import csv import datetime import logging import numpy as np import pandas as pd import os from collections import defaultdict from itertools import chain from pandas.tseries.offsets import BDay logger = logging.getLogger(__name__) def convert(x): try: return float(x[:-1]) except ValueError: return None def get_index_list(database, workdate): with database.cursor() as c: c.execute( "SELECT distinct index, series FROM index_maturity " "WHERE issue_date IS NOT NULL and issue_date <= %s + 10 " "AND maturity >= %s", (workdate, workdate), ) for index, series in c: yield index + str(series) database.commit() DOC_CLAUSE_MAPPING14 = { "Full Restructuring": "MM14", "No Restructuring": "XR14", "Modified Modified Restructurin": "MM14", } DOC_CLAUSE_MAPPING = { "Full Restructuring": "MM", "No Restructuring": "XR", "Modified Modified Restructurin": "MM", } def get_markit_bbg_mapping(database, basketid_list, workdate): markit_bbg_mapping = defaultdict(set) all_tickers = set([]) with database.cursor() as c: c.execute( "SELECT markit_ticker, markit_tier, spread, currency, cds_curve, " " short_code FROM historical_cds_issuers(%s) " "JOIN basket_constituents USING (company_id, seniority) " "WHERE basketid=ANY(%s)", (workdate, list(basketid_list)), ) for line in c: all_tickers.add((line.markit_ticker, line.markit_tier)) key = ( line.markit_ticker, line.markit_tier, line.currency, line.short_code, float(line.spread) / 10000, ) ## each markit ticker can be mapped to multiple bbg tickers ## these bbg tickers can have different curves (ok) ## or same curves (not ok since date, curve_ticker needs to be unique) ## therefore we keep them in a set structure markit_bbg_mapping[key].add(tuple(line.cds_curve)) database.commit() return (all_tickers, markit_bbg_mapping) def get_bbg_tickers(database, basketid_list, workdate): with database.cursor() as c: c.execute( "SELECT distinct cds_curve FROM historical_cds_issuers(%s) " "JOIN basket_constituents USING(company_id, seniority) " "WHERE basketid=ANY(%s)", (workdate, list(basketid_list)), ) yield from chain.from_iterable(e[0] for e in c) database.commit() def get_basketids(database, index_list, workdate): with database.cursor() as c: for index in index_list: c.execute("SELECT * FROM nameToBasketID(%s, %s)", (index, workdate)) yield c.fetchone()[0] database.commit() def get_current_tickers(database, workdate): index_list = get_index_list(database, workdate) basketid_list = get_basketids(database, index_list, workdate) return get_markit_bbg_mapping(database, basketid_list, workdate) def insert_cds(database, workdate): """insert Markit index quotes into the database :param database: psycopg2 connection to the database. :param workdate: """ all_tickers, markit_bbg_mapping = get_current_tickers(database, workdate) filename = "cds eod {0:%Y%m%d}.csv".format(workdate) colnames = [ "Upfront" + tenor for tenor in ["6m", "1y", "2y", "3y", "4y", "5y", "7y", "10y"] ] sqlstr = ( "INSERT INTO cds_quotes(date, curve_ticker, upfrontbid, upfrontask," "runningbid, runningask, source, recovery) VALUES(%s, %s, %s, %s, %s, %s, %s, %s) " "ON CONFLICT DO NOTHING" ) tickers_found = set() with open( os.path.join(os.environ["BASE_DIR"], "Tranche_data", "CDS", filename) ) as fh: csvreader = csv.DictReader(fh) with database.cursor() as c: for line in csvreader: spread = float(line["RunningCoupon"]) k = ( line["Ticker"], line["Tier"], line["Ccy"], line["DocClause"], spread, ) if k in markit_bbg_mapping: for curves in markit_bbg_mapping[k]: c.executemany( sqlstr, [ ( workdate, t, convert(line[col]), convert(line[col]), spread * 10000, spread * 10000, "MKIT", convert(line["RealRecovery"]) / 100, ) for col, t in zip(colnames, curves) ], ) tickers_found.add((line["Ticker"], line["Tier"])) database.commit() logger.warning("missing_quotes for {0}".format(all_tickers - tickers_found)) def get_date(f): with open(f) as fh: next(fh) next(fh) next(fh) date = next(fh).split(",", 1)[0][1:-1] return datetime.datetime.strptime(date, "%d-%b-%y").date() def insert_index(engine, workdate=None): """insert Markit index quotes into the database :param engine: sqlalchemy engine to the database :param workdate: date. If None, we will try to reinsert all files """ basedir = os.path.join(os.environ["BASE_DIR"], "Tranche_data", "Composite_reports") filenames = [ os.path.join(basedir, f) for f in os.listdir(basedir) if "Indices" in f ] name_mapping = { "CDXNAHY": "HY", "CDXNAIG": "IG", "iTraxx Eur": "EU", "iTraxx Eur Xover": "XO", } cols = ["close_price", "close_spread", "model_price", "model_spread"] colmapping = { "Date": "date", "Name": "index", "Series": "series", "Version": "version", "Term": "tenor", "Composite Price": "close_price", "Composite Spread": "close_spread", "Model Price": "model_price", "Model Spread": "model_spread", } ext_cols = ["date", "index", "series", "version", "tenor"] + cols + ["source"] dates_to_files = {} for f in filenames: d = get_date(f) if d in dates_to_files: dates_to_files[d].append(f) else: dates_to_files[d] = [f] if workdate is None: filenames = dates_to_files[max(dates_to_files.keys())] else: filenames = dates_to_files[workdate] for f in filenames: data = pd.read_csv(f, skiprows=2, parse_dates=[0, 7], engine="python") data = data.rename(columns=colmapping) data.dropna(subset=["close_price"], inplace=True) for col in cols: data[col] = data[col].str.replace("%", "").astype("float") data["tenor"] = data["tenor"].apply(lambda x: x.lower() + "r") data["index"] = data["index"].apply( lambda x: name_mapping[x] if x in name_mapping else np.NaN ) data = data.dropna(subset=["index"]) data["close_spread"] *= 100 data["model_spread"] *= 100 ## we renumbered the version for HY9, 10 and 11 data.loc[data.series.isin([9, 10, 11]) & (data.index == "HY"), "version"] -= 3 # data = data.groupby(['index', 'series', 'tenor', 'date'], as_index=False).last() data["source"] = "MKIT" data[ext_cols].to_sql( "index_quotes_pre", engine, if_exists="append", index=False ) def insert_tranche(engine, workdate=None): """insert Markit index quotes into the database :param engine: sqlalchemy engine to the database :param workdate: If None, we will try to reinsert all files :type workdate: pd.Timestamp """ basedir = os.path.join(os.environ["BASE_DIR"], "Tranche_data", "Composite_reports") filenames = [ os.path.join(basedir, f) for f in os.listdir(basedir) if f.startswith("Tranche Composites") ] index_version = pd.read_sql_table("index_version", engine, index_col="redindexcode") for f in filenames: if ( workdate is None or datetime.datetime.fromtimestamp(os.path.getmtime(f)).date() == (workdate + BDay(1)).date() ): df = pd.read_csv(f, skiprows=2, parse_dates=["Date"]) df.rename( columns={ "Date": "quotedate", "Index Term": "tenor", "Attachment": "attach", "Detachment": "detach", "Tranche Upfront Bid": "upfront_bid", "Tranche Upfront Mid": "upfront_mid", "Tranche Upfront Ask": "upfront_ask", "Index Price Mid": "index_price", "Tranche Spread Mid": "tranche_spread", "Red Code": "redindexcode", }, inplace=True, ) df.attach = df.attach * 100 df.detach = df.detach * 100 df.tranche_spread = df.tranche_spread * 10000 df.tenor = df.tenor.str.lower() + "r" df.set_index("redindexcode", inplace=True) df = df.join(index_version) df = df.filter( [ "basketid", "quotedate", "tenor", "attach", "detach", "upfront_bid", "upfront_ask", "upfront_mid", "tranche_spread", "index_price", ] ) df.to_sql("markit_tranche_quotes", engine, if_exists="append", index=False)