def cdf_charts(good_prob, bad_prob):
"""Plot CDF chart."""
source1 = pd.DataFrame(
{
"Good loans": np.arange(len(good_prob)) / len(good_prob),
},
index=good_prob)
c1 = altair.generate_chart("line", source1)
source2 = pd.DataFrame(
{
"Bad loans": np.arange(len(bad_prob)) / len(bad_prob),
},
index=bad_prob)
c2 = altair.generate_chart("line", source2)
return c1 + c2
def test_date_column_utc_scale(self):
"""Test that columns with date values have UTC time scale"""
df = pd.DataFrame(
{"index": [date(2019, 8, 9), date(2019, 8, 10)], "numbers": [1, 10]}
).set_index("index")
chart = altair.generate_chart("line", df)
st.altair_chart(chart)
c = self.get_delta_from_queue().new_element.vega_lite_chart
spec_dict = json.loads(c.spec)
# The x axis should have scale="utc", because it uses date values.
x_scale = _deep_get(spec_dict, "encoding", "x", "scale", "type")
self.assertEqual(x_scale, "utc")
# The y axis should _not_ have scale="utc", because it doesn't
# use date values.
y_scale = _deep_get(spec_dict, "encoding", "y", "scale", "type")
self.assertNotEqual(y_scale, "utc")
def page_charts(today_date=date.today() - timedelta(days=1)):
st.subheader("Shiller charts")
df0 = load_ie_data()
c1 = altair.generate_chart("line", df0[["Real_Price",
"10xReal_Earnings"]]).properties(
title="Index Plot",
height=200,
width=260,
)
c2 = altair.generate_chart("line", df0[["CAPE", "10xLong_IR"]]).properties(
title="PE (CAPE) Plot",
height=200,
width=260,
)
st.altair_chart(alt.concat(c1, c2, columns=2), use_container_width=True)
st.subheader("Stock charts")
start_date = get_start_date(today_date, options=("3Y", "2Y", "1Y"))
dates = pd.date_range(today_date - timedelta(days=365 * 2), today_date)
# MSCI
symbols = ["URTH", "EEM", "SPY", "ES3.SI"]
colnames = ["MSCI World", "MSCI EM", "S&P500", "ES3"]
df1 = load_data(dates, symbols, "SPY")
df1.columns = colnames
rebased_df1 = rebase(df1[df1.index >= start_date])
chart1 = altair.generate_chart("line", rebased_df1).properties(
title="MSCI",
height=200,
width=260,
)
# VIX
symbols = ["^VIX"]
colnames = ["VIX"]
df2 = load_data(dates, symbols)[symbols]
df2.columns = colnames
chart2 = altair.generate_chart("line",
df2[df2.index >= start_date]).properties(
title="VIX",
height=200,
width=260,
)
st.altair_chart(alt.concat(chart1, chart2, columns=2),
use_container_width=True)
# etfs
symbols = ["IWDA", "EIMI"]
colnames = ["World", "EM"]
df3a = load_data(dates, symbols)
df3a.columns = colnames
rebased_df3a = rebase(df3a[df3a.index >= start_date])
chart3a = altair.generate_chart("line", rebased_df3a).properties(
title="ETF",
height=200,
width=260,
)
symbols = ["O87.SI", "ES3.SI", "CLR.SI"]
colnames = ["GLD", "ES3", "Lion-Phillip"]
df3b = load_data(dates, symbols)
df3b.columns = colnames
rebased_df3b = rebase(df3b[df3b.index >= start_date])
chart3b = altair.generate_chart("line", rebased_df3b).properties(
title="ETF SGX",
height=200,
width=260,
)
st.altair_chart(alt.concat(chart3a, chart3b, columns=2),
use_container_width=True)
# industrial
symbols = [
"ES3.SI", "O5RU.SI", "A17U.SI", "J91U.SI", "BUOU.SI", "ME8U.SI",
"M44U.SI"
]
colnames = ["ES3", "AA", "Ascendas", "ESR", "FLCT", "MIT", "MLT"]
df4 = load_data(dates, symbols)
df4.columns = colnames
rebased_df4 = rebase(df4[df4.index >= start_date])
chart4a = altair.generate_chart(
"line",
rebased_df4[["ES3", "Ascendas", "FLCT", "MIT", "MLT"]],
).properties(
title="Industrial 1",
height=200,
width=260,
)
chart4b = altair.generate_chart(
"line",
rebased_df4[["ES3", "AA", "ESR"]],
).properties(
title="Industrial 2",
height=200,
width=260,
)
st.altair_chart(alt.concat(chart4a, chart4b, columns=2),
use_container_width=True)
# retail
symbols = ["ES3.SI", "C38U.SI", "J69U.SI", "N2IU.SI"]
colnames = ["ES3", "CICT", "FCT", "MCT"]
df5 = load_data(dates, symbols)
df5.columns = colnames
rebased_df5 = rebase(df5[df5.index >= start_date])
chart5 = altair.generate_chart("line", rebased_df5).properties(
title="Retail & Commercial",
height=200,
width=250,
)
# banks
symbols = ["ES3.SI", "D05.SI", "O39.SI", "U11.SI"]
colnames = ["ES3", "DBS", "OCBC", "UOB"]
df6 = load_data(dates, symbols)
df6.columns = colnames
rebased_df6 = rebase(df6[df6.index >= start_date])
chart6 = altair.generate_chart("line", rebased_df6).properties(
title="Banks",
height=200,
width=250,
)
st.altair_chart(alt.concat(chart5, chart6, columns=2),
use_container_width=True)