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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, use_gini=False, use_log=True, dr=None):
index = MarkitBasketIndex(index_type, series, ["5yr"])
if dr is None:
dr = pd.bdate_range(
index.issue_date, datetime.datetime.today() - pd.offsets.BDay(1)
)
dispersion = []
for d in dr:
# print(d)
index.value_date = d
dispersion.append(index.dispersion(use_gini, use_log))
return pd.DataFrame(dispersion, index=dr, columns=["dispersion"])
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 risk_numbers "
"LEFT JOIN index_version USING (index, series, version) "
"WHERE index = %s"
)
df = pd.read_sql_query(
sql_string, engine, parse_dates={"date": {"utc": True}}, params=[index_type]
)
del df["basketid"]
df.date = df.date.dt.normalize().dt.tz_convert(None)
df = df.groupby(
["date", "index", "series", "version", "tenor", "attach"], as_index=False
).mean()
df = df.assign(
exp_percentage=lambda x: x.expected_loss / x.index_expected_loss,
attach_adj=lambda x: np.maximum(
(x.attach - x.cumulativeloss) / df.indexfactor, 0
),
detach_adj=lambda x: np.minimum(
(x.detach - x.cumulativeloss) / df.indexfactor, 1
),
)
df = df.assign(
moneyness=lambda x: (x.detach_adj + x.attach_adj)
/ 2
/ x.indexfactor
/ x.index_expected_loss,
)
df.set_index(
["date", "index", "series", "tenor", "attach"], append=True, inplace=True
)
df.reset_index(level=0, drop=True, inplace=True)
return df
def create_models(df, use_gini=False, use_log=True):
# Takes the output of get_tranche_data
dispersion = {}
for g, _ in df.groupby(["series", "index"]):
temp = df.xs(g[0], level="series")
date_range = temp.index.get_level_values("date").unique()
dispersion[g[0]] = get_dispersion(
g[1], g[0], use_gini=use_gini, use_log=use_log, dr=date_range
)
dispersion = pd.concat(dispersion)
dispersion.index.rename("series", level=0, inplace=True)
df = df.merge(dispersion, left_index=True, right_index=True)
df.dropna(subset=["dispersion"], inplace=True)
gini_model, gini_calc = {}, {}
for attach in df.index.get_level_values("attach").unique():
gini_calc[attach] = df.xs(
["5yr", attach], level=["tenor", "attach"], drop_level=False
)
gini_model[attach] = smf.ols(
"np.log(exp_percentage) ~ "
"dispersion + "
"np.log(index_duration) + "
"np.log(moneyness)",
data=df.xs(attach, level="attach"),
).fit()
gini_calc[attach]["predict"] = np.exp(
gini_model[attach].predict(gini_calc[attach])
)
gini_calc = pd.concat(gini_calc, sort=False).reset_index(level=0, drop=True)
normalization = gini_calc.groupby(["date", "index", "series", "tenor"])[
"predict"
].sum()
gini_calc = gini_calc.merge(
normalization, left_index=True, right_index=True, suffixes=["_preN", "_sum"]
)
gini_calc["predict_N"] = gini_calc["predict_preN"] / gini_calc["predict_sum"]
gini_calc["mispricing"] = (
(gini_calc["exp_percentage"] - gini_calc["predict_N"])
* gini_calc["index_expected_loss"]
/ (gini_calc["detach_adj"] - gini_calc["attach_adj"])
/ gini_calc["indexfactor"]
* 10000
)
return gini_model, gini_calc
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":
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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