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Add ARIMA Normalization Functionality #89

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Add ARIMA Normalization Functionality #89

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409e5ba
add arima functionality
ml874 Jan 29, 2020
e6ab976
fixed arima functionality
ml874 Feb 13, 2020
a577fa5
added moving average component to arima
ml874 Feb 26, 2020
5967956
additional changes, pep8 formatting
ml874 Mar 18, 2020
d9918e0
additional changes, modified test file
ml874 Apr 2, 2020
68d3cc2
moved arima normalization to econometrics.py
ml874 Apr 7, 2020
36d3294
remove arima from timeseries __init__.py
ml874 Apr 8, 2020
94a1596
pep8 and added code coverage for econometrics
ml874 Apr 20, 2020
e835ddc
pep8 changes
ml874 Apr 20, 2020
6b9cc40
more code coverage
ml874 Apr 20, 2020
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199 changes: 199 additions & 0 deletions gs_quant/test/timeseries/test_arima.py
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"""
Copyright 2020 Goldman Sachs.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
from datetime import date
from math import isclose

import pytest
import pandas as pd
import numpy as np

from pandas import Timestamp
from pandas.util.testing import assert_series_equal
from numpy.testing import assert_raises, assert_array_equal, assert_allclose

import gs_quant.timeseries as ts

def test_arima_fit():
test_dict = {
'High':
{Timestamp('1989-01-03 00:00:00'): 3.575721263885498,
Timestamp('1989-01-04 00:00:00'): 3.5857372283935547,
Timestamp('1989-01-05 00:00:00'): 3.62580132484436,
Timestamp('1989-01-06 00:00:00'): 3.62580132484436,
Timestamp('1989-01-09 00:00:00'): 3.575721263885498,
Timestamp('1989-01-10 00:00:00'): 3.575721263885498,
Timestamp('1989-01-11 00:00:00'): 3.5657050609588623,
Timestamp('1989-01-12 00:00:00'): 3.635817289352417,
Timestamp('1989-01-13 00:00:00'): 3.615785360336304,
Timestamp('1989-01-16 00:00:00'): 3.615785360336304,
Timestamp('1989-01-17 00:00:00'): 3.635817289352417,
Timestamp('1989-01-18 00:00:00'): 3.675881385803223,
Timestamp('1989-01-19 00:00:00'): 3.695913553237915,
Timestamp('1989-01-20 00:00:00'): 3.665865421295166,
Timestamp('1989-01-23 00:00:00'): 3.675881385803223,
Timestamp('1989-01-24 00:00:00'): 3.675881385803223,
Timestamp('1989-01-25 00:00:00'): 3.695913553237915,
Timestamp('1989-01-26 00:00:00'): 3.7760417461395264,
Timestamp('1989-01-27 00:00:00'): 3.8561699390411377,
Timestamp('1989-01-30 00:00:00'): 3.8561699390411377},
'Low':
{Timestamp('1989-01-03 00:00:00'): 3.4855768680572514,
Timestamp('1989-01-04 00:00:00'): 3.5356571674346924,
Timestamp('1989-01-05 00:00:00'): 3.575721263885498,
Timestamp('1989-01-06 00:00:00'): 3.575721263885498,
Timestamp('1989-01-09 00:00:00'): 3.5356571674346924,
Timestamp('1989-01-10 00:00:00'): 3.5356571674346924,
Timestamp('1989-01-11 00:00:00'): 3.5256409645080566,
Timestamp('1989-01-12 00:00:00'): 3.5456731319427486,
Timestamp('1989-01-13 00:00:00'): 3.5857372283935547,
Timestamp('1989-01-16 00:00:00'): 3.5957531929016118,
Timestamp('1989-01-17 00:00:00'): 3.5857372283935547,
Timestamp('1989-01-18 00:00:00'): 3.615785360336304,
Timestamp('1989-01-19 00:00:00'): 3.655849456787109,
Timestamp('1989-01-20 00:00:00'): 3.62580132484436,
Timestamp('1989-01-23 00:00:00'): 3.615785360336304,
Timestamp('1989-01-24 00:00:00'): 3.615785360336304,
Timestamp('1989-01-25 00:00:00'): 3.655849456787109,
Timestamp('1989-01-26 00:00:00'): 3.665865421295166,
Timestamp('1989-01-27 00:00:00'): 3.79607367515564,
Timestamp('1989-01-30 00:00:00'): 3.786057710647583},
'Open':
{Timestamp('1989-01-03 00:00:00'): 3.575721263885498,
Timestamp('1989-01-04 00:00:00'): 3.5556890964508057,
Timestamp('1989-01-05 00:00:00'): 3.5857372283935547,
Timestamp('1989-01-06 00:00:00'): 3.605769157409668,
Timestamp('1989-01-09 00:00:00'): 3.5456731319427486,
Timestamp('1989-01-10 00:00:00'): 3.575721263885498,
Timestamp('1989-01-11 00:00:00'): 3.5456731319427486,
Timestamp('1989-01-12 00:00:00'): 3.5456731319427486,
Timestamp('1989-01-13 00:00:00'): 3.605769157409668,
Timestamp('1989-01-16 00:00:00'): 3.5957531929016118,
Timestamp('1989-01-17 00:00:00'): 3.5957531929016118,
Timestamp('1989-01-18 00:00:00'): 3.635817289352417,
Timestamp('1989-01-19 00:00:00'): 3.6858973503112793,
Timestamp('1989-01-20 00:00:00'): 3.665865421295166,
Timestamp('1989-01-23 00:00:00'): 3.6458332538604736,
Timestamp('1989-01-24 00:00:00'): 3.62580132484436,
Timestamp('1989-01-25 00:00:00'): 3.6858973503112793,
Timestamp('1989-01-26 00:00:00'): 3.675881385803223,
Timestamp('1989-01-27 00:00:00'): 3.79607367515564,
Timestamp('1989-01-30 00:00:00'): 3.806089639663696},
'Close':
{Timestamp('1989-01-03 00:00:00'): 3.5256409645080566,
Timestamp('1989-01-04 00:00:00'): 3.5857372283935547,
Timestamp('1989-01-05 00:00:00'): 3.575721263885498,
Timestamp('1989-01-06 00:00:00'): 3.575721263885498,
Timestamp('1989-01-09 00:00:00'): 3.575721263885498,
Timestamp('1989-01-10 00:00:00'): 3.5556890964508057,
Timestamp('1989-01-11 00:00:00'): 3.5556890964508057,
Timestamp('1989-01-12 00:00:00'): 3.605769157409668,
Timestamp('1989-01-13 00:00:00'): 3.605769157409668,
Timestamp('1989-01-16 00:00:00'): 3.5957531929016118,
Timestamp('1989-01-17 00:00:00'): 3.62580132484436,
Timestamp('1989-01-18 00:00:00'): 3.675881385803223,
Timestamp('1989-01-19 00:00:00'): 3.665865421295166,
Timestamp('1989-01-20 00:00:00'): 3.6458332538604736,
Timestamp('1989-01-23 00:00:00'): 3.62580132484436,
Timestamp('1989-01-24 00:00:00'): 3.675881385803223,
Timestamp('1989-01-25 00:00:00'): 3.675881385803223,
Timestamp('1989-01-26 00:00:00'): 3.756009578704834,
Timestamp('1989-01-27 00:00:00'): 3.79607367515564,
Timestamp('1989-01-30 00:00:00'): 3.846153736114502},
'Volume':
{Timestamp('1989-01-03 00:00:00'): 21873600.0,
Timestamp('1989-01-04 00:00:00'): 13487100.0,
Timestamp('1989-01-05 00:00:00'): 20733000.0,
Timestamp('1989-01-06 00:00:00'): 20654400.0,
Timestamp('1989-01-09 00:00:00'): 21478000.0,
Timestamp('1989-01-10 00:00:00'): 15541300.0,
Timestamp('1989-01-11 00:00:00'): 11465300.0,
Timestamp('1989-01-12 00:00:00'): 26481300.0,
Timestamp('1989-01-13 00:00:00'): 10236000.0,
Timestamp('1989-01-16 00:00:00'): 8888200.0,
Timestamp('1989-01-17 00:00:00'): 12934200.0,
Timestamp('1989-01-18 00:00:00'): 25965800.0,
Timestamp('1989-01-19 00:00:00'): 25556500.0,
Timestamp('1989-01-20 00:00:00'): 13779100.0,
Timestamp('1989-01-23 00:00:00'): 13680500.0,
Timestamp('1989-01-24 00:00:00'): 16870400.0,
Timestamp('1989-01-25 00:00:00'): 16959000.0,
Timestamp('1989-01-26 00:00:00'): 29040900.0,
Timestamp('1989-01-27 00:00:00'): 50615100.0,
Timestamp('1989-01-30 00:00:00'): 27567000.0},
'Adj Close':
{Timestamp('1989-01-03 00:00:00'): 0.13199026882648468,
Timestamp('1989-01-04 00:00:00'): 0.13424012064933774,
Timestamp('1989-01-05 00:00:00'): 0.1338651180267334,
Timestamp('1989-01-06 00:00:00'): 0.1338651180267334,
Timestamp('1989-01-09 00:00:00'): 0.1338651180267334,
Timestamp('1989-01-10 00:00:00'): 0.13311512768268585,
Timestamp('1989-01-11 00:00:00'): 0.13311512768268585,
Timestamp('1989-01-12 00:00:00'): 0.13499003648757935,
Timestamp('1989-01-13 00:00:00'): 0.13499003648757935,
Timestamp('1989-01-16 00:00:00'): 0.13461506366729736,
Timestamp('1989-01-17 00:00:00'): 0.13573989272117615,
Timestamp('1989-01-18 00:00:00'): 0.13761481642723086,
Timestamp('1989-01-19 00:00:00'): 0.13723985850811005,
Timestamp('1989-01-20 00:00:00'): 0.13648992776870728,
Timestamp('1989-01-23 00:00:00'): 0.13573989272117615,
Timestamp('1989-01-24 00:00:00'): 0.13761481642723086,
Timestamp('1989-01-25 00:00:00'): 0.13761481642723086,
Timestamp('1989-01-26 00:00:00'): 0.14061467349529266,
Timestamp('1989-01-27 00:00:00'): 0.14211450517177582,
Timestamp('1989-01-30 00:00:00'): 0.14398930966854095}}
test_df = pd.DataFrame(test_dict)
arima = ts.arima()
arima.fit(test_df, train_size=0.8, freq='B', q_vals=[0])
transformed_test_df = arima.transform(test_df)

for col in transformed_test_df.keys():
count_nans = arima.best_params[col].p + arima.best_params[col].d
assert(count_nans == transformed_test_df[col].isna().sum())

# Test (1,2,0) Model
diff_test_df_high = test_df['High'].diff().diff()
assert(transformed_test_df['High'][3] == (arima.best_params['High'].const + diff_test_df_high[2] * arima.best_params['High'].ar_coef[0]))
assert(transformed_test_df['High'][4] == (arima.best_params['High'].const + diff_test_df_high[3] * arima.best_params['High'].ar_coef[0]))
assert(transformed_test_df['High'][-1] == (arima.best_params['High'].const + diff_test_df_high[-2] * arima.best_params['High'].ar_coef[0]))

# Test (2,1,0) Model
diff_test_df_low = test_df['Low'].diff()
assert(isclose(transformed_test_df['Low'][3], (arima.best_params['Low'].const + diff_test_df_low[2] * arima.best_params['Low'].ar_coef[0] + diff_test_df_low[1] * arima.best_params['Low'].ar_coef[1]), abs_tol=1e-8))
assert(isclose(transformed_test_df['Low'][4], (arima.best_params['Low'].const + diff_test_df_low[3] * arima.best_params['Low'].ar_coef[0] + diff_test_df_low[2] * arima.best_params['Low'].ar_coef[1]), abs_tol=1e-8))
assert(isclose(transformed_test_df['Low'][-1], (arima.best_params['Low'].const + diff_test_df_low[-2] * arima.best_params['Low'].ar_coef[0] + diff_test_df_low[-3] * arima.best_params['Low'].ar_coef[1]), abs_tol=1e-8))

# Test (1,2,0) Model
diff_test_df_close = test_df['Close'].diff()[1:].diff()
first_day = pd.Series([np.nan])
first_day.index = [diff_test_df_close.index[0] - pd.DateOffset(days=1)]
first_day.name = 'Close'
diff_test_df_close = pd.concat([first_day, diff_test_df_close])
diff_test_df_close.index.name = "Date"

assert(transformed_test_df['Close'][4] == (arima.best_params['Close'].const + diff_test_df_close[3] * arima.best_params['Close'].ar_coef[0]))
assert(transformed_test_df['Close'][5] == (arima.best_params['Close'].const + diff_test_df_close[4] * arima.best_params['Close'].ar_coef[0]))
assert(transformed_test_df['Close'][-1] == (arima.best_params['Close'].const+ diff_test_df_close[-2] * arima.best_params['Close'].ar_coef[0]))

# # Test (0,2,0) Model
# diff_test_df_volumne = test_df['Volume'].diff()[1:].diff()
# first_day = pd.Series([np.nan])
# first_day.index = [diff_test_df_volumne.index[0] - pd.DateOffset(days=1)]
# first_day.name = 'Volume'
# diff_test_df_volumne = pd.concat([first_day, diff_test_df_volumne])
# diff_test_df_volumne.index.name = "Date"
# assert(transformed_test_df['Volume'][2] == arima.best_params['Volume'].const + diff_test_df_volumne[2])
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test_arima_fit()
1 change: 1 addition & 0 deletions gs_quant/timeseries/__init__.py
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Expand Up @@ -22,5 +22,6 @@
from .technicals import *
from .measures import *
from .helper import *
from .arima import *

__name__ = 'timeseries'