-
Notifications
You must be signed in to change notification settings - Fork 31
/
Copy pathvol_estimator.py
148 lines (111 loc) · 3.75 KB
/
vol_estimator.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
import math
import numpy as np
def ret_vol_est(df, window=30, trading_periods=252, clean=True):
log_return = (df['close'] / df['close'].shift(1)).apply(np.log)
result = log_return.rolling(
window=window,
center=False
).std() * math.sqrt(trading_periods)
if clean:
return result.dropna()
else:
return result
def kurt_estimator(df, window=30, clean=True):
log_return = (df['close'] / df['close'].shift(1)).apply(np.log)
result = log_return.rolling(
window=window,
center=False
).kurt()
if clean:
return result.dropna()
else:
return result
def skew_estimator(df, window=30, clean=True):
log_return = (df['close'] / df['close'].shift(1)).apply(np.log)
result = log_return.rolling(
window=window,
center=False
).skew()
if clean:
return result.dropna()
else:
return result
def gk_vol_est(df, window = 30, trading_periods=252, clean=True):
log_hl = (df['high'] / df['low']).apply(np.log)
log_cc = (df['close'] / df['close'].shift(1)).apply(np.log)
rs = 0.5 * log_hl ** 2 - (2 * math.log(2) - 1) * log_cc ** 2
def f(v):
return (trading_periods * v.mean()) ** 0.5
result = rs.rolling(window=window, center=False).apply(func=f)
if clean:
return result.dropna()
else:
return result
def ht_vol_est(df, window=30, trading_periods=252, clean=True):
log_return = (df['close'] / df['close'].shift(1)).apply(np.log)
vol = log_return.rolling(
window=window,
center=False
).std() * math.sqrt(trading_periods)
h = window
n = (log_return.count() - h) + 1
adj_factor = 1.0 / (1.0 - (h / n) + ((h ** 2 - 1) / (3 * n ** 2)))
result = vol * adj_factor
if clean:
return result.dropna()
else:
return result
def pk_vol_est(df, window=30, trading_periods=252, clean=True):
rs = (1.0 / (4.0 * math.log(2.0))) * ((df['high'] / df['low']).apply(np.log)) ** 2.0
def f(v):
return trading_periods * v.mean() ** 0.5
result = rs.rolling(
window=window,
center=False
).apply(func=f)
if clean:
return result.dropna()
else:
return result
def rs_vol_est(df, window=30, trading_periods=252, clean=True):
log_ho = (df['high'] / df['open']).apply(np.log)
log_lo = (df['low'] / df['open']).apply(np.log)
log_co = (df['close'] / df['open']).apply(np.log)
rs = log_ho * (log_ho - log_co) + log_lo * (log_lo - log_co)
def f(v):
return trading_periods * v.mean() ** 0.5
result = rs.rolling(
window=window,
center=False
).apply(func=f)
if clean:
return result.dropna()
else:
return result
def yz_vol_est(df, window=30, trading_periods=252, clean=True):
log_ho = (df['high'] / df['open']).apply(np.log)
log_lo = (df['low'] / df['open']).apply(np.log)
log_co = (df['close'] / df['open']).apply(np.log)
log_oc = (df['open'] / df['close'].shift(1)).apply(np.log)
log_oc_sq = log_oc ** 2
log_cc = (df['close'] / df['close'].shift(1)).apply(np.log)
log_cc_sq = log_cc ** 2
rs = log_ho * (log_ho - log_co) + log_lo * (log_lo - log_co)
close_vol = log_cc_sq.rolling(
window=window,
center=False
).sum() * (1.0 / (window - 1.0))
open_vol = log_oc_sq.rolling(
window=window,
center=False
).sum() * (1.0 / (window - 1.0))
window_rs = rs.rolling(
window=window,
center=False
).sum() * (1.0 / (window - 1.0))
k = 0.34 / (1 + (window + 1) / (window - 1))
result = (open_vol + k * close_vol + (1 - k) * window_rs).apply(np.sqrt) * math.sqrt(trading_periods)
if clean:
return result.dropna()
else:
return result