Credit Creation Monitor¶

# Costs of debt
industrials_bbplus = get_haver_series("FMLCT@USECON").ts.rename(
    "BAML U.S. Corp. Bond Yield"
)

mortgage_30y = get_haver_series("MTGF30@USECON").ts.rename("FRM 30-Year Mortgage Rate")

otr_treasury_10y = get_haver_series("F10JON@USECON").ts.rename(
    "U.S. 10-Year Treasury OTR Yield"
)
# Nominal GDP
us_gdp = get_haver_series("S111NGDP@G10").ts.rolling(4).sum()

# Unit is SAAR billions
savings_vs_borrowing = get_haver_collections(
    {
        "Domestic Non Financial Sectors": "FQ38NIF5@FFUNDS",
        "Households and Nonprofit Organizations": "FQ15NIF5@FFUNDS",
        "Non Financial Corporations": "FQ14NIF5@FFUNDS",
        "Government": "FQ36NIF5@FFUNDS",
    }
)

savings_vs_borrowing_df = savings_vs_borrowing.df.rename(
    columns=savings_vs_borrowing.titles
)
savings_vs_borrowing_df = savings_vs_borrowing_df.loc["1970":,]
savings_vs_borrowing_df_pgdp = savings_vs_borrowing_df.truediv(
    us_gdp.loc["1970":,], axis=0
)
ma = savings_vs_borrowing_df_pgdp.rolling(4).mean()
ma = ma.add_suffix(" (4Q MA)")

df_out = pd.concat([savings_vs_borrowing_df_pgdp, ma], axis=1)

MAIN_SERIES = "Domestic Non Financial Sectors"
WINDOW = 4

fig = plot_lines(
    df_out,
    show_0=True,
    tick_format="0.0%",
    years_limit=10,
    top_margin=180,
    figsize=(900, 500),
    title="<b>U.S. Savings vs. Borrowing by Sector (Percent of GDP)</b> <br>Net Lending (+) or Borrowing (-)",
)

for tr in fig.data:
    # Base name = strip MA suffix if present
    base = tr.name.replace(" (4Q MA)", "")

    if base not in COLOR_MAP:
        continue

    tr.legendgroup = base
    tr.line.color = COLOR_MAP[base]

    if tr.name.endswith("(4Q MA)"):
        tr.showlegend = True
        tr.line.width = 3
        tr.visible = True if base == MAIN_SERIES else "legendonly"
    else:
        tr.showlegend = False
        tr.line.width = 1
        tr.visible = True if base == MAIN_SERIES else False

fig.update_layout(
    legend=dict(
        font=dict(size=10),
        itemsizing="constant",
        itemwidth=30,
        tracegroupgap=2,
    )
)

fig.show()

df_to_show = ma.dropna(how="all").iloc[:, 1:]
df_to_show.columns = [
    "HH",
    "NFC",
    "Gov",
]
last_row = df_to_show.iloc[-1]
four_periods_prior = df_to_show.iloc[-5]
change = last_row - four_periods_prior

def lighten_hex(hex_color, factor=0.4):
    """Lighten a #RRGGBB color by mixing it with white."""
    hex_color = hex_color.lstrip("#")
    r, g, b = (int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
    r_l = int(r + (255 - r) * factor)
    g_l = int(g + (255 - g) * factor)
    b_l = int(b + (255 - b) * factor)
    return f"#{r_l:02x}{g_l:02x}{b_l:02x}"


colors_recent = ["#ff8333", "#ff3333", "#3372ff"]  # NFC, HH, Gov
colors_prior = [lighten_hex(c, factor=0.5) for c in colors_recent]

bar_chart = plot_bar(
    pd.DataFrame(
        {
            "Four Periods Prior": four_periods_prior,
            "Most Recent Period": last_row,
        }
    ),
    tick_format="0.1%",
    top_margin=100,
    figsize=(400, 600),
    title="Most Recent vs. Four Periods Prior",
)

bar_chart.data[0].marker.color = colors_prior
bar_chart.data[1].marker.color = colors_recent

bar_chart.update_layout(
    template="plotly_white",
    height=500,
    width=500,
    font=dict(
        family='"Avenir Next LT Pro", "Open Sans", verdana, arial, sans-serif',
        color="#333133",
    ),
    yaxis=dict(tickformat="0.1%", zeroline=True, zerolinecolor="gray"),
)


# Calculate the change from 4 periods prior for the last row

# Create a bar chart
chg_fig = go.Figure()

chg_fig.add_trace(
    go.Bar(
        x=change.index,
        y=change.values,
        marker_color=["#ff8333", "#ff3333", "#3372ff"],
        text=[f"{v:.2%}" for v in change.values],
        textposition="outside",
    )
)

chg_fig.update_layout(
    title="Diff",
    xaxis_title="Sector",
    yaxis_title="Change (% of GDP)",
    template="plotly_white",
    height=500,
    width=500,
    showlegend=False,
    yaxis=dict(tickformat="0.1%", zeroline=True, zerolinecolor="gray"),
)
display(
    HTML(
        "<center><b> U.S. Savings vs. Borrowing by Sector (Percent of GDP)</b> <br>Net Lending (+) or Borrowing (-)</center>"
    )
)
with columns(2, gap="1px", vertical_alignment="bottom") as cols:
    cols[0].plot(bar_chart)
    cols[1].plot(chg_fig)

sub_fig = {}
for g in ["HH", "NFC", "Gov"]:
    fig = plot_lines(
        df_to_show.filter(like=g),
        years_limit=10,
        default_y_range=(-0.1, 0.3),
        figsize=(400, 600),
        # title=f"<b>US {g} Credit Creation as % of GDP</b>",
        top_margin=170,
        tick_format="0.1%",
        show_0=True,
    )
    style_cc_plot(fig)
    sub_fig[g] = fig

sub_fig["HH"] = add_line_on_opposite_axis(
    sub_fig["HH"],
    mortgage_30y,
    years_limit=5,
    invert=True,
    color="black",
    tick_suffix={"y1": "%", "y2": "%"},
)

sub_fig["Gov"] = add_line_on_opposite_axis(
    sub_fig["Gov"],
    otr_treasury_10y,
    years_limit=5,
    invert=True,
    color="black",
    tick_suffix={"y1": "%", "y2": "%"},
)
sub_fig["NFC"] = add_line_on_opposite_axis(
    sub_fig["NFC"],
    industrials_bbplus,
    years_limit=5,
    invert=True,
    color="black",
    tick_suffix={"y1": "%", "y2": "%"},
)
display(
    HTML(
        "<center>Against approximate borrow costs <br><sup>Positive values indicate <b>net saving</b>, negative values indicate <b>net borrowing</b></sup></center>"
    )
)
with columns(3, gap="1px", vertical_alignment="bottom") as cols:
    cols[0].plot(sub_fig["HH"])
    cols[1].plot(sub_fig["NFC"])
    cols[2].plot(sub_fig["Gov"])

nfc_dict = {
    "Net Acquisition of Assets": "FA14TAO5@FFUNDS",
    "Net Acquisition of Liabilities": "FL14TAO5@FFUNDS",
    "Net Debt Securities": "FL10AIJ5@FFUNDS",
    "Net Loans": "FL14ABN5@FFUNDS",
    "Net Equity": "FL14ADS5@FFUNDS",
    "Net Lending/Saving": "FQ14NIF5@FFUNDS",
}
nfc_collection = get_haver_collections(nfc_dict)
nfc_collection_df = nfc_collection.df.rename(columns=nfc_collection.titles)
nfc_collection_df = nfc_collection_df.loc["1970":,]
nfc_collection_df_pgdp = nfc_collection_df.truediv(us_gdp.loc["1970":,], axis=0)

YEARS_LIMIT = 10

fig = plot_lines(
    nfc_collection_df_pgdp.loc[
        :, ["Net Acquisition of Liabilities", "Net Debt Securities", "Net Loans"]
    ],
    years_limit=YEARS_LIMIT,
    connect_gaps=True,
    tick_format="0.1%",
    title="<b>U.S. Non-Financial Corporations: Credit Creation Components (% of GDP)</b>",
    top_margin=120,
    axis_title="% of GDP",
)

for tr, color in zip(fig.data, ["#ff8333", "#d39973", "#ac988b"]):
    # Base name = strip MA suffix if present
    tr.line.color = color
    tr.line.width = 3
    if tr.name == "Net Acquisition of Liabilities":
        tr.visible = "legendonly"


fig = add_line_on_opposite_axis(
    fig,
    otr_treasury_10y,
    invert=True,
    years_limit=YEARS_LIMIT,
    color="crimson",
    line_width=2,
    tick_suffix={"y1": "%", "y2": "%"},
)

fig = add_line_on_opposite_axis(
    fig,
    industrials_bbplus,
    invert=True,
    color="black",
    years_limit=YEARS_LIMIT,
    line_width=2,
    tick_suffix={"y1": "%", "y2": "%"},
    axis_title="Yield (%)",
)

# fig.update_yaxes(title_text="% of GDP", secondary_y=False)
# fig.update_yaxes(title_text="Yield (%)", secondary_y=True)
fig.show()

pit = nfc_collection_df.rolling(4).mean().iloc[-1, :]
ngdp = us_gdp.rolling(4).mean().iloc[-1]
fig = plot_bar(
    nfc_collection_df.rolling(4).mean().iloc[-1, :].rename("Last 4 Quarters"),
    default_y_range=(-500, 2000),
    figsize=(800, 500),
    top_margin=120,
    title="<b>U.S. Non-Financial Corporations: Components of Net Lending/Borrowing (SAAR Billions)</b><br><sup>Seasonally Adjusted Annual Rate</sup>",
)
fig.update_traces(
    text=[f"{v:,.1f}bln ({v / ngdp:.2%})" for v in pit], textposition="outside"
)

# Bond Channel - SIFMA Corporate Bond Issuance
from scipy import stats

url_sifma = "https://www.sifma.org/wp-content/uploads/2024/01/US-Corporate-Bonds-Statistics-SIFMA.xlsx"
sifma_issuance_data = pd.read_excel(
    url_sifma, sheet_name="Issuance", skiprows=6, header=[0, 1, 2]
)

# Extract period and total columns (column 0 = periods, column 13 = TOTAL)
periods = sifma_issuance_data.iloc[:, 0]
total = sifma_issuance_data.iloc[:, 13]

# Split by frequency (Column A has mixed: years, YTD, quarters, monthly)
annual_df = pd.DataFrame(
    {"Year": periods.iloc[:11].astype(int), "Total": total.iloc[:11]}
)
ytd_df = pd.DataFrame({"Period": periods.iloc[12:14], "Total": total.iloc[12:14]})
quarterly_df = pd.DataFrame(
    {"Quarter": periods.iloc[15:24], "Total": total.iloc[15:24]}
)
monthly_df = pd.DataFrame(
    {"Date": pd.to_datetime(periods.iloc[25:38]), "Total": total.iloc[25:38]}
).reset_index(drop=True)
# Annual Trend with CAGR
fig_annual = go.Figure()

fig_annual.add_trace(
    go.Bar(
        x=annual_df["Year"],
        y=annual_df["Total"],
        marker_color=[
            "#aec7e8"
            if y < 2020
            else "#ff7f0e"
            if y == 2020
            else "#1f77b4"
            if y < 2024
            else "#2ca02c"
            for y in annual_df["Year"]
        ],
        text=[f"${v:,.0f}B" for v in annual_df["Total"]],
        textposition="outside",
        textfont=dict(size=11),
        hovertemplate="<b>%{x}</b><br>Issuance: $%{y:,.1f}B<extra></extra>",
    )
)

# Add trend line
x_numeric = annual_df["Year"].values
y_values = annual_df["Total"].values
slope, intercept, r, p, se = stats.linregress(x_numeric, y_values)
trend_line = slope * x_numeric + intercept

fig_annual.add_trace(
    go.Scatter(
        x=annual_df["Year"],
        y=trend_line,
        mode="lines",
        line=dict(color="red", dash="dash", width=2),
        name="Trend",
        hoverinfo="skip",
    )
)

cagr = (
    (annual_df["Total"].iloc[-1] / annual_df["Total"].iloc[0]) ** (1 / 10) - 1
) * 100

fig_annual.update_layout(
    title=dict(
        text=f"<b>US Corporate Bond Issuance - Annual Trend</b><br><sup>Source: SIFMA | CAGR (2014-2024): {cagr:.1f}%</sup>",
        x=0.5,
        font=dict(size=16),
    ),
    xaxis_title="Year",
    yaxis_title="Issuance ($B)",
    template="plotly_white",
    height=450,
    width=900,
    showlegend=False,
    yaxis=dict(range=[0, max(annual_df["Total"]) * 1.15]),
)

fig_annual.add_annotation(
    x=2020,
    y=annual_df[annual_df["Year"] == 2020]["Total"].values[0],
    text="COVID<br>Spike",
    showarrow=True,
    arrowhead=2,
    ax=0,
    ay=-50,
    font=dict(size=10, color="#ff7f0e"),
)
fig_annual.show()

# Comprehensive Dashboard - All Frequencies
fig = make_subplots(
    rows=2,
    cols=2,
    subplot_titles=(
        "<b>Annual Issuance</b>",
        "<b>YTD Comparison (Jan-Nov)</b>",
        "<b>Quarterly Issuance</b>",
        "<b>Monthly Issuance (Recent)</b>",
    ),
    vertical_spacing=0.15,
    horizontal_spacing=0.1,
)

primary_color, highlight_color, accent_color = "#1f77b4", "#2ca02c", "#ff7f0e"

# 1. Annual
colors_annual = [primary_color] * len(annual_df)
colors_annual[-1] = highlight_color
colors_annual[6] = accent_color
fig.add_trace(
    go.Bar(
        x=annual_df["Year"],
        y=annual_df["Total"],
        marker_color=colors_annual,
        text=annual_df["Total"].round(0).astype(int),
        textposition="outside",
        textfont_size=9,
        name="Annual",
    ),
    row=1,
    col=1,
)

# 2. YTD
ytd_colors = [primary_color, highlight_color]
fig.add_trace(
    go.Bar(
        x=["YTD 2024", "YTD 2025"],
        y=ytd_df["Total"].values,
        marker_color=ytd_colors,
        text=[f"${v:,.0f}B" for v in ytd_df["Total"].values],
        textposition="outside",
        textfont_size=12,
        name="YTD",
    ),
    row=1,
    col=2,
)
ytd_change = (ytd_df["Total"].values[1] / ytd_df["Total"].values[0] - 1) * 100
fig.add_annotation(
    x=0.5,
    y=max(ytd_df["Total"].values) * 1.15,
    text=f"<b>+{ytd_change:.1f}% YoY</b>",
    showarrow=False,
    font=dict(size=14, color=highlight_color),
    xref="x2",
    yref="y2",
)

# 3. Quarterly
quarterly_colors = [
    highlight_color
    if "25" in str(q)
    else primary_color
    if "24" in str(q)
    else "#aec7e8"
    for q in quarterly_df["Quarter"]
]
fig.add_trace(
    go.Bar(
        x=quarterly_df["Quarter"],
        y=quarterly_df["Total"],
        marker_color=quarterly_colors,
        text=quarterly_df["Total"].round(0).astype(int),
        textposition="outside",
        textfont_size=9,
        name="Quarterly",
    ),
    row=2,
    col=1,
)

# 4. Monthly
fig.add_trace(
    go.Bar(
        x=monthly_df["Date"].dt.strftime("%b %Y"),
        y=monthly_df["Total"],
        marker_color=primary_color,
        opacity=0.7,
        name="Monthly",
    ),
    row=2,
    col=2,
)
fig.add_trace(
    go.Scatter(
        x=monthly_df["Date"].dt.strftime("%b %Y"),
        y=monthly_df["Total"].rolling(3, min_periods=1).mean(),
        mode="lines+markers",
        line=dict(color=accent_color, width=2),
        marker=dict(size=6),
        name="3M Avg",
    ),
    row=2,
    col=2,
)

fig.update_layout(
    height=700,
    width=1100,
    title=dict(
        text="<b>US Corporate Bond Issuance</b><br><sup>Source: SIFMA ($ Billions)</sup>",
        x=0.5,
        font=dict(size=18),
    ),
    showlegend=False,
    template="plotly_white",
)
fig.update_yaxes(title_text="$B", row=1, col=1)
fig.update_yaxes(title_text="$B", row=1, col=2)
fig.update_yaxes(title_text="$B", row=2, col=1)
fig.update_yaxes(title_text="$B", row=2, col=2)
fig.update_xaxes(tickangle=-45, row=2, col=1)
fig.update_xaxes(tickangle=-45, row=2, col=2)
fig.show()

def qtr_to_qe(q: str) -> pd.Timestamp:
    """'4Q23' -> 2023-12-31"""
    qtr, yr = int(q[0]), int(q[2:])
    yr += 2000 if yr < 100 else 0
    return pd.Timestamp(year=yr, month=qtr * 3, day=1) + pd.offsets.MonthEnd(0)


quarterly_df["date"] = quarterly_df["Quarter"].apply(qtr_to_qe)
quarterly_gross_issuance = quarterly_df.set_index("date")["Total"].rename(
    "SIFMA US Corporate Bond Issuance"
)

net_debt_securities_against_issuance = pd.concat(
    [
        nfc_collection_df["Net Debt Securities"],
        quarterly_df.set_index("date")["Total"].rename(
            "SIFMA US Corporate Bond Issuance"
        ),
    ],
    axis=1,
).loc[quarterly_gross_issuance.first_valid_index() :,]


diff_in_issuance = net_debt_securities_against_issuance.diff().iloc[-1, 1]
stat_df = net_debt_securities_against_issuance.diff().dropna()

X = stat_df["SIFMA US Corporate Bond Issuance"]
y = stat_df["Net Debt Securities"]
res = sm.OLS(y, X).fit()
beta = res.params
fig = plot_line(
    blue=net_debt_securities_against_issuance["Net Debt Securities"],
    red=net_debt_securities_against_issuance["SIFMA US Corporate Bond Issuance"],
    align_z=True,
)

prior_idx = net_debt_securities_against_issuance.index[-2]
last_idx = net_debt_securities_against_issuance.index[-1]
last_val = net_debt_securities_against_issuance["Net Debt Securities"].iloc[-2]
proj_val = (last_val + (diff_in_issuance * beta)).squeeze()

fig.add_scatter(
    x=[prior_idx, last_idx],  # or offset second: last_idx + pd.DateOffset(months=3)
    y=[last_val, proj_val],
    mode="lines+markers",
    marker=dict(color="blue", size=10),
    line=dict(color="blue", dash="dash"),
    showlegend=False,
)
fig.show()

banks_loans_CI = get_haver_series("FABWCN@USECON").ts.rename(
    "Banks Loans to NFCs"
)  # this is a level
banks_loans_CI_net_change = banks_loans_CI.resample("QE").last().diff(1)
pd.concat(
    [
        nfc_collection_df["Net Loans"],
        banks_loans_CI_net_change,
    ],
    axis=1,
)

plot_line(
    blue=nfc_collection_df["Net Loans"], red=banks_loans_CI_net_change, years_limit=12
)

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Year": [
        "2015",
        "2016",
        "2017",
        "2018",
        "2019",
        "2020",
        "2021",
        "2022",
        "2023",
        "2024",
        "YTD 2024",
        "YTD 2025",
    ],
    "Capital Raised ($M)": [
        1989,
        1596,
        4347,
        2635,
        4876,
        8509,
        14790,
        3369,
        4064,
        10267,
        9947,
        9828,
    ],
}

df = pd.DataFrame(data)

# Define colors - darker blue for YTD entries
colors = ["#1f77b4" if "YTD" not in year else "#003f7f" for year in df["Year"]]

fig, ax = plt.subplots(figsize=(12, 6))
df.set_index("Year")["Capital Raised ($M)"].plot(kind="bar", ax=ax, color=colors)
ax.set_ylabel("Capital Raised ($M)")
ax.set_title("Public BDC Financing Activity")
plt.tight_layout()
plt.show()

from tulip.data.bloomberg import BloombergClient as bb

market_val_us_lev_loans = bb.bdh(ticker="I38932US Index", fields=["INDX_MARKET_CAP"])
banks_loans_CI = get_haver_series("FABWCN@USECON").ts.rename(
    "Banks Loans to NFCs"
)  # this is a level
leveraged_loans_CI_net_change = (
    market_val_us_lev_loans.resample("QE").last().diff().squeeze()
)
pd.concat(
    [
        nfc_collection_df["Net Loans"],
        leveraged_loans_CI_net_change.rename("Leveraged Loans Market Value Change")
        / 1e6,
    ],
    axis=1,
)

plot_line(
    blue=nfc_collection_df["Net Loans"],
    red=leveraged_loans_CI_net_change,
    years_limit=5,
)

hh_dict = {
    "Net Acquisition of Assets": "FA15TAO5@FFUNDS",
    "Net Acquisition of Liabilities": "FL15TAO5@FFUNDS",
    "Net Home Mortgages": "FL15HOM5@FFUNDS",
    "Net Consumer Credit": "FL15CNC0@FFUNDS",
    "Net Lending/Saving": "FQ15NIF5@FFUNDS",
}
hh_collection = get_haver_collections(hh_dict)
hh_collection_df = hh_collection.df.rename(columns=hh_collection.titles)
hh_collection_df = hh_collection_df.loc["1970":,]
hh_collection_df_pgdp = hh_collection_df.truediv(us_gdp.loc["1970":,], axis=0)

YEARS_LIMIT = 10

fig = plot_lines(
    hh_collection_df_pgdp.loc[
        :,
        ["Net Acquisition of Liabilities", "Net Home Mortgages", "Net Consumer Credit"],
    ],
    years_limit=YEARS_LIMIT,
    connect_gaps=True,
    tick_format="0.1%",
    title="<b>U.S. Households: Credit Creation Components (% of GDP)</b>",
    top_margin=120,
    axis_title="% of GDP",
)

for tr, color in zip(fig.data, ["#ff8333", "#d39973", "#ac988b"]):
    # Base name = strip MA suffix if present
    tr.line.color = color
    tr.line.width = 3
    if tr.name == "Net Acquisition of Liabilities":
        tr.visible = "legendonly"


fig = add_line_on_opposite_axis(
    fig,
    otr_treasury_10y,
    invert=True,
    years_limit=YEARS_LIMIT,
    color="crimson",
    line_width=2,
    axis_title="",
    tick_suffix={"y1": "%", "y2": "%"},
)

fig = add_line_on_opposite_axis(
    fig,
    mortgage_30y,
    invert=True,
    color="black",
    years_limit=YEARS_LIMIT,
    line_width=2,
    axis_title="Yield (%)",
    tick_suffix={"y1": "%", "y2": "%"},
)


fig.show()

pit = hh_collection_df.rolling(4).mean().iloc[-1, :]
ngdp = us_gdp.rolling(4).mean().iloc[-1]
fig = plot_bar(
    hh_collection_df.rolling(4).mean().iloc[-1, :].rename("Last 4 Quarters"),
    default_y_range=(-500, 5000),
    figsize=(800, 500),
    top_margin=120,
    title="<b> Households and Non-profits. Components of Net Lending/Borrowing (SAAR Billions)</b><br><sup>Seasonally Adjusted Annual Rate</sup>",
)
fig.update_traces(
    text=[f"{v:,.1f}bln ({v / ngdp:.2%})" for v in pit], textposition="outside"
)

hh_debt_index = {"Real Estate": "FABWRN@USECON", "Consumer Loans": "FABWQN@USECON"}

hh_debt_collection = get_haver_collections(hh_debt_index)
hh_debt_df = hh_debt_collection.df.rename(columns=hh_debt_collection.titles)
hh_debt_df = hh_debt_df.loc["1970":,]
hh_debt_df_pgdp = hh_debt_df.truediv(
    us_gdp.loc["1970":,].resample("ME").interpolate(), axis=0
)
hh_debt_df_chg_pgdp = hh_debt_df.diff(12).truediv(
    us_gdp.loc["1970":,].resample("ME").interpolate(), axis=0
)

plot_lines(
    hh_debt_df_chg_pgdp,
    years_limit=10,
    title="<b>Household Debt Growth (YoY Change as % of GDP)</b>",
    tick_format="0.2%",
    show_0=True,
    top_margin=120,
)

from tulip.data.bloomberg import BloombergClient as bb

mba_indeces = bb.create_collection(
    {
        "MBA US Purchase Index": "MBAVPRCH Index",
        "MBA US Refi Index": "MBAVREFI Index",
    }
)

mba_indices_df = mba_indeces.df
mba_indices_df.columns = ["MBA US Purchase Index", "MBA US Refinance Index"]
plot_line(
    blue=mba_indices_df.iloc[:, 0],
    red=mba_indices_df.iloc[:, 1],
    years_limit=5,
    title="MBA US Mortgage Indices",
)

# Billions
hh_credit_creation_series = {
    "Consumer Credit": "FOTN@USECON",
    "Student Loans": "FOSN@USECON",
    "Motor Vehicle Loans": "FOMVN@USECON",
}

# Fetch the collection
hh_credit_creation_series_collection = get_haver_collections(hh_credit_creation_series)
hh_credit_creation_series_df = hh_credit_creation_series_collection.df
hh_credit_creation_series_df.columns = (
    hh_credit_creation_series_collection.titles.values()
)
hh_credit_creation_series_df = hh_credit_creation_series_df.ffill().diff(12)
hh_credit_creation_series_df = hh_credit_creation_series_df.div(
    us_gdp.reindex(hh_credit_creation_series_df.index).ffill(), axis=0
)
plot_lines(
    hh_credit_creation_series_df,
    years_limit=10,
    title="<b>Household Credit Creation as % of GDP</b>",
    tick_format="0.1%",
    show_0=True,
)

plot_line(
    blue=hh_collection_df["Net Home Mortgages"],
    red=hh_debt_df["Real Estate"].resample("QE").last().diff(),
    years_limit=20,
)

plot_line(
    blue=hh_collection_df["Net Consumer Credit"],
    red=hh_debt_df["Consumer Loans"].resample("QE").last().diff(),
    years_limit=10,
)

from tulip_mania.notebook_related import notebook_updated

notebook_updated()