diff options
Diffstat (limited to 'python/exploration/beta_trade.py')
| -rw-r--r-- | python/exploration/beta_trade.py | 41 |
1 files changed, 16 insertions, 25 deletions
diff --git a/python/exploration/beta_trade.py b/python/exploration/beta_trade.py index 8f6825ef..b26eeedb 100644 --- a/python/exploration/beta_trade.py +++ b/python/exploration/beta_trade.py @@ -11,36 +11,27 @@ from statsmodels.tsa.ar_model import AR import matplotlib.pyplot as plt -def calc_returns(save_feather=False, index_pair=['HY','IG']): - returns = (index_returns(index=['EU','IG', 'HY'],tenor='5yr',years=None). - reset_index(['tenor','series'],drop=True)) - returns1 = {} - returns1[index_pair[0]] = (returns. - xs(index_pair[0], level=1). - dropna(). - groupby(level=['date'], as_index=False). - nth(-1)) - returns1[index_pair[1]] = returns.xs(index_pair[1], level=1) - df = pd.merge(returns1[index_pair[0]],returns1[index_pair[1]], - left_index=True,right_index=True, - suffixes=('_'+index_pair[0],'_'+index_pair[1])) - returns = df[['price_return_'+index_pair[0], 'price_return_'+index_pair[1]]] - returns.columns = [index_pair[0], index_pair[1]] - min_date = max(pd.Timestamp('20090320'),returns.index.min()[0]) - returns = returns.dropna()[min_date:] +def calc_returns(index_list=['HY', 'IG'], save_feather=False): + returns = (index_returns(index=index_list, tenor='5yr', years=None). + reset_index(['tenor','series'], drop=True). + set_index(['date', 'maturity'], append=True). + swaplevel(1, 0)) + returns = returns.unstack(level='index').dropna() + returns = returns.groupby(level='date').nth(-1)['price_return'] if save_feather: feather.write_dataframe(returns.reset_index(), os.path.join(os.environ["DATA_DIR"], "index_returns.fth")) - return returns.reset_index('maturity', drop=True) + return returns -def calc_betas(returns=None, spans=[5, 20], index_pair=['HY','IG']): +def calc_betas(returns=None, spans=[5, 20], index_list=['HY', 'IG']): if returns is None: - returns = calc_returns(index_pair=index_pair) - return [(returns. - ewm(span=span). - cov(). - groupby(level='date'). - apply(lambda df: df.values[0,1]/df.values[1,1])) for span in spans] + returns = calc_returns(index_list=index_list) + + return pd.concat([(returns. + ewm(span=span). + cov(). + groupby(level='date'). + apply(lambda df: df / np.diag(df))) for span in spans], axis=1, keys=spans) def plot_betas(betas=None): spans = [5, 20] |