diff options
Diffstat (limited to 'python/risk')
| -rw-r--r-- | python/risk/portfolio.py | 146 |
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 --- /dev/null +++ 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 |