import pandas as pd
from IPython.display import Markdown
from tulip.config import PATHS
from tulip.core import TulipLibrary
from tulip.plots import plot_scatter

market_data = TulipLibrary.market_data()

groups = {
    "AUD": "DM",
    "BRL": "EM",
    "CAD": "DM",
    "CHF": "DM",
    "CLP": "EM",
    "CNY": "EM",
    "COP": "EM",
    "CZK": "EM",
    "DEM": "DM",
    "ESP": "DM",
    "EUR": "DM",
    "FRF": "DM",
    "GBP": "DM",
    "HKD": "EM",
    "HUF": "EM",
    "IDR": "EM",
    "ILS": "EM",
    "INR": "EM",
    "ITL": "DM",
    "JPY": "DM",
    "KRW": "EM",
    "MXN": "EM",
    "MYR": "EM",
    "NLG": "DM",
    "NOK": "DM",
    "NZD": "DM",
    "PEN": "EM",
    "PHP": "EM",
    "PLN": "EM",
    "RON": "EM",
    "SEK": "DM",
    "SGD": "EM",
    "THB": "EM",
    "TRY": "EM",
    "TWD": "EM",
    "USD": "DM",
    "ZAR": "EM",
}
duration_carry = {}
for tenor, code in zip(
    [
        "2y",
        # "5y",
        "10y",
    ],
    [
        "duration_carry_2y",
        # "duration_carry_5y",
        "duration_carry_10y",
    ],
):
    duration_carry[tenor] = market_data.read(code).data.ffill().tail(1)

duration_carry = pd.concat(duration_carry)

ue_rate = {}
for tenor, code in zip(
    [
        # "UE",
        # "UE vs 5y Median",
        "UE vs 10y Median",
        "UE Change YoY",
    ],
    [
        # "ue_rate_3mma",
        # "ue_rate_3mma_vs_5ymm",
        "ue_rate_3mma_vs_10ymm",
        "ue_rate_3mma_yoy",
    ],
):
    ue_rate[tenor] = market_data.read(code).data.ffill().tail(1)

ue_rate = pd.concat(ue_rate)

last_reading = pd.concat([duration_carry, ue_rate], axis=0).T
last_update = min(last_reading.columns.levels[1].to_list())
last_reading["group"] = last_reading.index.to_series().replace(groups)
last_reading = last_reading.droplevel(1, axis=1)

Carry vs. Employment¶

This page levers Macrosynergy data. Here are the relevant links to the indicators being used

Duration vs Unemployment Gap¶

Duration Carry (DU10YCRY_NSA) vs Unemployment rate, seasonally adjusted 3-month moving average minus the 10-year median average (UNEMPLRATE_SA_3MMAv10YMM)

fig = plot_scatter(
    last_reading,
    x="UE vs 10y Median",
    y="10y",
    groups="group",
    title="<b>Duration Carry vs. Employment Gap</b>",
    xaxis_title="Employment Gap (3mma vs 10Y median)",
    yaxis_title="Duration Carry",
    # default_y_range = (-3, 3),
    # default_x_range = (-3, 3)
)

fig.show()

Changes in Employment vs Unemployment Gap¶

Duration Carry vs Change in unemployment rate over a year ago

fig = plot_scatter(
    last_reading,
    x="UE Change YoY",
    y="10y",
    groups="group",
    title="<b>Duration Carry vs. Change in Unemployment</b>",
    xaxis_title="Change in unemployment rate over a year ago",
    yaxis_title="Duration Carry",
    # default_y_range = (-3, 3),
    # default_x_range = (-3, 3)
)

fig.show()

Markdown(
    f"*Data as of {last_update.strftime('%Y-%m-%d %H:%M')} - Notebook updated at {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M')}*"
)

from tulip_mania.notebook_related import notebook_updated

notebook_updated()