import pandas as pd
from IPython.display import Markdown, display

from tulip.core.collection import TulipCollection
from tulip.data.bloomberg import BloombergClient as bb
from tulip.data.haver import HaverClient as hc
from tulip.data.gs import GSClient as gs
from tulip.data.haver import (
    get_series as get_series_from_haver,
    get_collection as get_collection_from_haver,
)
from tulip.plots import plot_line, plot_lines, plot_area, plot_bar
from tulip.genai import iris
from tulip.analysis.country_related.analytics import summarize_gs_eco_fct
from tulip_mania.notebook_related import notebook_updated

import pycountry

Australia¶

# ========================================
# PARAMETERS & DATA FETCHING
# ========================================
# Country identifiers
ISO_2 = "AU"
ctry_name = "Australia"
goldman_ctry_id = ISO_2
bloomberg_ctry_prefix = ISO_2
pyctry = pycountry.countries.get(alpha_2=ISO_2)

# ========================================
# DATA FETCHING - All data retrieval consolidated here
# ========================================

# Melbourne Institute Leading Growth Indicators
mi_leading_growth_dict = {
    "AU WMI Lead Index (NSA, 1996=100)": "AUNEVLDE@ANZ",
    "WMI Lead Index – Mo. Dev. from Trend (% NSA)": "AUNEVLDM@ANZ",
    "WMI Lead Index – 6M Ann. Growth (% NSA)": "AUNEVLAG@ANZ",
    "6M Ann. Growth Contrib. – S&P/ASX 200 (%Pt NSA)": "AUNEVLSP@ANZ",
    "6M Ann. Growth Contrib. – WMI CSI Exp. Idx (%Pt NSA)": "AUNEVLEI@ANZ",
    "6M Ann. Growth Contrib. – US Ind. Prod. (%Pt NSA)": "AUNEVLIP@ANZ",
    "6M Ann. % Contrib. – RBA Comm. Prices AUD (%Pt NSA)": "AUNEVLCP@ANZ",
    "6M Ann. Growth Contrib. – Dwelling App (%Pt NSA)": "AUNEVLDA@ANZ",
    "6M Ann. Growth Contrib. – Yield Spread (%Pt NSA)": "AUNEVLRG@ANZ",
    "6M Ann. Growth Contrib. – Westpac-MI Unemp. Exp Idx (%Pt NSA)": "AUNEVLUE@ANZ",
    "6M Ann. Growth Contrib. – Agg. Mo. Hrs Worked (%Pt NSA)": "AUNEVLHW@ANZ",
}

# GDP Level Stats
level_stats_dict = {
    "Real GDP": "AUSNGDPC@ANZ",
    "Fixed Investment": "AUSNFC@ANZ",
    "Inventories": "AUSNBC@ANZ",
    "Consumption": "AUSNCC@ANZ",
    "HH Consumption": "AUSNHC@ANZ",
    "Gov Consumption": "AUSNGC@ANZ",
    "Exports": "AUSNXC@ANZ",
    "Imports": "AUSNMC@ANZ",
}

# GDP Growth Stats
growth_stats_dict = {
    "Real GDP": "yryr%(AUSNGDPC@ANZ)",
    "Fixed Investment": "yryr%(AUSNFC@ANZ)",
    "Inventories": "AUSNBC@ANZ",
    "Consumption": "yryr%(AUSNCC@ANZ)",
    "HH Consumption": "yryr%(AUSNHC@ANZ)",
    "Gov Consumption": "yryr%(AUSNGC@ANZ)",
    "Exports": "yryr%(AUSNXC@ANZ)",
    "Imports": "yryr%(AUSNMC@ANZ)",
}

# Prices
prices_dict = {
    "CPI": "yryr%(AUWPC@ANZ)",
    "Core CPI": "yryr%(AUWPCXH@ANZ)",
    "CPI NSA": "yryr%(AUMPC@ANZ)",
}

# Melbourne Inflation Gauge
melbourne_inflation_dict = {
    "Melbourne Ins. Monthly Inflation Gauge": "yryr%(AUNIGI@ANZ)",
    "Core excluding volatile items": "yryr%(AUNIGIC@ANZ)",
    "Trimmed mean": "yryr%(AUNIGIT@ANZ)",
}

# Inflation Surveys
inflation_surveys_dict = {
    "Trimmed mean (headline)": "AUNVIT30@ANZ",
    "Weighted mean (full sample)": "AUNVIW@ANZ",
    "Weighted mean (within 0% to 5% range)": "AUNVIW05@ANZ",
}

# Bloomberg PMIs
pmi_dict = {
    "S&P Manufacturing PMI": f"MPMI{ISO_2}SA Index",
    "S&P Composite PMI": f"MPMI{ISO_2}CA Index",
    "Australia General Industry PMI": "AGIIPMI  Index",
}

# Retail Sales
retail_sales_dict = {
    "Australia Retail Sales YoY": "AURSTYSA Index",
    "Australia Retail Sales MoM": "AURSTSA  Index",
}

1. Growth: Activity & Output¶

1.1 Economic Forecasts (Consensus)¶

# Goldman Sachs Economic Forecasts
try:
    gs_eco_fct = gs.get_eco_forecast(geographyId=goldman_ctry_id)
    gs_summary = summarize_gs_eco_fct(gs_eco_fct)
    gs_summary = gs_summary[~gs_summary.index.str.contains("ngdp")].to_frame().T
    gs_summary.style.set_caption(
        f"Goldman Sachs {ctry_name} Economic Forecasts"
    ).format(precision=2)
except Exception as e:
    display(Markdown(f"*Goldman Sachs forecasts not available for {ctry_name}: {e}*"))

1.2 Growth Nowcasts & Real-Time Indicators¶

# Goldman Sachs Current Activity Indicator (CAI)
try:
    cai_series = gs.get_CAI_series(
        geographyId=goldman_ctry_id,
        metricName=[
            "CAI_HEADLINE",
            "CAI_CONTRIBUTION_TYPE_HARD",
            "CAI_CONTRIBUTION_TYPE_SOFT",
        ],
        startDate="1980-01-01",
    )
    cai_series = cai_series.set_index("metricName", append=True)["metricValue"].unstack(
        "metricName"
    )
    cai_series.columns = ["Hard", "Soft", "Headline"]

    cai_plot = plot_lines(
        cai_series,
        show_0=True,
        title=f"<b>{ctry_name} Current Activity Indicator</b> Updated: {pd.Timestamp.today().strftime('%Y-%m-%d')}",
        years_limit=4,
        tick_suffix="%",
        source="Goldman Sachs",
    )
    cai_plot.show()
except Exception as e:
    display(Markdown(f"*CAI not available for {ctry_name}: {e}*"))

Melbourne Institute Nowcasts and Leading Growth Indicators¶

Latest Melbourne Institute lowers its nowcast of Australian economic growth in the June quarter

Latest Weekly Indicators Commentary

mi_leading_growth = hc.create_collection(mi_leading_growth_dict)

leading_insight = iris.summarize(
    mi_leading_growth,
    "what trends do you see here? Could you also review the text of this page? https://melbourneinstitute.unimelb.edu.au/publications/macroeconomic-reports/latest-news/leading-index",
)
leading_insight.html()

mi_leading_growth.dashboard.table()

mi_leading_growth.dashboard.plots(years_limit=4, show_0=True)

# Bloomberg Nowcast
try:
    ifs_nowcast = bb.get_series("BENWAUGC Index")
    ifs_nowcast.plot(years_limit=2, tick_suffix="%")
except Exception as e:
    display(Markdown(f"*Bloomberg nowcast not available for {ctry_name}*"))

1.3 GDP Growth & Components¶

nom_gdp_yoy = hc.get_series("AUSNGDP@ANZ")
nom_gdp_yoy.ts = nom_gdp_yoy.ts.pct_change(4).mul(100)
real_gdp_yoy = hc.get_series("AUSNGDPC@ANZ")
real_gdp_yoy.ts = real_gdp_yoy.ts.pct_change(4).mul(100)
TulipCollection([nom_gdp_yoy, real_gdp_yoy]).dashboard.table()

plot_lines(
    [nom_gdp_yoy.ts.rename("Nominal"), real_gdp_yoy.ts.rename("Real")],
    tick_suffix="%",
    title="<b>Australian GDP YoY</b>",
    years_limit=10,
    show_0=True,
)

GDP Detail¶

level_collection = hc.create_collection(level_stats_dict)
level_collection[-1].good_is = -1
level_collection.dashboard.table()

growth_stats = hc.create_collection(growth_stats_dict)
growth_stats[-1].good_is = -1
growth_stats.dashboard.table()

growth_stats.dashboard.plots(show_0=True, years_limit=3, tick_suffix="%")

1.4 Business Activity Surveys¶

pmi_collection = bb.create_collection(pmi_dict)
pmi_collection.dashboard.table()

pmi_collection.dashboard.plots(hlines=50, show_0=True)

1.5 Retail Sales & Consumer Activity¶

retail_sales = bb.create_collection(retail_sales_dict)
retail_sales.dashboard.table()

retail_sales[0].plot(years_limit=4, tick_suffix="%", show_0=True)

1.6 Industrial Production & Manufacturing¶

1.7 Growth Summary (AI-Generated)¶

2. Prices: Inflation & Wages¶

2.1 Headline & Core Inflation¶

prices_collection = hc.create_collection(prices_dict)
prices_collection.good_is = -1
prices_collection.dashboard.table()

prices_df = prices_collection.df
prices_df.columns = list(prices_dict.keys())
fig = plot_lines(
    prices_df,
    years_limit=5,
    title="<b>Inflation Measures</b>",
    tick_suffix="%",
)
fig.add_hline(2, line_width=1, annotation_text="Target")

Melbourne Inflation Gauge¶

melbourne_inflation_metrics = hc.create_collection(melbourne_inflation_dict)
melbourne_inflation_metrics.good_is = -1
melbourne_inflation_metrics.dashboard.table()

Melbourne Institute Survey of Consumer Inflationary Expectations (Expected Inflation Rate Over the Next 12 Months)

df = melbourne_inflation_metrics.df
df.columns = melbourne_inflation_dict.keys()

fig = plot_lines(
    df,
    years_limit=5,
    title="<b>Melbourne Institution Inflation Gauge Measures</b>",
    tick_suffix="%",
    logo=False,
)
fig.add_hline(2, line_width=1, annotation_text="Target")

2.2 Inflation Expectations & Surveys¶

add_inflation_surveys = hc.create_collection(inflation_surveys_dict)
add_inflation_surveys.dashboard.table()

df = add_inflation_surveys.df
df.columns = inflation_surveys_dict.keys()

fig = plot_lines(
    df, years_limit=5, title="<b>Inflation Surveys</b>", tick_suffix="%", logo=False
)
fig.add_hline(2, line_width=1, annotation_text="Target")

2.3 Inflation Nowcasts & Forecasts¶

2.4 Wage Growth & Labor Costs¶

2.5 Inflation Components (Goods vs Services)¶

3. Slack: Output Gap & Capacity Utilization¶

3.1 Output Gap Estimates¶

3.2 Capacity Utilization¶

4. Employment: Labor Market Conditions¶

4.1 Unemployment Rate & Labor Force¶

4.2 Employment Growth & Payrolls¶

4.3 Jobless Claims & Leading Indicators¶

4.4 Job Openings & Labor Turnover¶

4.5 Alternative Employment Data¶

5. Financial Conditions¶

5.1 Credit Growth & Debt Levels¶

5.2 Financial Conditions Indices¶

5.3 Credit Spreads & Market Stress¶

5.4 External Balance (Current Account, Trade)¶

6. Fiscal Conditions¶

6.1 Budget Balance & Deficit¶

6.2 Public Debt Levels¶

6.3 Fiscal Policy Outlook¶

from tulip_mania.notebook_related import notebook_updated

notebook_updated()