diff options
Diffstat (limited to 'python/analytics/tranche_data.py')
| -rw-r--r-- | python/analytics/tranche_data.py | 65 |
1 files changed, 37 insertions, 28 deletions
diff --git a/python/analytics/tranche_data.py b/python/analytics/tranche_data.py index f4a13a9e..e3c2de96 100644 --- a/python/analytics/tranche_data.py +++ b/python/analytics/tranche_data.py @@ -30,10 +30,10 @@ def get_tranche_quotes( op = "IN" return "{} IN %({})s".format(key, key) elif key == "from_date": - col_key = "quotedate" + col_key = "date" op = ">=" elif key == "end_date": - col_key = "quotedate" + col_key = "date" op = "<=" else: op = "=" @@ -44,8 +44,8 @@ def get_tranche_quotes( ) sql_str = ( "SELECT * from " - "(SELECT quotedate, b.index, b.series, a.tenor, b.version, " - "a.attach, a.detach, upfront_mid, a.index_price, indexfactor, " + "(SELECT quotedate as date, b.index, b.series, a.tenor, b.version, " + "a.attach, a.detach, (1-upfront_mid) as close_price, a.index_price, indexfactor, " "cumulativeloss, c.delta, a.tranche_spread " "from markit_tranche_quotes a " "left join index_version b using (basketid)" @@ -66,19 +66,14 @@ def get_tranche_quotes( df = pd.read_sql_query( sql_str, serenitas_engine, - parse_dates={"date": {"utc": True}}, - index_col=["quotedate", "index", "series", "version"], + parse_dates={"date"}, + index_col=["date", "index", "series", "version"], params=make_params(args), ) df.tenor = df.tenor.astype(tenor_t) df = df.set_index("tenor", append=True) df.sort_index(inplace=True) df = df.assign( - close_price=lambda x: x.upfront_mid - if index in ["IG", "EU"] - else 1 - x.upfront_mid - ) - df = df.assign( attach_adj=lambda x: np.maximum( (x.attach - x.cumulativeloss) / df.indexfactor, 0 ), @@ -118,27 +113,41 @@ def tranche_returns( """ if df is None: df = get_tranche_quotes(index, series, tenor, from_date, end_date, years) - - df = df.groupby(level=["quotedate", "index", "series", "tenor", "attach"]).nth(0) - g = df.groupby(level=["index", "series", "tenor", "attach"]) - tranche_return = g.close_price.shift(0) * g.adj_thickness.shift( - 0 - ) / g.adj_thickness.shift(1) - g.close_price.shift(1) - index_return = g.index_price.diff() - returns = pd.concat( - [index_return, tranche_return], axis=1, keys=["index_return", "tranche_return"] + df = df.groupby(level=["date", "index", "series", "tenor", "attach"]).nth(0) + coupon_data = pd.read_sql_query( + "SELECT index, series, tenor, coupon * 1e-4 AS coupon " + " FROM index_maturity WHERE coupon is NOT NULL", + serenitas_engine, + index_col=["index", "series", "tenor"], ) - df = df.merge(returns, left_index=True, right_index=True) + df = df.join(coupon_data) + df["date_1"] = df.index.get_level_values(level="date") - df["date"] = df.index.get_level_values(level="quotedate") - df["day_frac"] = df.groupby(level=["index", "series", "tenor", "attach"])[ - "date" - ].transform(lambda s: s.diff().astype("timedelta64[D]") / 360) - df["tranche_return"] += df.day_frac * df.tranche_spread / 10000 + # skip missing dates + returns = [] + for i, g in df.groupby(level=["index", "series", "tenor", "attach"]): + g = g.dropna() + day_frac = g["date_1"].transform( + lambda s: s.diff().astype("timedelta64[D]") / 360 + ) + tranche_return = g.close_price.shift(0) * g.adj_thickness.shift( + 0 + ) / g.adj_thickness.shift(1) - g.close_price.shift(1) + index_return = g.index_price.diff() + tranche_return += day_frac * g.tranche_spread / 10000 + index_return += day_frac * g.coupon + deladj_return = tranche_return - g.delta.shift(1) * index_return + returns.append( + pd.concat( + [index_return, tranche_return, deladj_return], + axis=1, + keys=["index_return", "tranche_return", "deladj_return"], + ) + ) - df = df.assign(deladj_return=lambda x: x.tranche_return - x.delta * x.index_return) + df = df.merge(pd.concat(returns), left_index=True, right_index=True, how="left") df = df.drop( - ["date", "day_frac", "tranche_spread", "cumulativeloss", "detach"], axis=1 + ["date_1", "tranche_spread", "cumulativeloss", "detach", "coupon"], axis=1 ) return df |