-
Notifications
You must be signed in to change notification settings - Fork 86
/
vadj_multi.py
204 lines (169 loc) · 8.86 KB
/
vadj_multi.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
194
195
196
197
198
199
200
201
202
203
#!/usr/bin/env python
from regress import *
from calc import *
from loaddata import *
from util import *
def wavg(group):
b = group['pbeta']
d = group['log_ret']
w = group['mkt_cap_y'] / 1e6
res = b * ((d * w).sum() / w.sum())
return res
def wavg2(group):
b = group['pbeta']
d = group['cur_log_ret']
w = group['mkt_cap_y'] / 1e6
res = b * ((d * w).sum() / w.sum())
return res
def wavg_ind(group):
d = group['vadj0_B']
w = group['mkt_cap_y'] / 1e6
res = ((d * w).sum() / w.sum())
return res
def calc_vadj_daily(daily_df, horizon):
print "Caculating daily vadj..."
result_df = filter_expandable(daily_df)
print "Calculating vadj0..."
result_df['rv'] = result_df['tradable_volume'].astype(float) / result_df['tradable_med_volume_21']
result_df.replace([np.inf, -np.inf], np.nan, inplace=True)
result_df = result_df.dropna(subset=['log_ret', 'pbeta', 'mkt_cap_y', 'gdate'])
result_df['bret'] = result_df[['log_ret', 'pbeta', 'mkt_cap_y', 'gdate']].groupby('gdate').apply(wavg).reset_index(level=0)['pbeta']
result_df['badjret'] = result_df['log_ret'] - result_df['bret']
result_df['vadj0'] = result_df['rv'] * result_df['badjret']
result_df = result_df.dropna(subset=['vadj0'])
result_df['vadj0_B'] = winsorize_by_date(result_df['vadj0'])
demean = lambda x: (x - x.mean())
indgroups = result_df[['vadj0_B', 'gdate', 'ind1']].groupby(['gdate', 'ind1'], sort=False).transform(demean)
result_df['vadj0_B_ma'] = indgroups['vadj0_B']
print "Calulating lags..."
for lag in range(1,horizon+1):
shift_df = result_df.unstack().shift(lag).stack()
result_df['vadj' + str(lag) + '_B_ma'] = shift_df['vadj0_B_ma']
print "Calculated {} values".format(len(result_df['vadj0_B_ma'].dropna()))
return result_df
def calc_vadj_intra(intra_df):
print "Calculating vadj intra..."
result_df = filter_expandable(intra_df)
print "Calulating vadjC..."
result_df['cur_log_ret'] = result_df['overnight_log_ret'] + (np.log(result_df['iclose']/result_df['dopen']))
# result_df['c2c_badj'] = result_df['cur_log_ret'] / result_df['pbeta']
result_df['bret'] = result_df[['cur_log_ret', 'pbeta', 'mkt_cap_y', 'giclose_ts']].groupby(['giclose_ts'], sort=False).apply(wavg2).reset_index(level=0)['pbeta']
result_df['badjret'] = result_df['cur_log_ret'] - result_df['bret']
result_df['rv_i'] = (result_df['dvolume'].astype(float) * result_df['dvwap']) / result_df['dpvolume_med_21']
result_df['vadjC'] = result_df['rv_i'] * result_df['badjret']
result_df['vadjC_B'] = winsorize_by_ts(result_df['vadjC'])
print "Calulating vadjC_ma..."
demean = lambda x: (x - x.mean())
indgroups = result_df[['vadjC_B', 'date', 'ind1']].groupby(['date', 'ind1'], sort=False).transform(demean)
result_df['vadjC_B_ma'] = indgroups['vadjC_B']
print "Calculated {} values".format(len(result_df['vadjC_B_ma'].dropna()))
return result_df
def vadj_fits(daily_df, intra_df, horizon, name, middate=None):
insample_intra_df = intra_df
insample_daily_df = daily_df
outsample_intra_df = intra_df
if middate is not None:
insample_intra_df = intra_df[ intra_df['date'] < middate ]
insample_daily_df = daily_df[ daily_df.index.get_level_values('date') < middate ]
outsample_intra_df = intra_df[ intra_df['date'] >= middate ]
outsample_intra_df['vadj_b'] = np.nan
outsample_intra_df[ 'vadjC_B_ma_coef' ] = np.nan
for lag in range(1, horizon+1):
outsample_intra_df[ 'vadj' + str(lag) + '_B_ma_coef' ] = np.nan
# fits_df = pd.DataFrame(columns=['horizon', 'coef', 'indep', 'tstat', 'nobs', 'stderr'])
# fitresults_df = regress_alpha(insample_intra_df, 'vadjC_B_ma', horizon, True, 'intra')
# fits_df = fits_df.append(fitresults_df, ignore_index=True)
# plot_fit(fits_df, "vadj_intra_"+name+"_" + df_dates(insample_intra_df))
# fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
# unstacked = outsample_intra_df[ ['ticker'] ].unstack()
# coefs = dict()
# coefs[1] = unstacked.between_time('09:30', '10:31').stack().index
# coefs[2] = unstacked.between_time('10:30', '11:31').stack().index
# coefs[3] = unstacked.between_time('11:30', '12:31').stack().index
# coefs[4] = unstacked.between_time('12:30', '13:31').stack().index
# coefs[5] = unstacked.between_time('13:30', '14:31').stack().index
# coefs[6] = unstacked.between_time('14:30', '15:31').stack().index
# print fits_df.head()
# for ii in range(1,7):
# outsample_intra_df.ix[ coefs[ii], 'vadjC_B_ma_coef' ] = fits_df.ix['vadjC_B_ma'].ix[ii].ix['coef']
fits_df = pd.DataFrame(columns=['horizon', 'coef', 'indep', 'tstat', 'nobs', 'stderr'])
for lag in range(1,horizon+1):
fitresults_df = regress_alpha(insample_daily_df, 'vadj0_B_ma', lag, True, 'daily')
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "vadj_daily_"+name+"_" + df_dates(insample_daily_df))
fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
coef0 = fits_df.ix['vadj0_B_ma'].ix[horizon].ix['coef']
# outsample_intra_df[ 'vadjC_B_ma_coef' ] = coef0
for lag in range(1,horizon):
coef = coef0 - fits_df.ix['vadj0_B_ma'].ix[lag].ix['coef']
print "Coef{}: {}".format(lag, coef)
outsample_intra_df[ 'vadj'+str(lag)+'_B_ma_coef' ] = coef
# outsample_intra_df[ 'vadj_b'] = outsample_intra_df['vadjC_B_ma'] * outsample_intra_df['vadjC_B_ma_coef']
outsample_intra_df[ 'vadj_b'] = 0
for lag in range(1,horizon):
outsample_intra_df[ 'vadj_b'] += outsample_intra_df['vadj'+str(lag)+'_B_ma'] * outsample_intra_df['vadj'+str(lag)+'_B_ma_coef']
return outsample_intra_df
def calc_vadj_forecast(daily_df, intra_df, horizon, middate):
daily_results_df = calc_vadj_daily(daily_df, horizon)
forwards_df = calc_forward_returns(daily_df, horizon)
daily_results_df = pd.concat( [daily_results_df, forwards_df], axis=1)
#intra_results_df = calc_vadj_intra(intra_df)
intra_results_df = intra_df
intra_results_df = merge_intra_data(daily_results_df, intra_results_df)
sector_name = 'Energy'
print "Running vadj for sector {}".format(sector_name)
sector_df = daily_results_df[ daily_results_df['sector_name'] == sector_name ]
sector_intra_results_df = intra_results_df[ intra_results_df['sector_name'] == sector_name ]
result1_df = vadj_fits(sector_df, sector_intra_results_df, horizon, "ex", middate)
print "Running vadj for sector {}".format(sector_name)
sector_df = daily_results_df[ daily_results_df['sector_name'] != sector_name ]
sector_intra_results_df = intra_results_df[ intra_results_df['sector_name'] != sector_name ]
result2_df = vadj_fits(sector_df, sector_intra_results_df, horizon, "in", middate)
result_df = pd.concat([result1_df, result2_df], verify_integrity=True)
return result_df
def memtest():
start = "20110101"
end = "20120101"
pname = "./vadj_b" + start + "." + end
daily_df = pd.read_hdf(pname+"_daily.h5", 'table')
intra_df = pd.read_hdf(pname+"_intra.h5", 'table')
outsample_df = calc_vadj_forecast(daily_df, intra_df, 3, True)
if __name__=="__main__":
parser = argparse.ArgumentParser(description='G')
parser.add_argument("--start",action="store",dest="start",default=None)
parser.add_argument("--end",action="store",dest="end",default=None)
parser.add_argument("--mid",action="store",dest="mid",default=None)
parser.add_argument("--horizon",action="store",dest="horizon",default=3)
args = parser.parse_args()
start = args.start
end = args.end
lookback = 30
horizon = int(args.horizon)
pname = "./vadj_b" + start + "." + end
start = dateparser.parse(start)
end = dateparser.parse(end)
middate = dateparser.parse(args.mid)
loaded = False
try:
daily_df = pd.read_hdf(pname+"_daily.h5", 'table')
intra_df = pd.read_hdf(pname+"_intra.h5", 'table')
loaded = True
except:
print "Could not load cached data..."
if not loaded:
uni_df = get_uni(start, end, lookback)
BARRA_COLS = ['ind1', 'pbeta']
barra_df = load_barra(uni_df, start, end, BARRA_COLS)
PRICE_COLS = ['close', 'overnight_log_ret', 'tradable_volume', 'tradable_med_volume_21']
price_df = load_prices(uni_df, start, end, PRICE_COLS)
uni_df = price_df[['ticker']]
DBAR_COLS = ['close', 'dopen', 'dvolume', 'dvwap']
intra_df = load_daybars(uni_df, start, end, DBAR_COLS)
daily_df = merge_barra_data(price_df, barra_df)
intra_df = merge_intra_data(daily_df, intra_df)
intra_df = calc_vol_profiles(intra_df)
dump_hd5(daily_df, pname+"_daily.h5")
dump_hd5(intra_df, pname+"_intra.h5")
outsample_df = calc_vadj_forecast(daily_df, intra_df, horizon, middate)
dump_alpha(outsample_df, 'vadj_b')
dump_hd5(outsample_df)