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Adapt tests for new column names

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Matthias 2020-06-26 09:21:28 +02:00
parent b068e7c564
commit 2881718733
7 changed files with 34 additions and 34 deletions
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24
tests/data/test_btanalysis.py
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@ -47,7 +47,7 @@ def test_load_trades_from_db(default_conf, fee, mocker):
assert len(trades) == 3 assert len(trades) == 3
assert isinstance(trades, DataFrame) assert isinstance(trades, DataFrame)
assert "pair" in trades.columns assert "pair" in trades.columns
assert "open_time" in trades.columns assert "open_date" in trades.columns
assert "profit_percent" in trades.columns assert "profit_percent" in trades.columns
for col in BT_DATA_COLUMNS: for col in BT_DATA_COLUMNS:
@ -67,13 +67,13 @@ def test_extract_trades_of_period(testdatadir):
{'pair': [pair, pair, pair, pair], {'pair': [pair, pair, pair, pair],
'profit_percent': [0.0, 0.1, -0.2, -0.5], 'profit_percent': [0.0, 0.1, -0.2, -0.5],
'profit_abs': [0.0, 1, -2, -5], 'profit_abs': [0.0, 1, -2, -5],
'open_time': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime, 'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
Arrow(2017, 11, 14, 9, 41, 0).datetime, Arrow(2017, 11, 14, 9, 41, 0).datetime,
Arrow(2017, 11, 14, 14, 20, 0).datetime, Arrow(2017, 11, 14, 14, 20, 0).datetime,
Arrow(2017, 11, 15, 3, 40, 0).datetime, Arrow(2017, 11, 15, 3, 40, 0).datetime,
], utc=True ], utc=True
), ),
'close_time': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime, 'close_date': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
Arrow(2017, 11, 14, 10, 41, 0).datetime, Arrow(2017, 11, 14, 10, 41, 0).datetime,
Arrow(2017, 11, 14, 15, 25, 0).datetime, Arrow(2017, 11, 14, 15, 25, 0).datetime,
Arrow(2017, 11, 15, 3, 55, 0).datetime, Arrow(2017, 11, 15, 3, 55, 0).datetime,
@ -82,10 +82,10 @@ def test_extract_trades_of_period(testdatadir):
trades1 = extract_trades_of_period(data, trades) trades1 = extract_trades_of_period(data, trades)
# First and last trade are dropped as they are out of range # First and last trade are dropped as they are out of range
assert len(trades1) == 2 assert len(trades1) == 2
assert trades1.iloc[0].open_time == Arrow(2017, 11, 14, 9, 41, 0).datetime assert trades1.iloc[0].open_date == Arrow(2017, 11, 14, 9, 41, 0).datetime
assert trades1.iloc[0].close_time == Arrow(2017, 11, 14, 10, 41, 0).datetime assert trades1.iloc[0].close_date == Arrow(2017, 11, 14, 10, 41, 0).datetime
assert trades1.iloc[-1].open_time == Arrow(2017, 11, 14, 14, 20, 0).datetime assert trades1.iloc[-1].open_date == Arrow(2017, 11, 14, 14, 20, 0).datetime
assert trades1.iloc[-1].close_time == Arrow(2017, 11, 14, 15, 25, 0).datetime assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
def test_analyze_trade_parallelism(default_conf, mocker, testdatadir): def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
@ -174,7 +174,7 @@ def test_create_cum_profit1(testdatadir):
filename = testdatadir / "backtest-result_test.json" filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename) bt_data = load_backtest_data(filename)
# Move close-time to "off" the candle, to make sure the logic still works # Move close-time to "off" the candle, to make sure the logic still works
bt_data.loc[:, 'close_time'] = bt_data.loc[:, 'close_time'] + DateOffset(seconds=20) bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
timerange = TimeRange.parse_timerange("20180110-20180112") timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe='5m', df = load_pair_history(pair="TRX/BTC", timeframe='5m',
@ -213,11 +213,11 @@ def test_calculate_max_drawdown2():
-0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711] -0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711]
dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))] dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
df = DataFrame(zip(values, dates), columns=['profit', 'open_time']) df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
# sort by profit and reset index # sort by profit and reset index
df = df.sort_values('profit').reset_index(drop=True) df = df.sort_values('profit').reset_index(drop=True)
df1 = df.copy() df1 = df.copy()
drawdown, h, low = calculate_max_drawdown(df, date_col='open_time', value_col='profit') drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
# Ensure df has not been altered. # Ensure df has not been altered.
assert df.equals(df1) assert df.equals(df1)
@ -226,6 +226,6 @@ def test_calculate_max_drawdown2():
assert h < low assert h < low
assert drawdown == 0.091755 assert drawdown == 0.091755
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_time']) df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'): with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
calculate_max_drawdown(df, date_col='open_time', value_col='profit') calculate_max_drawdown(df, date_col='open_date', value_col='profit')

16
tests/edge/test_edge.py
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@ -163,8 +163,8 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades): for c, trade in enumerate(data.trades):
res = results.iloc[c] res = results.iloc[c]
assert res.exit_type == trade.sell_reason assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None) assert res.open_date == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None) assert res.close_date == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None)
def test_adjust(mocker, edge_conf): def test_adjust(mocker, edge_conf):
@ -354,8 +354,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1, 'open_index': 1,
'close_index': 1, 'close_index': 1,
'trade_duration': '', 'trade_duration': '',
@ -367,8 +367,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4, 'open_index': 4,
'close_index': 4, 'close_index': 4,
'trade_duration': '', 'trade_duration': '',
@ -380,8 +380,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6, 'open_index': 6,
'close_index': 7, 'close_index': 7,
'trade_duration': '', 'trade_duration': '',

4
tests/optimize/test_backtest_detail.py
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@ -395,5 +395,5 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades): for c, trade in enumerate(data.trades):
res = results.iloc[c] res = results.iloc[c]
assert res.sell_reason == trade.sell_reason assert res.sell_reason == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick) assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick) assert res.close_date == _get_frame_time_from_offset(trade.close_tick)

16
tests/optimize/test_backtesting.py
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@ -459,10 +459,10 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
{'pair': [pair, pair], {'pair': [pair, pair],
'profit_percent': [0.0, 0.0], 'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0], 'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime, 'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
), ),
'close_time': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime, 'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime,
Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True), Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
'open_index': [78, 184], 'open_index': [78, 184],
'close_index': [125, 192], 'close_index': [125, 192],
@ -475,12 +475,12 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
pd.testing.assert_frame_equal(results, expected) pd.testing.assert_frame_equal(results, expected)
data_pair = processed[pair] data_pair = processed[pair]
for _, t in results.iterrows(): for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]] ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate # Check open trade rate alignes to open rate
assert ln is not None assert ln is not None
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6) assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low # check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_time"]] ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round( round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)) t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
@ -756,10 +756,10 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'], pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
'profit_percent': [0.0, 0.0], 'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0], 'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00', ], utc=True '2018-01-30 03:30:00', ], utc=True
), ),
'close_time': pd.to_datetime(['2018-01-29 20:45:00', 'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00', ], utc=True), '2018-01-30 05:35:00', ], utc=True),
'open_index': [78, 184], 'open_index': [78, 184],
'close_index': [125, 192], 'close_index': [125, 192],
@ -772,11 +772,11 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'], pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
'profit_percent': [0.03, 0.01, 0.1], 'profit_percent': [0.03, 0.01, 0.1],
'profit_abs': [0.01, 0.02, 0.2], 'profit_abs': [0.01, 0.02, 0.2],
'open_time': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00', '2018-01-30 03:30:00',
'2018-01-30 05:30:00'], utc=True '2018-01-30 05:30:00'], utc=True
), ),
'close_time': pd.to_datetime(['2018-01-29 20:45:00', 'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00', '2018-01-30 05:35:00',
'2018-01-30 08:30:00'], utc=True), '2018-01-30 08:30:00'], utc=True),
'open_index': [78, 184, 185], 'open_index': [78, 184, 185],

2
tests/optimize/test_hyperopt.py
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@ -46,7 +46,7 @@ def hyperopt_results():
'profit_abs': [-0.2, 0.4, 0.6], 'profit_abs': [-0.2, 0.4, 0.6],
'trade_duration': [10, 30, 10], 'trade_duration': [10, 30, 10],
'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI], 'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
'close_time': 'close_date':
[ [
datetime(2019, 1, 1, 9, 26, 3, 478039), datetime(2019, 1, 1, 9, 26, 3, 478039),
datetime(2019, 2, 1, 9, 26, 3, 478039), datetime(2019, 2, 1, 9, 26, 3, 478039),

4
tests/optimize/test_optimize_reports.py
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@ -204,11 +204,11 @@ def test_backtest_record(default_conf, fee, mocker):
"UNITTEST/BTC", "UNITTEST/BTC"], "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780], "profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003], "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_time": [Arrow(2017, 11, 14, 19, 32, 00).datetime, "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime, Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime, Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime], Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_time": [Arrow(2017, 11, 14, 21, 35, 00).datetime, "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime, Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime, Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime], Arrow(2017, 11, 14, 22, 58, 00).datetime],

2
tests/test_plotting.py
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@ -267,7 +267,7 @@ def test_generate_profit_graph(testdatadir):
trades = load_backtest_data(filename) trades = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112") timerange = TimeRange.parse_timerange("20180110-20180112")
pairs = ["TRX/BTC", "XLM/BTC"] pairs = ["TRX/BTC", "XLM/BTC"]
trades = trades[trades['close_time'] < pd.Timestamp('2018-01-12', tz='UTC')] trades = trades[trades['close_date'] < pd.Timestamp('2018-01-12', tz='UTC')]
data = history.load_data(datadir=testdatadir, data = history.load_data(datadir=testdatadir,
pairs=pairs, pairs=pairs,