# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument import json import math import random from typing import List from unittest.mock import MagicMock import numpy as np import pandas as pd import pytest from arrow import Arrow from freqtrade import DependencyException, constants from freqtrade.arguments import Arguments, TimeRange from freqtrade.data import history from freqtrade.optimize import get_timeframe from freqtrade.optimize.backtesting import (Backtesting, setup_configuration, start) from freqtrade.strategy.default_strategy import DefaultStrategy from freqtrade.strategy.interface import SellType from freqtrade.tests.conftest import log_has, patch_exchange 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 = TimeRange(None, 'line', 0, -101) data = history.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, mocker) -> None: patch_exchange(mocker) config['ticker_interval'] = '1m' backtesting = Backtesting(config) data = load_data_test(contour) processed = backtesting.strategy.tickerdata_to_dataframe(data) min_date, max_date = get_timeframe(processed) assert isinstance(processed, dict) results = backtesting.backtest( { 'stake_amount': config['stake_amount'], 'processed': processed, 'max_open_trades': 1, 'position_stacking': False, 'start_date': min_date, 'end_date': max_date, } ) # results :: assert len(results) == num_results def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False, timerange=None, exchange=None): tickerdata = history.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange) pairdata = {'UNITTEST/BTC': tickerdata} return pairdata # use for mock ccxt.fetch_ohlvc' def _load_pair_as_ticks(pair, tickfreq): ticks = history.load_data(None, ticker_interval=tickfreq, pairs=[pair]) ticks = trim_dictlist(ticks, -201) return ticks[pair] # FIX: fixturize this? def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None): data = history.load_data(None, ticker_interval='1m', pairs=[pair]) data = trim_dictlist(data, -201) patch_exchange(mocker) backtesting = Backtesting(conf) processed = backtesting.strategy.tickerdata_to_dataframe(data) min_date, max_date = get_timeframe(processed) return { 'stake_amount': conf['stake_amount'], 'processed': processed, 'max_open_trades': 10, 'position_stacking': False, 'record': record, 'start_date': min_date, 'end_date': max_date, } 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, metadata=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 # Unit tests def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None: mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'DefaultStrategy', '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( 'Using data folder: {} ...'.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 'position_stacking' not in config assert not log_has('Parameter --enable-position-stacking 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: mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'DefaultStrategy', '--datadir', '/foo/bar', 'backtesting', '--ticker-interval', '1m', '--live', '--enable-position-stacking', '--disable-max-market-positions', '--refresh-pairs-cached', '--timerange', ':100', '--export', '/bar/foo', '--export-filename', 'foo_bar.json' ] 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( 'Using data folder: {} ...'.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 'position_stacking' in config assert log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples) assert 'use_max_market_positions' in config assert log_has('Parameter --disable-max-market-positions detected ...', caplog.record_tuples) assert log_has('max_open_trades set to unlimited ...', 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 ) assert 'exportfilename' in config assert log_has( 'Storing backtest results to {} ...'.format(config['exportfilename']), caplog.record_tuples ) def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None: default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'DefaultStrategy', 'backtesting' ] with pytest.raises(DependencyException, match=r'.*stake amount.*'): setup_configuration(get_args(args)) def test_start(mocker, fee, default_conf, caplog) -> None: start_mock = MagicMock() mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) patch_exchange(mocker) 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', 'DefaultStrategy', '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: patch_exchange(mocker) get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5)) backtesting = Backtesting(default_conf) assert backtesting.config == default_conf assert backtesting.ticker_interval == '5m' assert callable(backtesting.strategy.tickerdata_to_dataframe) assert callable(backtesting.advise_buy) assert callable(backtesting.advise_sell) get_fee.assert_called() assert backtesting.fee == 0.5 def test_tickerdata_to_dataframe(default_conf, mocker) -> None: patch_exchange(mocker) timerange = TimeRange(None, 'line', 0, -100) tick = history.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange) tickerlist = {'UNITTEST/BTC': tick} backtesting = Backtesting(default_conf) data = backtesting.strategy.tickerdata_to_dataframe(tickerlist) assert len(data['UNITTEST/BTC']) == 99 # Load strategy to compare the result between Backtesting function and strategy are the same strategy = DefaultStrategy(default_conf) data2 = strategy.tickerdata_to_dataframe(tickerlist) assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC']) def test_generate_text_table(default_conf, mocker): patch_exchange(mocker) backtesting = Backtesting(default_conf) results = pd.DataFrame( { 'pair': ['ETH/BTC', 'ETH/BTC'], 'profit_percent': [0.1, 0.2], 'profit_abs': [0.2, 0.4], 'trade_duration': [10, 30], 'profit': [2, 0], 'loss': [0, 0] } ) result_str = ( '| pair | buy count | avg profit % | cum profit % | ' 'total profit BTC | avg duration | profit | loss |\n' '|:--------|------------:|---------------:|---------------:|' '-------------------:|:---------------|---------:|-------:|\n' '| ETH/BTC | 2 | 15.00 | 30.00 | ' '0.60000000 | 0:20:00 | 2 | 0 |\n' '| TOTAL | 2 | 15.00 | 30.00 | ' '0.60000000 | 0:20:00 | 2 | 0 |' ) assert backtesting._generate_text_table(data={'ETH/BTC': {}}, results=results) == result_str def test_generate_text_table_sell_reason(default_conf, mocker): patch_exchange(mocker) backtesting = Backtesting(default_conf) results = pd.DataFrame( { 'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'], 'profit_percent': [0.1, 0.2, 0.3], 'profit_abs': [0.2, 0.4, 0.5], 'trade_duration': [10, 30, 10], 'profit': [2, 0, 0], 'loss': [0, 0, 1], 'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS] } ) result_str = ( '| Sell Reason | Count |\n' '|:--------------|--------:|\n' '| roi | 2 |\n' '| stop_loss | 1 |' ) assert backtesting._generate_text_table_sell_reason( data={'ETH/BTC': {}}, results=results) == result_str def test_generate_text_table_strategyn(default_conf, mocker): """ Test Backtesting.generate_text_table_sell_reason() method """ patch_exchange(mocker) backtesting = Backtesting(default_conf) results = {} results['ETH/BTC'] = pd.DataFrame( { 'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'], 'profit_percent': [0.1, 0.2, 0.3], 'profit_abs': [0.2, 0.4, 0.5], 'trade_duration': [10, 30, 10], 'profit': [2, 0, 0], 'loss': [0, 0, 1], 'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS] } ) results['LTC/BTC'] = pd.DataFrame( { 'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'], 'profit_percent': [0.4, 0.2, 0.3], 'profit_abs': [0.4, 0.4, 0.5], 'trade_duration': [15, 30, 15], 'profit': [4, 1, 0], 'loss': [0, 0, 1], 'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS] } ) result_str = ( '| Strategy | buy count | avg profit % | cum profit % ' '| total profit BTC | avg duration | profit | loss |\n' '|:-----------|------------:|---------------:|---------------:' '|-------------------:|:---------------|---------:|-------:|\n' '| ETH/BTC | 3 | 20.00 | 60.00 ' '| 1.10000000 | 0:17:00 | 3 | 0 |\n' '| LTC/BTC | 3 | 30.00 | 90.00 ' '| 1.30000000 | 0:20:00 | 3 | 0 |' ) print(backtesting._generate_text_table_strategy(all_results=results)) assert backtesting._generate_text_table_strategy(all_results=results) == result_str def test_backtesting_start(default_conf, mocker, caplog) -> None: def get_timeframe(input1): return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59) mocker.patch('freqtrade.data.history.load_data', mocked_load_data) mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe) mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock()) patch_exchange(mocker) mocker.patch.multiple( 'freqtrade.optimize.backtesting.Backtesting', backtest=MagicMock(), _generate_text_table=MagicMock(return_value='1'), ) default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] default_conf['ticker_interval'] = '1m' default_conf['live'] = False default_conf['datadir'] = None default_conf['export'] = None default_conf['timerange'] = '-100' backtesting = Backtesting(default_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_backtesting_start_no_data(default_conf, mocker, caplog) -> None: def get_timeframe(input1): return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59) mocker.patch('freqtrade.data.history.load_data', MagicMock(return_value={})) mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe) mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock()) patch_exchange(mocker) mocker.patch.multiple( 'freqtrade.optimize.backtesting.Backtesting', backtest=MagicMock(), _generate_text_table=MagicMock(return_value='1'), ) default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] default_conf['ticker_interval'] = "1m" default_conf['live'] = False default_conf['datadir'] = None default_conf['export'] = None default_conf['timerange'] = '20180101-20180102' backtesting = Backtesting(default_conf) backtesting.start() # check the logs, that will contain the backtest result assert log_has('No data found. Terminating.', caplog.record_tuples) def test_backtest(default_conf, fee, mocker) -> None: mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) patch_exchange(mocker) backtesting = Backtesting(default_conf) pair = 'UNITTEST/BTC' data = history.load_data(None, ticker_interval='5m', pairs=['UNITTEST/BTC']) data = trim_dictlist(data, -200) data_processed = backtesting.strategy.tickerdata_to_dataframe(data) min_date, max_date = get_timeframe(data_processed) results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], 'processed': data_processed, 'max_open_trades': 10, 'position_stacking': False, 'start_date': min_date, 'end_date': max_date, } ) assert not results.empty assert len(results) == 2 expected = pd.DataFrame( {'pair': [pair, pair], 'profit_percent': [0.0, 0.0], 'profit_abs': [0.0, 0.0], 'open_time': [Arrow(2018, 1, 29, 18, 40, 0).datetime, Arrow(2018, 1, 30, 3, 30, 0).datetime], 'close_time': [Arrow(2018, 1, 29, 22, 35, 0).datetime, Arrow(2018, 1, 30, 4, 15, 0).datetime], 'open_index': [77, 183], 'close_index': [124, 192], 'trade_duration': [235, 45], 'open_at_end': [False, False], 'open_rate': [0.104445, 0.10302485], 'close_rate': [0.104969, 0.103541], 'sell_reason': [SellType.ROI, SellType.ROI] }) pd.testing.assert_frame_equal(results, expected) data_pair = data_processed[pair] for _, t in results.iterrows(): ln = data_pair.loc[data_pair["date"] == t["open_time"]] # Check open trade rate alignes to open rate assert ln is not None assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6) # check close trade rate alignes to close rate or is between high and low ln = data_pair.loc[data_pair["date"] == t["close_time"]] assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or round(ln.iloc[0]["low"], 6) < round( t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)) def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None: mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) patch_exchange(mocker) backtesting = Backtesting(default_conf) # Run a backtesting for an exiting 5min ticker_interval data = history.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC']) data = trim_dictlist(data, -200) processed = backtesting.strategy.tickerdata_to_dataframe(data) min_date, max_date = get_timeframe(processed) results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], 'processed': processed, 'max_open_trades': 1, 'position_stacking': False, 'start_date': min_date, 'end_date': max_date, } ) assert not results.empty assert len(results) == 1 def test_processed(default_conf, mocker) -> None: patch_exchange(mocker) backtesting = Backtesting(default_conf) dict_of_tickerrows = load_data_test('raise') dataframes = backtesting.strategy.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(default_conf, fee, mocker) -> None: mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) tests = [['raise', 18], ['lower', 0], ['sine', 19]] # We need to enable sell-signal - otherwise it sells on ROI!! default_conf['experimental'] = {"use_sell_signal": True} for [contour, numres] in tests: simple_backtest(default_conf, contour, numres, mocker) def test_backtest_clash_buy_sell(mocker, default_conf): # Override the default buy trend function in our default_strategy def fun(dataframe=None, pair=None): buy_value = 1 sell_value = 1 return _trend(dataframe, buy_value, sell_value) backtest_conf = _make_backtest_conf(mocker, conf=default_conf) backtesting = Backtesting(default_conf) backtesting.advise_buy = fun # Override backtesting.advise_sell = fun # Override results = backtesting.backtest(backtest_conf) assert results.empty def test_backtest_only_sell(mocker, default_conf): # Override the default buy trend function in our default_strategy def fun(dataframe=None, pair=None): buy_value = 0 sell_value = 1 return _trend(dataframe, buy_value, sell_value) backtest_conf = _make_backtest_conf(mocker, conf=default_conf) backtesting = Backtesting(default_conf) backtesting.advise_buy = fun # Override backtesting.advise_sell = fun # Override results = backtesting.backtest(backtest_conf) assert results.empty def test_backtest_alternate_buy_sell(default_conf, fee, mocker): mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) mocker.patch('freqtrade.optimize.backtesting.file_dump_json', MagicMock()) backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC') # We need to enable sell-signal - otherwise it sells on ROI!! default_conf['experimental'] = {"use_sell_signal": True} default_conf['ticker_interval'] = '1m' backtesting = Backtesting(default_conf) backtesting.advise_buy = _trend_alternate # Override backtesting.advise_sell = _trend_alternate # Override results = backtesting.backtest(backtest_conf) backtesting._store_backtest_result("test_.json", results) # 200 candles in backtest data # won't buy on first (shifted by 1) # 100 buys signals assert len(results) == 99 # One trade was force-closed at the end assert len(results.loc[results.open_at_end]) == 0 def test_backtest_multi_pair(default_conf, fee, mocker): def evaluate_result_multi(results, freq, max_open_trades): # Find overlapping trades by expanding each trade once per period # and then counting overlaps dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq)) for row in results[['open_time', 'close_time']].iterrows()] deltas = [len(x) for x in dates] dates = pd.Series(pd.concat(dates).values, name='date') df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns) df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"}) df2 = pd.concat([dates, df2], axis=1) df2 = df2.set_index('date') df_final = df2.resample(freq)[['pair']].count() return df_final[df_final['pair'] > max_open_trades] def _trend_alternate_hold(dataframe=None, metadata=None): """ Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit) """ multi = 8 dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0) dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0) if metadata['pair'] in('ETH/BTC', 'LTC/BTC'): dataframe['buy'] = dataframe['buy'].shift(-4) dataframe['sell'] = dataframe['sell'].shift(-4) return dataframe mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) patch_exchange(mocker) pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC'] data = history.load_data(None, ticker_interval='5m', pairs=pairs) data = trim_dictlist(data, -500) # We need to enable sell-signal - otherwise it sells on ROI!! default_conf['experimental'] = {"use_sell_signal": True} default_conf['ticker_interval'] = '5m' backtesting = Backtesting(default_conf) backtesting.advise_buy = _trend_alternate_hold # Override backtesting.advise_sell = _trend_alternate_hold # Override data_processed = backtesting.strategy.tickerdata_to_dataframe(data) min_date, max_date = get_timeframe(data_processed) backtest_conf = { 'stake_amount': default_conf['stake_amount'], 'processed': data_processed, 'max_open_trades': 3, 'position_stacking': False, 'start_date': min_date, 'end_date': max_date, } results = backtesting.backtest(backtest_conf) # Make sure we have parallel trades assert len(evaluate_result_multi(results, '5min', 2)) > 0 # make sure we don't have trades with more than configured max_open_trades assert len(evaluate_result_multi(results, '5min', 3)) == 0 backtest_conf = { 'stake_amount': default_conf['stake_amount'], 'processed': data_processed, 'max_open_trades': 1, 'position_stacking': False, 'start_date': min_date, 'end_date': max_date, } results = backtesting.backtest(backtest_conf) assert len(evaluate_result_multi(results, '5min', 1)) == 0 def test_backtest_record(default_conf, fee, mocker): names = [] records = [] patch_exchange(mocker) mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) mocker.patch( 'freqtrade.optimize.backtesting.file_dump_json', new=lambda n, r: (names.append(n), records.append(r)) ) backtesting = Backtesting(default_conf) results = pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"], "profit_percent": [0.003312, 0.010801, 0.013803, 0.002780], "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003], "open_time": [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_time": [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], "open_index": [1, 119, 153, 185], "close_index": [118, 151, 184, 199], "trade_duration": [123, 34, 31, 14], "open_at_end": [False, False, False, True], "sell_reason": [SellType.ROI, SellType.STOP_LOSS, SellType.ROI, SellType.FORCE_SELL] }) backtesting._store_backtest_result("backtest-result.json", results) assert len(results) == 4 # 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) == 4 # reset test to test with strategy name names = [] records = [] backtesting._store_backtest_result("backtest-result.json", results, "DefStrat") assert len(results) == 4 # Assert file_dump_json was only called once assert names == ['backtest-result-DefStrat.json'] records = records[0] # Ensure records are of correct type assert len(records) == 4 # ('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, openr, closer, open_at_end, sell_reason) in records: assert pair == 'UNITTEST/BTC' assert isinstance(profit, float) # FIX: buy/sell should be converted to ints assert isinstance(date_buy, float) assert isinstance(date_sell, float) assert isinstance(openr, float) assert isinstance(closer, float) assert isinstance(open_at_end, bool) assert isinstance(sell_reason, 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(default_conf, mocker, caplog): default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] async def load_pairs(pair, timeframe, since): return _load_pair_as_ticks(pair, timeframe) api_mock = MagicMock() api_mock.fetch_ohlcv = load_pairs patch_exchange(mocker, api_mock) 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(default_conf) )) args = [ '--config', 'config.json', '--strategy', 'DefaultStrategy', '--datadir', 'freqtrade/tests/testdata', 'backtesting', '--ticker-interval', '1m', '--live', '--timerange', '-100', '--enable-position-stacking', '--disable-max-market-positions' ] 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 ...', 'Ignoring max_open_trades (--disable-max-market-positions was used) ...', 'Parameter --timerange detected: -100 ...', 'Using data folder: freqtrade/tests/testdata ...', 'Using stake_currency: BTC ...', 'Using stake_amount: 0.001 ...', 'Downloading data for all pairs in whitelist ...', 'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:57:00+00:00 (0 days)..', 'Parameter --enable-position-stacking detected ...' ] for line in exists: assert log_has(line, caplog.record_tuples) def test_backtest_start_multi_strat(default_conf, mocker, caplog): default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC'] async def load_pairs(pair, timeframe, since): return _load_pair_as_ticks(pair, timeframe) api_mock = MagicMock() api_mock.fetch_ohlcv = load_pairs patch_exchange(mocker, api_mock) backtestmock = MagicMock() mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock) gen_table_mock = MagicMock() mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', gen_table_mock) gen_strattable_mock = MagicMock() mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table_strategy', gen_strattable_mock) mocker.patch('freqtrade.configuration.open', mocker.mock_open( read_data=json.dumps(default_conf) )) args = [ '--config', 'config.json', '--datadir', 'freqtrade/tests/testdata', 'backtesting', '--ticker-interval', '1m', '--live', '--timerange', '-100', '--enable-position-stacking', '--disable-max-market-positions', '--strategy-list', 'DefaultStrategy', 'TestStrategy', ] args = get_args(args) start(args) # 2 backtests, 4 tables assert backtestmock.call_count == 2 assert gen_table_mock.call_count == 4 assert gen_strattable_mock.call_count == 1 # check the logs, that will contain the backtest result exists = [ 'Parameter -i/--ticker-interval detected ...', 'Using ticker_interval: 1m ...', 'Parameter -l/--live detected ...', 'Ignoring max_open_trades (--disable-max-market-positions was used) ...', 'Parameter --timerange detected: -100 ...', 'Using data folder: freqtrade/tests/testdata ...', 'Using stake_currency: BTC ...', 'Using stake_amount: 0.001 ...', 'Downloading data for all pairs in whitelist ...', 'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:57:00+00:00 (0 days)..', 'Parameter --enable-position-stacking detected ...', 'Running backtesting for Strategy DefaultStrategy', 'Running backtesting for Strategy TestStrategy', ] for line in exists: assert log_has(line, caplog.record_tuples)