change analyze tests to use full json dump from bittrex
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@ -1,4 +1,5 @@
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# pragma pylint: disable=missing-docstring
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# pragma pylint: disable=missing-docstring
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import json
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import pytest
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import pytest
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import arrow
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import arrow
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from pandas import DataFrame
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from pandas import DataFrame
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@ -6,31 +7,19 @@ from pandas import DataFrame
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from freqtrade.analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, \
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from freqtrade.analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, \
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get_buy_signal
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get_buy_signal
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RESULT_BITTREX = {
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'success': True,
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'message': '',
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'result': [
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{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 22.17210568, 'T': '2017-08-30T10:40:00', 'BV': 0.01448082},
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{'O': 0.00066194, 'H': 0.00066195, 'L': 0.00066194, 'C': 0.00066195, 'V': 33.4727437, 'T': '2017-08-30T10:34:00', 'BV': 0.02215696},
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{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 53.85127609, 'T': '2017-08-30T10:37:00', 'BV': 0.0351708},
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{'O': 0.00066194, 'H': 0.00066194, 'L': 0.00065311, 'C': 0.00065311, 'V': 46.29210665, 'T': '2017-08-30T10:42:00', 'BV': 0.03063118},
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]
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}
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@pytest.fixture
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@pytest.fixture
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def result():
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def result():
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return parse_ticker_dataframe(RESULT_BITTREX['result'], arrow.get('2017-08-30T10:00:00'))
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with open('freqtrade/tests/testdata/btc-eth.json') as data_file:
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data = json.load(data_file)
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return parse_ticker_dataframe(data['result'], arrow.get('2017-08-30T10:00:00'))
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def test_dataframe_has_correct_columns(result):
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def test_dataframe_has_correct_columns(result):
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assert result.columns.tolist() == \
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assert result.columns.tolist() == \
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['close', 'high', 'low', 'open', 'date', 'volume']
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['close', 'high', 'low', 'open', 'date', 'volume']
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def test_orders_by_date(result):
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def test_dataframe_has_correct_length(result):
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assert result['date'].tolist() == \
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assert len(result.index) == 5751
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['2017-08-30T10:34:00',
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'2017-08-30T10:37:00',
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'2017-08-30T10:40:00',
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'2017-08-30T10:42:00']
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def test_populates_buy_trend(result):
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def test_populates_buy_trend(result):
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dataframe = populate_buy_trend(populate_indicators(result))
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dataframe = populate_buy_trend(populate_indicators(result))
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