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import bottleneck as bn
import datetime
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from analytics.basket_index import MarkitBasketIndex
from analytics import CreditIndex
from dateutil.relativedelta import relativedelta
from utils.db import dbengine
def get_dispersion(index_type, series, end_date=datetime.date.today()):
index = MarkitBasketIndex(index_type, series, ["5yr"])
dr = pd.bdate_range(index.issue_date, end_date)
dispersion = []
for d in dr:
index.value_date = d
dispersion.append(bn.nanstd(np.log(index.spreads())))
return pd.Series(dispersion, index=dr, name="dispersion")
def add_cumloss(df, index_type, series, engine):
cumloss = pd.read_sql_query(
"SELECT lastdate, cumulativeloss AS cumloss FROM index_version "
"WHERE index=%s and series=%s order by lastdate",
engine,
params=(index_type, series),
parse_dates=["lastdate"],
)
cumloss.iat[-1, 0] = pd.Timestamp.max
cumloss = (
cumloss.set_index("lastdate").sort_index().reindex(df.index, method="bfill")
)
return df.join(cumloss)
def get_corr_data(index_type, series, engine):
sql_str = (
"SELECT quotedate::date, indexrefspread, indexrefprice, index_duration, "
"index_expected_loss, corr_at_detach "
"FROM tranche_risk JOIN tranche_quotes "
"ON tranche_risk.tranche_id=tranche_quotes.id "
"WHERE index=%s and series=%s and tenor='5yr' and detach=%s order by quotedate desc"
)
df = pd.read_sql_query(
sql_str,
engine,
params=(index_type, series, 3 if index_type == "IG" else 15),
index_col=["quotedate"],
parse_dates=["quotedate"],
)
if index_type == "HY":
spread_equivalent = []
index = CreditIndex(index_type, series, "5yr")
for k, v in df.iterrows():
index.value_date = k
index.ref = v["indexrefprice"]
spread_equivalent.append(index.spread)
df["indexrefspread"] = spread_equivalent
df = df.assign(
fisher=lambda x: 0.5 * np.log((1 + x.corr_at_detach) / (1 - x.corr_at_detach))
)
return df
def get_tranche_data(index_type, engine):
sql_string = "select * from index_version where index = %s"
idx_ver = pd.read_sql_query(
sql_string, engine, params=[index_type,], parse_dates=["lastdate"]
)
idx_ver["date"] = pd.to_datetime(
[
d.strftime("%Y-%m-%d") if not pd.isnull(d) else datetime.date(2050, 1, 1)
for d in idx_ver["lastdate"]
]
)
sql_string = "select * from risk_numbers where index = %s"
df = pd.read_sql_query(
sql_string, engine, parse_dates={"date": {"utc": True}}, params=[index_type]
)
df["exp_percentage"] = df["expected_loss"] / df["index_expected_loss"]
df.date = df.date.dt.normalize().dt.tz_convert(None)
df = df.groupby(["date", "index", "series", "tenor", "attach"]).mean()
df.reset_index(inplace=True)
idx_ver.sort_values(by=["date"], inplace=True, ascending=True)
df = pd.merge_asof(
df,
idx_ver[["date", "series", "cumulativeloss", "indexfactor"]],
left_on=["date"],
right_on=["date"],
by="series",
direction="forward",
)
df.set_index("date", inplace=True)
df["moneyness"] = df.apply(
lambda df: (df.detach - df.cumulativeloss)
/ df.indexfactor
/ df.index_expected_loss,
axis=1,
)
return df
def gini(array):
"""Calculate the Gini coefficient of a numpy array."""
if np.amin(array) < 0:
array -= np.amin(array) # values cannot be negative
array += 0.0000001 # values cannot be 0
array = np.sort(array) # values must be sorted
index = np.arange(1, array.shape[0] + 1) # index per array element
n = array.shape[0] # number of array elements
return (np.sum((2 * index - n - 1) * array)) / (n * np.sum(array))
def get_gini_spreadstdev(index_type, series, tenor, date):
indices = MarkitBasketIndex(index_type, series, tenor, value_date=date)
spreads = indices.spreads()
spreads = spreads[spreads < 1]
return (gini(spreads), np.std(spreads))
if __name__ == "__main__":
index_type = "HY"
series = 29
serenitas_engine = dbengine("serenitasdb")
dispersion = get_dispersion(index_type, series)
df = get_corr_data(index_type, series, serenitas_engine)
df = df.join(dispersion)
if index_type == "HY":
df = add_cumloss(df, index_type, series, serenitas_engine)
if index_type == "HY":
formula = "fisher ~ np.log(dispersion) + cumloss + np.log(index_duration)"
else:
formula = "fisher ~ np.log(dispersion) + np.log(indexrefspread) + np.log(index_duration)"
mod = smf.ols(formula=formula, data=df)
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