stable/tests/optimize/test_optimize_reports.py

import re
from datetime import timedelta
from pathlib import Path
from shutil import copyfile

import joblib
import pandas as pd
import pytest
from arrow import Arrow

from freqtrade.configuration import TimeRange
from freqtrade.constants import 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 (_get_resample_from_period, generate_backtest_stats,
                                                 generate_daily_stats, generate_edge_table,
                                                 generate_exit_reason_stats, generate_pair_metrics,
                                                 generate_periodic_breakdown_stats,
                                                 generate_strategy_comparison,
                                                 generate_trading_stats, show_sorted_pairlist,
                                                 store_backtest_signal_candles,
                                                 store_backtest_stats, text_table_bt_results,
                                                 text_table_exit_reason, text_table_strategy)
from freqtrade.resolvers.strategy_resolver import StrategyResolver
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 % |   Cum Profit % |   Tot Profit BTC |   '
        'Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n'
        '|---------+-----------+----------------+----------------+------------------+'
        '----------------+----------------+-------------------------|\n'
        '| ETH/BTC |         3 |           8.33 |          25.00 |       0.50000000 |          '
        '12.50 |        0:20:00 |     2     0     1  66.7 |\n'
        '|   TOTAL |         3 |           8.33 |          25.00 |       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, tmpdir):
    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": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
                                               Arrow(2017, 11, 14, 21, 36, 00).datetime,
                                               Arrow(2017, 11, 14, 22, 12, 00).datetime,
                                               Arrow(2017, 11, 14, 22, 44, 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, 43, 00).datetime,
                                                Arrow(2017, 11, 14, 22, 58, 00).datetime],
                                 "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': Arrow.utcnow().int_timestamp,
        'backtest_end_time': Arrow.utcnow().int_timestamp,
        'run_id': '123',
        }
        }
    timerange = TimeRange.parse_timerange('1510688220-1510700340')
    min_date = Arrow.fromtimestamp(1510688220)
    max_date = Arrow.fromtimestamp(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 loosing 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": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
                           Arrow(2017, 11, 14, 21, 36, 00).datetime,
                           Arrow(2017, 11, 14, 22, 12, 00).datetime,
                           Arrow(2017, 11, 14, 22, 44, 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, 43, 00).datetime,
                            Arrow(2017, 11, 14, 22, 58, 00).datetime],
             "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': Arrow.utcnow().int_timestamp,
        'backtest_end_time': Arrow.utcnow().int_timestamp,
        '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 = Path(tmpdir / 'btresult.json')
    filename_last = Path(tmpdir / 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-<date>.json)
    last_fn = get_latest_backtest_filename(filename_last.parent)
    assert re.match(r"btresult-.*\.json", last_fn)

    filename1 = Path(tmpdir / 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.file_dump_json')

    store_backtest_stats(testdatadir, {'metadata': {}}, '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, {'metadata': {}}, '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-<timestamp>.json
    assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / 'testresult'))


def test_store_backtest_candles(testdatadir, mocker):

    dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_joblib')

    candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}

    # mock directory exporting
    store_backtest_signal_candles(testdatadir, candle_dict, '2022_01_01_15_05_13')

    assert dump_mock.call_count == 1
    assert isinstance(dump_mock.call_args_list[0][0][0], Path)
    assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl'))

    dump_mock.reset_mock()
    # mock file exporting
    filename = Path(testdatadir / 'testresult')
    store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
    assert dump_mock.call_count == 1
    assert isinstance(dump_mock.call_args_list[0][0][0], Path)
    # result will be testdatadir / testresult-<timestamp>_signals.pkl
    assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl'))
    dump_mock.reset_mock()


def test_write_read_backtest_candles(tmpdir):

    candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}

    # test directory exporting
    stored_file = store_backtest_signal_candles(Path(tmpdir), candle_dict, '2022_01_01_15_05_13')
    scp = open(stored_file, "rb")
    pickled_signal_candles = joblib.load(scp)
    scp.close()

    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 = Path(tmpdir / 'testresult')
    stored_file = store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
    scp = open(stored_file, "rb")
    pickled_signal_candles = joblib.load(scp)
    scp.close()

    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_new.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_new.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_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 |   Win  Draws  Loss  Win% |   Avg Profit % |   Cum Profit % |'
        '   Tot Profit BTC |   Tot Profit % |\n'
        '|---------------+---------+--------------------------+----------------+----------------+'
        '------------------+----------------|\n'
        '|           roi |       2 |      2     0     0   100 |             15 |             30 |'
        '              0.6 |             15 |\n'
        '|     stop_loss |       1 |      0     0     1     0 |            -10 |            -10 |'
        '             -0.2 |             -5 |'
    )

    exit_reason_stats = generate_exit_reason_stats(max_open_trades=2,
                                                   results=results)
    assert text_table_exit_reason(exit_reason_stats=exit_reason_stats,
                                  stake_currency='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_exit_reason_stats(max_open_trades=2,
                                                   results=results)
    roi_result = exit_reason_stats[0]
    assert roi_result['exit_reason'] == '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['exit_reason'] == '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 % |   Cum Profit % |   Tot Profit BTC |'
        '   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |              Drawdown |\n'
        '|----------------+-----------+----------------+----------------+------------------+'
        '----------------+----------------+-------------------------+-----------------------|\n'
        '| StrategyTestV2 |       179 |           0.08 |          14.39 |       0.02608550 |'
        '         260.85 |        3:40:00 |   170     0     9  95.0 | 0.00308222 BTC  8.67% |\n'
        '|   TestStrategy |       179 |           0.08 |          14.39 |       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_new.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'
    assert _get_resample_from_period('month') == '1M'
    with pytest.raises(ValueError, match=r"Period noooo is not supported."):
        _get_resample_from_period('noooo')


def test_show_sorted_pairlist(testdatadir, default_conf, capsys):
    filename = testdatadir / "backtest_results/backtest-result_new.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