def test_loading_time_series(self):
to_test = [
os.path.join(DATA_DIR, "2_Eurostoxx50/eur_ois.csv"),
os.path.join(DATA_DIR, "2_Eurostoxx50/eurostoxx50_prices_eod.csv"),
os.path.join(
DATA_DIR,
"2_Eurostoxx50/eurostoxx50_exp_tail_variation_measures.csv"),
os.path.join(DATA_DIR,
"2_Eurostoxx50/eurostoxx50_realized_volmeasures.csv"),
[
os.path.join(
DATA_DIR,
"2_Eurostoxx50/eurostoxx50_riskneutralmeasures.csv"), ";"
],
[os.path.join(DATA_DIR, "2_Eurostoxx50/eurostoxx50_vrp.csv"), ";"],
[
os.path.join(
DATA_DIR,
"2_Eurostoxx50/FamaFrench_Europe_3_Factors_Daily.csv"),
",", "%Y%m%d"
],
[
os.path.join(
DATA_DIR,
"2_Eurostoxx50/FamaFrench_Europe_MOM_Factor_Daily.csv"),
",", "%Y%m%d"
],
]
for charge in to_test:
if type(charge) == list:
self.assertFalse(load_time_series_csv(*charge).empty)
else:
self.assertFalse(load_time_series_csv(charge).empty)
def _make_return_df(return_periods):
eurostoxx = load_time_series_csv(EUROSTOXX_CSV)
for h in return_periods:
eurostoxx['log_ret_%i'%h] = np.log(eurostoxx.lastprice) - np.log(eurostoxx.lastprice.shift(h))
# compute last period return
eurostoxx['log_ret_last_period'] = (np.log(eurostoxx.lastprice) - np.log(eurostoxx.lastprice.shift(1))).shift(1)
return eurostoxx.drop(labels=['lastprice'], axis=1)
def _make_riskneutral_df(time_horizon):
cols_of_interest = ['bakshiSkew', 'bakshiKurt', 'SVIX',]
riskteural_measures = load_time_series_csv(RISKNEUTRAL_CSV, delimiter=';')
riskteural_measures = riskteural_measures[['daystomaturity'] + cols_of_interest]
riskteural_measures = riskteural_measures.dropna()
interpolated_df = pd.DataFrame()
for date in list(set(riskteural_measures.index)):
# filter all row for respective date
riskneutral_measures_per_day = riskteural_measures.ix[date]
# filer out all option-implied measures with computed based on a maturity of less than 7 days
riskneutral_measures_per_day = riskneutral_measures_per_day[riskneutral_measures_per_day['daystomaturity'] > 7]
# interpolate / extrapolate to get estimate for desired time_horizon
interpolated_values = [InterpolatedUnivariateSpline(np.array(riskneutral_measures_per_day['daystomaturity']),
np.asarray(riskneutral_measures_per_day[col_of_interest]),
k=1)(time_horizon) for col_of_interest in cols_of_interest]
# create df with estimated option-implied risk measures
update_dict = dict(zip(cols_of_interest, interpolated_values))
update_dict.update({'daystomaturity': time_horizon})
interpolated_df = interpolated_df.append(pd.DataFrame(update_dict, index=[date]))
del interpolated_df['daystomaturity']
return interpolated_df
def _make_fama_french_mom_df(): return load_time_series_csv(FAMA_FRENCH_MOMENTUM_CSV, time_format="%Y%m%d")
def _make_fama_french_df(): fama_french_factors = load_time_series_csv(FAMA_FRENCH_CSV, time_format="%Y%m%d") return fama_french_factors.loc[:, ['Mkt-RF', 'SMB', 'HML']]
def _make_variance_risk_premium_df(): return load_time_series_csv(VRP_CSV, delimiter=';')
def _make_realized_vol_df(): realized_vol = load_time_series_csv(REALIZED_VOL_CSV) return realized_vol.loc[:, ['RealizedVariation']]
def _make_exp_tail_variation_df(): return load_time_series_csv(EURO_TAIL_VARIATION_CSV)
def _make_risk_free_df(): euro_oid = load_time_series_csv(EURO_OIS_CSV) euro_oid = euro_oid[euro_oid.maturity == 1] euro_oid['log_risk_free_1d'] = np.log((euro_oid['yield']/365) + 1) return euro_oid.drop(labels=['maturity', 'yield'], axis=1)