1 files changed, 45 insertions, 0 deletions
diff --git a/python/exploration/hybb.py b/python/exploration/hybb.py
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+++ b/python/exploration/hybb.py
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+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
+import datetime
+import numpy as np
+import pandas as pd
+
+dr = pd.date_range("2015-09-27", "2019-11-25", freq=bus_day)
+prices = {}
+s = -1
+for d in dr:
+    otr = on_the_run("HY", d)
+    if otr > s:
+        s = otr
+        basket_index = BasketIndex("HYBB", s, ["5yr"], value_date=d)
+    basket_index.value_date = d
+    prices[(s, d)] = 100 * (1 - basket_index.pv()["5yr"])
+hybb = pd.DataFrame.from_dict(prices, orient="index")
+hybb.index = pd.MultiIndex.from_tuples(hybb.index)
+
+hybb.columns = ["HYBB"]
+hybb = hybb.pct_change().reset_index(level=0, drop=True)
+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,
+)
+hy_returns = df["price_return"].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"]