def test_generate_weighted_returns(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'returns':
pd.DataFrame(
[[np.nan, np.nan, np.nan], [1.59904743, 1.66397210, 1.67345829],
[-0.37065629, -0.36541822, -0.36015840],
[-0.41055669, 0.60004777, 0.00536958]], dates, tickers),
'weights':
pd.DataFrame([[0.03777059, 0.04733924, 0.05197790],
[0.82074874, 0.48533938, 0.75792752],
[0.10196420, 0.05866016, 0.09578226],
[0.03951647, 0.40866122, 0.09431233]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('weighted_returns',
pd.DataFrame(
[[np.nan, np.nan, np.nan], [1.31241616, 0.80759119, 1.26836009],
[-0.03779367, -0.02143549, -0.03449679],
[-0.01622375, 0.24521625, 0.00050642]], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_date_top_industries(fn):
tickers = generate_random_tickers(10)
dates = generate_random_dates(2)
fn_inputs = {
'prices':
pd.DataFrame(
[[
21.050810483942833, 17.013843810658827, 10.984503755486879,
11.248093428369392, 12.961712733997235, 482.34539247360806,
35.202580592515041, 3516.5416782257166, 66.405314327318209,
13.503960481087077
],
[
15.63570258751384, 14.69054309070934, 11.353027688995159,
475.74195118202061, 11.959640427803022, 10.918933017418304,
17.9086438675435, 24.801265417692324, 12.488954191854916,
15.63570258751384
]], dates, tickers),
'sector':
pd.Series([
'ENERGY', 'MATERIALS', 'ENERGY', 'ENERGY', 'TELECOM', 'FINANCIALS',
'TECHNOLOGY', 'HEALTH', 'MATERIALS', 'REAL ESTATE'
], tickers),
'date':
dates[-1],
'top_n':
4
}
fn_correct_outputs = OrderedDict([('top_industries',
{'ENERGY', 'HEALTH', 'TECHNOLOGY'})])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_simple_moving_average(fn):
tickers = generate_random_tickers(5)
dates = generate_random_dates(6)
fn_inputs = {
'rolling_window':
3,
'close':
pd.DataFrame(
[[
21.050810483942833, 17.013843810658827, 10.984503755486879,
11.248093428369392, 12.961712733997235
],
[
15.63570258751384, 14.69054309070934, 11.353027688995159,
475.74195118202061, 11.959640427803022
],
[
482.34539247360806, 35.202580592515041, 3516.5416782257166,
66.405314327318209, 13.503960481087077
],
[
10.918933017418304, 17.9086438675435, 24.801265417692324,
12.488954191854916, 10.52435923388642
],
[
10.675971965144655, 12.749401436636365, 11.805257579935713,
21.539039489843024, 19.99766036804861
],
[
11.545495378369814, 23.981468434099405, 24.974763062186504,
36.031962102997689, 14.304332320024963
]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('simple_moving_average',
pd.DataFrame([
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[
173.01063518, 22.30232250, 1179.62640322, 184.46511965,
12.80843788
],
[
169.63334269, 22.60058918, 1184.23199044, 184.87873990,
11.99598671
],
[
167.98009915, 21.95354197, 1184.38273374, 33.47776934,
14.67532669
],
[11.04680012, 18.21317125, 20.52709535, 23.35331859, 14.94211731]
], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_tracking_error(fn):
dates = generate_random_dates(4)
fn_inputs = {
'benchmark_returns_by_date':
pd.Series([np.nan, 0.99880148, 0.99876653, 1.00024411], dates),
'etf_returns_by_date':
pd.Series([np.nan, 0.63859274, 0.93475823, 2.57295727], dates)
}
fn_correct_outputs = OrderedDict([('tracking_error', 16.5262431971)])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_rebalance_portfolio(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(11)
fn_inputs = {
'returns':
pd.DataFrame(
[[np.nan, np.nan, np.nan], [-0.02202381, 0.02265285, 0.01441961],
[0.01947657, 0.00551985, 0.00047382],
[0.00537313, -0.00803232, 0.01160313],
[0.00593824, -0.00567773, 0.02247191],
[0.02479339, 0.01758824, -0.00824176],
[-0.0109447, -0.00383568, 0.01361958],
[0.01164822, 0.01558719, 0.00614894],
[0.0109384, -0.00182079, 0.02900868],
[0.01138952, 0.00218049, -0.00954495],
[0.0106982, 0.00644535, -0.01815329]], dates, tickers),
'index_weights':
pd.DataFrame([[0.00449404, 0.11586048, 0.00359727],
[
0.00403487,
0.12534048,
0.0034428,
], [0.00423485, 0.12854258, 0.00347404],
[0.00395679, 0.1243466, 0.00335064],
[0.00368729, 0.11750295, 0.00333929],
[0.00369562, 0.11447422, 0.00325973],
[
0.00379612,
0.11088075,
0.0031734,
], [0.00366501, 0.10806014, 0.00314648],
[0.00361268, 0.10376514, 0.00323257],
[0.00358844, 0.10097531, 0.00319009],
[0.00362045, 0.09791232, 0.00318071]], dates, tickers),
'shift_size':
2,
'chunk_size':
4
}
fn_correct_outputs = OrderedDict([('all_rebalance_weights', [
np.array([0.29341237, 0.41378419, 0.29280344]),
np.array([0.29654088, 0.40731481, 0.29614432]),
np.array([0.29868214, 0.40308791, 0.29822995]),
np.array([0.30100044, 0.39839644, 0.30060312])
])])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_cumulative_returns(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'returns':
pd.DataFrame(
[[np.nan, np.nan, np.nan], [1.59904743, 1.66397210, 1.67345829],
[-0.37065629, -0.36541822, -0.36015840],
[-0.41055669, 0.60004777, 0.00536958]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('cumulative_returns',
pd.Series([np.nan, 5.93647782, -0.57128454, -0.68260542], dates))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_returns(fn):
tickers = generate_random_tickers(5)
dates = generate_random_dates(6)
fn_inputs = {
'close':
pd.DataFrame(
[[
21.050810483942833, 17.013843810658827, 10.984503755486879,
11.248093428369392, 12.961712733997235
],
[
15.63570258751384, 14.69054309070934, 11.353027688995159,
475.74195118202061, 11.959640427803022
],
[
482.34539247360806, 35.202580592515041, 3516.5416782257166,
66.405314327318209, 13.503960481087077
],
[
10.918933017418304, 17.9086438675435, 24.801265417692324,
12.488954191854916, 10.52435923388642
],
[
10.675971965144655, 12.749401436636365, 11.805257579935713,
21.539039489843024, 19.99766036804861
],
[
11.545495378369814, 23.981468434099405, 24.974763062186504,
36.031962102997689, 14.304332320024963
]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('returns',
pd.DataFrame([
[np.nan, np.nan, np.nan, np.nan, np.nan],
[-0.25723988, -0.13655355, 0.03354944, 41.29534136, -0.07731018],
[29.84897463, 1.39627496, 308.74483411, -0.86041737, 0.12912763],
[-0.97736283, -0.49126900, -0.99294726, -0.81192839, -0.22064647],
[-0.02225135, -0.28808672, -0.52400584, 0.72464717, 0.90013092],
[0.08144677, 0.88098779, 1.11556274, 0.67286764, -0.28469971]
], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_dividend_weights(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'dividends':
pd.DataFrame([[0.0, 0.0, 0.0], [0.0, 0.0, 0.1], [0.0, 1.0, 0.3],
[0.0, 0.2, 0.0]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('dividend_weights',
pd.DataFrame(
[[np.nan, np.nan, np.nan], [0.00000000, 0.00000000, 1.00000000],
[0.00000000, 0.71428571, 0.28571429],
[0.00000000, 0.75000000, 0.25000000]], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_generate_returns(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'prices':
pd.DataFrame([[35.4411, 34.1799, 34.0223], [92.1131, 91.0543, 90.9572],
[57.9708, 57.7814, 58.1982], [34.1705, 92.453, 58.5107]],
dates, tickers)
}
fn_correct_outputs = OrderedDict([
('returns',
pd.DataFrame(
[[np.nan, np.nan, np.nan], [1.59904743, 1.66397210, 1.67345829],
[-0.37065629, -0.36541822, -0.36015840],
[-0.41055669, 0.60004777, 0.00536958]], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_covariance_returns(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'returns':
pd.DataFrame(
[[np.nan, np.nan, np.nan], [1.59904743, 1.66397210, 1.67345829],
[-0.37065629, -0.36541822, -0.36015840],
[-0.41055669, 0.60004777, 0.00536958]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('returns_covariance',
np.array([[0.89856076, 0.7205586, 0.8458721],
[0.7205586, 0.78707297, 0.76450378],
[0.8458721, 0.76450378, 0.83182775]]))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_generate_positions(fn):
tickers = generate_random_tickers(5)
dates = generate_random_dates(6)
fn_inputs = {
'prices':
pd.DataFrame(
[[
65.40757705426432, 27.556319958924323, 50.59935209411175,
56.274712269629134, 99.9873070881051
],
[
47.82126720752715, 56.812865745668375, 40.75685814634723,
27.469680989736023, 41.449858088448735
],
[
88.20038097315815, 45.573972499280494, 36.592711369868724,
21.36570423559795, 0.698919959739297
],
[
14.670236824202721, 49.557949251949054, 18.935364730808935,
23.163368660093298, 8.075599541367884
],
[
41.499140208637705, 9.75987296846733, 66.08677766062186,
37.927861417544385, 10.792730405945827
],
[
86.26923464863536, 32.12679487375028, 15.621592524570282,
77.1908860965619, 52.733950486350444
]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('final_positions',
pd.DataFrame([[30, 0, 30, 30, 30], [0, 30, 0, 0, 0], [
30, 0, 0, 0, -10
], [-10, 0, -10, 0, -10], [0, -10, 30, 0, -10], [30, 0, -10, 30, 30]],
dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_arithmetic_rate_of_return(fn):
tickers = generate_random_tickers(5)
dates = generate_random_dates(6)
fn_inputs = {
'close':
pd.DataFrame(
[[
21.050810483942833, 17.013843810658827, 10.984503755486879,
11.248093428369392, 12.961712733997235
],
[
15.63570258751384, 14.69054309070934, 11.353027688995159,
475.74195118202061, 11.959640427803022
],
[
482.34539247360806, 35.202580592515041, 3516.5416782257166,
66.405314327318209, 13.503960481087077
],
[
10.918933017418304, 17.9086438675435, 24.801265417692324,
12.488954191854916, 10.52435923388642
],
[
10.675971965144655, 12.749401436636365, 11.805257579935713,
21.539039489843024, 19.99766036804861
],
[
11.545495378369814, 23.981468434099405, 24.974763062186504,
36.031962102997689, 14.304332320024963
]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('arithmetic_returns',
pd.Series(
[4.77892789, 0.22689225, 51.39616553, 6.83675173, 0.07443370],
tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_generate_dollar_volume_weights(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'close':
pd.DataFrame([[35.4411, 34.1799, 34.0223], [92.1131, 91.0543, 90.9572],
[57.9708, 57.7814, 58.1982], [34.1705, 92.453, 58.5107]],
dates, tickers),
'volume':
pd.DataFrame([[9.83683e+06, 1.78072e+07, 8.82982e+06],
[8.22427e+07, 6.85315e+07, 4.81601e+07],
[1.62348e+07, 1.30527e+07, 9.51201e+06],
[1.06742e+07, 5.68313e+07, 9.31601e+06]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('dollar_volume_weights',
pd.DataFrame([[0.27719777, 0.48394253, 0.23885970],
[0.41632975, 0.34293308, 0.24073717],
[0.41848548, 0.33536102, 0.24615350],
[0.05917255, 0.85239760, 0.08842984]], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)