1 files changed, 0 insertions, 51 deletions
diff --git a/python/insert_composite_quotes.py b/python/insert_composite_quotes.py
deleted file mode 100644
index 8d82bc32..00000000
--- a/python/insert_composite_quotes.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from sqlalchemy import Table, create_engine, MetaData
-import os
-import csv
-import datetime
-
-##script to load quotes from markit composite quotes csv file
-engine = create_engine('postgresql://serenitas_user@debian/serenitasdb')
-metadata = MetaData(bind = engine)
-quotes = Table('tranche_quotes', metadata, autoload = True)
-ins = quotes.insert()
-
-root_dir = '/home/share/CorpCDOs'
-
-def convert(x):
-    try:
-        return float(x)
-    except:
-        return None
-
-tenordict = {'3Y': '3yr', '5Y': '5yr', '7Y':'7yr', '10Y':'10yr'}
-
-with open(os.path.join(root_dir, 'Tranche_data', 'CDXNAIG Tranches.csv')) as fh:
-    reader = csv.DictReader(fh)
-    data = []
-    for csvdict in reader:
-        timestamp =  datetime.datetime.strptime(csvdict['Date'], "%m/%d/%Y")
-        if timestamp==datetime.datetime(2010, 7, 5):
-            continue
-        attach = int(float(csvdict['Attachment'])*100)
-        detach = int(float(csvdict['Detachment'])*100)
-        series = int(csvdict['Index Series'])
-        version = int(csvdict['Index Version'])
-
-        d = {'quotedate' : timestamp,
-             'indexrefprice': float(csvdict['Index Price Mid'])*100,
-             'indexrefspread': 80 if series==9 else 100,
-             'quotesource' : 'MKIT',
-             'trancheupfront' : convert(csvdict['Tranche Upfront Mid']),
-             'trancherunning' : convert(csvdict['Tranche Spread Mid']),
-             'tenor' : tenordict[csvdict['Index Term']],
-             'index' : 'IG',
-             'series': series,
-             'version': version,
-             'attach': attach,
-             'detach': detach,
-             'corratdetachment': convert(csvdict['Base Correlation'])
-             }
-        data.append(d)
-
-with engine.begin() as conn:
-    conn.execute(ins, data)