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rating_diff.py
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rating_diff.py
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#!/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) | (result_df['std_diff'].isnull()), 'rating_diff_mean'] = 0
print result_df['rating_diff_mean'].describe()
print "SEAN2"
print result_df.xs(10000708, level=1)
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):
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
fits_df = pd.DataFrame(columns=['horizon', 'coef', 'indep', 'tstat', 'nobs', 'stderr'])
for ii in range(1, horizon+1):
fitresults_df = regress_alpha(insample_daily_df, 'rtg0_ma', ii, True, 'daily', False)
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "rtg_daily_"+name+"_" + df_dates(insample_daily_df))
fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
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']
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'] += outsample_daily_df[lagname].fillna(0) * outsample_daily_df['rtg0_ma_coef'] * weight
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)
result_df = rtg_fits(daily_results_df, horizon, "", middate)
# 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=6)
# 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')