def process_args(api, args):
df = lpt.read_csv(args.filename)
date = lpt.to_date(args.date)
scope = args.scope
c1 = df.columns.values[0] # First column is the instrument
if args.fx:
scope = scope + "_FX"
df[c1] = "CCY_" + df[c1]
df["column3"] = None
else:
mi.map_instruments(api, df, c1)
# fix the column names
df.columns = ["instrument", "price", "ccy"]
def upsert_analytics(result=None):
analytics = [
api.models.InstrumentAnalytic(row["instrument"], row["price"],
row["ccy"])
for i, row in df.iterrows()
]
return api.call.set_analytics(scope, date.year, date.month, date.day,
analytics).bind(lambda r: None)
if args.update:
return upsert_analytics()
return api.call.create_analytic_store(
api.models.CreateAnalyticStoreRequest(scope,
date)).bind(upsert_analytics)
def transactions(r=None):
if args.transactions:
df = lpt.read_input(args.transactions)
if args.map:
mi.map_instruments(api, df, "instrument_uid"),
def load_transactions(portfolio, txns):
return api.call.upsert_transactions(
args.scope,
portfolio,
transactions=api.from_df(txns, api.models.TransactionRequest),
)
if args.portfolio.lower().startswith("col:"):
# Multiple portfolios contained in the file. Read the ID from the columns
portfolio_column = args.portfolio[4:]
def load_groups(iterator):
try:
portfolio, df = next(iterator)
print("Transactions: {}".format(portfolio))
return load_transactions(
portfolio, df.drop(portfolio_column, axis=1)
).bind(lambda r: load_groups(iterator))
except StopIteration:
return Either.Right(None)
return load_groups(iter(df.groupby(portfolio_column)))
else:
# one-off load. The portfolio id is provided
return load_transactions(args.portfolio, df)
return Either.Right(None)