1 files changed, 59 insertions, 21 deletions
diff --git a/python/bowdst.py b/python/bowdst.py
index f7fab0cc..51ed0a2b 100644
--- a/python/bowdst.py
+++ b/python/bowdst.py
@@ -29,32 +29,70 @@ def download_messages(em):
                     if not p.exists():
                         p.write_bytes(attach.content)
 
+
 def load_report(workdate: datetime.date):
 
     p = DAILY_DIR / str(workdate) / "Reports" / f"Asset Detail_{workdate:%d %b %Y}.csv"
-    df = pd.read_csv(p, thousands=",", parse_dates=["As Of Date", "Maturity Date", "Report Run Date"])
-    df = df.drop([
-        "Reporting Account Number", "Reporting Account Name",
-        "Source Account Name", "Xref Security ID", "Country Name",
-        "Country Code", "Local Currency Name", "Acct Base Currency Name",
-        "Acct Base Currency Code", "CINS", "Issuer ID", "SEDOL", "Valoren", "Sicovam",
-	"WPK", "Quick", "Underlying Sec ID", "Loan ID", "Manager",
-        "Book Yield Value",
-        "Counterparty", "Ticker with Exchange Code",
-	"Ticker with Yellow Key", "Acctg Status Update (EDT)",
-	"Accounting Status", "Primary GSP Account",
-        "Extended GSP Account Number", "Percent Of Total"], axis=1)
+    df = pd.read_csv(
+        p, thousands=",", parse_dates=["As Of Date", "Maturity Date", "Report Run Date"]
+    )
+    df = df.drop(
+        [
+            "Reporting Account Number",
+            "Reporting Account Name",
+            "Source Account Name",
+            "Xref Security ID",
+            "Country Name",
+            "Country Code",
+            "Local Currency Name",
+            "Acct Base Currency Name",
+            "Acct Base Currency Code",
+            "CINS",
+            "Issuer ID",
+            "SEDOL",
+            "Valoren",
+            "Sicovam",
+            "WPK",
+            "Quick",
+            "Underlying Sec ID",
+            "Loan ID",
+            "Manager",
+            "Book Yield Value",
+            "Counterparty",
+            "Ticker with Exchange Code",
+            "Ticker with Yellow Key",
+            "Acctg Status Update (EDT)",
+            "Accounting Status",
+            "Primary GSP Account",
+            "Extended GSP Account Number",
+            "Percent Of Total",
+        ],
+        axis=1,
+    )
     df["Source Account Number"] = df["Source Account Number"].str[-4:].astype("int")
     df.columns = df.columns.str.replace(" ", "_").str.lower()
-    df = df.rename(columns={
-        "shares/par": "current_notional",
-        "local_unrealized_gain/loss": "local_unrealized_pnl",
-        "base_unrealized_gain/loss": "base_unrealized_pnl"})
-    for col in ['current_notional', 'local_price', 'base_price', 'local_cost',
-                'base_cost',
-                'local_market_value', 'base_market_value', 'local_unrealized_pnl',
-                'base_unrealized_pnl', 'local_notional_cost',
-                'base_notional_cost', 'local_notional_value', 'base_notional_value']:
+    df = df.rename(
+        columns={
+            "shares/par": "current_notional",
+            "local_unrealized_gain/loss": "local_unrealized_pnl",
+            "base_unrealized_gain/loss": "base_unrealized_pnl",
+        }
+    )
+    for col in [
+        "current_notional",
+        "local_price",
+        "base_price",
+        "local_cost",
+        "base_cost",
+        "local_market_value",
+        "base_market_value",
+        "local_unrealized_pnl",
+        "base_unrealized_pnl",
+        "local_notional_cost",
+        "base_notional_cost",
+        "local_notional_value",
+        "base_notional_value",
+    ]:
         df[col] = df[col].apply(lambda s: s[1:-1] if s.startswith("(") else s)
         df[col] = pd.to_numeric(df[col].str.replace(",", ""))
     df["row"] = df.index