def test_get_top_n(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'prev_returns': pd.DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051]],
dates, tickers),
'top_n': 3}
fn_correct_outputs = OrderedDict([
(
'top_stocks',
pd.DataFrame(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 0, 1, 1, 0],
[0, 1, 0, 1, 1]],
dates, tickers))])
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_get_top_n(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'prev_returns': pd.DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051]],
dates, tickers),
'top_n': 3}
fn_correct_outputs = OrderedDict([
(
'top_stocks',
pd.DataFrame(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 0, 1, 1, 0],
[0, 1, 0, 1, 1]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_resample_prices(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-19', '2008-09-08', '2008-09-28', '2008-10-18', '2008-11-07', '2008-11-27'])
resampled_dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'close_prices': 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),
'freq': 'M'}
fn_correct_outputs = OrderedDict([
(
'prices_resampled',
pd.DataFrame(
[
[21.05081048, 17.01384381, 10.98450376, 11.24809343, 12.96171273],
[482.34539247, 35.20258059, 3516.54167823, 66.40531433, 13.50396048],
[10.91893302, 17.90864387, 24.80126542, 12.48895419, 10.52435923],
[11.54549538, 23.98146843, 24.97476306, 36.03196210, 14.30433232]],
resampled_dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_resample_prices(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-19', '2008-09-08', '2008-09-28', '2008-10-18', '2008-11-07', '2008-11-27'])
resampled_dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'close_prices': 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),
'freq': 'M'}
fn_correct_outputs = OrderedDict([
(
'prices_resampled',
pd.DataFrame(
[
[21.05081048, 17.01384381, 10.98450376, 11.24809343, 12.96171273],
[482.34539247, 35.20258059, 3516.54167823, 66.40531433, 13.50396048],
[10.91893302, 17.90864387, 24.80126542, 12.48895419, 10.52435923],
[11.54549538, 23.98146843, 24.97476306, 36.03196210, 14.30433232]],
resampled_dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_shift_returns(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'returns': pd.DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995]],
dates, tickers),
'shift_n': 1}
fn_correct_outputs = OrderedDict([
(
'shifted_returns',
pd.DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_high_lows_lookback(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'high':
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),
'low':
pd.DataFrame([[15.6718, 75.1392, 34.0527], [27.1834, 12.3453, 95.9373],
[28.2503, 24.2854, 23.2932], [86.3725, 32.223, 38.4107]],
dates, tickers),
'lookback_days':
2
}
fn_correct_outputs = OrderedDict([
('lookback_high',
pd.DataFrame([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan],
[92.11310000, 91.05430000, 90.95720000],
[92.11310000, 91.05430000, 90.95720000]], dates,
tickers)),
('lookback_low',
pd.DataFrame([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan],
[15.67180000, 12.34530000, 34.05270000],
[27.18340000, 12.34530000, 23.29320000]], dates,
tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_date_top_industries(fn=date_top_industries):
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_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_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)
def test_get_long_short(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'close':
pd.DataFrame([[25.6788, 35.1392, 34.0527], [25.1884, 14.3453, 39.9373],
[78.2803, 34.3854, 23.2932], [88.8725, 52.223, 34.4107]],
dates, tickers),
'lookback_high':
pd.DataFrame(
[[np.nan, np.nan, np.nan], [92.11310000, 91.05430000, 90.95720000],
[35.4411, 34.1799, 34.0223],
[92.11310000, 91.05430000, 90.95720000]], dates, tickers),
'lookback_low':
pd.DataFrame([[np.nan, np.nan, np.nan], [34.1705, 92.453, 58.5107],
[15.67180000, 12.34530000, 34.05270000],
[27.18340000, 12.34530000, 23.29320000]], dates, tickers)
}
fn_correct_outputs = OrderedDict([
('long_short',
pd.DataFrame([[0, 0, 0], [-1, -1, -1], [1, 1, -1], [0, 0, 0]], dates,
tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
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_portfolio_returns(fn=utils.portfolio_returns):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(
['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'df_long':
pd.DataFrame([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 0, 1, 1, 0],
[0, 1, 0, 1, 1]], dates, tickers),
'df_short':
pd.DataFrame([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 1, 1],
[1, 1, 1, 0, 0]], dates, tickers),
'lookahead_returns':
pd.DataFrame(
[[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995],
[1.25459098, 6.87369275, 2.58265839, 6.92676837, 0.84632677]],
dates, tickers),
'n_stocks':
3
}
fn_correct_outputs = OrderedDict([
('portfolio_returns',
pd.DataFrame([
[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000],
[-0.00000000, -0.00000000, -0.00000000, -0.00000000, -0.00000000],
[0.01859903, -0.09733263, 0.00232372, 0.00000000, -0.10228998],
[-0.41819699, 0.00000000, -0.86088613, 2.30892279, 0.28210892]
], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_signal_return(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(5)
fn_inputs = {
'signal':
pd.DataFrame(
[[0, 0, 0], [-1, -1, -1], [1, 0, 0], [0, 0, 0], [0, 1, 0]], dates,
tickers),
'lookahead_returns':
pd.DataFrame(
[[0.88702896, 0.96521098, 0.65854789],
[1.13391240, 0.87420969, -0.53914925],
[0.35450805, -0.56900529, -0.64808965],
[0.38572896, -0.94655617, 0.123564379], [np.nan, np.nan, np.nan]],
dates, tickers)
}
fn_correct_outputs = OrderedDict([
('signal_return',
pd.DataFrame(
[[0, 0, 0], [-1.13391240, -0.87420969, 0.53914925],
[0.35450805, 0, 0], [0, 0, 0], [np.nan, np.nan, np.nan]], dates,
tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_lookahead_prices(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(5)
fn_inputs = {
'close': pd.DataFrame(
[
[25.6788, 35.1392, 34.0527],
[25.1884, 14.3453, 39.9373],
[62.3457, 92.2524, 65.7893],
[78.2803, 34.3854, 23.2932],
[88.8725, 52.223, 34.4107]],
dates, tickers),
'lookahead_days': 2}
fn_correct_outputs = OrderedDict([
(
'lookahead_prices',
pd.DataFrame(
[
[62.34570000, 92.25240000, 65.78930000],
[78.28030000, 34.38540000, 23.29320000],
[88.87250000, 52.22300000, 34.41070000],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_return_lookahead(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(5)
fn_inputs = {
'close': pd.DataFrame(
[
[25.6788, 35.1392, 34.0527],
[25.1884, 14.3453, 39.9373],
[62.3457, 92.2524, 65.7893],
[78.2803, 34.3854, 23.2932],
[88.8725, 52.223, 34.4107]],
dates, tickers),
'lookahead_prices': pd.DataFrame(
[
[62.34570000, 92.25240000, 65.78930000],
[78.28030000, 34.38540000, 23.29320000],
[88.87250000, 52.22300000, 34.41070000],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan]],
dates, tickers)}
fn_correct_outputs = OrderedDict([
(
'lookahead_returns',
pd.DataFrame(
[
[0.88702896, 0.96521098, 0.65854789],
[1.13391240, 0.87420969, -0.53914925],
[0.35450805, -0.56900529, -0.64808965],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan]],
dates, 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)
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_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_kstest(fn):
tickers = generate_random_tickers(3)
fn_inputs = {
"long_short_signal_returns":
pd.DataFrame({
"ticker":
tickers * 5,
"signal_return": [
0.12,
-0.83,
0.37,
0.83,
-0.34,
0.27,
-0.68,
0.29,
0.69,
0.57,
0.39,
0.56,
-0.97,
-0.72,
0.26,
],
})
}
fn_correct_outputs = OrderedDict([
("ks_values", pd.Series([0.29787827, 0.35221525, 0.63919407],
tickers)),
("p_values", pd.Series([0.67234149, 0.46161172, 0.01650327], tickers)),
])
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 = {
'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),
'rolling_window': 3}
fn_correct_outputs = OrderedDict([
(
'returns',
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_shift_returns(fn=utils.shift_returns):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(
['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'returns':
pd.DataFrame(
[[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995]],
dates, tickers),
'shift_n':
1
}
fn_correct_outputs = OrderedDict([
('shifted_returns',
pd.DataFrame([
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051]
], dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_return_lookahead(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(5)
fn_inputs = {
'close':
pd.DataFrame([[25.6788, 35.1392, 34.0527], [25.1884, 14.3453, 39.9373],
[62.3457, 92.2524, 65.7893], [78.2803, 34.3854, 23.2932],
[88.8725, 52.223, 34.4107]], dates, tickers),
'lookahead_prices':
pd.DataFrame([[62.34570000, 92.25240000, 65.78930000],
[78.28030000, 34.38540000, 23.29320000],
[88.87250000, 52.22300000, 34.41070000],
[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]],
dates, tickers)
}
fn_correct_outputs = OrderedDict([
('lookahead_returns',
pd.DataFrame([[0.88702896, 0.96521098, 0.65854789],
[1.13391240, 0.87420969, -0.53914925],
[0.35450805, -0.56900529, -0.64808965],
[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]],
dates, tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_compute_log_returns(fn=utils.compute_log_returns):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(
['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'prices':
pd.DataFrame([[
21.05081048, 17.01384381, 10.98450376, 11.24809343, 12.96171273
], [
482.34539247, 35.20258059, 3516.54167823, 66.40531433, 13.50396048
], [
10.91893302, 17.90864387, 24.80126542, 12.48895419, 10.52435923
], [11.54549538, 23.98146843, 24.97476306, 36.03196210, 14.30433232]],
dates, tickers)
}
fn_correct_outputs = OrderedDict([
('log_returns',
pd.DataFrame([
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995]
], 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_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_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_get_signal_return(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(5)
fn_inputs = {
'signal': pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 0, 0],
[0, 0, 0],
[0, 1, 0]],
dates, tickers),
'lookahead_returns': pd.DataFrame(
[
[0.88702896, 0.96521098, 0.65854789],
[1.13391240, 0.87420969, -0.53914925],
[0.35450805, -0.56900529, -0.64808965],
[0.38572896, -0.94655617, 0.123564379],
[np.nan, np.nan, np.nan]],
dates, tickers)}
fn_correct_outputs = OrderedDict([
(
'signal_return',
pd.DataFrame(
[
[0, 0, 0],
[-1.13391240, -0.87420969, 0.53914925],
[0.35450805, 0, 0],
[0, 0, 0],
[np.nan, np.nan, np.nan]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_find_outliers(fn):
tickers = generate_random_tickers(3)
fn_inputs = {
"ks_values": pd.Series([0.20326939, 0.34826827, 0.60256811], tickers),
"p_values": pd.Series([0.98593727, 0.48009144, 0.02898631], tickers),
"ks_threshold": 0.5,
"pvalue_threshold": 0.05,
}
fn_correct_outputs = OrderedDict([("outliers", set([tickers[2]]))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_find_outliers(fn=utils.find_outliers):
tickers = generate_random_tickers(3)
fn_inputs = {
'ks_values': pd.Series([0.20326939, 0.34826827, 0.60256811], tickers),
'p_values': pd.Series([0.98593727, 0.48009144, 0.02898631], tickers),
'ks_threshold': 0.5,
'pvalue_threshold': 0.05
}
fn_correct_outputs = OrderedDict([('outliers', set([tickers[2]]))])
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_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_filter_signals(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(10)
fn_inputs = {
"signal":
pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 0, -1],
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, -1, 1],
[-1, 0, 0],
[0, 0, 0],
],
dates,
tickers,
),
"lookahead_days":
3,
}
fn_correct_outputs = OrderedDict([(
"filtered_signal",
pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, -1, 0],
[-1, 0, 0],
[0, 0, 0],
],
dates,
tickers,
),
)])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_find_outliers(fn):
tickers = generate_random_tickers(3)
fn_inputs = {
'ks_values': pd.Series(
[0.20326939, 0.34826827, 0.60256811],
tickers),
'p_values': pd.Series(
[0.98593727, 0.48009144, 0.02898631],
tickers),
'ks_threshold': 0.5,
'pvalue_threshold': 0.05}
fn_correct_outputs = OrderedDict([
(
'outliers',
set([tickers[2]]))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_high_lows_lookback(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'high': 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),
'low': pd.DataFrame(
[
[15.6718, 75.1392, 34.0527],
[27.1834, 12.3453, 95.9373],
[28.2503, 24.2854, 23.2932],
[86.3725, 32.223, 38.4107]],
dates, tickers),
'lookback_days': 2}
fn_correct_outputs = OrderedDict([
(
'lookback_high',
pd.DataFrame(
[
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[92.11310000, 91.05430000, 90.95720000],
[92.11310000, 91.05430000, 90.95720000]],
dates, tickers)),
(
'lookback_low',
pd.DataFrame(
[
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[15.67180000, 12.34530000, 34.05270000],
[27.18340000, 12.34530000, 23.29320000]],
dates, tickers))
])
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_kstest(fn):
tickers = generate_random_tickers(3)
fn_inputs = {
'long_short_signal_returns':
pd.DataFrame({
'ticker':
tickers * 5,
'signal_return': [
0.12, -0.83, 0.37, 0.83, -0.34, 0.27, -0.68, 0.29, 0.69, 0.57,
0.39, 0.56, -0.97, -0.72, 0.26
]
})
}
fn_correct_outputs = OrderedDict([
('ks_values', pd.Series([0.28999582, 0.34484969, 0.63466098],
tickers)),
('p_values', pd.Series([0.73186935, 0.49345487, 0.01775987], tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_kstest(fn=utils.calculate_kstest):
tickers = generate_random_tickers(3)
fn_inputs = {
'long_short_signal_returns':
pd.DataFrame({
'ticker':
tickers * 5,
'signal_return': [
0.12, -0.83, 0.37, 0.83, -0.34, 0.27, -0.68, 0.29, 0.69, 0.57,
0.39, 0.56, -0.97, -0.72, 0.26
]
})
}
fn_correct_outputs = OrderedDict([
('ks_values', pd.Series([0.29787827, 0.35221525, 0.63919407],
tickers)),
('p_values', pd.Series([0.69536353, 0.46493498, 0.01650327], 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_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_portfolio_returns(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'df_long': pd.DataFrame(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 0, 1, 1, 0],
[0, 1, 0, 1, 1]],
dates, tickers),
'df_short': pd.DataFrame(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, 0, 1, 1],
[1, 1, 1, 0, 0]],
dates, tickers),
'lookahead_returns': pd.DataFrame(
[
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995],
[1.25459098, 6.87369275, 2.58265839, 6.92676837, 0.84632677]],
dates, tickers),
'n_stocks': 3}
fn_correct_outputs = OrderedDict([
(
'portfolio_returns',
pd.DataFrame(
[
[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000],
[-0.00000000, -0.00000000, -0.00000000, -0.00000000, -0.00000000],
[0.01859903, -0.09733263, 0.00232372, 0.00000000, -0.10228998],
[-0.41819699, 0.00000000, -0.86088613, 2.30892279, 0.28210892]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_filter_signals(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(10)
fn_inputs = {
'signal':
pd.DataFrame(
[[0, 0, 0], [-1, -1, -1], [1, 0, -1], [0, 0, 0], [1, 0, 0],
[0, 1, 0], [0, 0, 1], [0, -1, 1], [-1, 0, 0], [0, 0, 0]], dates,
tickers),
'lookahead_days':
3
}
fn_correct_outputs = OrderedDict([
('filtered_signal',
pd.DataFrame(
[[0, 0, 0], [-1, -1, -1], [1, 0, 0], [0, 0, 0], [0, 0, 0],
[0, 1, 0], [0, 0, 1], [0, -1, 0], [-1, 0, 0], [0, 0, 0]], dates,
tickers))
])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_calculate_kstest(fn):
tickers = generate_random_tickers(3)
fn_inputs = {
'long_short_signal_returns': pd.DataFrame(
{
'ticker': tickers * 5,
'signal_return': [0.12, -0.83, 0.37, 0.83, -0.34, 0.27, -0.68, 0.29, 0.69,
0.57, 0.39, 0.56, -0.97, -0.72, 0.26]})}
fn_correct_outputs = OrderedDict([
(
'ks_values',
pd.Series(
[0.29787827, 0.35221525, 0.63919407],
tickers)),
(
'p_values',
pd.Series(
[0.69536353, 0.46493498, 0.01650327],
tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_get_long_short(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(4)
fn_inputs = {
'close': pd.DataFrame(
[
[25.6788, 35.1392, 34.0527],
[25.1884, 14.3453, 39.9373],
[78.2803, 34.3854, 23.2932],
[88.8725, 52.223, 34.4107]],
dates, tickers),
'lookback_high': pd.DataFrame(
[
[np.nan, np.nan, np.nan],
[92.11310000, 91.05430000, 90.95720000],
[35.4411, 34.1799, 34.0223],
[92.11310000, 91.05430000, 90.95720000]],
dates, tickers),
'lookback_low': pd.DataFrame(
[
[np.nan, np.nan, np.nan],
[34.1705, 92.453, 58.5107],
[15.67180000, 12.34530000, 34.05270000],
[27.18340000, 12.34530000, 23.29320000]],
dates, tickers)}
fn_correct_outputs = OrderedDict([
(
'long_short',
pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 1, -1],
[0, 0, 0]],
dates, tickers))])
assert_output(fn, fn_inputs, fn_correct_outputs)
def test_filter_signals(fn):
tickers = generate_random_tickers(3)
dates = generate_random_dates(10)
fn_inputs = {
'signal': pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 0, -1],
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, -1, 1],
[-1, 0, 0],
[0, 0, 0]],
dates, tickers),
'lookahead_days': 3}
fn_correct_outputs = OrderedDict([
(
'filtered_signal',
pd.DataFrame(
[
[0, 0, 0],
[-1, -1, -1],
[1, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, -1, 0],
[-1, 0, 0],
[0, 0, 0]],
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_compute_log_returns(fn):
tickers = generate_random_tickers(5)
dates = pd.DatetimeIndex(['2008-08-31', '2008-09-30', '2008-10-31', '2008-11-30'])
fn_inputs = {
'prices': pd.DataFrame(
[
[21.05081048, 17.01384381, 10.98450376, 11.24809343, 12.96171273],
[482.34539247, 35.20258059, 3516.54167823, 66.40531433, 13.50396048],
[10.91893302, 17.90864387, 24.80126542, 12.48895419, 10.52435923],
[11.54549538, 23.98146843, 24.97476306, 36.03196210, 14.30433232]],
dates, tickers)}
fn_correct_outputs = OrderedDict([
(
'log_returns',
pd.DataFrame(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[3.13172138, 0.72709204, 5.76874778, 1.77557845, 0.04098317],
[-3.78816218, -0.67583590, -4.95433863, -1.67093250, -0.24929051],
[0.05579709, 0.29199789, 0.00697116, 1.05956179, 0.30686995]],
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_find_outliers(fn):
tickers = generate_random_tickers(7)
dates = generate_random_dates(40)
outlier_values = [{0.0: 1, 0.5: 3, 0.75: 13, 1.0: 12, 1.25: 8, 1.5: 3}]
norm_values = [{
-1.0: 6,
-0.5: 9,
0.0: 8,
0.5: 11,
1.0: 6
}, {
-1.0: 4,
-0.5: 13,
0.0: 9,
0.5: 11,
1.0: 3
}, {
-1.0: 7,
-0.5: 11,
0.0: 7,
0.5: 9,
1.0: 6
}, {
-1.0: 5,
-0.5: 9,
0.0: 11,
0.5: 10,
1.0: 5
}, {
-1.0: 3,
-0.5: 12,
0.0: 14,
0.5: 8,
1.0: 3
}, {
-1.0: 5,
-0.5: 13,
0.0: 7,
0.5: 9,
1.0: 6
}]
# Build the values for the signal_return parameter
signal_return_values = []
for values in outlier_values + norm_values:
current_signal_return_values = []
for value, value_count in values.items():
current_signal_return_values.extend(
[x for x in [value] * value_count])
signal_return_values.append(current_signal_return_values)
signal_return_values = np.array(signal_return_values).T
# Create signals that aren't the same for each date
tile_value = [1, 1, 1, 0, 1]
signal_values = np.tile(
tile_value,
int(np.product(signal_return_values.shape) / len(tile_value))).reshape(
signal_return_values.shape)
fn_inputs = {
'signal': pd.DataFrame(signal_values, dates, tickers),
'signal_return': pd.DataFrame(signal_return_values, dates, tickers),
'ks_threshold': 0.8
}
fn_correct_outputs = OrderedDict([('outliers', [tickers[0]])])
assert_output(fn, fn_inputs, fn_correct_outputs)
