def latex_table(inner_tabular: str,
position="H",
caption=None,
escape_caption=False,
label=None,
output_filepath=None):
table = Table(position=position)
table.append(NoEscape(inner_tabular))
if caption:
caption_ = caption if escape_caption else NoEscape(caption)
table.add_caption(caption_)
if label:
table.append(NoEscape("\label{{tab:{label}}}".format(label=label)))
if output_filepath:
table.generate_tex(output_filepath)
return table.dumps()
data="HICP"), MultiColumn(2, align='|c', data="UNEM")))
tabr.add_hline(start=2, end=3, cmidruleoption="lr")
tabr.add_hline(start=4, end=5, cmidruleoption="lr")
tabr.add_hline(start=6, end=7, cmidruleoption="lr")
tabr.add_row([""] + ["1Y", "2Y"] + ["1Y", "2Y"] + ["1Y", "2Y"])
tabr.add_hline()
# fill in the rows of tabular
for i in range(9):
tabr.add_row([ds_stats[i]] +
["{:.2f}".format(item) for item in spf_desc_stat.iloc[i, :]])
# end of table
tabr.add_hline()
# add tabular to table
tabl.append(tabr)
# export the table
tabl.generate_tex(tab_path + "spf_Desc_Stats")
#########################################
# PLOT OF INDIVIDUAL FORECASTS VS TARGET#
#########################################
# SPF individual forecasts and the target
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(14, 15))
# RGDP (1Y)
a = axes[0, 0]
col_cyc = cycler('color', ['red'] + ['dimgrey'] * spf_bal_RGDP_1Y.shape[1])
mar_cyc = cycler('marker', ['.'] + [''] * spf_bal_RGDP_1Y.shape[1])
a.set_prop_cycle(col_cyc + mar_cyc)
spf_bal_RGDP_1Y.plot(ax=a, linewidth=0.5)
a.set_title('Real GDP Growth (1 Year Horizon)')
a.set_xlabel('Time')
tabr.add_row(
[""] + [spf_multitable.index[i]] +
["{:.2f}".format(item) for item in spf_multitable.iloc[i, :]])
tabr.add_hline()
for i in range(29, 32):
tabr.add_row(
[""] + [spf_multitable.index[i]] +
["{:.2f}".format(item) for item in spf_multitable.iloc[i, :]])
# end of table
tabr.add_hline()
# add tabular to table
tabl.append(tabr)
# export the table
tabl.generate_tex(tab_path + "spf_comb_perf_" + str(w))
# FORECAST COMBINATION FIGURES
comb_methods = [
'ECB', 'Equal Weights', 'Bates-Granger (1)', 'Bates-Granger (2)',
'Bates-Granger (3)', 'Bates-Granger (4)', 'Bates-Granger (5)',
'Granger-Ramanathan (1)', 'Granger-Ramanathan (2)',
'Granger-Ramanathan (3)', 'AFTER', 'Median Forecast',
'Trimmed Mean Forecast', 'PEW', 'Principal Component Forecast',
'Principal Component Forecast (AIC)', 'Principal Component Forecast (BIC)',
'Empirical Bayes Estimator', 'Kappa-Shrinkage',
'Two-Step Egalitarian LASSO', 'BMA (Marginal Likelihood)',
'BMA (Predictive Likelihood)', 'ANN', 'EP-NN', 'Bagging',
'Componentwise Boosting', 'AdaBoost', 'c-APM (Constant)',
'c-APM (Q-learning)', 'Market for Kernels'
]
def gen_tex_table(tbl, cap, file_name, r):
"""
The function creates a tex file to which it export the given table from
python.
Parameters
----------
tbl : DataFrame
DataFrame containing the table to be exported.
cap : Str
Table caption.
file_name : Str
Name of the tex file in the "Tables" directory.
r : Int
Number of decimals to round up the metrics.
"""
# create tabule object
tabl = Table()
tabl.add_caption(cap)
tabl.append(NoEscape("\label{tab: " + cap + "}"))
# create tabular object
tabr = Tabular(table_spec="lccc")
tabr.add_hline()
tabr.add_hline()
# header row
tabr.add_row(["Forecast Combination Method"] + list(tbl))
tabr.add_hline()
# number of combination methods + additional rows
R = tbl.shape[0]
# specify format
fmt = "{:." + str(r) + "f}"
# fill in the rows for each combination method (-3 for individuals)
for i in range(R-3):
tabr.add_row([tbl.index[i]] + [
fmt.format(item) for item in tbl.iloc[i, :]])
tabr.add_hline()
# additional rows
for i in range(R-3, R):
tabr.add_row([tbl.index[i]] + [
fmt.format(item) for item in tbl.iloc[i, :]])
# end of table
tabr.add_hline()
tabr.add_hline()
# add tabular to table
tabl.append(tabr)
# export the table
tabl.generate_tex("C:/Users/Marek/Dropbox/Master_Thesis/Latex/Tables/" +
file_name)
return
tabr.add_hline()
tabr.add_hline()
# header row
tabr.add_row(["Statistic"] + tickers)
tabr.add_hline()
# fill in the rows of tabular
for i in range(9):
tabr.add_row([ds_stats[i]] +
["{:.7f}".format(item) for item in ret_desc_stat.iloc[i, :]])
# end of table
tabr.add_hline()
tabr.add_hline()
# add tabular to table
tabl.append(tabr)
# export the table
tabl.generate_tex(tab_path + "bond_ret_desc_stats")
# REALIZED VOLATILITY
rvol_desc_stat = pd.DataFrame(data=np.full((9, 4), np.nan, dtype=np.float),
columns=tickers,
index=ds_stats)
rvol_desc_stat.iloc[0, :] = np.mean(rvol.values, axis=0)
rvol_desc_stat.iloc[1, :] = np.median(rvol.values, axis=0)
rvol_desc_stat.iloc[2, :] = stats.mode(rvol.values, axis=0)[0]
rvol_desc_stat.iloc[3, :] = np.std(rvol.values, axis=0)
rvol_desc_stat.iloc[4, :] = np.var(rvol.values, axis=0)
rvol_desc_stat.iloc[5, :] = np.min(rvol.values, axis=0)
rvol_desc_stat.iloc[6, :] = np.max(rvol.values, axis=0)
rvol_desc_stat.iloc[7, :] = stats.kurtosis(rvol.values, axis=0)
rvol_desc_stat.iloc[8, :] = stats.skew(rvol.values, axis=0)
tabr.add_row(
("", MultiColumn(2, align='|c|', data="Pilot 1"),
MultiColumn(2, align='|c|',
data="Pilot 2"), MultiColumn(2, align='|c|', data="Pilot 1"),
MultiColumn(2, align='|c', data="Pilot 2")))
tabr.add_hline(start=2, end=9, cmidruleoption="lr")
tabr.add_row([""] + 4 * ["Coeff.", "p-val."])
tabr.add_hline()
# specify format
fmt = "{:.3f}"
# fill in the rows for each combination method (-3 for individuals)
for i in range(3):
tabr.add_row([output_table.index[i]] +
[fmt.format(item) for item in output_table.iloc[i, :]])
# end of table
tabr.add_hline()
tabr.add_hline()
# add tabular to table
tabl.append(tabr)
# export the table
tabl.generate_tex(table_path + "output_table")
###############
# END OF FILE #
###############
