import re from datetime import timedelta from pathlib import Path from shutil import copyfile import joblib import pandas as pd import pytest from freqtrade.configuration import TimeRange from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN from freqtrade.data import history from freqtrade.data.btanalysis import ( get_latest_backtest_filename, load_backtest_data, load_backtest_stats, ) from freqtrade.edge import PairInfo from freqtrade.enums import ExitType from freqtrade.optimize.optimize_reports import ( generate_backtest_stats, generate_daily_stats, generate_edge_table, generate_pair_metrics, generate_periodic_breakdown_stats, generate_strategy_comparison, generate_trading_stats, show_sorted_pairlist, store_backtest_analysis_results, store_backtest_stats, text_table_bt_results, text_table_strategy, ) from freqtrade.optimize.optimize_reports.bt_output import text_table_tags from freqtrade.optimize.optimize_reports.optimize_reports import ( _get_resample_from_period, calc_streak, generate_tag_metrics, ) from freqtrade.resolvers.strategy_resolver import StrategyResolver from freqtrade.util import dt_ts from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc from tests.conftest import CURRENT_TEST_STRATEGY from tests.data.test_history import _clean_test_file def _backup_file(file: Path, copy_file: bool = False) -> None: """ Backup existing file to avoid deleting the user file :param file: complete path to the file :param copy_file: keep file in place too. :return: None """ file_swp = str(file) + ".swp" if file.is_file(): file.rename(file_swp) if copy_file: copyfile(file_swp, file) def test_text_table_bt_results(): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"], "profit_ratio": [0.1, 0.2, -0.05], "profit_abs": [0.2, 0.4, -0.1], "trade_duration": [10, 30, 20], } ) result_str = ( "| Pair | Entries | Avg Profit % | Tot Profit BTC | " "Tot Profit % | Avg Duration | Win Draw Loss Win% |\n" "|---------+-----------+----------------+------------------+" "----------------+----------------+-------------------------|\n" "| ETH/BTC | 3 | 8.33 | 0.50000000 | " "12.50 | 0:20:00 | 2 0 1 66.7 |\n" "| TOTAL | 3 | 8.33 | 0.50000000 | " "12.50 | 0:20:00 | 2 0 1 66.7 |" ) pair_results = generate_pair_metrics( ["ETH/BTC"], stake_currency="BTC", starting_balance=4, results=results ) assert text_table_bt_results(pair_results, stake_currency="BTC") == result_str def test_generate_backtest_stats(default_conf, testdatadir, tmp_path): default_conf.update({"strategy": CURRENT_TEST_STRATEGY}) StrategyResolver.load_strategy(default_conf) results = { "DefStrat": { "results": pd.DataFrame( { "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"], "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780], "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003], "open_date": [ dt_utc(2017, 11, 14, 19, 32, 00), dt_utc(2017, 11, 14, 21, 36, 00), dt_utc(2017, 11, 14, 22, 12, 00), dt_utc(2017, 11, 14, 22, 44, 00), ], "close_date": [ dt_utc(2017, 11, 14, 21, 35, 00), dt_utc(2017, 11, 14, 22, 10, 00), dt_utc(2017, 11, 14, 22, 43, 00), dt_utc(2017, 11, 14, 22, 58, 00), ], "open_rate": [0.002543, 0.003003, 0.003089, 0.003214], "close_rate": [0.002546, 0.003014, 0.003103, 0.003217], "trade_duration": [123, 34, 31, 14], "is_open": [False, False, False, True], "is_short": [False, False, False, False], "stake_amount": [0.01, 0.01, 0.01, 0.01], "exit_reason": [ ExitType.ROI, ExitType.STOP_LOSS, ExitType.ROI, ExitType.FORCE_EXIT, ], } ), "config": default_conf, "locks": [], "final_balance": 1000.02, "rejected_signals": 20, "timedout_entry_orders": 0, "timedout_exit_orders": 0, "canceled_trade_entries": 0, "canceled_entry_orders": 0, "replaced_entry_orders": 0, "backtest_start_time": dt_ts() // 1000, "backtest_end_time": dt_ts() // 1000, "run_id": "123", } } timerange = TimeRange.parse_timerange("1510688220-1510700340") min_date = dt_from_ts(1510688220) max_date = dt_from_ts(1510700340) btdata = history.load_data( testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True ) stats = generate_backtest_stats(btdata, results, min_date, max_date) assert isinstance(stats, dict) assert "strategy" in stats assert "DefStrat" in stats["strategy"] assert "strategy_comparison" in stats strat_stats = stats["strategy"]["DefStrat"] assert strat_stats["backtest_start"] == min_date.strftime(DATETIME_PRINT_FORMAT) assert strat_stats["backtest_end"] == max_date.strftime(DATETIME_PRINT_FORMAT) assert strat_stats["total_trades"] == len(results["DefStrat"]["results"]) # Above sample had no losing trade assert strat_stats["max_drawdown_account"] == 0.0 # Retry with losing trade results = { "DefStrat": { "results": pd.DataFrame( { "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"], "profit_ratio": [0.003312, 0.010801, -0.013803, 0.002780], "profit_abs": [0.000003, 0.000011, -0.000014, 0.000003], "open_date": [ dt_utc(2017, 11, 14, 19, 32, 00), dt_utc(2017, 11, 14, 21, 36, 00), dt_utc(2017, 11, 14, 22, 12, 00), dt_utc(2017, 11, 14, 22, 44, 00), ], "close_date": [ dt_utc(2017, 11, 14, 21, 35, 00), dt_utc(2017, 11, 14, 22, 10, 00), dt_utc(2017, 11, 14, 22, 43, 00), dt_utc(2017, 11, 14, 22, 58, 00), ], "open_rate": [0.002543, 0.003003, 0.003089, 0.003214], "close_rate": [0.002546, 0.003014, 0.0032903, 0.003217], "trade_duration": [123, 34, 31, 14], "is_open": [False, False, False, True], "is_short": [False, False, False, False], "stake_amount": [0.01, 0.01, 0.01, 0.01], "exit_reason": [ ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS, ExitType.FORCE_EXIT, ], } ), "config": default_conf, "locks": [], "final_balance": 1000.02, "rejected_signals": 20, "timedout_entry_orders": 0, "timedout_exit_orders": 0, "canceled_trade_entries": 0, "canceled_entry_orders": 0, "replaced_entry_orders": 0, "backtest_start_time": dt_ts() // 1000, "backtest_end_time": dt_ts() // 1000, "run_id": "124", } } stats = generate_backtest_stats(btdata, results, min_date, max_date) assert isinstance(stats, dict) assert "strategy" in stats assert "DefStrat" in stats["strategy"] assert "strategy_comparison" in stats strat_stats = stats["strategy"]["DefStrat"] assert pytest.approx(strat_stats["max_drawdown_account"]) == 1.399999e-08 assert strat_stats["drawdown_start"] == "2017-11-14 22:10:00" assert strat_stats["drawdown_end"] == "2017-11-14 22:43:00" assert strat_stats["drawdown_end_ts"] == 1510699380000 assert strat_stats["drawdown_start_ts"] == 1510697400000 assert strat_stats["pairlist"] == ["UNITTEST/BTC"] # Test storing stats filename = tmp_path / "btresult.json" filename_last = tmp_path / LAST_BT_RESULT_FN _backup_file(filename_last, copy_file=True) assert not filename.is_file() store_backtest_stats(filename, stats, "2022_01_01_15_05_13") # get real Filename (it's btresult-.json) last_fn = get_latest_backtest_filename(filename_last.parent) assert re.match(r"btresult-.*\.json", last_fn) filename1 = tmp_path / last_fn assert filename1.is_file() content = filename1.read_text() assert "max_drawdown_account" in content assert "strategy" in content assert "pairlist" in content assert filename_last.is_file() _clean_test_file(filename_last) filename1.unlink() def test_store_backtest_stats(testdatadir, mocker): dump_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.file_dump_json") data = {"metadata": {}, "strategy": {}, "strategy_comparison": []} store_backtest_stats(testdatadir, data, "2022_01_01_15_05_13") assert dump_mock.call_count == 3 assert isinstance(dump_mock.call_args_list[0][0][0], Path) assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "backtest-result")) dump_mock.reset_mock() filename = testdatadir / "testresult.json" store_backtest_stats(filename, data, "2022_01_01_15_05_13") assert dump_mock.call_count == 3 assert isinstance(dump_mock.call_args_list[0][0][0], Path) # result will be testdatadir / testresult-.json assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "testresult")) def test_store_backtest_stats_real(tmp_path): data = {"metadata": {}, "strategy": {}, "strategy_comparison": []} store_backtest_stats(tmp_path, data, "2022_01_01_15_05_13") assert (tmp_path / "backtest-result-2022_01_01_15_05_13.json").is_file() assert (tmp_path / "backtest-result-2022_01_01_15_05_13.meta.json").is_file() assert not (tmp_path / "backtest-result-2022_01_01_15_05_13_market_change.feather").is_file() assert (tmp_path / LAST_BT_RESULT_FN).is_file() fn = get_latest_backtest_filename(tmp_path) assert fn == "backtest-result-2022_01_01_15_05_13.json" store_backtest_stats(tmp_path, data, "2024_01_01_15_05_25", market_change_data=pd.DataFrame()) assert (tmp_path / "backtest-result-2024_01_01_15_05_25.json").is_file() assert (tmp_path / "backtest-result-2024_01_01_15_05_25.meta.json").is_file() assert (tmp_path / "backtest-result-2024_01_01_15_05_25_market_change.feather").is_file() assert (tmp_path / LAST_BT_RESULT_FN).is_file() # Last file reference should be updated fn = get_latest_backtest_filename(tmp_path) assert fn == "backtest-result-2024_01_01_15_05_25.json" def test_store_backtest_candles(testdatadir, mocker): dump_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.file_dump_joblib") candle_dict = {"DefStrat": {"UNITTEST/BTC": pd.DataFrame()}} # mock directory exporting store_backtest_analysis_results(testdatadir, candle_dict, {}, "2022_01_01_15_05_13") assert dump_mock.call_count == 2 assert isinstance(dump_mock.call_args_list[0][0][0], Path) assert str(dump_mock.call_args_list[0][0][0]).endswith("_signals.pkl") dump_mock.reset_mock() # mock file exporting filename = Path(testdatadir / "testresult") store_backtest_analysis_results(filename, candle_dict, {}, "2022_01_01_15_05_13") assert dump_mock.call_count == 2 assert isinstance(dump_mock.call_args_list[0][0][0], Path) # result will be testdatadir / testresult-_signals.pkl assert str(dump_mock.call_args_list[0][0][0]).endswith("_signals.pkl") dump_mock.reset_mock() def test_write_read_backtest_candles(tmp_path): candle_dict = {"DefStrat": {"UNITTEST/BTC": pd.DataFrame()}} # test directory exporting sample_date = "2022_01_01_15_05_13" store_backtest_analysis_results(tmp_path, candle_dict, {}, sample_date) stored_file = tmp_path / f"backtest-result-{sample_date}_signals.pkl" with stored_file.open("rb") as scp: pickled_signal_candles = joblib.load(scp) assert pickled_signal_candles.keys() == candle_dict.keys() assert pickled_signal_candles["DefStrat"].keys() == pickled_signal_candles["DefStrat"].keys() assert pickled_signal_candles["DefStrat"]["UNITTEST/BTC"].equals( pickled_signal_candles["DefStrat"]["UNITTEST/BTC"] ) _clean_test_file(stored_file) # test file exporting filename = tmp_path / "testresult" store_backtest_analysis_results(filename, candle_dict, {}, sample_date) stored_file = tmp_path / f"testresult-{sample_date}_signals.pkl" with stored_file.open("rb") as scp: pickled_signal_candles = joblib.load(scp) assert pickled_signal_candles.keys() == candle_dict.keys() assert pickled_signal_candles["DefStrat"].keys() == pickled_signal_candles["DefStrat"].keys() assert pickled_signal_candles["DefStrat"]["UNITTEST/BTC"].equals( pickled_signal_candles["DefStrat"]["UNITTEST/BTC"] ) _clean_test_file(stored_file) def test_generate_pair_metrics(): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC"], "profit_ratio": [0.1, 0.2], "profit_abs": [0.2, 0.4], "trade_duration": [10, 30], "wins": [2, 0], "draws": [0, 0], "losses": [0, 0], } ) pair_results = generate_pair_metrics( ["ETH/BTC"], stake_currency="BTC", starting_balance=2, results=results ) assert isinstance(pair_results, list) assert len(pair_results) == 2 assert pair_results[-1]["key"] == "TOTAL" assert ( pytest.approx(pair_results[-1]["profit_mean_pct"]) == pair_results[-1]["profit_mean"] * 100 ) assert pytest.approx(pair_results[-1]["profit_sum_pct"]) == pair_results[-1]["profit_sum"] * 100 def test_generate_daily_stats(testdatadir): filename = testdatadir / "backtest_results/backtest-result.json" bt_data = load_backtest_data(filename) res = generate_daily_stats(bt_data) assert isinstance(res, dict) assert round(res["backtest_best_day"], 4) == 0.1796 assert round(res["backtest_worst_day"], 4) == -0.1468 assert res["winning_days"] == 19 assert res["draw_days"] == 0 assert res["losing_days"] == 2 # Select empty dataframe! res = generate_daily_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :]) assert isinstance(res, dict) assert round(res["backtest_best_day"], 4) == 0.0 assert res["winning_days"] == 0 assert res["draw_days"] == 0 assert res["losing_days"] == 0 def test_generate_trading_stats(testdatadir): filename = testdatadir / "backtest_results/backtest-result.json" bt_data = load_backtest_data(filename) res = generate_trading_stats(bt_data) assert isinstance(res, dict) assert res["winner_holding_avg"] == timedelta(seconds=1440) assert res["loser_holding_avg"] == timedelta(days=1, seconds=21420) assert "wins" in res assert "losses" in res assert "draws" in res # Select empty dataframe! res = generate_trading_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :]) assert res["wins"] == 0 assert res["losses"] == 0 def test_calc_streak(testdatadir): df = pd.DataFrame( { "profit_ratio": [0.05, -0.02, -0.03, -0.05, 0.01, 0.02, 0.03, 0.04, -0.02, -0.03], } ) # 4 consecutive wins, 3 consecutive losses res = calc_streak(df) assert res == (4, 3) assert isinstance(res[0], int) assert isinstance(res[1], int) # invert situation df1 = df.copy() df1["profit_ratio"] = df1["profit_ratio"] * -1 assert calc_streak(df1) == (3, 4) df_empty = pd.DataFrame( { "profit_ratio": [], } ) assert df_empty.empty assert calc_streak(df_empty) == (0, 0) filename = testdatadir / "backtest_results/backtest-result.json" bt_data = load_backtest_data(filename) assert calc_streak(bt_data) == (7, 18) def test_text_table_exit_reason(): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"], "profit_ratio": [0.1, 0.2, -0.1], "profit_abs": [0.2, 0.4, -0.2], "trade_duration": [10, 30, 10], "wins": [2, 0, 0], "draws": [0, 0, 0], "losses": [0, 0, 1], "exit_reason": [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS], } ) result_str = ( "| Exit Reason | Exits | Avg Profit % | Tot Profit BTC | Tot Profit % |" " Avg Duration | Win Draw Loss Win% |\n" "|---------------+---------+----------------+------------------+----------------+" "----------------+-------------------------|\n" "| roi | 2 | 15.00 | 0.60000000 | 2.73 |" " 0:20:00 | 2 0 0 100 |\n" "| stop_loss | 1 | -10.00 | -0.20000000 | -0.91 |" " 0:10:00 | 0 0 1 0 |\n" "| TOTAL | 3 | 6.67 | 0.40000000 | 1.82 |" " 0:17:00 | 2 0 1 66.7 |" ) exit_reason_stats = generate_tag_metrics( "exit_reason", starting_balance=22, results=results, skip_nan=False ) assert text_table_tags("exit_tag", exit_reason_stats, "BTC") == result_str def test_generate_sell_reason_stats(): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"], "profit_ratio": [0.1, 0.2, -0.1], "profit_abs": [0.2, 0.4, -0.2], "trade_duration": [10, 30, 10], "wins": [2, 0, 0], "draws": [0, 0, 0], "losses": [0, 0, 1], "exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value], } ) exit_reason_stats = generate_tag_metrics( "exit_reason", starting_balance=22, results=results, skip_nan=False ) roi_result = exit_reason_stats[0] assert roi_result["key"] == "roi" assert roi_result["trades"] == 2 assert pytest.approx(roi_result["profit_mean"]) == 0.15 assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2) assert pytest.approx(roi_result["profit_mean"]) == 0.15 assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2) stop_result = exit_reason_stats[1] assert stop_result["key"] == "stop_loss" assert stop_result["trades"] == 1 assert pytest.approx(stop_result["profit_mean"]) == -0.1 assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2) assert pytest.approx(stop_result["profit_mean"]) == -0.1 assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2) def test_text_table_strategy(testdatadir): filename = testdatadir / "backtest_results/backtest-result_multistrat.json" bt_res_data = load_backtest_stats(filename) bt_res_data_comparison = bt_res_data.pop("strategy_comparison") result_str = ( "| Strategy | Entries | Avg Profit % | Tot Profit BTC |" " Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n" "|----------------+-----------+----------------+------------------+" "----------------+----------------+-------------------------+-----------------------|\n" "| StrategyTestV2 | 179 | 0.08 | 0.02608550 |" " 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n" "| TestStrategy | 179 | 0.08 | 0.02608550 |" " 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |" ) strategy_results = generate_strategy_comparison(bt_stats=bt_res_data["strategy"]) assert strategy_results == bt_res_data_comparison assert text_table_strategy(strategy_results, "BTC") == result_str def test_generate_edge_table(): results = {} results["ETH/BTC"] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60) assert generate_edge_table(results).count("+") == 7 assert generate_edge_table(results).count("| ETH/BTC |") == 1 assert ( generate_edge_table(results).count( "| Risk Reward Ratio | Required Risk Reward | Expectancy |" ) == 1 ) def test_generate_periodic_breakdown_stats(testdatadir): filename = testdatadir / "backtest_results/backtest-result.json" bt_data = load_backtest_data(filename).to_dict(orient="records") res = generate_periodic_breakdown_stats(bt_data, "day") assert isinstance(res, list) assert len(res) == 21 day = res[0] assert "date" in day assert "draws" in day assert "loses" in day assert "wins" in day assert "profit_abs" in day # Select empty dataframe! res = generate_periodic_breakdown_stats([], "day") assert res == [] def test__get_resample_from_period(): assert _get_resample_from_period("day") == "1d" assert _get_resample_from_period("week") == "1W-MON" assert _get_resample_from_period("month") == "1ME" with pytest.raises(ValueError, match=r"Period noooo is not supported."): _get_resample_from_period("noooo") for period in BACKTEST_BREAKDOWNS: assert isinstance(_get_resample_from_period(period), str) def test_show_sorted_pairlist(testdatadir, default_conf, capsys): filename = testdatadir / "backtest_results/backtest-result.json" bt_data = load_backtest_stats(filename) default_conf["backtest_show_pair_list"] = True show_sorted_pairlist(default_conf, bt_data) out, _err = capsys.readouterr() assert "Pairs for Strategy StrategyTestV3: \n[" in out assert "TOTAL" not in out assert '"ETH/BTC", // ' in out