def update_bar_inver(value, start_date, end_date):
df_nacio = query_by_daterange("inversion_nacional", start_date,
end_date).dropna()
df_inter = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
fig = plots.bar_inversion(df_nacio, df_inter, value)
return fig
def update_resumen(start_date, end_date):
# dataframes
df_vf = query_by_daterange("valor_fondos", start_date, end_date)
df_vf = df_vf[(df_vf != 0).all(1)]
df_q = query_by_daterange("q_index", start_date, end_date)
df_q = df_q[(df_q != 0).all(1)]
usdclp = query_by_daterange("usdclp", start_date, end_date)
usdclp = usdclp.drop_duplicates(subset=['Fecha'],
keep='first',
inplace=False,
ignore_index=True).reset_index()
df_nacio = query_by_daterange("inversion_nacional", start_date,
end_date).dropna()
df_inter = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
df_fn = query_by_daterange("forwards_nacionales", start_date, end_date)
dfc = df_fn[df_fn['Nombre'] == 'Compra']
dfv = df_fn[df_fn['Nombre'] == 'Venta']
df_activos = query_by_daterange("activos", start_date, end_date).dropna()
df_bonos_clp = df_activos[df_activos['Nombre'] == 'Bonos CLP']
df_bonos_uf = df_activos[df_activos['Nombre'] == 'Bonos UF']
df_inter = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
df_ex = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
df_ex = df_ex[df_ex['Nombre'] == 'INVERSIÓN EXTRANJERA']
# plots
patrimonio_total = plots.patrimonio_ajustado(df_vf, df_q, usdclp, True)
patrimonio_afps = plots.patrimonio_ajustado(df_vf, df_q, usdclp, False)
inversiones_total = plots.bar_inversion(df_nacio, df_inter, 'TOTAL')
inter_hedge = plots.fig_hedge(df_inter, dfc, dfv, df_fn, usdclp, True)
inter_hedge_total = plots.fig_hedge_total(df_inter, dfc, dfv, df_fn,
usdclp, True)
inversiones_nacional = plots.bar_inversion_nacional(df_nacio, 'TOTAL')
inversiones_internacional = plots.bar_inversion_internacional(
df_inter, 'TOTAL')
inversiones_nacional_monedas = plots.bar_inversion_nacional_monedas(
df_bonos_clp, df_bonos_uf, 'TOTAL')
extranjeros_total = plots.fig_total_ex_fwd(df_ex, dfc, dfv, df_fn)
extranjeros_fondos = plots.fig_inversiones(df_inter,
'INVERSIÓN EXTRANJERA', False)
forwards_n = plots.fig_forwards_nacional(dfc, dfv, df_fn, usdclp, True)
return patrimonio_total, patrimonio_afps, inversiones_total, inter_hedge_total, inter_hedge, inversiones_nacional, inversiones_nacional_monedas, inversiones_internacional, extranjeros_total, extranjeros_fondos, forwards_n
def update_output_ex_fwd(start_date, end_date):
df_ex = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
df_ex = df_ex[df_ex['Nombre'] == 'INVERSIÓN EXTRANJERA']
df_fn = query_by_daterange("forwards_nacionales", start_date, end_date)
dfc = df_fn[df_fn['Nombre'] == 'Compra']
dfv = df_fn[df_fn['Nombre'] == 'Venta']
fig = plots.fig_total_ex_fwd(df_ex, dfc, dfv, df_fn)
return fig
def update_bar_nacio_monedas(value, start_date, end_date):
df = query_by_daterange("activos", start_date, end_date).dropna()
df_bonos_clp = df[df['Nombre'] == 'Bonos CLP']
df_bonos_uf = df[df['Nombre'] == 'Bonos UF']
fig = plots.bar_inversion_nacional_monedas(df_bonos_clp, df_bonos_uf,
value)
return fig
def update_output_ex_rf(value, start_date, end_date):
flag = "MMUSD"
if value is not None:
if len(value) != 0:
flag = "porcentaje"
df_inter = query_by_daterange("inversion_internacional", start_date,
end_date).dropna()
fig = plots.fig_inversiones(df_inter, 'RENTA FIJA', flag)
return fig
def update_activos(start_date, end_date):
# dataset
df_activos = query_by_daterange("activos", start_date, end_date).dropna()
# layout activos de pensiones
fig_act_bclp = plots.fig_activos(df_activos, 'Bonos CLP', 'porcentaje')
fig_act_buf = plots.fig_activos(df_activos, 'Bonos UF', 'porcentaje')
fig_act_ex = plots.fig_activos(df_activos, 'TOTAL EXTRANJERO',
'porcentaje')
return fig_act_bclp, fig_act_buf, fig_act_ex
def update_output_dp(value, start_date, end_date):
flag = "MMUSD"
if value is not None:
if len(value) != 0:
flag = "porcentaje"
# dataset
df_nacio = query_by_daterange("inversion_nacional", start_date,
end_date).dropna()
fig = plots.fig_inversiones(df_nacio, 'Depósitos a Plazo', flag)
return fig
def update_output(value, start_date, end_date):
flag = "MMUSD"
if value is not None:
if len(value) != 0:
flag = "porcentaje"
# dataset
df_nacio = query_by_daterange("inversion_nacional", start_date,
end_date).dropna()
fig = plots.fig_inversiones(df_nacio, 'INVERSIÓN NACIONAL TOTAL', flag)
return fig
def update_fig_fn(start_date, end_date):
# DATAFRAMES
df_fn = query_by_daterange("forwards_nacionales", start_date, end_date)
dfc = df_fn[df_fn['Nombre'] == 'Compra']
dfv = df_fn[df_fn['Nombre'] == 'Venta']
df_vf = query_by_daterange("valor_fondos", start_date, end_date)
df_vf = df_vf[(df_vf != 0).all(1)]
df_q = query_by_daterange("q_index", start_date, end_date)
df_q = df_q[(df_q != 0).all(1)]
usdclp = query_by_daterange("usdclp", start_date, end_date)
usdclp = usdclp.drop_duplicates(subset=['Fecha'],
keep='first',
inplace=False,
ignore_index=True).reset_index()
df_inter = query_by_daterange("inversion_internacional", start_date,
end_date)
# FIGURES
fig_fn = plots.fig_forwards_nacional(dfc, dfv, df_fn, usdclp)
fig_inter_hedge = plots.fig_hedge(df_inter, dfc, dfv, df_fn, usdclp)
fig_inter_hedge_total = plots.fig_hedge_total(df_inter, dfc, dfv, df_fn,
usdclp)
fig_afp = plots.fig_afp(df_vf, usdclp)
fig_fn_afp = plots.fig_forwards_nacional_afp(dfc, dfv, df_fn, usdclp,
df_vf, df_q)
return fig_fn, fig_inter_hedge_total, fig_inter_hedge, fig_afp, fig_fn_afp
import dash_core_components as dcc
import dash_html_components as html
import plots
from api import query_by_daterange
from datetime import date, timedelta, datetime, time
from plots import fx_dv01_participacion_reajuste
import time
end_date = date.today()
start_date = end_date - timedelta(days=3 * 25)
#t0 = time.time()
df_irf = query_by_daterange('irf', start_date, end_date)
#t1 = time.time()
#print('elapsed time:', t1-t0)
usdclp = query_by_daterange("usdclp", start_date, end_date)
# dataframes
header = html.Div(
[
html.Div([
html.H2('IRF Data', ),
html.H6('Versión 2.0.1', className='no-print'),
],
className='twelve columns',
style={'text-align': 'center'})
],
className='row',
def update_fig_inver(start_date, end_date):
df_total = query_by_daterange("inversion_total", start_date,
end_date).dropna()
fig = plots.fig_inversiones(df_total, 'TOTAL ACTIVOS', 'MMUSD')
return fig
def update_activos(start_date, end_date):
df_extranjeros = query_by_daterange("extranjeros", start_date,
end_date).dropna()
fig_ex_reg = plots.fig_extranjeros(df_extranjeros)
return fig_ex_reg