def test_performance(client, data):
d = post(client=client, data=data, url="/engine/performance").data.decode()
x = pd.read_json(d, typ="series")
assert x.to_dict() == {
'Return': '-28.27%',
'# Events': '602',
'# Events per year': '261',
'Annua Return': '-14.40%',
'Annua Volatility': '18.01%',
'Annua Sharpe Ratio (r_f = 0)': '-0.80',
'Max Drawdown': '32.61%',
'Max % return': '4.07%',
'Min % return': '-9.11%',
'MTD': '1.43%',
'YTD': '1.33%',
'Current Nav': '1200.59',
'Max Nav': '1692.70',
'Current Drawdown': '29.07%',
'Calmar Ratio (3Y)': '-0.44',
'# Positive Events': '286',
'# Negative Events': '316',
'Value at Risk (alpha = 95)': '1.91%',
'Conditional Value at Risk (alpha = 95)': '2.76%',
'First at': '2013-01-01',
'Last at': '2015-04-22',
'Kurtosis': '8.75'
}
def test_volatility(client, data, price):
d = json.loads(
post(client=client, data=data, url="/engine/volatility").data.decode())
x = parse(d)
pt.assert_series_equal(from_nav(price).ewm_volatility().dropna(),
x,
check_names=False)
def test_month(client, data, price):
d = post(client=client, data=data, url="/engine/month").data.decode()
x = pd.read_json(d, orient="table")
pt.assert_frame_equal(
x,
from_nav(price).monthlytable.applymap(
lambda x: "{0:.2f}%".format(float(100.0 * x)).replace("nan%", "")),
check_names=False)
def test_post(client):
data = post(client, url="/post", data={}).data
assert data.decode() == "Hello Thomas!"
def test_drawdown(client, data, price):
d = json.loads(
post(client=client, data=data, url="/engine/drawdown").data.decode())
x = parse(d)
pt.assert_series_equal(from_nav(price).drawdown, x, check_names=False)