diff options
Diffstat (limited to 'python/calibrate_tranches_BC.py')
| -rw-r--r-- | python/calibrate_tranches_BC.py | 184 |
1 files changed, 94 insertions, 90 deletions
diff --git a/python/calibrate_tranches_BC.py b/python/calibrate_tranches_BC.py index 3da19a69..7ea71277 100644 --- a/python/calibrate_tranches_BC.py +++ b/python/calibrate_tranches_BC.py @@ -36,7 +36,7 @@ def build_sql_str(df, use_markit=False): if __name__ == "__main__": from serenitas.utils import SerenitasFileHandler - from serenitas.utils.db import dbconn + from serenitas.utils.db2 import serenitas_pool from serenitas.utils.env import CONFIG_DIR logger = logging.getLogger("tranche_calib") @@ -128,102 +128,106 @@ if __name__ == "__main__": "eu36": datetime.date(2021, 9, 24), } - serenitas_conn = dbconn("serenitasdb") - if args.config is None: - if args.index is None: - raise ValueError("Please provide an index to run") - config = {"runs": [(args.index, args.tenor, args.skewtype)]} - else: - with (CONFIG_DIR / args.config).open("r") as fh: - config = full_load(fh) - - for index, tenor, skewtype in config["runs"]: - begin_date = None - index, series = index[:2].upper(), int(index[2:]) - if args.start_from is not None: - begin_date = args.start_from - if args.update: - begin_date = get_lastdate(serenitas_conn, index, series, tenor) - if begin_date is None: - continue - if not args.update and begin_date is None: - try: - begin_date = start_dates[f"{index.lower()}{series}"] - except KeyError: - print(index, series) - continue + with serenitas_pool.connection() as serenitas_conn: + if args.config is None: + if args.index is None: + raise ValueError("Please provide an index to run") + config = {"runs": [(args.index, args.tenor, args.skewtype)]} + else: + with (CONFIG_DIR / args.config).open("r") as fh: + config = full_load(fh) - dr = pd.bdate_range(begin_date, args.until) - if dr.empty: - continue - logger.info(f"calibrating {index}, {series}, {tenor}") - tranche_index = None + for index, tenor, skewtype in config["runs"]: + begin_date = None + index, series = index[:2].upper(), int(index[2:]) + if args.start_from is not None: + begin_date = args.start_from + if args.update: + begin_date = get_lastdate(serenitas_conn, index, series, tenor) + if begin_date is None: + continue + if not args.update and begin_date is None: + try: + begin_date = start_dates[f"{index.lower()}{series}"] + except KeyError: + print(index, series) + continue - data = {} - for d in dr.date: - logger.debug(f"calibrating for {d}") - try: - if tranche_index is None: - tranche_index = TrancheBasket(index, series, tenor, value_date=d) - else: - tranche_index.value_date = d - except (RuntimeError, ValueError) as e: - logger.error(e) + dr = pd.bdate_range(begin_date, args.until) + if dr.empty: continue + logger.info(f"calibrating {index}, {series}, {tenor}") + tranche_index = None - try: - tranche_index.tweak() - except ValueError as e: - logger.error(e) - break - try: - tranche_index.build_skew(skewtype) - except ValueError as e: - logger.error(e) - logger.debug("Trying topdown") - tranche_index.rho[:] = np.nan + data = {} + for d in dr.date: + logger.debug(f"calibrating for {d}") try: - tranche_index.build_skew("topdown") - except ValueError: + if tranche_index is None: + tranche_index = TrancheBasket( + index, series, tenor, value_date=d + ) + else: + tranche_index.value_date = d + except (RuntimeError, ValueError) as e: logger.error(e) continue - df = pd.concat( - [ - tranche_index.tranche_deltas(), - tranche_index.tranche_fwd_deltas(), - tranche_index.tranche_durations(), - tranche_index.tranche_EL(), - tranche_index.tranche_spreads(), - ], - axis=1, - ) - try: - df["theta"] = tranche_index.tranche_thetas(method="TLP") - except ValueError: - df["theta"] = None + try: + tranche_index.tweak() + except ValueError as e: + logger.error(e) + break + try: + tranche_index.build_skew(skewtype) + except ValueError as e: + logger.error(e) + logger.debug("Trying topdown") + tranche_index.rho[:] = np.nan + try: + tranche_index.build_skew("topdown") + except ValueError: + logger.error(e) + continue + + df = pd.concat( + [ + tranche_index.tranche_deltas(), + tranche_index.tranche_fwd_deltas(), + tranche_index.tranche_durations(), + tranche_index.tranche_EL(), + tranche_index.tranche_spreads(), + ], + axis=1, + ) + try: + df["theta"] = tranche_index.tranche_thetas(method="TLP") + except ValueError: + df["theta"] = None - ( - df["index_duration"], - df["index_expected_loss"], - df["index_price"], - ) = tranche_index.index_pv(clean=True) - df["index_expected_loss"] *= -1 - df["index_basis"] = tranche_index.tweaks[0] - df["index_theta"] = tranche_index.theta()[tenor] - df["tranche_id"] = tranche_index.tranche_quotes.id.values - df["corr_at_detach"] = tranche_index.rho[1:] - df["corr01"] = tranche_index.tranche_corr01() - del df["fwd_gamma"] - df["quote_price"] = ( - 1 - tranche_index.tranche_quotes.quotes.values - tranche_index._accrued - ) - df["calibrated_price"] = tranche_index.tranche_pvs().bond_price - data[d] = df + ( + df["index_duration"], + df["index_expected_loss"], + df["index_price"], + ) = tranche_index.index_pv(clean=True) + df["index_expected_loss"] *= -1 + df["index_basis"] = tranche_index.tweaks[0] + df["index_theta"] = tranche_index.theta()[tenor] + df["tranche_id"] = tranche_index.tranche_quotes.id.values + df["corr_at_detach"] = tranche_index.rho[1:] + df["corr01"] = tranche_index.tranche_corr01() + del df["fwd_gamma"] + df["quote_price"] = ( + 1 + - tranche_index.tranche_quotes.quotes.values + - tranche_index._accrued + ) + df["calibrated_price"] = tranche_index.tranche_pvs().bond_price + data[d] = df - if data: - data = pd.concat(data) - sql_str = build_sql_str(data, args.markit) - with serenitas_conn.cursor() as c: - c.executemany(sql_str, data.to_dict(orient="records")) - serenitas_conn.commit() + if data: + data = pd.concat(data) + sql_str = build_sql_str(data, args.markit) + with serenitas_conn.cursor() as c: + c.executemany(sql_str, data.to_dict(orient="records")) + serenitas_conn.commit() |