from analytics import on_the_run
from analytics.basket_index import BasketIndex
from analytics.utils import bus_day
from analytics.index_data import index_returns
from utils.db import serenitas_pool
import datetime
import numpy as np
import pandas as pd
from typing import Tuple
from cds_rebook import default_adjustment


def get_defaulted_info(conn, auction_date: datetime.date) -> Tuple[int, str]:
    with conn.cursor() as c:
        c.execute(
            "SELECT id, seniority FROM defaulted WHERE auction_date=%s", (auction_date,)
        )
        cid, seniority = c.fetchone()
    conn.commit()
    return cid, seniority


dr = pd.date_range("2015-09-27", "2019-11-25", freq=bus_day)
prices = {}
s = -1
index_list = []
conn = serenitas_pool.getconn()
for d in dr:
    print(d)
    otr = on_the_run("HY", d)
    if otr > s:
        s = otr
        index_list.append(BasketIndex("HYBB", s, ["5yr"], value_date=d))
    for index in index_list:
        index.value_date = d
        prices[(index.series, index.version, d)] = 100 * (1 - index.pv()["5yr"])
        if index._lastdate == d.date():
            cid, seniority = get_defaulted_info(conn, d.date())
            accrual_days, fee = default_adjustment(cid, seniority, index.maturities[0])
            weight = next(w for _, w, c in index.items() if c.defaulted)
            old_price = 1 - index.pv()["5yr"]
            new_price = (index.factor * old_price - weight * (1 - fee)) / (
                index.factor - weight
            )
            prices[(index.series, index.version + 1, d)] = new_price * 100
serenitas_pool.putconn(conn)

hybb = pd.DataFrame.from_dict(prices, orient="index")
hybb.index = pd.MultiIndex.from_tuples(hybb.index)
hybb.index.names = ["series", "version", "date"]
hybb.columns = ["HYBB"]
hybb = hybb.sort_index()
hybb = hybb.groupby(["series", "version"]).pct_change()
hybb["day_frac"] = hybb.groupby(["series", "version"]).transform(
    lambda s: s.index.get_level_values("date")
    .to_series()
    .diff()
    .astype("timedelta64[D]")
    / 360
)
hybb["HYBB"] += hybb.day_frac * 0.05
hybb = hybb.dropna().groupby("date").nth(-1)
df = index_returns(
    index="HY",
    tenor="5yr",
    series=[25, 26, 27, 28, 29, 30, 31, 32, 33],
    from_date=datetime.date(2015, 9, 27),
    years=None,
)
df = df["price_return"].reset_index(level=["index", "tenor"], drop=True)

hy_returns = df.dropna().groupby("date").nth(-1)
hy_returns.name = "HY"

returns = pd.concat([hybb["HYBB"], hy_returns], axis=1)
# weekly
beta_weekly = (
    returns.ewm(span=5).cov().groupby(level=0).apply(lambda df: df / np.diag(df))
)
beta_weekly = beta_weekly.xs("HY", level=1)["HYBB"]

## monthly
beta_monthly = (
    returns.ewm(span=20).cov().groupby(level=0).apply(lambda df: df / np.diag(df))
)
beta_monthly = beta_monthly.xs("HY", level=1)["HYBB"]