403 lines
17 KiB
Python
403 lines
17 KiB
Python
import re
|
|
from datetime import timedelta
|
|
from pathlib import Path
|
|
|
|
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 SellType
|
|
from freqtrade.optimize.optimize_reports import (_get_resample_from_period, generate_backtest_stats,
|
|
generate_daily_stats, generate_edge_table,
|
|
generate_pair_metrics,
|
|
generate_periodic_breakdown_stats,
|
|
generate_sell_reason_stats,
|
|
generate_strategy_comparison,
|
|
generate_trading_stats, show_sorted_pairlist,
|
|
store_backtest_stats, text_table_bt_results,
|
|
text_table_sell_reason, text_table_strategy)
|
|
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
|
from tests.data.test_history import _backup_file, _clean_test_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 | Buys | 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': 'StrategyTestV2'})
|
|
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],
|
|
"stake_amount": [0.01, 0.01, 0.01, 0.01],
|
|
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
|
|
SellType.ROI, SellType.FORCE_SELL]
|
|
}),
|
|
'config': default_conf,
|
|
'locks': [],
|
|
'final_balance': 1000.02,
|
|
'rejected_signals': 20,
|
|
'timedout_entry_orders': 0,
|
|
'timedout_exit_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],
|
|
"stake_amount": [0.01, 0.01, 0.01, 0.01],
|
|
"sell_reason": [SellType.ROI, SellType.ROI,
|
|
SellType.STOP_LOSS, SellType.FORCE_SELL]
|
|
}),
|
|
'config': default_conf,
|
|
'locks': [],
|
|
'final_balance': 1000.02,
|
|
'rejected_signals': 20,
|
|
'timedout_entry_orders': 0,
|
|
'timedout_exit_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)
|
|
|
|
# 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': {}})
|
|
|
|
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': {}})
|
|
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_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-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-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_sell_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],
|
|
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
|
}
|
|
)
|
|
|
|
result_str = (
|
|
'| Sell Reason | Sells | 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 |'
|
|
)
|
|
|
|
sell_reason_stats = generate_sell_reason_stats(max_open_trades=2,
|
|
results=results)
|
|
assert text_table_sell_reason(sell_reason_stats=sell_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],
|
|
'sell_reason': [SellType.ROI.value, SellType.ROI.value, SellType.STOP_LOSS.value]
|
|
}
|
|
)
|
|
|
|
sell_reason_stats = generate_sell_reason_stats(max_open_trades=2,
|
|
results=results)
|
|
roi_result = sell_reason_stats[0]
|
|
assert roi_result['sell_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 = sell_reason_stats[1]
|
|
|
|
assert stop_result['sell_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-result_multistrat.json"
|
|
bt_res_data = load_backtest_stats(filename)
|
|
|
|
bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
|
|
|
|
result_str = (
|
|
'| Strategy | Buys | 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-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-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 StrategyTestV2: \n[' in out
|
|
assert 'TOTAL' not in out
|
|
assert '"ETH/BTC", // ' in out
|