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diff --git a/python/exploration/beta_trade.py b/python/exploration/beta_trade.py
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+import math
+import pandas as pd
+import feather
+from index_data import index_returns
+from arch import arch_model
+from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
+
+returns = index_returns(index=['IG', 'HY'], tenor='5yr')
+returns = (returns.
+           reset_index(level='series').
+           groupby(level=['date','index']).
+           nth(-1)) ## lastest returns
+
+returns = (returns.
+           set_index('series', append=True)['price_return'].
+           unstack(level='index'))
+returns.columns = [col.lower() for col in returns.columns]
+feather.write_dataframe(returns.reset_index(), "/home/share/CorpCDOs/data/index_returns.fth")
+
+# three ways of computing the volatility
+# 20 days simple moving average
+vol_sma = returns.hy.rolling(20).std() * math.sqrt(252)
+vol_ewma = returns.hy.ewm(span=20).std() * math.sqrt(252)
+# GARCH(1,1)
+# we scale returns by 10 to help with the fitting
+scale = 10
+am = arch_model(scale * returns.hy.dropna())
+res = am.fit()
+vol_garch = res.conditional_volatility * math.sqrt(252)/scale
+vol = pd.concat([vol_sma, vol_ewma, vol_garch], axis=1)
+
+## let's get the betas
+beta_ewma = (returns.
+             ewm(span=20).
+             cov().
+             apply(lambda df:df.at['ig', 'hy']/df.at['ig', 'ig'],
+                   ('minor', 'major')))
+
+resids = returns.ig-beta_ewma*returns.hy
+
+# feather.write_dataframe(beta_ewma.reset_index(),
+#                         "/home/share/CorpCDOs/data/beta.fth")