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https://github.com/OpenBB-finance/OpenBB.git
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fabd814175
* Added loger to baseclass controller refined sequential logic of switch fixed prefix / for multiple commands at terminal startup
556 lines
15 KiB
Python
556 lines
15 KiB
Python
# IMPORTATION STANDARD
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import os
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from datetime import datetime
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# IMPORTATION THIRDPARTY
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import pandas as pd
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import pytest
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# IMPORTATION INTERNAL
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pred_controller = pytest.importorskip(
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modname="gamestonk_terminal.etf.prediction_techniques.pred_controller",
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reason="Requires prediction dependencies, like tensorflow",
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)
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# pylint: disable=E1101
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# pylint: disable=W0603
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# pytest.skip(allow_module_level=True)
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DF_ETF = pd.DataFrame.from_dict(
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data={
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pd.Timestamp("2020-11-30 00:00:00"): {
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"Open": 75.69999694824219,
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"High": 76.08999633789062,
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"Low": 75.41999816894531,
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"Close": 75.75,
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"Adj Close": 71.90919494628906,
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"Volume": 5539100,
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"date_id": 1,
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"OC_High": 75.75,
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"OC_Low": 75.69999694824219,
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},
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pd.Timestamp("2020-12-01 00:00:00"): {
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"Open": 76.0199966430664,
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"High": 77.12999725341797,
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"Low": 75.69000244140625,
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"Close": 77.02999877929688,
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"Adj Close": 73.1242904663086,
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"Volume": 6791700,
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"date_id": 2,
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"OC_High": 77.02999877929688,
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"OC_Low": 76.0199966430664,
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},
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},
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orient="index",
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)
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EMPTY_DF = pd.DataFrame()
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PRED_CONTROLLER = pred_controller.PredictionTechniquesController(
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ticker="MOCK_TICKER",
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start=datetime.strptime("2020-12-15", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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)
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"queue, expected",
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[
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(["pick", "help"], []),
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(["quit", "help"], ["help"]),
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],
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)
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def test_menu_with_queue(expected, mocker, queue):
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mocker.patch(
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target=(
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"gamestonk_terminal.etf.prediction_techniques.pred_controller."
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"PredictionTechniquesController.switch"
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),
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return_value=["quit"],
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)
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result_menu = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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queue=queue,
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).menu()
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assert result_menu == expected
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@pytest.mark.vcr(record_mode="none")
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def test_menu_without_queue_completion(mocker):
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# ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU
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mocker.patch(
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target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT",
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new=True,
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)
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mocker.patch(
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target="gamestonk_terminal.parent_classes.session",
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)
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mocker.patch(
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target="gamestonk_terminal.parent_classes.session.prompt",
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return_value="quit",
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)
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# DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER
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mocker.patch.object(
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target=pred_controller.gtff,
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attribute="USE_PROMPT_TOOLKIT",
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new=True,
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)
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mocker.patch(
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target="gamestonk_terminal.etf.prediction_techniques.pred_controller.session",
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)
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mocker.patch(
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target="gamestonk_terminal.etf.prediction_techniques.pred_controller.session.prompt",
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return_value="quit",
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)
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result_menu = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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queue=None,
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).menu()
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assert result_menu == []
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"mock_input",
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["picked", "load help", "mock"],
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)
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def test_menu_without_queue_sys_exit(mock_input, mocker):
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# DISABLE AUTO-COMPLETION
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mocker.patch.object(
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target=pred_controller.gtff,
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attribute="USE_PROMPT_TOOLKIT",
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new=False,
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)
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mocker.patch(
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target="gamestonk_terminal.etf.prediction_techniques.pred_controller.session",
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return_value=None,
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)
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# MOCK USER INPUT
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mocker.patch("builtins.input", return_value=mock_input)
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# MOCK SWITCH
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class SystemExitSideEffect:
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def __init__(self):
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self.first_call = True
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def __call__(self, *args, **kwargs):
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if self.first_call:
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self.first_call = False
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raise SystemExit()
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return ["quit"]
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mock_switch = mocker.Mock(side_effect=SystemExitSideEffect())
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mocker.patch(
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target=(
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"gamestonk_terminal.etf.prediction_techniques.pred_controller."
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"PredictionTechniquesController.switch"
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),
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new=mock_switch,
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)
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result_menu = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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queue=None,
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).menu()
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assert result_menu == []
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.record_stdout
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@pytest.mark.parametrize(
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"start",
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[
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datetime.strptime("2020-12-01", "%Y-%m-%d"),
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],
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)
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def test_print_help(start):
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controller = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=start,
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interval="1440min",
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data=DF_ETF,
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)
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controller.print_help()
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"an_input, expected_queue",
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[
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("", []),
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("/help", ["home", "help"]),
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("help/help", ["help", "help"]),
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("q", ["quit"]),
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("h", []),
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("r", ["quit", "quit", "reset", "etf", "load ARKW", "pred"]),
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],
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)
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def test_switch(an_input, expected_queue):
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controller = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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queue=None,
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)
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queue = controller.switch(an_input=an_input)
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assert queue == expected_queue
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@pytest.mark.vcr(record_mode="none")
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def test_call_cls(mocker):
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mocker.patch("os.system")
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controller = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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)
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controller.call_cls([])
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assert controller.queue == []
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os.system.assert_called_once_with("cls||clear")
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"func, queue, expected_queue",
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[
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(
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"call_exit",
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[],
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[
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"quit",
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"quit",
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"quit",
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],
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),
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("call_exit", ["help"], ["quit", "quit", "quit", "help"]),
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("call_home", [], ["quit", "quit"]),
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("call_help", [], []),
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("call_quit", [], ["quit"]),
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("call_quit", ["help"], ["quit", "help"]),
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(
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"call_reset",
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[],
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["quit", "quit", "reset", "etf", "load ARKW", "pred"],
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),
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(
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"call_reset",
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["help"],
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["quit", "quit", "reset", "etf", "load ARKW", "pred", "help"],
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),
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],
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)
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def test_call_func_expect_queue(expected_queue, queue, func):
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controller = pred_controller.PredictionTechniquesController(
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ticker="ARKW",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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interval="1440min",
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data=DF_ETF,
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queue=queue,
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)
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result = getattr(controller, func)([])
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assert result is None
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assert controller.queue == expected_queue
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"tested_func, mocked_func, other_args, called_with",
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[
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(
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"call_ets",
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"ets_view.display_exponential_smoothing",
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[
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"--day=1",
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"--trend=A",
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"--seasonal=M",
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"--period=2",
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"--end=2020-12-15",
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"--export=png",
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],
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dict(
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ticker="MOCK_TICKER",
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values=PRED_CONTROLLER.data["AdjClose"],
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n_predict=1,
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trend="A",
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seasonal="M",
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seasonal_periods=2,
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s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
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export="png",
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),
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),
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(
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"call_knn",
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"knn_view.display_k_nearest_neighbors",
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[
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"--input=1",
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"--days=2",
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"--jumps=3",
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"--neighbors=4",
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"--end=2020-12-01",
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"--test_size=0.1",
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"--no_shuffle",
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],
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dict(
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ticker="MOCK_TICKER",
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data=PRED_CONTROLLER.data["AdjClose"],
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n_neighbors=4,
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n_input_days=1,
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n_predict_days=2,
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test_size=0.1,
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end_date=datetime.strptime("2020-12-01", "%Y-%m-%d"),
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no_shuffle=False,
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),
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),
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(
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"call_regression",
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"regression_view.display_regression",
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[
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"4",
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"--input=1",
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"--days=2",
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"--jumps=3",
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"--end=2020-12-15",
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"--export=png",
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],
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dict(
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dataset="MOCK_TICKER",
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values=PRED_CONTROLLER.data["AdjClose"],
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poly_order=4,
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n_input=1,
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n_predict=2,
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n_jumps=3,
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s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
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export="png",
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),
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),
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(
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"call_arima",
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"arima_view.display_arima",
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[
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"--days=1",
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"--ic=bic",
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"--seasonal",
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"--order=p,q,d",
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"--results",
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"--end=2020-12-15",
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"--export=png",
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],
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dict(
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dataset="MOCK_TICKER",
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values=PRED_CONTROLLER.data["AdjClose"],
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arima_order="p,q,d",
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n_predict=1,
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seasonal=True,
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ic="bic",
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results=True,
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s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
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export="png",
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),
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),
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(
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"call_mlp",
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"neural_networks_view.display_mlp",
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[
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"--days=1",
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"--input=2",
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"--epochs=3",
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"--end=2020-12-15",
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"--batch_size=4",
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"--loops=5",
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# "--valid", "0.1",
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"--lr=0.01",
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"--no_shuffle",
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],
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dict(
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dataset="MOCK_TICKER",
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data=PRED_CONTROLLER.data["AdjClose"],
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n_input_days=2,
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n_predict_days=1,
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learning_rate=0.01,
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epochs=3,
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batch_size=4,
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test_size=0.1,
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n_loops=5,
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no_shuffle=False,
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),
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),
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(
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"call_rnn",
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"neural_networks_view.display_rnn",
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[
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"--days=1",
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"--input=2",
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"--epochs=3",
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"--end=2020-12-15",
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"--batch_size=4",
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"--loops=5",
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# "--valid", "0.1",
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"--lr=0.01",
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"--no_shuffle",
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],
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dict(
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dataset="MOCK_TICKER",
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data=PRED_CONTROLLER.data["AdjClose"],
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n_input_days=2,
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n_predict_days=1,
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learning_rate=0.01,
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epochs=3,
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batch_size=4,
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test_size=0.1,
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n_loops=5,
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no_shuffle=False,
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),
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),
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(
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"call_lstm",
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"neural_networks_view.display_lstm",
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[
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"--days=1",
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"--input=2",
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"--epochs=3",
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"--end=2020-12-15",
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"--batch_size=4",
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"--loops=5",
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# "--valid", "0.1",
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"--lr=0.01",
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"--no_shuffle",
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],
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dict(
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dataset="MOCK_TICKER",
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data=PRED_CONTROLLER.data["AdjClose"],
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n_input_days=2,
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n_predict_days=1,
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learning_rate=0.01,
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epochs=3,
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batch_size=4,
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test_size=0.1,
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n_loops=5,
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no_shuffle=False,
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),
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),
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(
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"call_conv1d",
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"neural_networks_view.display_conv1d",
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[
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"--days=1",
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"--input=2",
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"--epochs=3",
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"--end=2020-12-15",
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"--batch_size=4",
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"--loops=5",
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# "--valid", "0.1",
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"--lr=0.01",
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"--no_shuffle",
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],
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dict(
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dataset="MOCK_TICKER",
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data=PRED_CONTROLLER.data["AdjClose"],
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n_input_days=2,
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n_predict_days=1,
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learning_rate=0.01,
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epochs=3,
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batch_size=4,
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test_size=0.1,
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n_loops=5,
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no_shuffle=False,
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),
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),
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(
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"call_mc",
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"mc_view.display_mc_forecast",
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[
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"--days=1",
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# "--num=2",
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"--dist=normal",
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"--export=png",
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],
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dict(
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data=PRED_CONTROLLER.data["AdjClose"],
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n_future=1,
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n_sims=100,
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use_log=False,
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export="png",
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),
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),
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],
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)
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def test_call_func(tested_func, mocked_func, other_args, called_with, mocker):
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mock = mocker.Mock()
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mocker.patch(
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"gamestonk_terminal.etf.prediction_techniques.pred_controller." + mocked_func,
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new=mock,
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)
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getattr(PRED_CONTROLLER, tested_func)(other_args=other_args)
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if isinstance(called_with, dict):
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mock.assert_called_once_with(**called_with)
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elif isinstance(called_with, list):
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mock.assert_called_once_with(*called_with)
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else:
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mock.assert_called_once()
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|
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@pytest.mark.vcr(record_mode="none")
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@pytest.mark.parametrize(
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"func",
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[
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"call_load",
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"call_pick",
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"call_ets",
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"call_knn",
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"call_regression",
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"call_arima",
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"call_mlp",
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"call_rnn",
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"call_lstm",
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"call_conv1d",
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"call_mc",
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],
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)
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def test_call_func_no_parser(func, mocker):
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mock = mocker.Mock(return_value=None)
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mocker.patch.object(
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target=pred_controller,
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attribute="parse_known_args_and_warn",
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new=mock,
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)
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mocker.patch.object(
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target=pred_controller.pred_helper,
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attribute="parse_known_args_and_warn",
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new=mock,
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)
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controller = pred_controller.PredictionTechniquesController(
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ticker="MOCK_TICKER",
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start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
|
|
interval="1440min",
|
|
data=DF_ETF.copy(),
|
|
)
|
|
|
|
func_result = getattr(controller, func)(other_args=["PM"])
|
|
assert func_result is None
|
|
assert controller.queue == []
|
|
getattr(pred_controller, "parse_known_args_and_warn").assert_called_once()
|