def creditprof():
'''Credit profile derived from mortgage and corporate credit spreads.'''
# Derivation in fecon235/nb/fred-credit-spreads.ipynb
# See https://git.io/creditprof
# First, note the oldest start date common among all series herein:
start = '1991-08-30'
# ----- MORTGAGE CREDIT SPREAD
# Freddie Mac 15-Year Fixed Rate Mortgage v. Treasury 10-year bond.
# Freddie Mac series is updated weekly on Thursdays,
# so we apply daily interpolation to be
# frequency compatible with the other series in this function:
fmac = daily(get('MORTGAGE15US'))
# Retrieve daily rates for 10-year Treasuries...
ty = get(d4bond10)
# ... then compute the mortgage spread:
mort = todf(fmac - ty)
# Profile the mortgage credit spread:
mortmad = madmen(mort)
# ----- CORPORATE BOND SPREAD
# Examine daily BAA10Y spread between Moody's Seasoned Baa-rated
# Corporate Bonds and 10-year Treasury Constant Maturity:
baa = get('BAA10Y')
# Profile the corporate credit spread:
baamad = madmen(baa[start:])
# ----- UNIFIED PROFILE for generality,
# take the mean of all profiles:
return todf((mortmad + baamad) / 2)
def foreinfl(n=120, alpha=1.0, beta=0.3673):
'''Forecast Unified Inflation 1-year ahead per https://git.io/infl
which a rendering of fecon235/nb/fred-inflation.ipynb.
SUMMARY output: [Average, "infl-date", GMR, HW, BEI]
e.g. [2.2528, '2018-01-01', 1.5793, 3.0791, 2.1000]
where Average is the mean of three orthogonal methods:
GMR for geometric mean rate, HW for Holt-Winters time-series,
and BEI for Break-even Inflation from the Treasury bond market.
Default n denotes 120-month history, i.e. last 10 years.
'''
# Holt-Winters parameters alpha and beta are optimized
# from the 1960-2018 dataset, consisting of 697 monthly points.
# Each "way" is an orthogonal method, to be averaged into way[0].
way = [-9, -9, -9, -9, -9] # dummy placeholders.
inflall = get(m4infl) # synthetic Unified Inflation, monthly.
infl = tail(inflall, n)
way[1] = str(infl.index[-1]).replace(" 00:00:00", "")
# ^Most recent month for CPI, CPIc, PCE, PCEc data.
gm = gemrat(infl, yearly=12)
way[2] = gm[0] # Geometric Mean Rate over n months.
hw = foreholt(infl, 12, alpha, beta) # Holt-Winters model.
way[3] = (tailvalue(hw) - 1) * 100 # Convert forecasted level to rate.
bond10 = get(m4bond10)
tips10 = get(m4tips10)
bei = todf(bond10 - tips10) # 10-year BEI Break-even Inflation.
# ^Treasury bond market data will be much more recent than m4infl.
way[4] = tailvalue(bei)
# Final forecast is the AVERAGE of three orthogonal methods:
way[0] = sum(way[2:]) / len(way[2:])
return way
def forefunds(nearby='16m', distant='17m'):
'''Forecast distant Fed Funds rate using Eurodollar futures.'''
# Derivation in fecon235/nb/qdl-libor-fed-funds.ipynb
# See https://git.io/fedfunds
ffer = get('DFF')
# ^Retrieve Fed Funds effective rate, daily since 1954.
ffer_ema = ema(ffer['1981':], 0.0645)
# ^Eurodollar futures debut.
# ^Exponentially Weighted Moving Average, 30-period.
libor_nearby = get('f4libor' + nearby)
libor_distant = get('f4libor' + distant)
libor_spread = todf(libor_nearby - libor_distant)
# spread in forward style quote since futures uses 100-rate.
return todf(ffer_ema + libor_spread)
def test_group_fecon236_GET_w4cotr_metals_from_QUANDL_vSlow_oLocal():
'''Test get() which uses getqdl() in qdl module.
Here we get the CFTC Commitment of Traders Reports
for gold and silver expressed as our position indicator.
>>> print(qdl.w4cotr_metals)
w4cotr_metals
'''
metals = get(qdl.w4cotr_metals)
assert round(tool.tailvalue(metals[:'2015-07-28']), 3) == 0.461
def foreholt(data, h=12, alpha=hw_alpha, beta=hw_beta, maxi=0):
'''Data slang aware Holt-Winters holtforecast(), h-periods ahead.
Thus "data" can be a fredcode, quandlcode, stock slang,
OR a DataFrame should be detected.
'''
if not isinstance(data, pd.DataFrame):
try:
data = get(data, maxi)
except Exception:
raise ValueError("INVALID data argument.")
holtdf = holt(data, alpha, beta)
return holtforecast(holtdf, h)
def groupget(ggdic=group4d, maxi=0):
'''Retrieve and create group dataframe, given group dictionary.'''
# Since dictionaries are unordered, create SORTED list of keys:
keys = [key for key in sorted(ggdic)]
# Download individual dataframes as values into a dictionary:
dfdic = {key: get(ggdic[key], maxi) for key in keys}
# ^Illustrates dictionary comprehension.
# Paste together dataframes into one large sorted dataframe:
groupdf = tool.paste([dfdic[key] for key in keys])
# Name the columns:
groupdf.columns = keys
return groupdf
def forecast(data, h=12, grids=0, maxi=0):
'''h-period ahead forecasts by holtforecast or optimize_holtforecast,
where "data" may be fredcode, quandlcode, stock slang, or DataFrame.
Given default grids argument, forecast is very QUICK since we use
FIXED parameters implicitly: alpha=hw_alpha and beta=hw_beta.
Recommend grids=50 for reasonable results, but it is TIME-CONSUMING
for search grids > 49 to find OPTIMAL alpha and beta.
'''
if not isinstance(data, pd.DataFrame):
try:
data = get(data, maxi)
# ^Expecting fredcode, quandlcode, or stock slang.
except Exception:
raise ValueError("INVALID data argument.")
if grids > 0:
opt = optimize_holtforecast(data, h, grids=grids)
system.warn(str(opt[1]), stub="OPTIMAL alpha, beta, losspc, loss:")
return opt[0]
else:
holtdf = holt(data)
system.warn("Holt-Winters parameters have NOT been optimized.")
return holtforecast(holtdf, h)
def test_group_fecon236_GET_d7xbtusd_from_QUANDL_vSlow_oLocal():
'''Test get() which uses getqdl() in qdl module.
Here we get a Bitcoin price from Quandl.
'''
xbt = get(qdl.d7xbtusd)
assert tool.tailvalue(xbt[:'2018-06-14']) == 6315.7