def test_encoding_schemes(self):
"""
Test quantile and sigma encoding
"""
values = np.arange(0, 1000, 1)
quantile_dict = quantile_mapping(values, num_letters=10)
sigma_dict = sigma_mapping(values, step=20)
self.assertEqual(len(quantile_dict), 10)
self.assertEqual(quantile_dict[229.77], '\x02')
self.assertEqual(len(sigma_dict), np.ceil((max(values) - min(values)) / 20))
self.assertEqual(sigma_dict[100], '\x05')
with self.assertRaises(ValueError):
sigma_mapping(values, step=1) # Length of dice > ASCII table
def test_inter_bar_feature_values(self):
"""
Test entropy, misc, inter-bar feature generation
"""
# Encode volumes and pct changes
df_trades = pd.read_csv(self.trades_path, parse_dates=[0])
df_trades['log_ret'] = np.log(df_trades.Price /
df_trades.Price.shift(1)).dropna()
unique_volumes = df_trades.Volume.drop_duplicates()
non_null_log_ret = df_trades[df_trades.log_ret != 0].log_ret.dropna()
volume_mapping = quantile_mapping(unique_volumes, num_letters=10)
returns_mapping = quantile_mapping(non_null_log_ret, num_letters=10)
# Compress bars from ticks
compressed_bars = get_volume_bars(self.trades_path,
threshold=20,
verbose=False)
compressed_bars.set_index('date_time', inplace=True)
compressed_bars.index = pd.to_datetime(compressed_bars.index)
gen = MicrostructuralFeaturesGenerator(self.trades_path,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping)
features = gen.get_features(to_csv=False, verbose=False)
features.set_index('date_time', inplace=True)
# Check individual feature values
# Avg tick size
self.assertAlmostEqual(features.avg_tick_size.max(), 8.0, delta=1e-1)
self.assertAlmostEqual(features.avg_tick_size.mean(),
3.1931,
delta=1e-4)
self.assertAlmostEqual(features.avg_tick_size[3], 1.6153, delta=1e-3)
# Tick rule sum
self.assertAlmostEqual(features.tick_rule_sum.max(), 7.0, delta=1e-1)
self.assertAlmostEqual(features.tick_rule_sum.mean(), -3.4, delta=1e-4)
self.assertAlmostEqual(features.tick_rule_sum[3], -11.0, delta=1e-3)
# VWAP
self.assertAlmostEqual(features.vwap.max(), 1311.663, delta=1e-1)
self.assertAlmostEqual(features.vwap.mean(), 1304.94542, delta=1e-4)
self.assertAlmostEqual(features.vwap[3], 1304.5119, delta=1e-3)
# Kyle lambda
self.assertAlmostEqual(features.kyle_lambda.max(), 197.958, delta=1e-1)
self.assertAlmostEqual(features.kyle_lambda.mean(),
23.13859,
delta=1e-4)
self.assertAlmostEqual(features.kyle_lambda[3], 0.007936, delta=1e-3)
# Amihud lambda
self.assertAlmostEqual(features.amihud_lambda.max(),
8.291e-5,
delta=1e-7)
self.assertAlmostEqual(features.amihud_lambda.mean(),
1.001e-5,
delta=1e-8)
self.assertAlmostEqual(features.amihud_lambda[3],
4.663786e-9,
delta=1e-11)
# Hasbrouck lambda
self.assertAlmostEqual(features.hasbrouck_lambda.max(),
0.0025621,
delta=1e-5)
self.assertAlmostEqual(features.hasbrouck_lambda.mean(),
0.00018253,
delta=1e-5)
self.assertAlmostEqual(features.hasbrouck_lambda[3],
2.42e-11,
delta=1e-13)
# Tick rule entropy shannon
self.assertAlmostEqual(features.tick_rule_entropy_shannon.max(),
1.52192,
delta=1e-4)
self.assertAlmostEqual(features.tick_rule_entropy_shannon.mean(),
0.499,
delta=1e-4)
self.assertAlmostEqual(features.tick_rule_entropy_shannon[3],
0.39124,
delta=1e-4)
# Volume entropy plug-in
self.assertAlmostEqual(features.volume_entropy_plug_in.max(),
1.92192,
delta=1e-4)
self.assertAlmostEqual(features.volume_entropy_plug_in.mean(),
1.052201,
delta=1e-5)
self.assertAlmostEqual(features.volume_entropy_plug_in[3],
0.41381,
delta=1e-4)
# Volume entropy Lempel-Ziv
self.assertAlmostEqual(features.volume_entropy_lempel_ziv.max(),
1.0,
delta=1e-4)
self.assertAlmostEqual(features.volume_entropy_lempel_ziv.mean(),
0.5904612,
delta=1e-4)
self.assertAlmostEqual(features.volume_entropy_lempel_ziv[3],
0.46153,
delta=1e-4)
# Pct entropy Lempel-Ziv
self.assertAlmostEqual(features.pct_entropy_lempel_ziv.max(),
0.8,
delta=1e-4)
self.assertAlmostEqual(features.pct_entropy_lempel_ziv.mean(),
0.56194,
delta=1e-5)
self.assertAlmostEqual(features.pct_entropy_lempel_ziv[3],
0.46153,
delta=1e-5)
# Pct entropy Konto
self.assertAlmostEqual(features.pct_entropy_konto.max(),
1.361,
delta=1e-4)
self.assertAlmostEqual(features.pct_entropy_konto.mean(),
0.83039791,
delta=1e-5)
self.assertAlmostEqual(features.pct_entropy_konto[3],
1.067022,
delta=1e-5)
def test_feature_generator_function(self):
"""
Test validity of MicrostructuralFeaturesGenerator
"""
# Encode volumes and pct changes
df_trades = pd.read_csv(self.trades_path, parse_dates=[0])
df_trades['log_ret'] = np.log(df_trades.Price /
df_trades.Price.shift(1)).dropna()
non_null_log_ret = df_trades[df_trades.log_ret != 0].log_ret.dropna()
# Take unique volumes only
volume_mapping = quantile_mapping(df_trades.Volume.drop_duplicates(),
num_letters=10)
returns_mapping = quantile_mapping(non_null_log_ret, num_letters=10)
# Compress bars from ticks
compressed_bars = get_volume_bars(self.trades_path,
threshold=20,
verbose=False)
compressed_bars.set_index('date_time', inplace=True)
compressed_bars.index = pd.to_datetime(compressed_bars.index)
bar_index = compressed_bars.index
# Test None input ValureError raise
with self.assertRaises(ValueError):
MicrostructuralFeaturesGenerator(None,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping)
gen = MicrostructuralFeaturesGenerator(self.trades_path,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping)
gen_no_entropy = MicrostructuralFeaturesGenerator(
self.trades_path,
compressed_bars.tick_num,
volume_encoding=None,
pct_encoding=None)
gen_csv = MicrostructuralFeaturesGenerator(
self.trades_path,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping)
gen_1 = MicrostructuralFeaturesGenerator(
self.trades_path,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping,
batch_size=1)
gen_20 = MicrostructuralFeaturesGenerator(
self.trades_path,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping,
batch_size=20)
gen_df = MicrostructuralFeaturesGenerator(
df_trades,
compressed_bars.tick_num,
volume_encoding=volume_mapping,
pct_encoding=returns_mapping,
batch_size=20)
features = gen.get_features(to_csv=False, verbose=False)
features_1 = gen_1.get_features(to_csv=False, verbose=False)
features_20 = gen_20.get_features(to_csv=False, verbose=False)
features_from_df = gen_df.get_features(to_csv=False, verbose=False)
features_no_entropy = gen_no_entropy.get_features(verbose=False)
# No volume/pct entropy columns check
with self.assertRaises(KeyError):
features['tick_rule_entropy'] += features_no_entropy[
'volume_entropy_plug_in']
with self.assertRaises(KeyError):
features['tick_rule_entropy'] = features_no_entropy[
'pct_entropy_plug_in']
gen_csv.get_features(to_csv=True, output_path='features.csv')
features_from_csv = pd.read_csv('features.csv', parse_dates=[0])
self.assertTrue(
(features.dropna().values == features_1.dropna().values).all())
self.assertTrue(
(features.dropna().values == features_20.dropna().values).all())
self.assertTrue(
(features.dropna().values == features_from_df.dropna().values
).all())
features.set_index('date_time', inplace=True)
features_from_csv.set_index('date_time', inplace=True)
self.assertAlmostEqual((features - features_from_csv).sum().sum(),
0,
delta=1e-6)
self.assertEqual(bar_index.shape[0], features.shape[0])
self.assertEqual(compressed_bars.loc[features.index].shape[0],
compressed_bars.shape[0])
os.remove('features.csv')