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wiseplat
GitHub Repository: wiseplat/python-code
 Path: blob/master/ invest-robot-contest_tinkoff-invest-volume-analysis-robot-master/tests/test_merge_two_frames.py
5931 views
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import unittest

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import pandas as pd

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from pandas.util.testing import assert_frame_equal

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from utils.strategy_util import merge_two_frames

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class TestMergeTwoFrames(unittest.TestCase):

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    def setUp(self):

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        source = [

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.06, "quantity": 15,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 67.06, "quantity": 1,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 67.06, "quantity": 4,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.07, "quantity": 18,

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             "time": "2022-05-06 07:00:00.084909+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.075, "quantity": 15,

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             "time": "2022-05-06 07:00:00.084909+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.9, "quantity": 2,

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             "time": "2022-05-06 07:00:01.119835+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.81, "quantity": 15,

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             "time": "2022-05-06 07:00:05.408809+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.81, "quantity": 3,

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             "time": "2022-05-06 07:00:15.605217+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.745, "quantity": 10,

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             "time": "2022-05-06 07:00:46.862681+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:00:55.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:01.531078+00:00"},

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            # условно потерял данные с 07:01:01 по 07:02:08

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.795, "quantity": 50,

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             "time": "2022-05-06 07:02:08.652788+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.79, "quantity": 5,

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             "time": "2022-05-06 07:02:09.338233+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 25,

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             "time": "2022-05-06 07:02:30.858362+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 1,

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             "time": "2022-05-06 07:02:31.232032+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.9525, "quantity": 1,

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             "time": "2022-05-06 07:02:58.749556+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.9, "quantity": 1,

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             "time": "2022-05-06 07:03:01.484374+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.895, "quantity": 30,

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             "time": "2022-05-06 07:03:55.090231+00:00"}

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        ]

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        self.source_df = pd.DataFrame(source, columns=["figi", "direction", "price", "quantity", "time"])

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        # запрос данных с 07:01:01 по 07:02:08

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        # api вернет с 07:01:00 по 07:02:59

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        response = [

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:01.531078+00:00"},

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            # region новые данные

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:05.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:20.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:45.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:59.531078+00:00"},

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            # endregion новые данные

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.795, "quantity": 50,

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             "time": "2022-05-06 07:02:08.652788+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.79, "quantity": 5,

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             "time": "2022-05-06 07:02:09.338233+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 25,

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             "time": "2022-05-06 07:02:30.858362+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 1,

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             "time": "2022-05-06 07:02:31.232032+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.9525, "quantity": 1,

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             "time": "2022-05-06 07:02:58.749556+00:00"},

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        ]

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        self.response_df = pd.DataFrame(response, columns=["figi", "direction", "price", "quantity", "time"])

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        self.empty_df = pd.DataFrame([], columns=["figi", "direction", "price", "quantity", "time"])

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    def test_merge_two_frames(self):

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        expected = [

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.06, "quantity": 15,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 67.06, "quantity": 1,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 67.06, "quantity": 4,

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             "time": "2022-05-06 06:59:44.000144+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.07, "quantity": 18,

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             "time": "2022-05-06 07:00:00.084909+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 67.075, "quantity": 15,

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             "time": "2022-05-06 07:00:00.084909+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.9, "quantity": 2,

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             "time": "2022-05-06 07:00:01.119835+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.81, "quantity": 15,

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             "time": "2022-05-06 07:00:05.408809+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.81, "quantity": 3,

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             "time": "2022-05-06 07:00:15.605217+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.745, "quantity": 10,

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             "time": "2022-05-06 07:00:46.862681+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:00:55.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:01.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:05.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:20.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:45.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.6775, "quantity": 7,

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             "time": "2022-05-06 07:01:59.531078+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.795, "quantity": 50,

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             "time": "2022-05-06 07:02:08.652788+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.79, "quantity": 5,

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             "time": "2022-05-06 07:02:09.338233+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 25,

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             "time": "2022-05-06 07:02:30.858362+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.88, "quantity": 1,

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             "time": "2022-05-06 07:02:31.232032+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 1, "price": 66.9525, "quantity": 1,

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             "time": "2022-05-06 07:02:58.749556+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.9, "quantity": 1,

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             "time": "2022-05-06 07:03:01.484374+00:00"},

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            {"figi": "BBG0013HGFT4", "direction": 2, "price": 66.895, "quantity": 30,

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             "time": "2022-05-06 07:03:55.090231+00:00"}

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        ]

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        expected_df = pd.DataFrame(expected, columns=["figi", "direction", "price", "quantity", "time"])

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        assert_frame_equal(merge_two_frames(self.source_df, self.response_df), expected_df)

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    def test_boundary(self):

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        assert_frame_equal(merge_two_frames(self.source_df, self.empty_df), self.source_df)

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        assert_frame_equal(merge_two_frames(self.empty_df, self.response_df), self.response_df)

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        assert_frame_equal(merge_two_frames(self.empty_df, self.empty_df), self.empty_df)

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if __name__ == "__main__":

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    unittest.main()

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