred < 0]) == -121.0
def test_signals(self, lab_inst):
assert sum(abs(lab_inst.df_1.signals_pred)) == 34
def test_signals_size_pred(self, lab_inst):
assert sum(lab_inst.df_1.signals_size_pred[lab_inst.df_1.signals_size_pred > 0]) == 17
assert sum(lab_inst.df_1.signals_size_pred[lab_inst.df_1.signals_size_pred < 0]) == -17
def test_signals_size_true(self, lab_inst):
assert sum(lab_inst.df_1.signals_size_true[lab_inst.df_1.signals_size_true > 0]) == 17
assert sum(lab_inst.df_1.signals_size_true[lab_inst.df_1.signals_size_true < 0]) == -17
def test_hitrate_textitem(self, lab_inst):
assert lab_inst.hit_trads_textitem.toPlainText() == "HIT-RATE: 24.2% n-HITS: 8 n-LOSSES: 25 n-TRADS: 33 "
def test_scores_textitem(self, lab_inst):
assert lab_inst.scores_textitem.toPlainText() == "SCORE: 1.00 F1-SCORE: 1.00 "
def test_show_distribution(self, lab_inst):
lab_inst._show_pricedistribution()
assert lab_inst.dist_textitem.toPlainText() == "MEAN: 55321.7 MEDIAN: 56199.2 ASYMMETRY: -0.5 STD: 4191.0 "
def test_drawdown_textitem(self, lab_inst):
lab_inst.showplt1 = 2
lab_inst._show_plot()
assert lab_inst.drawdown_textitem.toPlainText() == "DD-MAX: 32.30% DD-DURATION: 1992"
def test_drawdowns(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.drawdown[0]), 4) == -369.6303
def test_drawdowns_duration(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.drawdown[1]), 4) == 1985049.0
def test_pnl_textitem(self, lab_inst):
lab_inst._show_plot()
assert lab_inst.pnl_textitem.toPlainText() == "PNL: -32.09%"
def test_market_returns_cum(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.market_returns_cum), 4) == 52.5716
def test_strategy_returns_cum(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.strategy_returns_cum), 4) == -365.3629
def test_equity_curve_true(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.equity_curve_true), 4) == 1966.4971
def test_equity_curve_pred(self, lab_inst):
lab_inst._show_plot()
assert round(sum(lab_inst.equity_curve_pred), 4) == 1634.6371
def test_risk_metrics_textitem(self, lab_inst):
lab_inst._show_plot()
assert lab_inst.risk_metrics_textitem.toPlainText() == "SHARPE: -2.68 SORTINO: -1.25 CALMAR: -0.18 "
def test_show_returns_1(self, lab_inst):
lab_inst.showplt11 = 1
lab_inst._show_returns()
assert lab_inst.value_var_hist_axis.value == 0
def test_show_returns_2(self, lab_inst):
lab_inst.showplt11 = 2
lab_inst._show_returns()
assert lab_inst.value_var_hist_axis.value == 10
assert round(sum(lab_inst.strategy_returns_pred), 4) == -0.3209
assert round(sum(lab_inst.strategy_returns_true), 4) == 0.0091
assert lab_inst.returns_textitem.toPlainText() == "STRATEGY (MEAN: -0.02% STD: 0.31%) "
def test_show_returns_3(self, lab_inst):
lab_inst.showplt11 = 3
lab_inst._show_returns()
assert lab_inst.value_var_hist_axis.value == 10
assert round(sum(lab_inst.trads_pct_changes), 4) == 0.0091
assert lab_inst.returns_textitem.toPlainText() == "n-TRADS: 33 (MEAN: 0.05% STD: 2.78%) "
def test_show_returns_4(self, lab_inst):
lab_inst.showplt11 = 4
lab_inst._show_returns()
assert lab_inst.value_var_hist_axis.value == 10
def test_show_returns_5(self, lab_inst):
lab_inst.showplt11 = 5
lab_inst._show_returns()
assert lab_inst.value_var_hist_axis.value == 10
assert lab_inst.returns_textitem.toPlainText() == "P: (MEAN: 2.89% STD: 1.10%) L: (MEAN: 2.47% STD: 0.28%) "
def test_show_returns_6(self, lab_inst):
lab_inst.showplt11 = 6
lab_inst._show_returns()
assert round(sum(lab_inst.strategy_returns_cum), 4) == -365.3629
assert round(sum(lab_inst.market_returns_cum), 4) == 52.5716
assert lab_inst.value_var_hist_axis.value == 0
def test_show_cumulative_gains_1(self, lab_inst):
lab_inst.showplt4 = 1
lab_inst._show_cumulative_gains()
assert round(sum(lab_inst.df_1.cumul_gains_str.fillna(0)), 4) == 1634.6371
assert round(sum(lab_inst.df_1.cumul_gains_hold.fillna(0)), 4) == 2051.5614
def test_show_cumulative_gains_2(self, lab_inst):
lab_inst.showplt4 = 2
lab_inst._show_cumulative_gains()
assert round(sum(lab_inst.df_1.cumul_gains_reappl_str.fillna(0)), 4) == 1663.3355
assert round(sum(lab_inst.df_1.cumul_gains_reappl_hold.fillna(0)), 4) == 1968.8835
def test_show_features(self, lab_inst):
lab_inst.showplt3 = 1
lab_inst._show_features()
data_plt_1 = []
for item in lab_inst.plt_1.listDataItems():
y_data = item.yData
data_plt_1.append(y_data)
data_plt_1 = np.nan_to_num(data_plt_1, nan=0.0)
data_plt_3 = []
for item in lab_inst.plt_3.listDataItems():
y_data = item.yData
data_plt_3.append(y_data)
data_plt_3 = np.nan_to_num(data_plt_3, nan=0.0)
x_long_pred, _ = lab_inst.scatter_long_pred.getData()
x_short_pred, _ = lab_inst.scatter_short_pred.getData()
assert np.round(sum(data_plt_1[0]), 1) == 108346643.8
assert np.round(sum(x_long_pred), 1) == 7707.0
assert np.round(sum(x_short_pred), 1) == 8717.0
assert np.round(sum(data_plt_3[0]), 1) == 104306792.0