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 (
    get_series as get_series_from_haver,
    get_collection as get_collection_from_haver,
)
from tulip.data.gs import GSClient as gs
from tulip.data.haver import get_urate
from tulip.plots import plot_line, plot_lines, switch_trace_to_secondary_axis
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

Sweden¶

# ========================================
# PARAMETERS & DATA FETCHING
# ========================================

# Country identifiers
ISO_2 = "SE"
haver_code = 144
ctry_name = "Sweden"
bloomberg_ctry_prefix = "SE"
goldman_ctry_id = ISO_2
pyctry = pycountry.countries.get(alpha_2=ISO_2)

# ========================================
# DATA FETCHING - All Haver Series
# ========================================

# Core economic indicators
core_series = {
    "Economic Tendency": "SENVET@NORDIC",
    "PMI": "SESVPTG@NORDIC",
    "Confidence Indicator": "SESVXJ@NORDIC",
    "Unemployment Rate": "SESELUR@NORDIC",
    "Unemployment Rate 2": "SESELUCR@NORDIC",
    "NAIRU": "SEAVELUR@NORDIC",
    "Job Starts": "E144TVNS@INTWKLY",
}
core_collection = get_collection_from_haver(core_series)

# GDP and growth data
growth_series = {
    "Real GDP": "SESNGDP@NORDIC",
    "Fixed Investment": "SESNI@NORDIC",
    "Inventories": "SESNB@NORDIC",
    "Gov Consumption": "SESNCP@NORDIC",
    "HH Consumption": "SESNCV@NORDIC",
    "Exports": "SESNX@NORDIC",
    "Imports": "SESNM@NORDIC",
}
growth_collection = get_collection_from_haver(growth_series)
growth_collection["SESNM@NORDIC"].good_is = -1

# Nominal GDP
ngdp = get_series_from_haver(f"H{haver_code}NGDP@G10")
ngdp_ann = ngdp.ts.rolling(4).sum()

# Flash GDP
flash_gdp = get_series_from_haver("SEWNGCY@NORDIC")

# Trade data
trade_balance = get_series_from_haver("SETIB@NORDIC").ts.rename(
    "Trade Balance (SEK bn)"
)
sek_usd = get_series_from_haver("SENXUSV@NORDIC").ts.rename("SEK/USD")

# Riksbank sentiment indicators
riksbank_series = {
    "Business Price Expectations": "SESVBSPM@NORDIC",
    "Consumer Confidence": "SESCCI@NORDIC",
    "Consumer Confidence Macro": "SESCCIMA@NORDIC",
    "Consumer Confidence Micro": "SESCCIMI@NORDIC",
    "Business Demand Assessment": "SESVBODM@NORDIC",
    "1Y CPIF Expectations": "SENJ1FA@NORDIC",
    "2Y CPIF Expectations": "SENJ2FA@NORDIC",
    "5Y CPIF Expectations": "SENJ5FA@NORDIC",
}
riksbank_collection = get_collection_from_haver(riksbank_series)

# Inflation
cpi = get_series_from_haver("SENPCY@NORDIC")

# Unemployment data
unemployment_rate = get_series_from_haver("SESELUR@NORDIC")
labor_force = get_series_from_haver("SESELLF@NORDIC")
unemployed_weekly = get_series_from_haver("E144TVAR@INTWKLY")
nairu_cb = get_series_from_haver("SEAVELUR@NORDIC")

# Fiscal data
fiscal_series = {
    "Budget Deficit": "SENFGB@NORDIC",
    "General Govt Budget Deficit": "SEAFTNL@NORDIC",
    "Defense Expenditure": "SENFEND@NORDIC",
    "Central Govt Debt": "SENFPD@NORDIC",
}
fiscal_collection = get_collection_from_haver(fiscal_series)

# Debt levels
debt_series = {
    "Domestic Debt": "SESZLDDG@NORDIC",
    "Household Debt": "SESZDHP@NORDIC",
    "Nonfinancial Corporations Debt": "SESZDNP@NORDIC",
    "Financial Corporations Debt": "SESZDFP@NORDIC",
    "Government Debt": "SESZDGP@NORDIC",
    "Nonprofit Institutions Serving Households' Debt": "SESZLSP@NORDIC",
}
debt_collection = get_collection_from_haver(debt_series)

# Debt to income
debt_income_series = {
    "Outstanding Debt to Disposable Income": "SESZLHDI@NORDIC",
    "Interest Rate": "SENRREPV@NORDIC",
}
debt_income_collection = get_collection_from_haver(debt_income_series)

# Real estate
real_estate_series = {
    "Policy Rate": "SENRREPV@NORDIC",
    "2Y Mortgage Yield": "SENRM2@NORDIC",
    "5Y Mortgage Yield": "SENRM5@NORDIC",
    "Housing Prices": "SEAPH001@NORDIC",
}
real_estate_collection = get_collection_from_haver(real_estate_series)

# Stock indices
omx = get_series_from_haver("S144O30@INTWKLY").ts.rename("OMXS30")
spx = get_series_from_haver("S111SP5@INTWKLY").ts.rename("S&P500")

# IRIS SUMMARY
iris.summarize(
    core_collection, "What trends do you see in Sweden's economic indicators?"
).html()

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}*"))

# Bloomberg Nowcast (if available)
try:
    nowcast_ticker = f"BENW{bloomberg_ctry_prefix}GC Index"
    bloomberg_nowcast = bb.get_series(nowcast_ticker)
    bloomberg_nowcast.plot(
        years_limit=3, tick_suffix="%", title=f"<b>{ctry_name} Bloomberg Nowcast</b>"
    )
except Exception as e:
    display(Markdown(f"*Bloomberg nowcast not available for {ctry_name}*"))

1.3 GDP Growth & Components¶

# Flash GDP
flash_gdp.plot(tick_suffix="%", show_0=True, title="<b>Sweden Flash GDP</b>")

# GDP Components Dashboard
growth_collection.dashboard.table()

growth_collection.dashboard.plots(show_0=True)

# Nominal GDP Growth
plot_line(
    ngdp_ann.pct_change(4),
    title="<b>Nominal GDP Growth (%)</b>",
    tick_format="0.0%",
    show_0=True,
)

1.4 Business Activity Surveys¶

# Economic Tendency
economic_tendency = core_collection["SENVET@NORDIC"]
fig = economic_tendency.plot(title="<b>Sweden Economic Tendency</b>")
fig.add_hline(y=100, line_width=1, line_dash="dash", line_color="black")
fig.show()

# PMI
pmi = core_collection["SESVPTG@NORDIC"]
fig = pmi.plot(title="<b>Sweden PMI</b>")
fig.add_hline(y=50, line_width=1, line_dash="dash", line_color="black")
fig.show()

# Confidence Indicator
confidence_indicator = core_collection["SESVXJ@NORDIC"]
fig = confidence_indicator.plot(title="<b>Sweden Confidence Indicator</b>")
fig.add_hline(y=100, line_width=1, line_dash="dash", line_color="black")
fig.show()

# Riksbank Metrics
riksbank_collection.dashboard.table()

1.5 Retail Sales & Consumer Activity¶

1.6 Industrial Production & Manufacturing¶

1.7 Trade¶

# Trade Balance vs SEK/USD
fig = plot_line(
    blue=trade_balance,
    red=sek_usd,
    title="<b>Sweden: Trade Balance vs SEK/USD Exchange Rate</b>",
    tick_suffix="",
    years_limit=10,
    watermark=False,
    source="Haver",
)

fig.data[1].update(yaxis="y2")

fig.update_layout(
    yaxis=dict(
        title=dict(text="Trade Balance (SEK bn)", font=dict(color="blue")),
        tickfont=dict(color="blue"),
    ),
    yaxis2=dict(
        title=dict(text="SEK/USD", font=dict(color="red")),
        tickfont=dict(color="red"),
        overlaying="y",
        side="right",
    ),
)

fig.show()

1.8 Growth Summary (AI-Generated)¶

2. Prices: Inflation & Wages¶

2.1 Headline & Core Inflation¶

# CPI Inflation
cpi.plot(title="<b>Sweden CPI Inflation</b>", tick_suffix="%")

2.2 Inflation Expectations & Surveys¶

# CPIF Expectations from Riksbank collection
cpif_expectations = riksbank_collection.df[
    ["SENJ1FA@NORDIC", "SENJ2FA@NORDIC", "SENJ5FA@NORDIC"]
]
cpif_expectations.columns = ["1Y CPIF", "2Y CPIF", "5Y CPIF"]
plot_lines(
    cpif_expectations,
    title="<b>Sweden CPIF Inflation Expectations</b>",
    tick_suffix="%",
    years_limit=5,
)

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¶

# Labor Force Survey Unemployment Rate
plot_line(
    unemployment_rate.ts,
    title="<b>Sweden: Labor Force Survey Unemployment Rate (SA, %)</b>",
    tick_suffix="%",
)

# Unemployment vs NAIRU
urate = get_urate("SWE")
plot_lines(
    [urate.ts.rename("Unemployment"), nairu_cb.ts.rename("Nairu Estimate")],
    default_x_range=("2010-01-01", "2025-12-31"),
    title="<b>Swedish Unemployment vs NAIRU</b>",
    tick_suffix="%",
)

4.2 Weekly Unemployment (SPES-Based)¶

Sweden publishes a weekly figure on unemployment. We normalize by the labor force to create a faster measure.

# Weekly unemployment calculation
unemployed = unemployed_weekly.ts.rename("All Registered Unemployed")
lf = labor_force.ts.rename("Labor Force")
lf = lf.mul(100).resample("W-MON").bfill()

weekly_unemployment = (
    pd.concat(
        [
            unemployed,
            lf,
            unemployment_rate.ts.div(100)
            .resample("W-MON")
            .bfill()
            .rename("Labor Force Survey Unemployment"),
        ],
        axis=1,
    )
    .ffill()
    .dropna()
)
weekly_unemployment["Fast Unemployment"] = (
    weekly_unemployment["All Registered Unemployed"]
    / weekly_unemployment["Labor Force"]
)

fig = plot_lines(
    weekly_unemployment[["Labor Force Survey Unemployment", "Fast Unemployment"]],
    title="<b>SPES-Based Unemployment Rate (Weekly figure)</b>",
    axis_title="Unemployment Rate (%)",
    years_limit=2,
    source="Haver",
    watermark=False,
    tick_format="0.1%",
)

fig.show()

4.3 Employment Growth & Payrolls¶

4.4 Jobless Claims & Leading Indicators¶

4.5 Job Openings & Labor Turnover¶

4.6 Alternative Employment Data¶

5. Financial Conditions¶

5.1 Credit Growth & Debt Levels¶

# Outstanding Debt Levels
combined_df = debt_collection.df
combined_df.columns = debt_collection.titles.values()

fig = plot_lines(
    combined_df,
    title="<b>Sweden Debt Outstanding as % of GDP</b>",
    tick_suffix="%",
    y_axis_label="Debt Outstanding as a % of SA GDP",
    x_axis_label="Date",
    watermark=False,
    years_limit=10,
    logo=False,
    figsize=(1200, 500),
)

fig.show()

# Debt Creation (YoY Change)
yoy_df = combined_df.diff(6) * 2
yoy_df = yoy_df.dropna()

fig = plot_lines(
    yoy_df,
    title="<b>Sweden Debt Creation</b>",
    tick_suffix="%",
    y_axis_label="Debt Creation (Ann. Chg in % of GDP)",
    x_axis_label="Date",
    watermark=False,
    years_limit=10,
    source="Haver",
    logo=False,
    figsize=(1200, 500),
)

fig.show()

5.2 Housing, Consumption and Debt¶

# Debt to Income vs Interest Rate
df_od = debt_income_collection.df
df_od.columns = debt_income_collection.titles.values()
df_od.dropna(inplace=True)

fig = plot_line(
    blue=df_od["Outstanding Debt to Disposable Income"],
    red=df_od["Interest Rate"],
    title="<b>% of Outstanding Debt to Disposable Income vs Interest Rate</b>",
    tick_suffix="%",
    y_axis_label="Debt to Disposable Income (%)",
    y2_axis_label="Interest Rate (%)",
    source="Haver",
    watermark=False,
    years_limit=10,
    figsize=(1200, 500),
)

fig.show()

5.3 Financial Conditions Indices¶

5.4 Credit Spreads & Market Stress¶

5.5 Foreign Exchange Rate¶

# SEK/USD Exchange Rate
get_series_from_haver("SENXUSV@NORDIC").plot(title="<b>SEK/USD Exchange Rate</b>")

5.6 External Balance (Current Account, Trade)¶

6. Fiscal Conditions¶

6.1 Budget Balance & Deficit¶

# Deficit as % of GDP
deficit_as_PGDP = (
    fiscal_collection[0].ts.rolling(12).sum() / ngdp_ann.resample("ME").ffill()
)
plot_lines(
    deficit_as_PGDP.rename("Deficit as percentage of GDP"),
    tick_format="0.0%",
    title="<b>Sweden Deficit as percentage of GDP</b>",
    years_limit=3,
    show_0=True,
)

6.2 Public Debt Levels¶

6.3 Fiscal Policy Outlook¶

7. Assets & Markets¶

7.1 Real Estate¶

# Swedish Mortgage Yields, Policy Rate, and Housing Prices
df_all = real_estate_collection.df
df_all.columns = real_estate_collection.titles.values()
df_all = df_all.dropna()

fig = plot_lines(
    df_all,
    title="<b>Swedish Mortgage Yields, Policy Rate, and Housing Prices</b>",
    width=1000,
    height=600,
    tick_suffix="%",
    watermark=False,
    default_y_range=(-10, 15),
)

switch_trace_to_secondary_axis(
    fig,
    trace_names="Housing Prices",
    secondary_axis_title="Housing Price Index (1981 = 100)",
    tick_suffix="",
)

fig.update_layout(yaxis=dict(title="Mortgage Yields & Policy Rate (%)"))

fig.show()

7.2 Equities¶

# OMXS30 vs S&P 500
df = pd.concat([omx, spx], axis=1).dropna()
df = df[df.index >= "2020-01-01"]

df_rebased = df / df.iloc[0] * 100

fig = plot_lines(
    df_rebased,
    title="<b>Rebased Index Comparison: OMXS30 vs. S&P 500 (Start = 100)</b>",
    axis_title="Index Level (Rebased)",
    source="Haver",
    watermark=False,
    width=1000,
    height=600,
)

fig.show()

8. Riksbank Sentiment Indicator¶

Riksbank Sentiment Index see

Source: J.P. Morgan, SCB, Riksbank, NIER, Origo. *Z scores of NIER industry demand exp., NIER industry price exp., HH conf., Origo 5-year CPI exp. Four variables have equal weights

riksbank_collection.dashboard.plots()

# Riksbank Sentiment Index (Leads Interest Rate Cuts)
sentiment_indicator = pd.DataFrame(
    {
        "industry_demands": riksbank_collection["SESVBODM@NORDIC"].ts,
        "industry_price_expectations": riksbank_collection["SESVBSPM@NORDIC"].ts,
        "confidence_indicator": riksbank_collection["SESCCI@NORDIC"].ts,
        "cpi_expectations": riksbank_collection["SENJ5FA@NORDIC"].ts,
    }
)
ssi = (
    sentiment_indicator.sub(sentiment_indicator.mean())
    .div(sentiment_indicator.std())
    .mean(axis=1)
)

policy_rate_target = bb.get_series("SWRRATEI Index")
ssi = ssi.rename("Riksbank Sentiment Index").to_frame()
ssi["Policy Rate Target"] = policy_rate_target.ts.resample("ME").last().ffill()
ssi["Policy Rate Change"] = ssi["Policy Rate Target"].diff()

fig = plot_line(
    blue=ssi.loc["2003":, "Policy Rate Change"],
    red=ssi.loc["2003":, "Riksbank Sentiment Index"],
    show_0=True,
    align_0=True,
    title="<b>Riksbank Sentiment Index vs Policy Rate Change</b>",
)
fig.show()

from tulip_mania.notebook_related import notebook_updated

notebook_updated()