from IPython.display import Markdown
import pandas as pd
from tulip.analysis.equity_related.analytics import compute_ieg, organize_ieg_summary
from tulip.analysis.equity_related.visualization import style_ieg_summary
from tulip_mania.notebook_related import display_table_and_two_charts_flexible
from tulip_mania.helper_functions.fetch_with_cache import (
    fetch_equity_data,
    fetch_nominal_yields,
    fetch_breakevens,
)
from tulip.plots import plot_lines

import yaml

covid_start_date = "2020-03-01"
covid_end_date = "2022-03-01"

ERP = 0.02
DIV_PO = 0.5

with open("equity_indices.yaml") as f:
    data = yaml.safe_load(f)

EQUITY_INDICES = {
    k: [v["index"], v["nominal_bond"], v["breakeven"]]
    for k, v in data["equity_indices"].items()
}

equity_indices = [v[0] for v in EQUITY_INDICES.values()]
nominal_bonds = {v[1] for v in EQUITY_INDICES.values()}
break_evens_indices = {v[2] for v in EQUITY_INDICES.values() if v[2] is not None}

# Fetch data using cached functions
equity_df = fetch_equity_data(
    tuple(equity_indices), pd.Timestamp("1980-01-01"), pd.Timestamp.today()
)

nominal_yields = fetch_nominal_yields(
    tuple(nominal_bonds), pd.Timestamp("1980-01-01"), pd.Timestamp.today()
)

breakevens = fetch_breakevens(
    tuple(break_evens_indices), pd.Timestamp("1980-01-01"), pd.Timestamp.today()
)

ieg_calcs = {}
for s, list_ticker in EQUITY_INDICES.items():
    has_bei = list_ticker[2] in breakevens.index.get_level_values(0)

    ieg_calcs[s] = compute_ieg(
        pe_ratio=equity_df.xs(list_ticker[0])["BEST_PE_RATIO"],
        nominal_yield=nominal_yields.xs(list_ticker[1])["PX_LAST"],
        bei=breakevens.xs(list_ticker[2])["PX_LAST"] if has_bei else None,
        equity_risk_premium=ERP,
        dividend_payout_ratio=DIV_PO,
    )

ieg_summary = {}
sieg_summary = {}
for k, v in ieg_calcs.items():
    # Because of when countries close their markets some markets might not have yield data on certain dates
    ieg_summary[k] = organize_ieg_summary(
        v.dropna(subset=["EY-NY"]), covid_start_date, covid_end_date
    )
    sieg_summary[k] = style_ieg_summary(ieg_summary[k])

# Combine all the summaries first
combined_df = pd.concat(ieg_summary, axis=1)
avg_rows = [
    row for row in combined_df.index if row.startswith("Avg") and row != "Avg ex. Covid"
]
avg_full_history = combined_df.loc[avg_rows].mean()
combined_df = combined_df.drop(avg_rows)
combined_df.loc["Avg Full History"] = avg_full_history
desired_order = [
    "Last",
    "One Month Ago",
    "Delta",
    "Avg Full History",
    "Avg ex. Covid",
    "Last 5Y",
    "Percentile Rank",
]
combined_df = combined_df.reindex(desired_order)

State of Risk Premiums¶

Summary¶

style_ieg_summary(
    combined_df.loc[:, pd.IndexSlice[["USA|SPX", "USA|SPX EW", "USA|Nasdaq"], :]]
)

style_ieg_summary(
    combined_df.loc[:, pd.IndexSlice[["USA|Ex-Mag 7", "USA|Mag 7", "USA|R2K"], :]]
)

style_ieg_summary(
    combined_df.loc[
        :,
        pd.IndexSlice[["EUR|Stoxx 50", "EUR|Stoxx 600", "EUR|DAX"], :],
    ]
)

style_ieg_summary(
    combined_df.loc[
        :,
        pd.IndexSlice[["GBP|FTSE 100", "CHF|SMI", "ESP|IBEX 35"], :],
    ]
)

style_ieg_summary(
    combined_df.loc[
        :,
        pd.IndexSlice[["JPN|Nikkei", "JPN|Topix"], :],
    ]
)

# todo: me quede aqui
style_ieg_summary(
    combined_df.loc[
        :,
        pd.IndexSlice[["CHN|CSI 300", "CHN|Hang Seng", "CHN|Hang Seng Tech"], :],
    ]
)

style_ieg_summary(
    combined_df.loc[
        :,
        pd.IndexSlice[["IND|Nifty 50", "KOR|KOSPI", "BRA|Bovespa"], :],
    ]
)

Individual Markets¶

# # asset_class = "Equities"
# _emy = data["USA - SPX"]["ieg"][["EY-NY"]]
# chart2 = plot_lines(
#     _emy,
#     years_limit=5,
#     tick_format=".1%",
#     title=f"<b>US Equity Risk Premium (ERP)</b>",
#     show_0=True,
#     figsize=(600, 640),
#     logo=False,
#     #source=f"Kate Capital",
# )
# chart2.update_layout(showlegend=False)
# chart2.show()

for ctry in ieg_summary.keys():
    Markdown(f"#### {ctry}")

    table = sieg_summary[ctry]

    _ieg = ieg_calcs[ctry].dropna(how="all", axis=1)
    if "RIEG" in _ieg.columns:
        _ieg = _ieg.loc[:, ["NIEG", "RIEG"]]
    else:
        _ieg = _ieg.loc[:, ["NIEG"]]

    chart = plot_lines(
        _ieg,
        years_limit=5,
        tick_format=".1%",
        title=f"<b>IEG:</b> {ctry}",
        show_0=True,
        figsize=(450, 450),
        logo=False,
    )

    _emy = ieg_calcs[ctry][["EY-NY"]]

    chart2 = plot_lines(
        _emy,
        years_limit=5,
        tick_format=".1%",
        title=f"<b>ERP:</b> {ctry}",
        show_0=True,
        figsize=(450, 450),
        logo=False,
    )

    for c in [chart, chart2]:
        c.update_layout(
            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
        )

    display_table_and_two_charts_flexible(
        table=table.set_caption(ctry), chart1=chart, chart2=chart2
    )

    Markdown("***")

Explanation of Calculations:¶

1. Earnings Yield Minus Nominal Yield (EY - NY)¶

This metric measures the spread between equity earnings yield and the 10-year nominal government bond yield:

EY - NY = \left( \frac{1}{\text{BEST\_PE\_RATIO}} \right) - \text{NY\_10Y}

(1)

The Bloomberg field BESTPE_RATIO is pulled with the _blended forward override, which gives a 12-month forward P/E ratio, derived by weighting this year’s and next year’s earnings estimates according to the number of days in each period. This ensures a continuous forward-looking valuation metric that updates daily throughout the year. It is also what we show under ‘PE’ in the tables.

2. Nominal Implied Earnings Growth (NIEG)¶

The nominal implied growth in earnings is estimated by adding the nominal yield and the equity risk premium, then subtracting the dividend yield:

NIEG = NY\_10Y + RP - DY

(2)

Where the dividend yield is calculated as the earnings yield multiplied by the dividend payout ratio:

DY = \left( \frac{1}{\text{BEST\_PE\_RATIO}} \right) \times \text{DVD\_PAYOUT\_RATIO}

(3)

This comes from reworking the equation that sets expected equity returns equal to those of nominal bonds:

NIEG + DY = NY\_10Y + RP

(4)

3. Real Implied Earnings Growth (RIEG)¶

To calculate real (inflation-adjusted) implied earnings growth, we subtract expected inflation from the nominal implied earnings growth:

RIEG = NIEG - BEI\_10Y

(5)

We use the 10Y breakeven from Bloomberg (XXXGBE10 Index) where XXX is the currency of the market.

from tulip_mania.notebook_related import notebook_updated

notebook_updated()