test_techindicator.py

__author__ = 'saeedamen'  # Saeed Amen

#
# Copyright 2016-2020 Cuemacro - https://www.cuemacro.com / @cuemacro
#
# 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:https://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.
#

import pytest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from finmarketpy.economics.techindicator import TechParams, TechIndicator

tech_params = TechParams(fillna=True, atr_period=14, sma_period=3,
                         green_n=4, green_count=9, red_n=2, red_count=13)
tech_ind = TechIndicator()
dates = pd.date_range(start='1/1/2018', end='1/08/2018')


def get_cols_name(n):
    return ['Asset%d.close' % x for x in range(1, n + 1)]


def test_sma():
    indicator_name = 'SMA'
    # Test Case 1: constant prices
    cols = get_cols_name(1)
    data_df = pd.DataFrame(index=dates, columns=cols, data=1)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=1)
    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 2: Normal case with one single security
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 3: Normal case with multiple securities
    cols = get_cols_name(10)
    col_prices = np.array(range(1, 9))
    data_df = pd.DataFrame(index=dates, columns=cols, data=np.tile(col_prices, (len(cols), 1)).T)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 4: Decreasing price with multiple securities
    cols = get_cols_name(10)
    col_prices = np.array(range(8, 0, -1))
    data_df = pd.DataFrame(index=dates, columns=cols, data=np.tile(col_prices, (len(cols), 1)).T)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 5: With SOME missing data
    cols = get_cols_name(1)
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    data_df.iloc[3] = np.nan

    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=[np.nan, np.nan, 2, 2.67, 3.67,
                                                                             4.67, 6, 7])
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan

    assert_frame_equal(df.apply(lambda x: round(x, 2)), expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 6: With not enough data
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_params.sma_period = 20

    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=np.nan)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=np.nan)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

def test_roc():
    indicator_name = 'ROC'
    # Test Case 1: constant prices
    cols = get_cols_name(1)
    tech_params.roc_period = 3
    data_df = pd.DataFrame(index=dates, columns=cols, data=1)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=0)
    expected_signal_df.iloc[:tech_params.roc_period] = np.nan
    expected_df.iloc[:tech_params.roc_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 2: Increasing prices, fixed rate
    cols = get_cols_name(1)
    tech_params.roc_period = 1
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=1)
    expected_signal_df.iloc[:tech_params.roc_period] = np.nan
    expected_df.iloc[:tech_params.roc_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    tech_params.roc_period = 2
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=3)
    expected_signal_df.iloc[:tech_params.roc_period] = np.nan
    expected_df.iloc[:tech_params.roc_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    tech_params.roc_period = 3
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=7)
    expected_signal_df.iloc[:tech_params.roc_period] = np.nan
    expected_df.iloc[:tech_params.roc_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 3: Missing values
    cols = get_cols_name(1)
    tech_params.roc_period = 1
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    data_df.iloc[3] = np.nan
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=[1, 1, 1, -1, 1, 1, 1, 1])
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=[1, 1, 1, 0, 3, 1, 1, 1])
    expected_signal_df.iloc[:tech_params.roc_period] = np.nan
    expected_df.iloc[:tech_params.roc_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

def test_sma2():
    indicator_name = 'SMA2'
    tech_params.sma_period = 2
    tech_params.sma2_period = 3

    # Test Case 1: Increasing prices
    cols = get_cols_name(1)
    signals = ['SMA', 'SMA2']
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, sig])
                                                     for col in cols for sig in signals],
                               data=[
                                   [np.nan, np.nan],
                                   [1.5, np.nan],
                                   [2.5, 2.0],
                                   [3.5, 3.0],
                                   [4.5, 4.0],
                                   [5.5, 5.0],
                                   [6.5, 6.0],
                                   [7.5, 7.0],
                               ])
    expected_signal_df.iloc[:tech_params.sma2_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 2: Decreasing prices
    cols = get_cols_name(1)
    signals = ['SMA', 'SMA2']
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(8, 0, -1)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, sig])
                                                     for col in cols for sig in signals],
                               data=[
                                   [np.nan, np.nan],
                                   [7.5, np.nan],
                                   [6.5, 7.0],
                                   [5.5, 6.0],
                                   [4.5, 5.0],
                                   [3.5, 4.0],
                                   [2.5, 3.0],
                                   [1.5, 2.0],
                               ])
    expected_signal_df.iloc[:tech_params.sma2_period] = np.nan
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 3: Costant prices
    cols = get_cols_name(1)
    signals = ['SMA', 'SMA2']
    data_df = pd.DataFrame(index=dates, columns=cols, data=1)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, sig])
                                                     for col in cols for sig in signals],
                               data=np.tile(np.ones(len(dates)), (2, 1)).T)
    expected_signal_df.iloc[:tech_params.sma2_period] = np.nan
    expected_df.iloc[:tech_params.sma2_period - 1] = np.nan
    expected_df.set_value('2018-01-02', 'Asset1.close SMA', 1.0)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)


def test_polarity():
    indicator_name = 'polarity'

    # Test Case 1: constant prices
    cols = get_cols_name(1)
    data_df = pd.DataFrame(index=dates, columns=cols, data=1)
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity', 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity'])
                                                     for col in cols], data=1)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df, check_dtype=False)

    # Test Case 2: Increasing prices
    cols = get_cols_name(1)
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity', 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity'])
                                                     for col in cols], data=data_df.values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df, check_dtype=False)

    # Test Case 3: Decreasing prices
    cols = get_cols_name(1)
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(-1, -9, -1)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity', 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity'])
                                                     for col in cols], data=data_df.values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df, check_dtype=False)

    # Test Case 4: Missing values
    cols = get_cols_name(1)
    tech_params.roc_period = 1
    data_df = pd.DataFrame(index=dates, columns=cols, data=[1, 2, 4, 8, 16, 32, 64, 128])
    data_df.iloc[3] = np.nan
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity', 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, 'Polarity'])
                                                     for col in cols],
                               data=[1.0, 2.0, 4.0, 4.0, 16.0, 32.0, 64.0, 128.0])
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df, check_dtype=False)


def test_attr():
    """Testing of attributes such as long only
    """
    indicator_name = 'SMA'
    tech_params = TechParams()

    # Test Case 1: Only longs or only shorts
    # Test Case 1.1: Only longs
    cols = get_cols_name(1)
    tech_params.sma_period = 3
    tech_params.only_allow_longs = True
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(8, 0, -1)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=0)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 1.1: Only shorts
    tech_params = TechParams()
    tech_params.sma_period = 3
    tech_params.only_allow_shorts = True
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=0)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(8, 0, -1)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 2: Check incompatibility between only_allow_longs and only_allow_shorts
    tech_params = TechParams()
    tech_params.only_allow_shorts = True
    with pytest.raises(Exception):
        tech_params.only_allow_longs = True

    tech_params = TechParams()
    tech_params.only_allow_longs = True
    with pytest.raises(Exception):
        tech_params.only_allow_shorts = True

    # Test Case 3: test strip_signal_name
    tech_params = TechParams()
    tech_params.sma_period = 3
    tech_params.strip_signal_name = True
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=cols, data=1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    # Test Case 4: test signal multiplier
    tech_params = TechParams()
    tech_params.sma_period = 3
    tech_params.signal_mult = -1
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=-1)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    tech_params.signal_mult = 0
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=0)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)

    tech_params.signal_mult = 3
    data_df = pd.DataFrame(index=dates, columns=cols, data=list(range(1, 9)))
    tech_ind.create_tech_ind(data_df, indicator_name, tech_params)
    expected_signal_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name, 'Signal'])
                                                            for col in cols], data=3)
    expected_df = pd.DataFrame(index=dates, columns=[' '.join([col, indicator_name])
                                                     for col in cols], data=data_df.shift().values)
    df = tech_ind.get_techind()
    signal_df = tech_ind.get_signal()

    expected_signal_df.iloc[:tech_params.sma_period] = np.nan
    expected_df.iloc[:tech_params.sma_period - 1] = np.nan

    assert_frame_equal(df, expected_df)
    assert_frame_equal(signal_df, expected_signal_df)