import pandas as pd
import numpy as np
from IPython.display import Markdown
from tulip.data.bloomberg import BloombergClient as bb

from tulip.data.dataquery import get_us_otr_yields
from tulip.plots import plot_lines, plot_line, switch_trace_to_secondary_axis
from dotenv import load_dotenv
from tulip.data.macrosynergy import get_downloader

load_dotenv()
import macrosynergy.panel as msp

xcats = [
    "GNBRGDP_NSA_3MMA",
    "GNBRGDP_NSA_6MMA",
    "GNBRGDP_NSA_D3M3ML12",
    "GNBRGDP_NSA_D6M6ML12",
]

kate_cids = ["USD", "DEM", "GBP", "JPY", "AUD", "CAD", "NZD", "SEK"]

import os

g20_tickers = [cid + "_" + xcat for cid in kate_cids for xcat in xcats]

JPMaQSDownloader = get_downloader()
with JPMaQSDownloader as dq:
    df = dq.download(
        tickers=g20_tickers,
        start_date="2000-01-01",
        suppress_warning=True,
        metrics=["all"],
        show_progress=True,
    )

Government Requirement Ratios¶

Evolution of Government Borrowing Requirements¶

3mma and 6mma of government borrowing requirements as a percentage of GDP¶

msp.view_timelines(
    df,
    xcats=["GNBRGDP_NSA_3MMA", "GNBRGDP_NSA_6MMA"],
    cids=kate_cids,
    ncol=3,
    cumsum=False,
    start="2015-01-01",
    same_y=True,
    all_xticks=True,
    # title="Cumulative FX returns",
    # xcat_labels=["FX returns", "FX forward return for 10% vol target"],
)

Since 2024

msp.view_timelines(
    df,
    xcats=["GNBRGDP_NSA_3MMA", "GNBRGDP_NSA_6MMA"],
    cids=kate_cids,
    ncol=3,
    cumsum=False,
    start="2024-01-01",
    same_y=True,
    all_xticks=True,
    # title="Cumulative FX returns",
    # xcat_labels=["FX returns", "FX forward return for 10% vol target"],
)

3mma and 6mma of government borrowing requirements as a percentage of GDP, along with their year-over-year changes.¶

msp.view_timelines(
    df,
    xcats=["GNBRGDP_NSA_D3M3ML12", "GNBRGDP_NSA_D6M6ML12"],
    cids=kate_cids,
    ncol=3,
    cumsum=False,
    start="2015-01-01",
    same_y=True,
    all_xticks=True,
    # title="Cumulative FX returns",
    # xcat_labels=["FX returns", "FX forward return for 10% vol target"],
)

Since 2024:

msp.view_timelines(
    df,
    xcats=["GNBRGDP_NSA_D3M3ML12", "GNBRGDP_NSA_D6M6ML12"],
    cids=kate_cids,
    ncol=3,
    cumsum=False,
    start="2024-01-01",
    same_y=True,
    all_xticks=True,
    # title="Cumulative FX returns",
    # xcat_labels=["FX returns", "FX forward return for 10% vol target"],
)

from tulip_mania.notebook_related import notebook_updated

notebook_updated()