def test_get_economy_calendar_events_no_response(mocker):
# MOCK GET
mock_response = requests.Response()
mock_response.status_code = 400
mocker.patch(target="requests.get",
new=mocker.Mock(return_value=mock_response))
result_df = finnhub_model.get_economy_calendar_events()
assert isinstance(result_df, pd.DataFrame)
assert result_df.empty
def test_get_economy_calendar_events_no_response(mocker):
# MOCK GET
attrs = {
"json.return_value": {"error": "mock error message"},
}
mock_response = mocker.Mock(**attrs)
mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response))
result_df = finnhub_model.get_economy_calendar_events()
assert isinstance(result_df, pd.DataFrame)
assert result_df.empty
def test_get_economy_calendar_events(mocker):
# MOCK JSON
mock_json = pd.DataFrame()
mock_json["economicCalendar"] = ["MOCK_ROW_1", "MOCK_ROW_2"]
# MOCK GET
attrs = {
"status_code": 200,
"json.return_value": mock_json,
}
mock_response = mocker.Mock(**attrs)
mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response))
result_df = finnhub_model.get_economy_calendar_events()
assert not result_df.empty
def economy_calendar_events(country: str, num: int, impact: str, export: str):
"""Output economy calendar impact events. [Source: Finnhub]
Parameters
----------
country : str
Country from where to get economy calendar impact events
num : int
Number economy calendar impact events to display
impact : str
Impact of the economy event
export : str
Export dataframe data to csv,json,xlsx file
"""
df_events = finnhub_model.get_economy_calendar_events()
if df_events.empty:
print("No latest economy calendar events found\n")
return
df_econ_calendar = df_events[df_events["country"] == country].sort_values(
"time", ascending=True)
if df_econ_calendar.empty:
print(
"No latest economy calendar events found in the specified country\n"
)
return
if impact != "all":
df_econ_calendar = df_econ_calendar[df_econ_calendar["impact"] ==
impact]
if df_econ_calendar.empty:
print(
"No latet economy calendar events found in the specified country with this impact\n"
)
return
df_econ_calendar = df_econ_calendar.fillna("").head(n=num)
d_econ_calendar_map = {
"actual": "Actual release",
"prev": "Previous release",
"country": "Country",
"unit": "Unit",
"estimate": "Estimate",
"event": "Event",
"impact": "Impact Level",
"time": "Release time",
}
df_econ_calendar = df_econ_calendar[[
"time", "event", "impact", "prev", "estimate", "actual", "unit"
]].rename(columns=d_econ_calendar_map)
df_econ_calendar.replace("", float("NaN"), inplace=True)
df_econ_calendar.dropna(how="all", axis=1, inplace=True)
print(
tabulate(
df_econ_calendar,
headers=df_econ_calendar.columns,
showindex=False,
floatfmt=".2f",
tablefmt="fancy_grid",
))
print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"events",
df_econ_calendar,
)