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vadj.py
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vadj.py
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#!/usr/bin/env python
from regress import *
from loaddata import *
from util import *
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 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 volmult_i(group):
d = group['dpvolume']
m = group['dpvolume_med_21']
adj = d.sum()/m.sum()
res = group['dpvolume'] / adj
return res
def volmult2(group):
d = group['tradable_volume']
m = group['tradable_med_volume_21']
adj = d.sum()/m.sum()
res = group['tradable_volume'] / adj
return res
def calc_vadj_daily(daily_df, horizon):
print "Caculating daily vadj..."
result_df = filter_expandable(daily_df)
print "Calculating vadj0..."
result_df['tradable_volume_adj'] = result_df[['tradable_med_volume_21', 'tradable_volume', 'gdate']].groupby('gdate').apply(volmult2).reset_index(level=0)['tradable_volume']
result_df['rv'] = result_df['tradable_volume_adj'].astype(float) / result_df['tradable_med_volume_21_y']
# result_df['dpvolume'] = result_df['dvolume'].astype(float) * result_df['dvwap']
# result_df['dpvolume_adj'] = result_df[['dpvolume_med_21', 'dpvolume', 'gdate']].groupby('gdate').apply(volmult).reset_index(level=0)['dpvolume']
# result_df['rv'] = (result_df['dpvolume_adj'] - result_df['dpvolume_med_21']) / result_df['dpvolume_std_21']
print result_df[['log_ret', 'pbeta', 'mkt_cap_y', 'gdate']].head()
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'] * np.sign(result_df['badjret']).fillna(0)
# 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['bret_i'] = 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_i'] = result_df['cur_log_ret'] - result_df['bret_i']
result_df['dpvolume'] = result_df['dvolume'].astype(float) * result_df['dvwap']
result_df['dpvolume_adj'] = result_df[['dpvolume_med_21', 'dpvolume', 'giclose_ts']].groupby('giclose_ts').apply(volmult_i).reset_index(level=0)['dpvolume']
result_df['rv_i'] = result_df['dpvolume_adj'].astype(float) / result_df['dpvolume_med_21']
# result_df['rv_i'] = (result_df['dpvolume_adj'] - result_df['dpvolume_med_21']) / result_df['dpvolume_std_21']
result_df['vadjC'] = result_df['rv_i'] * np.sign(result_df['badjret_i'])
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', 'giclose_ts', 'ind1']].groupby(['giclose_ts', '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, False, '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', '15:59').stack().index
print fits_df.head(10)
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, False, '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']
print "Coef0: {}".format(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']
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 = merge_intra_data(daily_results_df, intra_results_df)
sector_name = "Energy"
results = list()
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 ]
result_df = vadj_fits(sector_df, sector_intra_results_df, horizon, "in", middate)
results.append(result_df)
print "Running vadj excluding 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 ]
result_df = vadj_fits(sector_df, sector_intra_results_df, horizon, "ex", middate)
results.append(result_df)
result_df = pd.concat(results, 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("--freq",action="store",dest="freq",default='15Min')
parser.add_argument("--horizon",action="store",dest="horizon",default=2)
args = parser.parse_args()
start = args.start
end = args.end
lookback = 30
horizon = int(args.horizon)
freq = args.freq
pname = "./vadj_b" + start + "." + end
start = dateparser.parse(start)
end = dateparser.parse(end)
middate = dateparser.parse(args.mid)
loaded = False
try:
print "Looking " + pname+"_daily.h5"
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', 'volat_21']
price_df = load_prices(uni_df, start, end, PRICE_COLS)
DBAR_COLS = ['close', 'dopen', 'dvolume', 'dvwap']
intra_df = load_daybars(price_df[['ticker']], start, end, DBAR_COLS, freq)
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)
print "one"
print intra_df.columns
daily_df = merge_intra_eod(daily_df, intra_df)
print "two"
print daily_df.columns
daily_df.to_hdf(pname+"_daily.h5", 'table', complib='zlib')
intra_df.to_hdf(pname+"_intra.h5", 'table', complib='zlib')
outsample_df = calc_vadj_forecast(daily_df, intra_df, horizon, middate)
dump_alpha(outsample_df, 'vadj_b')
# dump_all(outsample_df)