299 lines
12 KiB
Python
299 lines
12 KiB
Python
from pathlib import Path
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from unittest.mock import MagicMock
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import pytest
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from arrow import Arrow
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from pandas import DataFrame, DateOffset, Timestamp, to_datetime
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism,
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calculate_market_change, calculate_max_drawdown,
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combine_dataframes_with_mean, create_cum_profit,
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extract_trades_of_period, get_latest_backtest_filename,
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load_backtest_data, load_trades, load_trades_from_db)
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from freqtrade.data.history import load_data, load_pair_history
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from freqtrade.optimize.backtesting import BacktestResult
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from tests.conftest import create_mock_trades
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from tests.conftest_trades import MOCK_TRADE_COUNT
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def test_get_latest_backtest_filename(testdatadir, mocker):
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with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
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get_latest_backtest_filename(testdatadir / 'does_not_exist')
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with pytest.raises(ValueError,
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match=r"Directory .* does not seem to contain .*"):
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get_latest_backtest_filename(testdatadir.parent)
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res = get_latest_backtest_filename(testdatadir)
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assert res == 'backtest-result_new.json'
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res = get_latest_backtest_filename(str(testdatadir))
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assert res == 'backtest-result_new.json'
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mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
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with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
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get_latest_backtest_filename(testdatadir)
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def test_load_backtest_data_old_format(testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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assert isinstance(bt_data, DataFrame)
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assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit_abs"]
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assert len(bt_data) == 179
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# Test loading from string (must yield same result)
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bt_data2 = load_backtest_data(str(filename))
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assert bt_data.equals(bt_data2)
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with pytest.raises(ValueError, match=r"File .* does not exist\."):
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load_backtest_data(str("filename") + "nofile")
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def test_load_backtest_data_new_format(testdatadir):
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filename = testdatadir / "backtest-result_new.json"
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bt_data = load_backtest_data(filename)
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assert isinstance(bt_data, DataFrame)
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assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
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assert len(bt_data) == 179
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# Test loading from string (must yield same result)
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bt_data2 = load_backtest_data(str(filename))
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assert bt_data.equals(bt_data2)
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# Test loading from folder (must yield same result)
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bt_data3 = load_backtest_data(testdatadir)
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assert bt_data.equals(bt_data3)
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with pytest.raises(ValueError, match=r"File .* does not exist\."):
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load_backtest_data(str("filename") + "nofile")
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with pytest.raises(ValueError, match=r"Unknown dataformat."):
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load_backtest_data(testdatadir / LAST_BT_RESULT_FN)
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def test_load_backtest_data_multi(testdatadir):
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filename = testdatadir / "backtest-result_multistrat.json"
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for strategy in ('DefaultStrategy', 'TestStrategy'):
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bt_data = load_backtest_data(filename, strategy=strategy)
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assert isinstance(bt_data, DataFrame)
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assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
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assert len(bt_data) == 179
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# Test loading from string (must yield same result)
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bt_data2 = load_backtest_data(str(filename), strategy=strategy)
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assert bt_data.equals(bt_data2)
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with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
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load_backtest_data(filename, strategy='XYZ')
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with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
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load_backtest_data(filename)
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@pytest.mark.usefixtures("init_persistence")
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def test_load_trades_from_db(default_conf, fee, mocker):
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create_mock_trades(fee)
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# remove init so it does not init again
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init_mock = mocker.patch('freqtrade.persistence.init', MagicMock())
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trades = load_trades_from_db(db_url=default_conf['db_url'])
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assert init_mock.call_count == 1
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assert len(trades) == MOCK_TRADE_COUNT
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assert isinstance(trades, DataFrame)
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assert "pair" in trades.columns
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assert "open_date" in trades.columns
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assert "profit_percent" in trades.columns
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for col in BT_DATA_COLUMNS:
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if col not in ['index', 'open_at_end']:
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assert col in trades.columns
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trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='DefaultStrategy')
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assert len(trades) == 3
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trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='NoneStrategy')
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assert len(trades) == 0
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def test_extract_trades_of_period(testdatadir):
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pair = "UNITTEST/BTC"
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# 2018-11-14 06:07:00
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timerange = TimeRange('date', None, 1510639620, 0)
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data = load_pair_history(pair=pair, timeframe='1m',
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datadir=testdatadir, timerange=timerange)
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trades = DataFrame(
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{'pair': [pair, pair, pair, pair],
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'profit_percent': [0.0, 0.1, -0.2, -0.5],
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'profit_abs': [0.0, 1, -2, -5],
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'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
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Arrow(2017, 11, 14, 9, 41, 0).datetime,
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Arrow(2017, 11, 14, 14, 20, 0).datetime,
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Arrow(2017, 11, 15, 3, 40, 0).datetime,
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], utc=True
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),
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'close_date': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
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Arrow(2017, 11, 14, 10, 41, 0).datetime,
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Arrow(2017, 11, 14, 15, 25, 0).datetime,
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Arrow(2017, 11, 15, 3, 55, 0).datetime,
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], utc=True)
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})
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trades1 = extract_trades_of_period(data, trades)
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# First and last trade are dropped as they are out of range
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assert len(trades1) == 2
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assert trades1.iloc[0].open_date == Arrow(2017, 11, 14, 9, 41, 0).datetime
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assert trades1.iloc[0].close_date == Arrow(2017, 11, 14, 10, 41, 0).datetime
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assert trades1.iloc[-1].open_date == Arrow(2017, 11, 14, 14, 20, 0).datetime
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assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
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def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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res = analyze_trade_parallelism(bt_data, "5m")
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assert isinstance(res, DataFrame)
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assert 'open_trades' in res.columns
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assert res['open_trades'].max() == 3
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assert res['open_trades'].min() == 0
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def test_load_trades(default_conf, mocker):
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db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
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bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
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load_trades("DB",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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no_trades=False,
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strategy="DefaultStrategy",
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)
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assert db_mock.call_count == 1
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assert bt_mock.call_count == 0
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db_mock.reset_mock()
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bt_mock.reset_mock()
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default_conf['exportfilename'] = Path("testfile.json")
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load_trades("file",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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)
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assert db_mock.call_count == 0
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assert bt_mock.call_count == 1
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db_mock.reset_mock()
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bt_mock.reset_mock()
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default_conf['exportfilename'] = "testfile.json"
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load_trades("file",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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no_trades=True
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)
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assert db_mock.call_count == 0
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assert bt_mock.call_count == 0
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def test_calculate_market_change(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
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result = calculate_market_change(data)
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assert isinstance(result, float)
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assert pytest.approx(result) == 0.00955514
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def test_combine_dataframes_with_mean(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
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df = combine_dataframes_with_mean(data)
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assert isinstance(df, DataFrame)
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assert "ETH/BTC" in df.columns
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assert "ADA/BTC" in df.columns
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assert "mean" in df.columns
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def test_create_cum_profit(testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe='5m',
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datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(df.set_index('date'),
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bt_data[bt_data["pair"] == 'TRX/BTC'],
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"cum_profits", timeframe="5m")
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]['cum_profits'] == 0
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assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005
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def test_create_cum_profit1(testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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# Move close-time to "off" the candle, to make sure the logic still works
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bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe='5m',
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datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(df.set_index('date'),
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bt_data[bt_data["pair"] == 'TRX/BTC'],
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"cum_profits", timeframe="5m")
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]['cum_profits'] == 0
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assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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create_cum_profit(df.set_index('date'), bt_data[bt_data["pair"] == 'NOTAPAIR'],
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"cum_profits", timeframe="5m")
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def test_calculate_max_drawdown(testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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drawdown, h, low = calculate_max_drawdown(bt_data)
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assert isinstance(drawdown, float)
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assert pytest.approx(drawdown) == 0.21142322
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assert isinstance(h, Timestamp)
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assert isinstance(low, Timestamp)
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assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
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assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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drawdown, h, low = calculate_max_drawdown(DataFrame())
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def test_calculate_max_drawdown2():
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values = [0.011580, 0.010048, 0.011340, 0.012161, 0.010416, 0.010009, 0.020024,
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-0.024662, -0.022350, 0.020496, -0.029859, -0.030511, 0.010041, 0.010872,
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-0.025782, 0.010400, 0.012374, 0.012467, 0.114741, 0.010303, 0.010088,
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-0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711]
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dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
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df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
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# sort by profit and reset index
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df = df.sort_values('profit').reset_index(drop=True)
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df1 = df.copy()
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drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
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# Ensure df has not been altered.
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assert df.equals(df1)
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assert isinstance(drawdown, float)
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# High must be before low
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assert h < low
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assert drawdown == 0.091755
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df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
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with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
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calculate_max_drawdown(df, date_col='open_date', value_col='profit')
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