-
Notifications
You must be signed in to change notification settings - Fork 86
/
rating_diff_updn.py
192 lines (148 loc) · 8.51 KB
/
rating_diff_updn.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
#!/usr/bin/env python
from regress import *
from loaddata import *
from util import *
from pandas.stats.moments import ewma
def wavg(group):
b = group['pbeta']
d = group['log_ret']
w = group['mkt_cap_y'] / 1e6
print "Mkt return: {} {}".format(group['gdate'], ((d * w).sum() / w.sum()))
res = b * ((d * w).sum() / w.sum())
return res
def calc_rtg_daily(daily_df, horizon):
print "Caculating daily rtg..."
result_df = filter_expandable(daily_df)
print "Calculating rtg0..."
# result_df['cum_ret'] = pd.rolling_sum(result_df['log_ret'], 6)
# result_df['med_diff'] = result_df['median'].unstack().diff().stack()
# result_df['rtg0'] = -1.0 * (result_df['median'] - 3) / ( 1.0 + result_df['std'] )
# result_df['rtg0'] = -1 * result_df['mean'] * np.abs(result_df['mean'])
# result_df['rtg0'] = -1.0 * result_df['med_diff_dk'] * result_df['cum_ret']
result_df['std_diff'] = result_df['rating_std'].unstack().diff().stack()
print "SEAN"
print result_df['rating_diff_mean'].describe()
result_df.loc[ result_df['std_diff'] <= 0, 'rating_diff_mean'] = 0
print result_df['rating_diff_mean'].describe()
result_df['rtg0'] = result_df['rating_diff_mean'] * result_df['rating_diff_mean'] * np.sign(result_df['rating_diff_mean'])
# result_df['rtg0'] = -1.0 * result_df['med_diff_dk']
# demean = lambda x: (x - x.mean())
# indgroups = result_df[['rtg0', 'gdate', 'ind1']].groupby(['gdate', 'ind1'], sort=True).transform(demean)
# result_df['rtg0_ma'] = indgroups['rtg0']
result_df['rtg0_ma'] = result_df['rtg0']
for lag in range(1,horizon+1):
shift_df = result_df.unstack().shift(lag).stack()
result_df['rtg'+str(lag)+'_ma'] = shift_df['rtg0_ma']
return result_df
def rtg_fits(daily_df, horizon, name, middate=None, intercepts=None):
insample_daily_df = daily_df
if middate is not None:
insample_daily_df = daily_df[ daily_df.index.get_level_values('date') < middate ]
outsample_daily_df = daily_df[ daily_df.index.get_level_values('date') >= middate ]
outsample_daily_df['rtg'] = np.nan
ESTIMATE = "rating"
insample_up_df = insample_daily_df[ insample_daily_df[ESTIMATE + "_diff_mean"] > 0 ]
fits_df = pd.DataFrame(columns=['horizon', 'coef', 'indep', 'tstat', 'nobs', 'stderr', 'intercept'])
for ii in range(1, horizon+1):
fitresults_df = regress_alpha(insample_up_df, 'rtg0_ma', ii, False, 'daily', True)
fitresults_df['intercept'] = fitresults_df['intercept'] - intercepts[ii]
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "rtg_up_"+name+"_" + df_dates(insample_up_df))
fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
coef0 = fits_df.ix['rtg0_ma'].ix[horizon].ix['coef']
intercept0 = fits_df.ix['rtg0_ma'].ix[horizon].ix['intercept']
print "Coef{}: {}".format(0, coef0)
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] > 0, 'rtg0_ma_coef' ] = coef0
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] > 0, 'rtg0_ma_intercept' ] = intercept0
for lag in range(1,horizon):
coef = coef0 - fits_df.ix['rtg0_ma'].ix[lag].ix['coef']
intercept = intercept0 - fits_df.ix['rtg0_ma'].ix[lag].ix['intercept']
print "Coef{}: {}".format(lag, coef)
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] > 0, 'rtg'+str(lag)+'_ma_coef' ] = coef
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] > 0, 'rtg'+str(lag)+'_ma_intercept' ] = intercept
insample_dn_df = insample_daily_df[ insample_daily_df[ESTIMATE + "_diff_mean"] <= 0 ]
fits_df = pd.DataFrame(columns=['horizon', 'coef', 'indep', 'tstat', 'nobs', 'stderr', 'intercept'])
for ii in range(1, horizon+1):
fitresults_df = regress_alpha(insample_dn_df, 'rtg0_ma', ii, False, 'daily', True)
fitresults_df['intercept'] = fitresults_df['intercept'] - intercepts[ii]
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "rtg_dn_"+name+"_" + df_dates(insample_dn_df))
fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
coef0 = fits_df.ix['rtg0_ma'].ix[horizon].ix['coef']
intercept0 = fits_df.ix['rtg0_ma'].ix[horizon].ix['intercept']
print "Coef{}: {}".format(0, coef0)
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] <= 0, 'rtg0_ma_coef' ] = coef0
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] <= 0, 'rtg0_ma_intercept' ] = intercept0
for lag in range(1,horizon):
coef = coef0 - fits_df.ix['rtg0_ma'].ix[lag].ix['coef']
intercept = intercept0 - fits_df.ix['rtg0_ma'].ix[lag].ix['intercept']
print "Coef{}: {}".format(lag, coef)
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] <= 0, 'rtg'+str(lag)+'_ma_coef' ] = coef
outsample_daily_df.loc[ outsample_daily_df[ESTIMATE + '_diff_mean'] <= 0, 'rtg'+str(lag)+'_ma_intercept' ] = intercept
coef0 = fits_df.ix['rtg0_ma'].ix[horizon].ix['coef']
print "Coef{}: {}".format(0, coef0)
outsample_daily_df[ 'rtg0_ma_coef' ] = coef0
outsample_daily_df[ 'rtg' ] = outsample_daily_df['rtg0_ma'].fillna(0) * outsample_daily_df['rtg0_ma_coef'] + outsample_daily_df['rtg0_ma_intercept']
for lag in range(1,horizon):
weight = (horizon - lag) / float(horizon)
lagname = 'rtg'+str(lag)+'_ma'
print "Running lag {} with weight: {}".format(lag, weight)
outsample_daily_df[ 'rtg'] += weight * (outsample_daily_df[lagname].fillna(0) * outsample_daily_df['rtg0_ma_coef'] + outsample_daily_df['rtg'+str(lag)+'_ma_intercept'])
print "Alpha Summary {}".format(name)
print outsample_daily_df['rtg'].describe()
return outsample_daily_df
def calc_rtg_forecast(daily_df, horizon, middate):
daily_results_df = calc_rtg_daily(daily_df, horizon)
forwards_df = calc_forward_returns(daily_df, horizon)
daily_results_df = pd.concat( [daily_results_df, forwards_df], axis=1)
# results = list()
# for sector_name in daily_results_df['sector_name'].dropna().unique():
# if sector_name == "Utilities" or sector_name == "HealthCare": continue
# print "Running rtg for sector {}".format(sector_name)
# sector_df = daily_results_df[ daily_results_df['sector_name'] == sector_name ]
# result_df = rtg_fits(sector_df, horizon, sector_name, middate)
# results.append(result_df)
# result_df = pd.concat(results, verify_integrity=True)
intercept_d = get_intercept(daily_results_df, horizon, 'rtg0_ma', middate)
result_df = rtg_fits(daily_results_df, horizon, "", middate, intercept_d)
# res1 = rtg_fits( daily_results_df[ daily_results_df['rating_diff_mean'] > 0 ], horizon, "up", middate)
# res2 = rtg_fits( daily_results_df[ daily_results_df['rating_diff_mean'] < 0 ], horizon, "dn", middate)
# result_df = pd.concat([res1, res2], verify_integrity=True)
return result_df
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("--lag",action="store",dest="lag",default=20)
# parser.add_argument("--horizon",action="store",dest="horizon",default=20)
args = parser.parse_args()
start = args.start
end = args.end
lookback = 30
horizon = int(args.lag)
pname = "./rtg" + start + "." + end
start = dateparser.parse(start)
end = dateparser.parse(end)
middate = dateparser.parse(args.mid)
lag = int(args.lag)
loaded = False
try:
daily_df = pd.read_hdf(pname+"_daily.h5", 'table')
loaded = True
except:
print "Did not load cached data..."
if not loaded:
uni_df = get_uni(start, end, lookback)
BARRA_COLS = ['ind1']
barra_df = load_barra(uni_df, start, end, BARRA_COLS)
PRICE_COLS = ['close']
price_df = load_prices(uni_df, start, end, PRICE_COLS)
daily_df = merge_barra_data(price_df, barra_df)
analyst_df = load_ratings_hist(price_df[['ticker']], start, end)
daily_df = merge_daily_calcs(analyst_df, daily_df)
daily_df.to_hdf(pname+"_daily.h5", 'table', complib='zlib')
result_df = calc_rtg_forecast(daily_df, horizon, middate)
print "Total Alpha Summary"
print result_df['rtg'].describe()
dump_daily_alpha(result_df, 'rtg')