# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument import json import math import random from copy import deepcopy from typing import List from unittest.mock import MagicMock import pytest import numpy as np import pandas as pd from arrow import Arrow from freqtrade import optimize from freqtrade.analyze import Analyze from freqtrade.arguments import Arguments from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration from freqtrade.tests.conftest import log_has # Avoid to reinit the same object again and again _BACKTESTING = None _BACKTESTING_INITIALIZED = False @pytest.fixture(scope='function') def init_backtesting(default_conf, mocker): global _BACKTESTING_INITIALIZED, _BACKTESTING if not _BACKTESTING_INITIALIZED: mocker.patch('freqtrade.exchange.validate_pairs', MagicMock(return_value=True)) _BACKTESTING = Backtesting(default_conf) _BACKTESTING_INITIALIZED = True def get_args(args) -> List[str]: return Arguments(args, '').get_parsed_arg() def trim_dictlist(dict_list, num): new = {} for pair, pair_data in dict_list.items(): new[pair] = pair_data[num:] return new def load_data_test(what): timerange = ((None, 'line'), None, -100) data = optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'], timerange=timerange) pair = data['UNITTEST/BTC'] datalen = len(pair) # Depending on the what parameter we now adjust the # loaded data looks: # pair :: [[ 1509836520000, unix timestamp in ms # 0.00162008, open # 0.00162008, high # 0.00162008, low # 0.00162008, close # 108.14853839 base volume # ]] base = 0.001 if what == 'raise': return {'UNITTEST/BTC': [ [ pair[x][0], # Keep old dates x * base, # But replace O,H,L,C x * base + 0.0001, x * base - 0.0001, x * base, pair[x][5], # Keep old volume ] for x in range(0, datalen) ]} if what == 'lower': return {'UNITTEST/BTC': [ [ pair[x][0], # Keep old dates 1 - x * base, # But replace O,H,L,C 1 - x * base + 0.0001, 1 - x * base - 0.0001, 1 - x * base, pair[x][5] # Keep old volume ] for x in range(0, datalen) ]} if what == 'sine': hz = 0.1 # frequency return {'UNITTEST/BTC': [ [ pair[x][0], # Keep old dates math.sin(x * hz) / 1000 + base, # But replace O,H,L,C math.sin(x * hz) / 1000 + base + 0.0001, math.sin(x * hz) / 1000 + base - 0.0001, math.sin(x * hz) / 1000 + base, pair[x][5] # Keep old volume ] for x in range(0, datalen) ]} return data def simple_backtest(config, contour, num_results) -> None: backtesting = _BACKTESTING data = load_data_test(contour) processed = backtesting.tickerdata_to_dataframe(data) assert isinstance(processed, dict) results = backtesting.backtest( { 'stake_amount': config['stake_amount'], 'processed': processed, 'max_open_trades': 1, 'realistic': True } ) # results :: assert len(results) == num_results def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False, timerange=None): tickerdata = optimize.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange) pairdata = {'UNITTEST/BTC': tickerdata} return pairdata # use for mock freqtrade.exchange.get_ticker_history' def _load_pair_as_ticks(pair, tickfreq): ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair]) ticks = trim_dictlist(ticks, -200) return ticks[pair] # FIX: fixturize this? def _make_backtest_conf(conf=None, pair='UNITTEST/BTC', record=None): data = optimize.load_data(None, ticker_interval='8m', pairs=[pair]) data = trim_dictlist(data, -200) return { 'stake_amount': conf['stake_amount'], 'processed': _BACKTESTING.tickerdata_to_dataframe(data), 'max_open_trades': 10, 'realistic': True, 'record': record } def _trend(signals, buy_value, sell_value): n = len(signals['low']) buy = np.zeros(n) sell = np.zeros(n) for i in range(0, len(signals['buy'])): if random.random() > 0.5: # Both buy and sell signals at same timeframe buy[i] = buy_value sell[i] = sell_value signals['buy'] = buy signals['sell'] = sell return signals def _trend_alternate(dataframe=None): signals = dataframe low = signals['low'] n = len(low) buy = np.zeros(n) sell = np.zeros(n) for i in range(0, len(buy)): if i % 2 == 0: buy[i] = 1 else: sell[i] = 1 signals['buy'] = buy signals['sell'] = sell return dataframe def _run_backtest_1(fun, backtest_conf): # strategy is a global (hidden as a singleton), so we # emulate strategy being pure, by override/restore here # if we dont do this, the override in strategy will carry over # to other tests old_buy = _BACKTESTING.populate_buy_trend old_sell = _BACKTESTING.populate_sell_trend _BACKTESTING.populate_buy_trend = fun # Override _BACKTESTING.populate_sell_trend = fun # Override results = _BACKTESTING.backtest(backtest_conf) _BACKTESTING.populate_buy_trend = old_buy # restore override _BACKTESTING.populate_sell_trend = old_sell # restore override return results # Unit tests def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None: """ Test setup_configuration() function """ mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'default_strategy', 'backtesting' ] config = setup_configuration(get_args(args)) assert 'max_open_trades' in config assert 'stake_currency' in config assert 'stake_amount' in config assert 'exchange' in config assert 'pair_whitelist' in config['exchange'] assert 'datadir' in config assert log_has( 'Parameter --datadir detected: {} ...'.format(config['datadir']), caplog.record_tuples ) assert 'ticker_interval' in config assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples) assert 'live' not in config assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples) assert 'realistic_simulation' not in config assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples) assert 'refresh_pairs' not in config assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples) assert 'timerange' not in config assert 'export' not in config def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None: """ Test setup_configuration() function """ mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'default_strategy', '--datadir', '/foo/bar', 'backtesting', '--ticker-interval', '1m', '--live', '--realistic-simulation', '--refresh-pairs-cached', '--timerange', ':100', '--export', '/bar/foo' ] config = setup_configuration(get_args(args)) assert 'max_open_trades' in config assert 'stake_currency' in config assert 'stake_amount' in config assert 'exchange' in config assert 'pair_whitelist' in config['exchange'] assert 'datadir' in config assert log_has( 'Parameter --datadir detected: {} ...'.format(config['datadir']), caplog.record_tuples ) assert 'ticker_interval' in config assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples) assert log_has( 'Using ticker_interval: 1m ...', caplog.record_tuples ) assert 'live' in config assert log_has('Parameter -l/--live detected ...', caplog.record_tuples) assert 'realistic_simulation' in config assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples) assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples) assert 'refresh_pairs' in config assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples) assert 'timerange' in config assert log_has( 'Parameter --timerange detected: {} ...'.format(config['timerange']), caplog.record_tuples ) assert 'export' in config assert log_has( 'Parameter --export detected: {} ...'.format(config['export']), caplog.record_tuples ) def test_start(mocker, init_backtesting, default_conf, caplog) -> None: """ Test start() function """ start_mock = MagicMock() mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock) mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'default_strategy', 'backtesting' ] args = get_args(args) start(args) assert log_has( 'Starting freqtrade in Backtesting mode', caplog.record_tuples ) assert start_mock.call_count == 1 def test_backtesting__init__(mocker, default_conf) -> None: """ Test Backtesting.__init__() method """ init_mock = MagicMock() mocker.patch('freqtrade.optimize.backtesting.Backtesting._init', init_mock) backtesting = Backtesting(default_conf) assert backtesting.config == default_conf assert backtesting.analyze is None assert backtesting.ticker_interval is None assert backtesting.tickerdata_to_dataframe is None assert backtesting.populate_buy_trend is None assert backtesting.populate_sell_trend is None assert init_mock.call_count == 1 def test_backtesting_init(mocker, default_conf) -> None: """ Test Backtesting._init() method """ mocker.patch('freqtrade.exchange.validate_pairs', MagicMock(return_value=True)) backtesting = Backtesting(default_conf) assert backtesting.config == default_conf assert isinstance(backtesting.analyze, Analyze) assert backtesting.ticker_interval == '5m' assert callable(backtesting.tickerdata_to_dataframe) assert callable(backtesting.populate_buy_trend) assert callable(backtesting.populate_sell_trend) def test_tickerdata_to_dataframe(init_backtesting, default_conf) -> None: """ Test Backtesting.tickerdata_to_dataframe() method """ timerange = ((None, 'line'), None, -100) tick = optimize.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange) tickerlist = {'UNITTEST/BTC': tick} backtesting = _BACKTESTING data = backtesting.tickerdata_to_dataframe(tickerlist) assert len(data['UNITTEST/BTC']) == 100 # Load Analyze to compare the result between Backtesting function and Analyze are the same analyze = Analyze(default_conf) data2 = analyze.tickerdata_to_dataframe(tickerlist) assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC']) def test_get_timeframe(init_backtesting) -> None: """ Test Backtesting.get_timeframe() method """ backtesting = _BACKTESTING data = backtesting.tickerdata_to_dataframe( optimize.load_data( None, ticker_interval='1m', pairs=['UNITTEST/BTC'] ) ) min_date, max_date = backtesting.get_timeframe(data) assert min_date.isoformat() == '2017-11-04T23:02:00+00:00' assert max_date.isoformat() == '2017-11-14T22:59:00+00:00' def test_generate_text_table(init_backtesting): """ Test Backtesting.generate_text_table() method """ backtesting = _BACKTESTING results = pd.DataFrame( { 'currency': ['ETH/BTC', 'ETH/BTC'], 'profit_percent': [0.1, 0.2], 'profit_BTC': [0.2, 0.4], 'duration': [10, 30], 'profit': [2, 0], 'loss': [0, 0] } ) result_str = ( 'pair buy count avg profit % ' 'total profit BTC avg duration profit loss\n' '------- ----------- -------------- ' '------------------ -------------- -------- ------\n' 'ETH/BTC 2 15.00 ' '0.60000000 20.0 2 0\n' 'TOTAL 2 15.00 ' '0.60000000 20.0 2 0' ) assert backtesting._generate_text_table(data={'ETH/BTC': {}}, results=results) == result_str def test_backtesting_start(default_conf, mocker, caplog) -> None: """ Test Backtesting.start() method """ def get_timeframe(input1, input2): return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59) mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock()) mocker.patch('freqtrade.optimize.load_data', mocked_load_data) mocker.patch('freqtrade.exchange.get_ticker_history') mocker.patch('freqtrade.exchange.validate_pairs', MagicMock(return_value=True)) mocker.patch.multiple( 'freqtrade.optimize.backtesting.Backtesting', backtest=MagicMock(), _generate_text_table=MagicMock(return_value='1'), get_timeframe=get_timeframe, ) conf = deepcopy(default_conf) conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] conf['ticker_interval'] = 1 conf['live'] = False conf['datadir'] = None conf['export'] = None conf['timerange'] = '-100' backtesting = Backtesting(conf) backtesting.start() # check the logs, that will contain the backtest result exists = [ 'Using local backtesting data (using whitelist in given config) ...', 'Using stake_currency: BTC ...', 'Using stake_amount: 0.001 ...', 'Measuring data from 2017-11-14T21:17:00+00:00 ' 'up to 2017-11-14T22:59:00+00:00 (0 days)..' ] for line in exists: assert log_has(line, caplog.record_tuples) def test_backtest(init_backtesting, default_conf) -> None: """ Test Backtesting.backtest() method """ backtesting = _BACKTESTING data = optimize.load_data(None, ticker_interval='5m', pairs=['UNITTEST/BTC']) data = trim_dictlist(data, -200) results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], 'processed': backtesting.tickerdata_to_dataframe(data), 'max_open_trades': 10, 'realistic': True } ) assert not results.empty def test_backtest_1min_ticker_interval(init_backtesting, default_conf) -> None: """ Test Backtesting.backtest() method with 1 min ticker """ backtesting = _BACKTESTING # Run a backtesting for an exiting 5min ticker_interval data = optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC']) data = trim_dictlist(data, -200) results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], 'processed': backtesting.tickerdata_to_dataframe(data), 'max_open_trades': 1, 'realistic': True } ) assert not results.empty def test_processed(init_backtesting) -> None: """ Test Backtesting.backtest() method with offline data """ backtesting = _BACKTESTING dict_of_tickerrows = load_data_test('raise') dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows) dataframe = dataframes['UNITTEST/BTC'] cols = dataframe.columns # assert the dataframe got some of the indicator columns for col in ['close', 'high', 'low', 'open', 'date', 'ema50', 'ao', 'macd', 'plus_dm']: assert col in cols def test_backtest_pricecontours(init_backtesting, default_conf, fee, mocker) -> None: mocker.patch('freqtrade.optimize.backtesting.exchange.get_fee', fee) tests = [['raise', 17], ['lower', 0], ['sine', 17]] for [contour, numres] in tests: simple_backtest(default_conf, contour, numres) # Test backtest using offline data (testdata directory) def test_backtest_ticks(init_backtesting, default_conf): ticks = [1, 5] fun = _BACKTESTING.populate_buy_trend for tick in ticks: backtest_conf = _make_backtest_conf(conf=default_conf) results = _run_backtest_1(fun, backtest_conf) assert not results.empty def test_backtest_clash_buy_sell(init_backtesting, default_conf): # Override the default buy trend function in our default_strategy def fun(dataframe=None): buy_value = 1 sell_value = 1 return _trend(dataframe, buy_value, sell_value) backtest_conf = _make_backtest_conf(conf=default_conf) results = _run_backtest_1(fun, backtest_conf) assert results.empty def test_backtest_only_sell(init_backtesting, default_conf): # Override the default buy trend function in our default_strategy def fun(dataframe=None): buy_value = 0 sell_value = 1 return _trend(dataframe, buy_value, sell_value) backtest_conf = _make_backtest_conf(conf=default_conf) results = _run_backtest_1(fun, backtest_conf) assert results.empty def test_backtest_alternate_buy_sell(init_backtesting, default_conf, fee, mocker): mocker.patch('freqtrade.optimize.backtesting.exchange.get_fee', fee) backtest_conf = _make_backtest_conf(conf=default_conf, pair='UNITTEST/BTC') results = _run_backtest_1(_trend_alternate, backtest_conf) assert len(results) == 3 def test_backtest_record(init_backtesting, default_conf, fee, mocker): names = [] records = [] mocker.patch('freqtrade.optimize.backtesting.exchange.get_fee', fee) mocker.patch( 'freqtrade.optimize.backtesting.file_dump_json', new=lambda n, r: (names.append(n), records.append(r)) ) backtest_conf = _make_backtest_conf( conf=default_conf, pair='UNITTEST/BTC', record="trades" ) results = _run_backtest_1(_trend_alternate, backtest_conf) assert len(results) == 3 # Assert file_dump_json was only called once assert names == ['backtest-result.json'] records = records[0] # Ensure records are of correct type assert len(records) == 3 # ('UNITTEST/BTC', 0.00331158, '1510684320', '1510691700', 0, 117) # Below follows just a typecheck of the schema/type of trade-records oix = None for (pair, profit, date_buy, date_sell, buy_index, dur) in records: assert pair == 'UNITTEST/BTC' isinstance(profit, float) # FIX: buy/sell should be converted to ints isinstance(date_buy, str) isinstance(date_sell, str) isinstance(buy_index, pd._libs.tslib.Timestamp) if oix: assert buy_index > oix oix = buy_index assert dur > 0 def test_backtest_start_live(init_backtesting, default_conf, mocker, caplog): conf = deepcopy(default_conf) conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] mocker.patch('freqtrade.exchange.get_ticker_history', new=lambda n, i: _load_pair_as_ticks(n, i)) mocker.patch('freqtrade.exchange.validate_pairs', MagicMock()) mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock()) mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock()) mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(conf) )) args = MagicMock() args.ticker_interval = 1 args.level = 10 args.live = True args.datadir = None args.export = None args.strategy = 'default_strategy' args.timerange = '-100' # needed due to MagicMock malleability args = [ '--config', 'config.json', '--strategy', 'default_strategy', 'backtesting', '--ticker-interval', '1m', '--live', '--timerange', '-100' ] args = get_args(args) start(args) # check the logs, that will contain the backtest result exists = [ 'Parameter -i/--ticker-interval detected ...', 'Using ticker_interval: 1m ...', 'Parameter -l/--live detected ...', 'Using max_open_trades: 1 ...', 'Parameter --timerange detected: -100 ..', 'Parameter --datadir detected: freqtrade/tests/testdata ...', 'Using stake_currency: BTC ...', 'Using stake_amount: 0.001 ...', 'Downloading data for all pairs in whitelist ...', 'Measuring data from 2017-11-14T19:32:00+00:00 up to 2017-11-14T22:59:00+00:00 (0 days)..' ] for line in exists: log_has(line, caplog.record_tuples)