1 files changed, 146 insertions, 0 deletions
diff --git a/python/risk/portfolio.py b/python/risk/portfolio.py
new file mode 100644
index 00000000..8a9b3001
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+++ b/python/risk/portfolio.py
@@ -0,0 +1,146 @@
+import analytics
+import pandas as pd
+
+from analytics import CreditIndex, Portfolio, BlackSwaptionVolSurface
+from copy import deepcopy
+from risk.tranches import get_tranche_portfolio
+from risk.swaptions import get_swaption_portfolio
+from risk.bonds import subprime_risk, clo_risk, crt_risk
+from utils.db import dbconn, dbengine, serenitas_engine, dawn_engine
+from pandas.tseries.offsets import BDay
+
+
+def build_portf(position_date, spread_date=None):
+    """
+    Output two portfolios:
+    1) All synthetic + curve with just delta-proxy + dummy index as cash bonds proxy (portf)
+    2) All synthetic (portf_syn)
+    """
+
+    analytics._local = False
+    if spread_date is None:
+        spread_date = position_date
+
+    analytics.init_ontr(spread_date)
+    conn = dawn_engine.raw_connection()
+    conn.autocommit = True
+
+    on_the_run_index = analytics._ontr["HY"]
+    on_the_run_index.value_date = position_date
+
+    portf = get_tranche_portfolio(position_date, conn, False, "SERCGMAST")
+    s_portf = get_swaption_portfolio(position_date, conn)
+    if bool(s_portf):
+        for t, id in zip(s_portf.trades, s_portf.trade_ids):
+            portf.add_trade(t, id)
+    portf_syn = deepcopy(portf)
+
+    df = pd.read_sql_query(
+        "SELECT * from list_cds_positions_by_strat(%s)",
+        dawn_engine,
+        params=(position_date,),
+    )
+
+    if not (df.empty):
+        for t in df.itertuples(index=False):
+            portf_syn.add_trade(
+                CreditIndex(
+                    redcode=t.security_id, maturity=t.maturity, notional=t.notional
+                ),
+                (t.folder, t.security_desc),
+            )
+
+        df_no_curve = df[~df.folder.str.contains("CURVE")]
+        for t in df_no_curve.itertuples(index=False):
+            portf.add_trade(
+                CreditIndex(
+                    redcode=t.security_id, maturity=t.maturity, notional=t.notional
+                ),
+                (t.folder, t.security_desc),
+            )
+
+        # separately add in curve delta
+        df_curve = df[df["folder"].str.contains("CURVE")]
+        curve_portf = Portfolio(
+            [
+                CreditIndex(
+                    redcode=t.security_id, maturity=t.maturity, notional=t.notional
+                )
+                for t in df_curve.itertuples(index=False)
+            ]
+        )
+        curve_portf.value_date = spread_date
+        curve_portf.mark()
+
+        hyontr = deepcopy(on_the_run_index)
+        hyontr.notional = curve_portf.hy_equiv
+        portf.add_trade(hyontr, ("curve_trades", ""))
+
+    # get bond risks:
+    sql_string = (
+        "SELECT distinct timestamp::date FROM priced where normalization = 'current_notional' and model_version = 1 "
+        "and date(timestamp) <= %s and date(timestamp) >= %s order by timestamp desc"
+    )
+    with dbconn("etdb") as etconn, dbconn("dawndb") as dawnconn:
+        timestamps = pd.read_sql_query(
+            sql_string,
+            dawn_engine,
+            parse_dates=["timestamp"],
+            params=[
+                position_date,
+                position_date - pd.tseries.offsets.DateOffset(15, "D"),
+            ],
+        )
+        rmbs_pos = subprime_risk(
+            position_date,
+            dawnconn,
+            dbengine("rmbs_model"),
+            timestamps.iloc[0][0].date(),
+        )
+        clo_pos = clo_risk(position_date, dawnconn, etconn)
+        crt_pos = crt_risk(
+            position_date, dawnconn, dbengine("crt"), model_version="hpi5_ir3_btm"
+        )
+        # CRT model version changes with time, need to check
+    rmbs_notional = 0
+    for pos in [rmbs_pos, crt_pos]:
+        rmbs_notional += pos["hy_equiv"].sum() if pos is not None else 0
+    hyontr_rmbs = deepcopy(on_the_run_index)
+    hyontr_rmbs.notional = -rmbs_notional
+    portf.add_trade(hyontr_rmbs, ("rmbs_bonds", ""))
+    if isinstance(clo_pos, pd.DataFrame):
+        hyontr_clos = deepcopy(on_the_run_index)
+        hyontr_clos.notional = -clo_pos["hy_equiv"].sum()
+        portf.add_trade(hyontr_clos, ("clo_bonds", ""))
+
+    for p in [portf, portf_syn]:
+        p.value_date = spread_date
+        p.mark(interp_method="bivariate_linear")
+        p.reset_pv()
+
+    return portf, portf_syn
+
+
+def generate_vol_surface(portf, try_days_back=5):
+
+    vol_surface = {}
+    for trade in portf.swaptions:
+        try:
+            vs = BlackSwaptionVolSurface(
+                trade.index.index_type,
+                trade.index.series,
+                value_date=portf.value_date,
+                interp_method="bivariate_linear",
+            )
+        except:
+            vs = BlackSwaptionVolSurface(
+                trade.index.index_type,
+                trade.index.series,
+                value_date=portf.value_date - BDay(try_days_back),
+                interp_method="bivariate_linear",
+            )
+        vol_surface[
+            (trade.index.index_type, trade.index.series, trade.option_type)
+        ] = vs[vs.list(source="MS", option_type=trade.option_type)[-1]]
+
+    return vol_surface