US Dollar Liquidity¶

import pandas as pd
from IPython.display import Markdown
from tulip.plots import plot_lines
from tulip.core import TulipSeries, TulipCollection
from tulip.data.fred import get_series_from_fred
from tulip.data.bloomberg import BloombergClient as bb
from tulip.plots import plot_line, switch_trace_to_secondary_axis

from tulip_mania.notebook_related import display_chart, display_two_charts

START_DATE = pd.Timestamp("today").normalize() - pd.offsets.MonthEnd(18)
END_DATE = pd.Timestamp("today").normalize() + pd.offsets.MonthEnd(2)

SOFR_T = {
    "Federal Funds Target Rate - Upper Bound": "FDTR Index",
    "United States SOFR Secured Overnight Financing Rate": "SOFRRATE Index",
    "Federal Funds Target Rate - Lower Bound": "FDTRFTRL Index",
}


GOVT_LIQUIDITY = {"US Govt Securities Liquidity Index": "GVLQUSD Index"}

XCCY_BASIS = {
    "EUR USD Basis Swap ESTRON vs SOFRRATE 12 Month": "EUXOQQ1 BGN Curncy",
    "GBP USD Basis Swap SONIA vs SOFRRATE 12Month": "BPXOQQ1 BGN Curncy",
    "JPY USD Basis Swap TONA vs SOFRRATE 12 Month": "JYBSS12M BGN Curncy",
    "CHF USD Basis Swap SARON vs SOFRRATE 12Month": "SFXOQQ1 BGN Curncy",
    "AUD-USD Basis Swap (OIS vs SOFR) 1Year": "ADOIQQ1 BGN Curncy",
}

# A spread based on the difference between the rate on Underlying Tkr1 and the rate on Underlying Tkr2.
SOFR_SWAP_SPREAD = {
    "SOFR-2Y Swap Spread": "USSFCT02 Curncy",  # USOSFR2 Curncy vs CT2 Govt
    "SOFR-3Y Swap Spread": "USSFCT03 Curncy",  # USOSFR3 Curncy vs CT3 Govt
    "SOFR-5Y Swap Spread": "USSFCT05 Curncy",  # USOSFR5 Curncy vs CT5 Govt
}

# dxy_facility = get_series_from_fred('??')
# dxy_facility.title = '?'
usd_twi = get_series_from_fred("DTWEXBGS")
usd_twi.title = "Nominal Broad U.S. Dollar Trade Weighted Index"
real_usd_twi = get_series_from_fred("RTWEXBGS")
real_usd_twi.title = "Real Broad U.S. Dollar Trade Weighted Index"

reverse_repo_facility = get_series_from_fred("RRPONTSYD")
reverse_repo_facility.title = "Overnight Reverse Repo Facility Use at the Fed"
repo_facility = get_series_from_fred("RPONTSYD")
repo_facility.title = "Overnight Reverse Repo Facility Use at the Fed"
CP_Spread = get_series_from_fred("CPFF")
CP_Spread.ts = CP_Spread.ts.ffill()
CP_Spread.title = "Commercial Paper Minus Federal Funds Rate"  # 3-Month Commercial Paper Minus Federal Funds Rate

US_IG_SPREAD = get_series_from_fred("BAMLC0A0CM")
US_HY_SPREAD = get_series_from_fred("BAMLH0A0HYM2")

MUNI_RATIOS = {
    "Muni ratio 1Yr": "MUNSMT01 Index",  #'CAAA01YR BVLI Index',
    "Muni ratio 2Yr": "MUNSMT02 Index",  #'CAAA02YR BVLI Index',
    "Muni ratio 5Yr": "MUNSMT05 Index",  #'CAAA05YR BVLI Index',
    "Muni ratio 10Yr": "MUNSMT10 Index",  #'CAAA10YR BVLI Index',
}

Dollar Trade Weighted Index (TWI)¶

fig = usd_twi.plot(
    source="FRED", default_x_range=(usd_twi.ts.index[0], END_DATE), figsize=(900, 400)
)

fig.update_layout(
    title="<b>Nominal Broad U.S. Dollar Trade Weighted Index</b>",
    showlegend=False,  # Set False to hide the legend
)

fig.show()

SOFR Secured Overnight Financing Rate¶

Versus Fed Fund Rates¶

The closer to the upper bound the tighter liquidity is.

sofr_col = bb.create_collection(SOFR_T)
sofr_df = sofr_col.df.copy()
sofr_df.rename(columns=sofr_col.titles, inplace=True)

plot_lines(
    timeseries=sofr_df,
    title="<b>Secured Overnight Financing vs. Fed Funds</b> - Double Click to zoom out",
    figsize=(900, 400),
    year_limit=10,
    tick_suffix="%",
)

Versus T-Bill (3M)¶

SOFR_SPREAD = {
    #  "SOFR-T-Bill Spread": "SFRTZR Comdty",
    "SOFR": "SFR1 Comdty",
    "T-Bill": "TZR1 Comdty",
    # "2-Year": "TU1 Comdty",
    # "3-Year": "3Y1 Comdty",
}

sofr_df = {}
for k, v in SOFR_SPREAD.items():
    metadata = bb.get_metadata(v)
    sofr_df[k] = bb.bdh(ticker=v, fields=["YLD_YTM_MID"])

sofr_yield = pd.concat(
    [
        sofr_df["SOFR"].droplevel(0, axis=1).loc[:, "YLD_YTM_MID"].rename("SOFR (3M)"),
        sofr_df["T-Bill"].droplevel(0, axis=1).loc[:, "YLD_YTM_MID"].rename("T-Bill"),
        #    series_data[ 'SOFR-T-Bill Spread'].iloc[:,0].rename('Spread')
    ],
    axis=1,
)

sofr_yield["Spread"] = sofr_yield["SOFR (3M)"].sub(sofr_yield["T-Bill"]).mul(100)

fig = plot_lines(
    timeseries=sofr_yield,
    default_y_range=(sofr_yield.iloc[-1, :2].min() - 1, 7),
    default_x_range=(START_DATE, END_DATE),
    title="<b>Secured Overnight Financing Rate vs. T-Bill rate</b> - Yield and Spread",
    figsize=(900, 400),
)
fig = fig.update_yaxes(ticksuffix="%")
switch_trace_to_secondary_axis(
    fig, trace_names="Spread", as_area=True, tick_suffix="bps"
)

fig.show()

SOFR BASIS SWAPs¶

These contracts represent the spread between a SOFR swap of a given tenor and the risk free tresury rate of that same tenor. For example SOFR-5Y Swap Spread is the spread between USOSFR5 Curncy and CT5 Govt. They are normally negative because of the convience of using the derivative (the cash-security yield is higher) This trade is related to the so-called “basis trade”. A hedge fund typically buys a Treasury bond in the cash market and sells a corresponding Treasury futures contract. To finance the bond, the fund may enter into a SOFR swap where it pays fixed and receives floating (tied to overnight repo rates). When the basis—defined as the spread between the swap and the cash bond widens negatively, the fund incurs losses because the value of the bond it holds declines more than the benefit it gains from the swap.

series_data = {}
for k, v in SOFR_SWAP_SPREAD.items():
    metadata = bb.get_metadata(v)
    series_data[k] = bb.bdh(ticker=v, fields=["PX_LAST"])


sofr_swap_spread_df = pd.concat(
    [
        series_data["SOFR-2Y Swap Spread"]
        .droplevel(0, axis=1)
        .iloc[:, 0]
        .rename("Spread")
        .rename("SOFR-2Y Swap Spread"),
        series_data["SOFR-3Y Swap Spread"]
        .droplevel(0, axis=1)
        .iloc[:, 0]
        .rename("Spread")
        .rename("SOFR-3Y Swap Spread"),
        series_data["SOFR-5Y Swap Spread"]
        .droplevel(0, axis=1)
        .iloc[:, 0]
        .rename("Spread")
        .rename("SOFR-5Y Swap Spread"),
    ],
    axis=1,
)

plot_lines(
    timeseries=sofr_swap_spread_df,
    title="<b>Secured Overnight Financing Rate Swap Spreads</b>",
    show_0=True,
    source="Bloomberg",
    years_limit=0.5,
    tick_suffix="bps",
    figsize=(900, 400),
)

Bond Liquidity¶

Charts US Govt Securities Liquidity Index (GVLQUSD Index). This index measures how dispersed Treasury prices are from a smoothed yield curve.

Official Description from Bloomberg:

The Index GVLQUSD is a measure of prevailing liquidity conditions in the US Treasury market. This Index displays the equally-weighted average yield error across the universe of US Treasury notes and bonds with remaining maturity 1-year or greater, based off the intra-day Bloomberg relative value curve fitter. When liquidity conditions are favorable the average yield errors are small as any dislocations from fair value are normalized within a short time frame. Under stressed liquidity conditions, dislocations from fair value implied by the curve fitter can remain persistent resulting in large average yield errors. CBBT is the pricing source for all Treasury notes and bonds used in this index calculation.

GOVT_LIQUIDITY = {"US Govt Securities Liquidity Index": "GVLQUSD Index"}
series_data = {}
for k, v in GOVT_LIQUIDITY.items():
    metadata = bb.get_metadata(v)
    series_data[k] = bb.bdh(ticker=v, fields=["PX_LAST"])


plot_line(
    blue=series_data["US Govt Securities Liquidity Index"]
    .droplevel(0, axis=1)
    .iloc[:, 0]
    .rename("US Govt Securities Liquidity Index"),
    default_x_range=(START_DATE, END_DATE),
    title="<b>Bloomberg US Govt Securities Liquidity Index</b>",
    source="Bloomberg",
    figsize=(900, 400),
)

Commercial Paper Spread¶

3-Month Commercial Paper Minus Federal Funds Rate

fig = CP_Spread.plot(
    default_x_range=(START_DATE, END_DATE),
    show_grid=True,
    default_y_range=(-1.5, 3.5),
    tick_suffix="%",
    figsize=(900, 400),
)
fig.update_yaxes(ticksuffix="%").show()

Corporate Bond Spreads (IG, HY)¶

SPREADS = TulipCollection([US_IG_SPREAD, US_HY_SPREAD])
SPREADS.dashboard.plots(source="From ICE, via FRED")

series_data = {}
for k, v in MUNI_RATIOS.items():
    metadata = bb.get_metadata(v)
    series_data[k] = bb.bdh(ticker=v)


muni_ratios_df = pd.concat(
    [
        series_data[k].droplevel(0, axis=1).iloc[:, 0].rename(k)
        for k, v in MUNI_RATIOS.items()
    ],
    axis=1,
)

plot_lines(
    timeseries=muni_ratios_df,
    default_y_range=(20, 100),
    title="<b>BVAL AAA Muni Yield % of Treasury Of Equivalent Tenor</b>",
    show_0=True,
    source="Bloomberg",
    years_limit=0.5,
    tick_suffix="%",
    figsize=(900, 400),
)

Fed Repo Facilities¶

Reverse Repo Facility (RRP)¶

Money market funds and other financial institutions use this facility to invest surplus cash by lending it overnight to the Fed at a fixed interest rate. The lower the use the more tight dollar liquidity is.

fig = reverse_repo_facility.plot(
    default_x_range=(START_DATE, END_DATE),
    tick_format="$,.0f",
    tick_suffix="bn",
    figsize=(900, 400),  # todo: not working?
)
fig.show(source="Fred")

Repo Facility¶

The opposite would be for the FED to Inject Liquidty. There is currently no injection, but if the Fed was to ease, we would see part of the action here.

fig = repo_facility.plot(
    default_x_range=(START_DATE, END_DATE),
    tick_format="$,.0f",
    tick_suffix="bn",
    figsize=(900, 400),  # todo: not working?
    connect_gaps=True,
)
fig.show()

Cross-Currency Basis¶

Stress outside the United States for Dollar Funding Negative means strong demand for dollars abroad

series_data = {}
for k, v in XCCY_BASIS.items():
    metadata = bb.get_metadata(v)
    series_data[k] = bb.bdh(ticker=v, fields=["PX_LAST"])

XCCY_BASIS_DF = pd.concat(
    [
        series_data["EUR USD Basis Swap ESTRON vs SOFRRATE 12 Month"]
        .iloc[:, 0]
        .rename("EUR 1Y Basis"),
        series_data["GBP USD Basis Swap SONIA vs SOFRRATE 12Month"]
        .iloc[:, 0]
        .rename("GBP 1Y Basis"),
        series_data["JPY USD Basis Swap TONA vs SOFRRATE 12 Month"]
        .iloc[:, 0]
        .rename("JPY 1Y Basis"),
        series_data["CHF USD Basis Swap SARON vs SOFRRATE 12Month"]
        .iloc[:, 0]
        .rename("CHF 1Y Basis"),
        series_data["AUD-USD Basis Swap (OIS vs SOFR) 1Year"]
        .iloc[:, 0]
        .rename("AUD 1Y Basis"),
    ],
    axis=1,
).sort_index()

start_date = XCCY_BASIS_DF.index[-1] - pd.offsets.MonthEnd(12)
end_date = XCCY_BASIS_DF.index[-1] + pd.offsets.MonthEnd(2)

fig = plot_lines(
    timeseries=XCCY_BASIS_DF,
    default_x_range=(start_date, end_date),
    title="<b>XCCY Basis Developed Markets against US SOFR</b>",
    figsize=(900, 400),
    connect_gaps=True,
)
fig.show()

from tulip_mania.notebook_related import notebook_updated

notebook_updated()