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badj2_multi.py
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badj2_multi.py
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#!/usr/bin/env python
from alphacalc import *
from dateutil import parser as dateparser
import argparse
def wavg(group):
b = group['pbeta']
d = group['log_ret']
w = group['capitalization'] / 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_o2c(daily_df, horizon):
print "Caculating daily o2c..."
result_df = daily_df.reset_index()
result_df = filter_expandable(result_df)
result_df = result_df[ ['log_ret', 'pbeta', 'date', 'ind1', 'sid', 'mkt_cap_y' ]]
print "Calculating o2c0..."
result_df['o2c0'] = result_df[['log_ret', 'pbeta', 'mkt_cap_y', 'date']].groupby(['date'], sort=False).apply(wavg)
result_df['o2c0_B'] = winsorize_by_group(result_df[ ['date', 'o2c0'] ], 'date')
demean = lambda x: (x - x.mean())
indgroups = result_df[['o2c0_B', 'date', 'ind1']].groupby(['date', 'ind1'], sort=False).transform(demean)
result_df['o2c0_B_ma'] = indgroups['o2c0_B']
result_df.set_index(keys=['date', 'sid'], inplace=True)
print "Calulating lags..."
for lag in range(1,horizon+1):
shift_df = result_df.unstack().shift(lag).stack()
result_df['o2c' + str(lag) + '_B_ma'] = shift_df['o2c0_B_ma']
result_df = merge_daily_calcs(daily_df, result_df)
return result_df
def calc_o2c_intra(intra_df, daily_df):
print "Calculating o2c intra..."
result_df = filter_expandable_intra(intra_df, daily_df)
result_df = result_df.reset_index()
result_df = result_df[ ['iclose_ts', 'iclose', 'dopen', 'overnight_log_ret', 'pbeta', 'date', 'ind1', 'sid', 'mkt_cap_y' ] ]
result_df = result_df.dropna(how='any')
print "Calulating o2cC..."
result_df['cur_log_ret'] = result_df['overnight_log_ret'] + (np.log(result_df['iclose']/result_df['dopen']))
result_df['o2cC'] = result_df[['cur_log_ret', 'pbeta', 'mkt_cap_y', 'iclose_ts']].groupby(['iclose_ts'], sort=False).apply(wavg2)
result_df['o2cC_B'] = winsorize_by_group(result_df[ ['iclose_ts', 'o2cC'] ], 'iclose_ts')
print "Calulating o2cC_ma..."
demean = lambda x: (x - x.mean())
indgroups = result_df[['o2cC_B', 'iclose_ts', 'ind1']].groupby(['iclose_ts', 'ind1'], sort=False).transform(demean)
result_df['o2cC_B_ma'] = indgroups['o2cC_B']
result_df = merge_intra_calcs(intra_df, result_df)
return result_df
def o2c_fits(daily_df, intra_df, full_df, horizon, name, middate=None):
if 'badj_m' not in full_df.columns:
print "Creating forecast columns..."
full_df['badj_m'] = np.nan
full_df[ 'o2cC_B_ma_coef' ] = np.nan
for lag in range(1, horizon+1):
full_df[ 'o2c' + str(lag) + '_B_ma_coef' ] = np.nan
insample_intra_df = intra_df
insample_daily_df = daily_df
outsample_intra_df = intra_df
outsample = False
if middate is not None:
outsample = True
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 ]
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, 'o2c0_B_ma', lag, outsample, 'daily')
fits_df = fits_df.append(fitresults_df, ignore_index=True)
plot_fit(fits_df, "badj_daily_"+name+"_" + df_dates(insample_daily_df))
fits_df.set_index(keys=['indep', 'horizon'], inplace=True)
coef0 = fits_df.ix['o2c0_B_ma'].ix[horizon].ix['coef']
full_df.ix[ outsample_intra_df.index, 'o2cC_B_ma_coef' ] = 0#coef0
print "{} Coef0: {}".format(name, coef0)
for lag in range(1,horizon):
coef = coef0 - fits_df.ix['o2c0_B_ma'].ix[lag].ix['coef']
print "{} Coef{}: {}".format(name, lag, coef)
full_df.ix[ outsample_intra_df.index, 'o2c'+str(lag)+'_B_ma_coef' ] = coef
full_df.ix[ outsample_intra_df.index, 'badj2_m'] = full_df['o2cC_B_ma'] * full_df['o2cC_B_ma_coef']
for lag in range(1,horizon):
full_df.ix[ outsample_intra_df.index, 'badj2_m'] += full_df['o2c'+str(lag)+'_B_ma'] * full_df['o2c'+str(lag)+'_B_ma_coef']
return full_df
def calc_o2c_forecast(daily_df, intra_df, horizon, outsample):
daily_df = calc_o2c(daily_df, horizon)
intra_df = calc_o2c_intra(intra_df, daily_df)
full_df = merge_intra_data(daily_df, intra_df)
middate = None
if outsample:
middate = intra_df.index[0][0] + (intra_df.index[len(intra_df)-1][0] - intra_df.index[0][0]) / 2
print "Setting fit period before {}".format(middate)
sector_name = 'Energy'
print "Running o2c for sector {}".format(sector_name)
sector_df = daily_df[ daily_df['sector_name'] == sector_name ]
sector_intra_df = intra_df[ intra_df['sector_name'] == sector_name ]
full_df = o2c_fits(sector_df, sector_intra_df, full_df, horizon, "in", middate)
print "Running o2c for sector {}".format(sector_name)
sector_df = daily_df[ daily_df['sector_name'] != sector_name ]
sector_intra_df = intra_df[ intra_df['sector_name'] != sector_name ]
full_df = o2c_fits(sector_df, sector_intra_df, full_df, horizon, "ex", middate)
if outsample:
full_df = full_df[ full_df['date'] > 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("--os",action="store",dest="outsample",default=False)
args = parser.parse_args()
start = args.start
end = args.end
outsample = args.outsample
lookback = 30
horizon = 3
pname = "./badj2_m" + start + "." + end
start = dateparser.parse(start)
end = dateparser.parse(end)
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_df = load_barra(uni_df, start, end)
price_df = load_prices(uni_df, start, end)
daily_df = merge_barra_data(price_df, barra_df)
daybar_df = load_daybars(uni_df, start, end)
intra_df = merge_intra_data(daily_df, daybar_df)
daily_df.to_hdf(pname+"_daily.h5", 'table', complib='zlib')
intra_df.to_hdf(pname+"_intra.h5", 'table', complib='zlib')
full_df = calc_o2c_forecast(daily_df, intra_df, horizon, outsample)
dump_alpha(outsample_df, 'badj2_m')
dump_all(outsample_df)