Merge pull request #249 from kryofly/tests_dec28
tests for dataframe, whitelist and backtesting
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commit
80e1e64eae
@ -1,5 +1,6 @@
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# pragma pylint: disable=missing-docstring,W0212
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import math
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import pandas as pd
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from freqtrade import exchange, optimize
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from freqtrade.exchange import Bittrex
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@ -46,3 +47,110 @@ def test_backtest_1min_ticker_interval(default_conf, mocker):
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data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])
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results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 1, True)
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assert not results.empty
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def trim_dataframe(df, num):
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new = dict()
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for pair, pair_data in df.items():
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new[pair] = pair_data[-num:] # last 50 rows
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return new
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def load_data_test(what):
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data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])
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data = trim_dataframe(data, -40)
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pair = data['BTC_UNITEST']
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# Depending on the what parameter we now adjust the
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# loaded data:
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# pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
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# 'C': 0.123, 'V': 123.123,
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# 'T': '2017-11-04T23:02:00', 'BV': 0.123}]
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if what == 'raise':
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o = 0.001
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h = 0.001
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ll = 0.001
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c = 0.001
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ll -= 0.0001
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h += 0.0001
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for frame in pair:
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o += 0.0001
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h += 0.0001
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ll += 0.0001
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c += 0.0001
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# save prices rounded to satoshis
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frame['O'] = round(o, 9)
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frame['H'] = round(h, 9)
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frame['L'] = round(ll, 9)
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frame['C'] = round(c, 9)
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if what == 'lower':
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o = 0.001
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h = 0.001
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ll = 0.001
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c = 0.001
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ll -= 0.0001
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h += 0.0001
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for frame in pair:
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o -= 0.0001
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h -= 0.0001
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ll -= 0.0001
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c -= 0.0001
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# save prices rounded to satoshis
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frame['O'] = round(o, 9)
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frame['H'] = round(h, 9)
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frame['L'] = round(ll, 9)
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frame['C'] = round(c, 9)
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if what == 'sine':
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i = 0
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o = (2 + math.sin(i/10)) / 1000
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h = o
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ll = o
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c = o
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h += 0.0001
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ll -= 0.0001
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for frame in pair:
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o = (2 + math.sin(i/10)) / 1000
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h = (2 + math.sin(i/10)) / 1000 + 0.0001
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ll = (2 + math.sin(i/10)) / 1000 - 0.0001
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c = (2 + math.sin(i/10)) / 1000 - 0.000001
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# save prices rounded to satoshis
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frame['O'] = round(o, 9)
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frame['H'] = round(h, 9)
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frame['L'] = round(ll, 9)
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frame['C'] = round(c, 9)
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i += 1
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return data
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def simple_backtest(config, contour, num_results):
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data = load_data_test(contour)
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processed = optimize.preprocess(data)
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assert isinstance(processed, dict)
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results = backtest(config['stake_amount'], processed, 1, True)
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# results :: <class 'pandas.core.frame.DataFrame'>
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assert len(results) == num_results
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# Test backtest on offline data
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# loaded by freqdata/optimize/__init__.py::load_data()
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def test_backtest2(default_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
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results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True)
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num_resutls = len(results)
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assert num_resutls > 0
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def test_processed(default_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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data = load_data_test('raise')
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assert optimize.preprocess(data)
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def test_raise(default_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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tests = [['raise', 359], ['lower', 0], ['sine', 1734]]
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for [contour, numres] in tests:
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simple_backtest(default_conf, contour, numres)
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81
freqtrade/tests/test_acl_pair.py
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81
freqtrade/tests/test_acl_pair.py
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@ -0,0 +1,81 @@
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from freqtrade.main import refresh_whitelist
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# whitelist, blacklist, filtering, all of that will
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# eventually become some rules to run on a generic ACL engine
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# perhaps try to anticipate that by using some python package
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def assert_list_equal(l1, l2):
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for pair in l1:
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assert pair in l2
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for pair in l2:
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assert pair in l1
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def whitelist_conf():
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return {
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"stake_currency": "BTC",
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"exchange": {
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"pair_whitelist": [
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"BTC_ETH",
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"BTC_TKN",
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"BTC_TRST",
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"BTC_SWT",
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"BTC_BCC"
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],
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},
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}
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def get_health():
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return [{'Currency': 'ETH',
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'IsActive': True
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},
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{'Currency': 'TKN',
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'IsActive': True
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}]
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def get_health_empty():
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return []
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# below three test could be merged into a single
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# test that ran randomlly generated health lists
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def test_refresh_whitelist(mocker):
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conf = whitelist_conf()
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mocker.patch.dict('freqtrade.main._CONF', conf)
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mocker.patch.multiple('freqtrade.main.exchange',
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get_wallet_health=get_health)
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# no argument: use the whitelist provided by config
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refresh_whitelist()
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whitelist = ['BTC_ETH', 'BTC_TKN']
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pairslist = conf['exchange']['pair_whitelist']
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# Ensure all except those in whitelist are removed
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assert_list_equal(whitelist, pairslist)
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def test_refresh_whitelist_dynamic(mocker):
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conf = whitelist_conf()
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mocker.patch.dict('freqtrade.main._CONF', conf)
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mocker.patch.multiple('freqtrade.main.exchange',
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get_wallet_health=get_health)
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# argument: use the whitelist dynamically by exchange-volume
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whitelist = ['BTC_ETH', 'BTC_TKN']
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refresh_whitelist(whitelist)
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pairslist = conf['exchange']['pair_whitelist']
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assert_list_equal(whitelist, pairslist)
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def test_refresh_whitelist_dynamic_empty(mocker):
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conf = whitelist_conf()
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mocker.patch.dict('freqtrade.main._CONF', conf)
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mocker.patch.multiple('freqtrade.main.exchange',
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get_wallet_health=get_health_empty)
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# argument: use the whitelist dynamically by exchange-volume
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whitelist = []
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conf['exchange']['pair_whitelist'] = []
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refresh_whitelist(whitelist)
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pairslist = conf['exchange']['pair_whitelist']
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assert_list_equal(whitelist, pairslist)
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27
freqtrade/tests/test_dataframe.py
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27
freqtrade/tests/test_dataframe.py
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import pandas
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from freqtrade import analyze
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import freqtrade.optimize
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_pairs = ['BTC_ETH']
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def load_dataframe_pair(pairs):
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ld = freqtrade.optimize.load_data(ticker_interval=5, pairs=pairs)
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assert isinstance(ld, dict)
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assert isinstance(pairs[0], str)
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dataframe = ld[pairs[0]]
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dataframe = analyze.analyze_ticker(dataframe)
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return dataframe
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def test_dataframe_load():
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dataframe = load_dataframe_pair(_pairs)
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assert isinstance(dataframe, pandas.core.frame.DataFrame)
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def test_dataframe_columns_exists():
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dataframe = load_dataframe_pair(_pairs)
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assert 'high' in dataframe.columns
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assert 'low' in dataframe.columns
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assert 'close' in dataframe.columns
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