def test_calc_annual_return(self):
historical_data = {"Adj Close": [3.0, 3.5, 4.2], "Close": [3.0, 5.0, 4.2]}
historical_index = pd.date_range("2012-01-01", periods=3, freq='2D')
metric = Metric(historical=pd.DataFrame(historical_data, index=historical_index))
annual_return1 = metric.calc_annual_return(pd.Timestamp("2012-01-02"), pd.Timestamp("2012-01-04"))
annual_return2 = metric.calc_annual_return(pd.Timestamp("2012-01-02"), pd.Timestamp("2012-01-04"), "Close")
self.assertAlmostEqual(annual_return1, 36.5)
self.assertAlmostEqual(annual_return2, -29.2)
def test_calc_debt_to_asset_ratio(self):
balance_sheet_data = {
"Total Liabilities / Total Assets": [0.69, 0.68, 0.67]
}
balance_sheet_index = pd.to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"])
metric = Metric(
balance_sheet=pd.DataFrame(balance_sheet_data, index=balance_sheet_index),
financial_report_preparation_lag=np.timedelta64(1, 'D')
)
debt_ratio = metric.calc_debt_to_asset_ratio()
# because of 1 day financial_report_preparation_lag
expected_index = pd.to_datetime(["2012-01-02", "2012-01-03", "2012-01-04"])
self.assertFrameEqual(debt_ratio, pd.Series([0.69, 0.68, 0.67], index=expected_index))
def test_calc_profit_margin(self):
income_statement_data = {
"Gross Income": [62, 50, 36],
"Sales/Revenue": [3.1, 2.0, 1.2]
}
income_statement_index = pd.to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"])
metric = Metric(
income_statement=pd.DataFrame(income_statement_data, index=income_statement_index),
financial_report_preparation_lag=np.timedelta64(1, 'D')
)
gross_profit_margin = metric.calc_profit_margin("gross")
# because of 1 day financial_report_preparation_lag
expected_index = pd.to_datetime(["2012-01-02", "2012-01-03", "2012-01-04"])
self.assertFrameEqual(gross_profit_margin, pd.Series([20, 25, 30], index=expected_index), check_dtype=False)
def test_at(self):
# make up some fake data for testing attribute transfer
data = lambda: {'d': np.random.randn(3)}
index = pd.date_range("2012-01-01", periods=3)
metric = Metric(
historical=pd.DataFrame(data(), index=index),
balance_sheet=pd.DataFrame(data(), index=index),
income_statement=pd.DataFrame(data(), index=index),
cash_flow=pd.DataFrame(data(), index=index)
)
date = pd.Timestamp("2012-12-21")
obj = metric.at(date)
self.assertEqual(obj.date, date)
other_attributes = {k: v for k, v in vars(obj).items() if k != "date"}
self.assertEqual(other_attributes, vars(metric))
self.assertIsInstance(obj, DatedMetric)
def test_calc_pe_ratio(self):
historical_data = {"Adj Close": [3.0, 3.5, 4.2], "Close": [3.0, 5.0, 4.2]}
historical_index = pd.to_datetime(["2012-01-01", "2012-01-03", "2012-01-05"])
income_statement_data = {"EPS (Basic)": [1.2, 2.5, 3.5], "EPS (Diluted)": [6.0, 4.0, 8.0]}
income_statement_index = pd.to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"])
metric = Metric(
historical=pd.DataFrame(historical_data, index=historical_index),
income_statement=pd.DataFrame(income_statement_data, index=income_statement_index),
financial_report_preparation_lag=np.timedelta64(1, 'D')
)
pe_ratios1 = metric.calc_pe_ratio()
pe_ratios2 = metric.calc_pe_ratio("Close", "EPS (Diluted)")
# because of 1 day financial_report_preparation_lag
expected_index = pd.to_datetime(["2012-01-02", "2012-01-03", "2012-01-04"])
self.assertFrameEqual(pe_ratios1, pd.Series([2.5, 1.4, 1], index=expected_index))
self.assertFrameEqual(pe_ratios2, pd.Series([0.5, 1.25, 0.625], index=expected_index))
def test_from_archive(self):
fake_load = lambda data_type, symbol: data_type[0]
with patch("fa.analysis.finance.load_fundamental_data", MagicMock(side_effect=fake_load)) as mock_load_fundamental_data, \
patch("fa.analysis.finance.Metric.__init__", MagicMock(return_value=None)) as mock_constructor:
obj = Metric.from_archive("C6L.SI", 1, b=2)
mock_load_fundamental_data.assert_has_calls([
call("price", "C6L.SI"),
call("balance_sheet", "C6L.SI"),
call("income_statement", "C6L.SI"),
call("cash_flow", "C6L.SI"),
])
mock_constructor.assert_called_once_with(1, b=2, historical='p', balance_sheet='b', cash_flow='c', income_statement='i')
self.assertIsInstance(obj, Metric)
initialize.init()
symbols = OrderedDict(
[
("C6L.SI", "Singapore Airlines"),
("J7X.SI", "Tiger Airways"),
("S53.SI", "SMRT"),
("C52.SI", "ComfortDelGro"),
("N03.SI", "Neptune Orient Lines"),
("Z74.SI", "Singtel"),
("CC3.SI", "Starhub"),
("B2F.SI", "M1"),
]
)
metrics = [Metric.from_archive(s) for s in symbols]
pe_ratios_list = [m.calc_pe_ratio() for m in metrics]
# figure 1
fig = plt.figure(figsize=(12, 10))
title = "P/E Ratio of stocks over Date\n"
xticks = reduce(lambda a, b: a | b, [ts.index for ts in pe_ratios_list])
with pd.plot_params.use("x_compat", True):
for series, label in zip(pe_ratios_list, symbols.values()):
graph = series.plot(label=label, legend=True, title=title, xticks=xticks)
graph.set_xlabel("Date")
graph.set_ylabel("P/E Ratio")
# prepare graph data