# pragma pylint: disable=missing-docstring, C0103 import logging from pathlib import Path from shutil import copyfile import numpy as np import pandas as pd import pytest from freqtrade.configuration.timerange import TimeRange from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format, ohlcv_fill_up_missing_data, ohlcv_to_dataframe, reduce_dataframe_footprint, trades_df_remove_duplicates, trades_dict_to_list, trades_remove_duplicates, trades_to_ohlcv, trim_dataframe) from freqtrade.data.history import (get_timerange, load_data, load_pair_history, validate_backtest_data) from freqtrade.data.history.idatahandler import IDataHandler from freqtrade.enums import CandleType from tests.conftest import generate_test_data, log_has, log_has_re from tests.data.test_history import _clean_test_file def test_dataframe_correct_columns(dataframe_1m): assert dataframe_1m.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume'] def test_ohlcv_to_dataframe(ohlcv_history_list, caplog): columns = ['date', 'open', 'high', 'low', 'close', 'volume'] caplog.set_level(logging.DEBUG) # Test file with BV data dataframe = ohlcv_to_dataframe(ohlcv_history_list, '5m', pair="UNITTEST/BTC", fill_missing=True) assert dataframe.columns.tolist() == columns assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog) def test_trades_to_ohlcv(trades_history_df, caplog): caplog.set_level(logging.DEBUG) with pytest.raises(ValueError, match="Trade-list empty."): trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), '1m') df = trades_to_ohlcv(trades_history_df, '1m') assert not df.empty assert len(df) == 1 assert 'open' in df.columns assert 'high' in df.columns assert 'low' in df.columns assert 'close' in df.columns assert df.loc[:, 'high'][0] == 0.019627 assert df.loc[:, 'low'][0] == 0.019626 def test_ohlcv_fill_up_missing_data(testdatadir, caplog): data = load_pair_history(datadir=testdatadir, timeframe='1m', pair='UNITTEST/BTC', fill_up_missing=False) caplog.set_level(logging.DEBUG) data2 = ohlcv_fill_up_missing_data(data, '1m', 'UNITTEST/BTC') assert len(data2) > len(data) # Column names should not change assert (data.columns == data2.columns).all() assert log_has_re(f"Missing data fillup for UNITTEST/BTC: before: " f"{len(data)} - after: {len(data2)}.*", caplog) # Test fillup actually fixes invalid backtest data min_date, max_date = get_timerange({'UNITTEST/BTC': data}) assert validate_backtest_data(data, 'UNITTEST/BTC', min_date, max_date, 1) assert not validate_backtest_data(data2, 'UNITTEST/BTC', min_date, max_date, 1) def test_ohlcv_fill_up_missing_data2(caplog): timeframe = '5m' ticks = [ [ 1511686200000, # 8:50:00 8.794e-05, # open 8.948e-05, # high 8.794e-05, # low 8.88e-05, # close 2255, # volume (in quote currency) ], [ 1511686500000, # 8:55:00 8.88e-05, 8.942e-05, 8.88e-05, 8.893e-05, 9911, ], [ 1511687100000, # 9:05:00 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05, 2251 ], [ 1511687400000, # 9:10:00 8.877e-05, 8.883e-05, 8.895e-05, 8.817e-05, 123551 ] ] # Generate test-data without filling missing data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False) assert len(data) == 3 caplog.set_level(logging.DEBUG) data2 = ohlcv_fill_up_missing_data(data, timeframe, "UNITTEST/BTC") assert len(data2) == 4 # 3rd candle has been filled row = data2.loc[2, :] assert row['volume'] == 0 # close should match close of previous candle assert row['close'] == data.loc[1, 'close'] assert row['open'] == row['close'] assert row['high'] == row['close'] assert row['low'] == row['close'] # Column names should not change assert (data.columns == data2.columns).all() assert log_has_re(f"Missing data fillup for UNITTEST/BTC: before: " f"{len(data)} - after: {len(data2)}.*", caplog) def test_ohlcv_to_dataframe_1M(): # Monthly ticks from 2019-09-01 to 2023-07-01 ticks = [ [1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999], [1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887], [1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725], [1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296], [1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358], [1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124], [1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479], [1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968], [1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02], [1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201], [1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926], [1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129], [1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051], [1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004], [1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549], [1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24], [1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079], [1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482], [1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267], [1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389], [1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443], [1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886], [1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175], [1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254], [1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869], [1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847], [1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463], [1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024], [1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277], [1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91], [1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586], [1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862], [1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774], [1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306], [1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513], [1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557], [1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414], [1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584], [1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013], [1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711], [1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645], [1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415], [1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541], [1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219], [1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284], [1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273], [1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377] ] data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT", fill_missing=False, drop_incomplete=False) assert len(data) == len(ticks) assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01' assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01' # Test with filling missing data data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT", fill_missing=True, drop_incomplete=False) assert len(data) == len(ticks) assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01' assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01' def test_ohlcv_drop_incomplete(caplog): timeframe = '1d' ticks = [ [ 1559750400000, # 2019-06-04 8.794e-05, # open 8.948e-05, # high 8.794e-05, # low 8.88e-05, # close 2255, # volume (in quote currency) ], [ 1559836800000, # 2019-06-05 8.88e-05, 8.942e-05, 8.88e-05, 8.893e-05, 9911, ], [ 1559923200000, # 2019-06-06 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05, 2251 ], [ 1560009600000, # 2019-06-07 8.877e-05, 8.883e-05, 8.895e-05, 8.817e-05, 123551 ] ] caplog.set_level(logging.DEBUG) data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False, drop_incomplete=False) assert len(data) == 4 assert not log_has("Dropping last candle", caplog) # Drop last candle data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False, drop_incomplete=True) assert len(data) == 3 assert log_has("Dropping last candle", caplog) def test_trim_dataframe(testdatadir) -> None: data = load_data( datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'] )['UNITTEST/BTC'] min_date = int(data.iloc[0]['date'].timestamp()) max_date = int(data.iloc[-1]['date'].timestamp()) data_modify = data.copy() # Remove first 30 minutes (1800 s) tr = TimeRange('date', None, min_date + 1800, 0) data_modify = trim_dataframe(data_modify, tr) assert not data_modify.equals(data) assert len(data_modify) < len(data) assert len(data_modify) == len(data) - 30 assert all(data_modify.iloc[-1] == data.iloc[-1]) assert all(data_modify.iloc[0] == data.iloc[30]) data_modify = data.copy() tr = TimeRange('date', None, min_date + 1800, 0) # Remove first 20 candles - ignores min date data_modify = trim_dataframe(data_modify, tr, startup_candles=20) assert not data_modify.equals(data) assert len(data_modify) < len(data) assert len(data_modify) == len(data) - 20 assert all(data_modify.iloc[-1] == data.iloc[-1]) assert all(data_modify.iloc[0] == data.iloc[20]) data_modify = data.copy() # Remove last 30 minutes (1800 s) tr = TimeRange(None, 'date', 0, max_date - 1800) data_modify = trim_dataframe(data_modify, tr) assert not data_modify.equals(data) assert len(data_modify) < len(data) assert len(data_modify) == len(data) - 30 assert all(data_modify.iloc[0] == data.iloc[0]) assert all(data_modify.iloc[-1] == data.iloc[-31]) data_modify = data.copy() # Remove first 25 and last 30 minutes (1800 s) tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800) data_modify = trim_dataframe(data_modify, tr) assert not data_modify.equals(data) assert len(data_modify) < len(data) assert len(data_modify) == len(data) - 55 # first row matches 25th original row assert all(data_modify.iloc[0] == data.iloc[25]) def test_trades_remove_duplicates(trades_history): trades_history1 = trades_history * 3 assert len(trades_history1) == len(trades_history) * 3 res = trades_remove_duplicates(trades_history1) assert len(res) == len(trades_history) for i, t in enumerate(res): assert t == trades_history[i] def test_trades_df_remove_duplicates(trades_history_df): trades_history1 = pd.concat([trades_history_df, trades_history_df, trades_history_df] ).reset_index(drop=True) assert len(trades_history1) == len(trades_history_df) * 3 res = trades_df_remove_duplicates(trades_history1) assert len(res) == len(trades_history_df) assert res.equals(trades_history_df) def test_trades_dict_to_list(fetch_trades_result): res = trades_dict_to_list(fetch_trades_result) assert isinstance(res, list) assert isinstance(res[0], list) for i, t in enumerate(res): assert t[0] == fetch_trades_result[i]['timestamp'] assert t[1] == fetch_trades_result[i]['id'] assert t[2] == fetch_trades_result[i]['type'] assert t[3] == fetch_trades_result[i]['side'] assert t[4] == fetch_trades_result[i]['price'] assert t[5] == fetch_trades_result[i]['amount'] assert t[6] == fetch_trades_result[i]['cost'] def test_convert_trades_format(default_conf, testdatadir, tmpdir): tmpdir1 = Path(tmpdir) files = [{'old': tmpdir1 / "XRP_ETH-trades.json.gz", 'new': tmpdir1 / "XRP_ETH-trades.json"}, {'old': tmpdir1 / "XRP_OLD-trades.json.gz", 'new': tmpdir1 / "XRP_OLD-trades.json"}, ] for file in files: copyfile(testdatadir / file['old'].name, file['old']) assert not file['new'].exists() default_conf['datadir'] = tmpdir1 convert_trades_format(default_conf, convert_from='jsongz', convert_to='json', erase=False) for file in files: assert file['new'].exists() assert file['old'].exists() # Remove original file file['old'].unlink() # Convert back convert_trades_format(default_conf, convert_from='json', convert_to='jsongz', erase=True) for file in files: assert file['old'].exists() assert not file['new'].exists() _clean_test_file(file['old']) if file['new'].exists(): file['new'].unlink() @pytest.mark.parametrize('file_base,candletype', [ (['XRP_ETH-5m', 'XRP_ETH-1m'], CandleType.SPOT), (['UNITTEST_USDT_USDT-1h-mark', 'XRP_USDT_USDT-1h-mark'], CandleType.MARK), (['XRP_USDT_USDT-1h-futures'], CandleType.FUTURES), ]) def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, candletype): tmpdir1 = Path(tmpdir) prependix = '' if candletype == CandleType.SPOT else 'futures/' files_orig = [] files_temp = [] files_new = [] for file in file_base: file_orig = testdatadir / f"{prependix}{file}.feather" file_temp = tmpdir1 / f"{prependix}{file}.feather" file_new = tmpdir1 / f"{prependix}{file}.json.gz" IDataHandler.create_dir_if_needed(file_temp) copyfile(file_orig, file_temp) files_orig.append(file_orig) files_temp.append(file_temp) files_new.append(file_new) default_conf['datadir'] = tmpdir1 default_conf['candle_types'] = [candletype] if candletype == CandleType.SPOT: default_conf['pairs'] = ['XRP/ETH', 'XRP/USDT', 'UNITTEST/USDT'] else: default_conf['pairs'] = ['XRP/ETH:ETH', 'XRP/USDT:USDT', 'UNITTEST/USDT:USDT'] default_conf['timeframes'] = ['1m', '5m', '1h'] assert not file_new.exists() convert_ohlcv_format( default_conf, convert_from='feather', convert_to='jsongz', erase=False, ) for file in (files_temp + files_new): assert file.exists() # Remove original files for file in (files_temp): file.unlink() # Convert back convert_ohlcv_format( default_conf, convert_from='jsongz', convert_to='feather', erase=True, ) for file in (files_temp): assert file.exists() for file in (files_new): assert not file.exists() def test_reduce_dataframe_footprint(): data = generate_test_data('15m', 40) data['open_copy'] = data['open'] data['close_copy'] = data['close'] data['close_copy'] = data['close'] assert data['open'].dtype == np.float64 assert data['open_copy'].dtype == np.float64 assert data['close_copy'].dtype == np.float64 df2 = reduce_dataframe_footprint(data) # Does not modify original dataframe assert data['open'].dtype == np.float64 assert data['open_copy'].dtype == np.float64 assert data['close_copy'].dtype == np.float64 # skips ohlcv columns assert df2['open'].dtype == np.float64 assert df2['high'].dtype == np.float64 assert df2['low'].dtype == np.float64 assert df2['close'].dtype == np.float64 assert df2['volume'].dtype == np.float64 # Changes dtype of returned dataframe assert df2['open_copy'].dtype == np.float32 assert df2['close_copy'].dtype == np.float32