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bd_intra.py
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bd_intra.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
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 calc_bd_intra(intra_df):
print "Calculating bd intra..."
result_df = filter_expandable(intra_df)
result_df['cur_log_ret'] = np.log(result_df['iclose']/result_df['bopen'])
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']
# decile = lambda x: 10.0 * x.rank()/float(len(x))
# result_df['cur_log_ret_decile'] = result_df[['cur_log_ret', 'giclose_ts']].groupby(['giclose_ts'], sort=False).transform(decile)['cur_log_ret']
print "Calulating bdC..."
result_df['bdC'] = (result_df['askHitDollars'] - result_df['bidHitDollars']) / (result_df['askHitDollars'] + result_df['midHitDollars'] + result_df['bidHitDollars'])
result_df['bdC_B'] = winsorize_by_ts(result_df['bdC'])
print "Calulating bdC_ma..."
demean = lambda x: (x - x.mean())
indgroups = result_df[['bdC_B', 'giclose_ts', 'ind1']].groupby(['giclose_ts', 'ind1'], sort=False).transform(demean)
result_df['bdC_B_ma'] = indgroups['bdC_B']
# result_df['bdC_B_ma'] = result_df['bdC_B_ma'] * np.abs(result_df['badjret'])
# result_df['bdC_B_ma'] = result_df['bdC_B_ma'].clip(0,1000) * np.sign(result_df['cur_log_ret'])
# result_df.ix[ (result_df['cur_log_ret_decile'] < 1) | (result_df['cur_log_ret_decile'] == 9), 'bdC_B_ma'] = np.nan
# result_df['bdC_B_ma'] = result_df['bdC_B_ma'] * (2 - result_df['cur_log_ret_r'])
# result_df['eod_ts'] = result_df['date'].apply(lambda x: x + timedelta(hours=15, minutes=30))
# result_df['scale'] = result_df['eod_ts'] - result_df['giclose_ts']
# result_df['scale'] = result_df['scale'].apply(lambda x: 1.0 - (x/np.timedelta64(1, 's'))/(360*60))
# result_df[ result_df['scale'] > 1 ] = 0
# result_df['bdC_B_ma_tod'] = result_df['bdC_B_ma'] * result_df['scale']
return result_df
def bd_fits(intra_df, horizon, name, middate):
insample_intra_df = intra_df
outsample_intra_df = intra_df
if middate is not None:
insample_intra_df = intra_df[ intra_df['date'] < middate ]
outsample_intra_df = intra_df[ intra_df['date'] >= middate ]
outsample_intra_df['bdma'] = np.nan
outsample_intra_df['bdC_B_ma_coef'] = np.nan
for lag in range(0, horizon+1):
outsample_intra_df[ 'bd' + 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, 'bdC_B_ma', horizon, True, 'intra_eod')
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "bdma_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()
for ii in range(1,7):
outsample_intra_df.ix[ coefs[ii], 'bdC_B_ma_coef' ] = fits_df.ix['bdC_B_ma'].ix[ii].ix['coef']
outsample_intra_df[ 'bdma_i'] = outsample_intra_df['bdC_B_ma'] * outsample_intra_df['bdC_B_ma_coef']
return outsample_intra_df
def calc_bd_forecast(daily_df, intra_df, horizon):
forwards_df = calc_forward_returns(daily_df, horizon)
daily_results_df = forwards_df
intra_results_df = calc_bd_intra(intra_df)
intra_results_df = merge_intra_data(daily_results_df, intra_results_df)
full_df = bd_fits(intra_results_df, horizon, "", middate)
return full_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=15)
args = parser.parse_args()
start = args.start
end = args.end
lookback = 30
horizon = 0
freq = int(args.freq)
pname = "./bd" + 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 "Did 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)
BAR_COLS = ['askHitDollars', 'midHitDollars', 'bidHitDollars', 'bopen']
intra_df = load_bars(price_df[['ticker']], start, end, BAR_COLS, freq)
daily_df = merge_barra_data(price_df, barra_df)
daily_df = merge_intra_eod(daily_df, intra_df)
intra_df = merge_intra_data(daily_df, intra_df)
daily_df.to_hdf(pname+"_daily.h5", 'table', complib='zlib')
intra_df.to_hdf(pname+"_intra.h5", 'table', complib='zlib')
outsample_df = calc_bd_forecast(daily_df, intra_df, horizon)
dump_alpha(outsample_df, 'bdma_i')