def test_annual_return():
"""
Test for the annual return calculation.
:return:
"""
data = pd.DataFrame({
'date':
['2020-10-03 12:09:07', '2020-10-04 16:10:05', '2020-10-05 12:03:00'],
'pnl': [1000, 1050, -787]
})
result = dash_app.pnl_summary(data)
ann_return = str(
round((1000 + 1050 - 787) / TOTAL_CAPITAL / (3 / 365) * 100, 2)) + '%'
assert ann_return == result['Value'].iloc[0]
def test_max_dropdown():
"""
Test for the Max Dropdown calculation.
:return:
"""
data = pd.DataFrame({
'date': [
'2020-10-04 12:09:07', '2020-10-05 16:10:05',
'2020-10-06 12:01:00', '2020-10-07 17:00:00'
],
'pnl': [1000, 900, 750, -1500]
})
result = dash_app.pnl_summary(data)
md = str((1000 - (-1500)) / np.max(data['pnl']))
assert md == result['Value'].iloc[4]
def test_kurtosis():
"""
Test for the kurtosis calculation.
:return:
"""
data = pd.DataFrame({
'date': [
'2020-10-04 12:09:07', '2020-10-05 16:10:05',
'2020-10-06 12:01:00', '2020-10-07 17:00:00'
],
'pnl': [1000, 900, 750, -1500]
})
result = dash_app.pnl_summary(data)
kui = str(round(data['pnl'].kurtosis(), 2))
assert kui == result['Value'].iloc[6]
def test_cumulative_return():
"""
Test for the cumulative return calculation.
:return:
"""
data = pd.DataFrame({
'date': [
'2020-10-04 12:09:07', '2020-10-05 16:10:05',
'2020-10-06 12:01:00', '2020-10-07 17:00:00'
],
'pnl': [100000, 90000, 75000, -150000]
})
result = dash_app.pnl_summary(data)
ann_return = str(
round((
(100000 + 90000 + 75000 - 150000) / TOTAL_CAPITAL) * 100, 2)) + '%'
assert ann_return == result['Value'].iloc[1]
def test_sharpe_ratio():
"""
Test for the Sharpe Ratio calculation.
:return:
"""
data = pd.DataFrame({
'date': [
'2020-10-04 12:09:07', '2020-10-05 16:10:05',
'2020-10-06 12:01:00', '2020-10-07 17:00:00'
],
'pnl': [1000, -800, 950, 600]
})
cum_shift = data['pnl']
std = np.std(cum_shift, ddof=1)
mean = cum_shift.mean()
result = dash_app.pnl_summary(data)
sr = str(round(mean / std * np.sqrt(365), 2))
assert sr == result['Value'].iloc[3]
def test_annual_volatility():
"""
Test for the annual return calculation.
:return:
"""
data = pd.DataFrame({
'date': [
'2020-10-04 12:09:07', '2020-10-04 12:09:07',
'2020-10-05 16:10:05', '2020-10-06 12:01:00', '2020-10-07 17:00:00'
],
'pnl': [0, 1000, 900, 750, -1500]
})
std = np.std([
1000 / TOTAL_CAPITAL, 900 / TOTAL_CAPITAL, 750 / TOTAL_CAPITAL,
-1500 / TOTAL_CAPITAL
],
ddof=1)
result = dash_app.pnl_summary(data)
volatility = str(round(std * np.sqrt(365), 2))
assert volatility == result['Value'].iloc[2]