import yaml
import pandas as pd

from tulip.plots import plot_histograms
from tulip_mania.helper_functions.fetch_with_cache import fetch_equity_data

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

ERP = 0.02
DIV_PO = 0.5
FIELDS_OF_INTEREST = [
    "BEST_SALES",
    "TRAIL_12M_SALES_PER_SH",
    "BEST_EBITDA",
    "TRAIL_12M_EBITDA_PER_SHARE",
    "BEST_EPS",
    "TRAIL_12M_EPS",
]


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()]

equity_df = fetch_equity_data(
    tuple(equity_indices), pd.Timestamp("1980-01-01"), pd.Timestamp.today()
)

eps_analysis = {
    k: dict() for k in equity_df.index.get_level_values("security").unique()
}


for s in eps_analysis.keys():
    # Get data for the selected market
    eps_analysis[s]["ts"] = ts = (
        equity_df.xs(s)[FIELDS_OF_INTEREST].ffill().resample("QE").last()
    )
    # Fix deprecation warning by specifying fill_method=None
    eps_analysis[s]["ann_chg"] = ann_chg = eps_analysis[s]["ts"].pct_change(4, fill_method=None)
    eps_analysis[s]["expectations"] = expectations = pd.DataFrame(
        index=eps_analysis[s]["ts"].index,
        data={
            "EXP_EPS": eps_analysis[s]["ts"]["BEST_EPS"]
            / eps_analysis[s]["ts"]["TRAIL_12M_EPS"]
            - 1,
            "EXP_EBITDA": eps_analysis[s]["ts"]["BEST_EBITDA"]
            / eps_analysis[s]["ts"]["TRAIL_12M_EBITDA_PER_SHARE"]
            - 1,
            "EXP_SALES": eps_analysis[s]["ts"]["BEST_SALES"]
            / eps_analysis[s]["ts"]["TRAIL_12M_SALES_PER_SH"]
            - 1,
        },
    )

    eps_analysis[s]["sales_yoy"] = sales_yoy = (
        ann_chg["TRAIL_12M_SALES_PER_SH"].dropna().rename("Sales YoY") * 100
    )
    eps_analysis[s]["sales_yoy_expectation"] = sales_yoy_expectation = (
        expectations["EXP_SALES"].dropna().iloc[-1] * 100
    )

    eps_analysis[s]["ebitda_yoy"] = ebitda_yoy = (
        ann_chg["TRAIL_12M_EBITDA_PER_SHARE"].dropna().rename("EBITDA YoY") * 100
    )
    eps_analysis[s]["ebitda_yoy_expectation"] = ebitda_yoy_expectation = (
        expectations["EXP_EBITDA"].dropna().iloc[-1] * 100
    )

    eps_analysis[s]["eps_yoy"] = eps_yoy = (
        ann_chg["TRAIL_12M_EPS"].dropna().rename("EPS Actual YoY") * 100
    )
    eps_analysis[s]["eps_yoy_expectation"] = eps_yoy_expectation = (
        expectations["EXP_EPS"].dropna().iloc[-1] * 100
    )

    eps_analysis[s]["eps_yoy_expectations"] = eps_yoy_expectations = (
        expectations["EXP_EPS"].dropna().rename("EPS Expected YoY") * 100
    )

    eps_analysis[s]["histogram_chart"] = plot_histograms(
        [sales_yoy, ebitda_yoy, eps_yoy, eps_yoy_expectations],
        thresholds=[
            sales_yoy_expectation,
            ebitda_yoy_expectation,
            eps_yoy_expectation,
            eps_yoy_expectation,
        ],
        titles=["Sales YoY", "EBITDA YoY", "EPS Actual YoY", "EPS Expected YoY"],
        title=f"<b>{s}</b>",
        xlabel="YoY Change (%)",
        layout="grid",
    )