bd = BusDay(sql)
crsp = CRSP(sql, bd, rdb)
logdir = os.path.join(settings['images'], 'weekrev')
# Construct weekly reversal
rebalbeg = 19730629 # increased stocks coverage in CRSP from around this date
rebalend = 20210101 # a Friday, so can include last week in 2020
wd = Weekly(sql, 'Fri', rebalbeg, rebalend) # Generate Friday-end weekly cal
# Retrieve weekly returns, standardize scores, and compute returns and i.c.
june_universe = 0 # to track when reached a June end to update universe
year = 0 # to track new year to retrieve prices in batch for screening
res = DataFrame()
tic = time.time()
for rebaldate in wd.date_range(rebalbeg, rebalend)[:-1]:
d = bd.june_universe(rebaldate)
if d != june_universe:
june_universe = d # update universe every June
univ = crsp.get_universe(june_universe) # usual CRSP universe screen
univ = univ[univ['decile'] < 10] # drop smalest decile stocks
start = wd.begwk(rebaldate) # starting date of rebalance week
beg = bd.offset(rebaldate, 1) # beginning date of holding week
end = wd.endwk(beg) # ending date of holding week
prcdate = bd.offset(start, -1) # require price available at start of week
prcyear = (prcdate // 10000) * 10000
if prcyear != year: # retrieve new batch of prices each new year
year = prcyear
prc = crsp.get_range('daily',
'prc',
'date',
ls = smf.ols("ret ~ BETA + BETA2 + RES", data=rets).fit()
print(ls.summary())
print(ls.get_robustcov_results('HC0').summary())
print(ls.get_robustcov_results('HAC', maxlags=3).summary())
print(
ls.get_robustcov_results('hac-panel', groups=rets['port'],
maxlags=3).summary())
print(ls.get_robustcov_results('cluster', groups=rets['port']).summary())
## Fama MacBeth with individual stocks and standardized scores as loadings
rebalbeg = 19640601
rebalend = LAST_DATE
rebaldates = crsp.bd.date_range(rebalbeg, rebalend, 'endmo')
loadings = dict()
for pordate in rebaldates: # retrieve signal values every month
date = bd.june_universe(pordate)
univ = crsp.get_universe(date)
cap = np.sqrt(crsp.get_cap(date)['cap'])
smb = -np.log(cap).rename('size')
hml = signals('hml', date, bd.endmo(date, -12))['hml'].rename('value')
beta = (signals('beta', pordate, bd.begmo(pordate))['beta'] * 2 / 3) + (1 /
3)
mom = signals('mom', pordate)['mom'].rename('momentum')
df = pd.concat(
(beta, hml, smb, mom), # inner join of signals with univ
join='inner',
axis=1).reindex(univ.index).dropna()
loadings[pordate] = winsorized(df, quantiles=[0.05, 0.95])
## Compute coefficients from FM cross-sectional regressions
riskpremium = RiskPremium(user, bench, 'RF', LAST_DATE)