609 lines
20 KiB
Python
609 lines
20 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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import json
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import random
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import math
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from typing import List
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from copy import deepcopy
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from unittest.mock import MagicMock
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from arrow import Arrow
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import pandas as pd
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import numpy as np
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from freqtrade import optimize
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from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
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from freqtrade.arguments import Arguments
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from freqtrade.analyze import Analyze
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from freqtrade.tests.conftest import default_conf, log_has
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# Avoid to reinit the same object again and again
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_BACKTESTING = Backtesting(default_conf())
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def get_args(args) -> List[str]:
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return Arguments(args, '').get_parsed_arg()
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def trim_dictlist(dict_list, num):
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new = {}
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for pair, pair_data in dict_list.items():
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new[pair] = pair_data[num:]
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return new
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def load_data_test(what):
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timerange = ((None, 'line'), None, -100)
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data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
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pair = data['BTC_UNITEST']
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datalen = len(pair)
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# Depending on the what parameter we now adjust the
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# loaded data looks:
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# pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
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# 'C': 0.123, 'V': 123.123,
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# 'T': '2017-11-04T23:02:00', 'BV': 0.123}]
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base = 0.001
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if what == 'raise':
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return {'BTC_UNITEST':
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[{'T': pair[x]['T'], # Keep old dates
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'V': pair[x]['V'], # Keep old volume
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'BV': pair[x]['BV'], # keep too
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'O': x * base, # But replace O,H,L,C
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'H': x * base + 0.0001,
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'L': x * base - 0.0001,
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'C': x * base} for x in range(0, datalen)]}
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if what == 'lower':
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return {'BTC_UNITEST':
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[{'T': pair[x]['T'], # Keep old dates
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'V': pair[x]['V'], # Keep old volume
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'BV': pair[x]['BV'], # keep too
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'O': 1 - x * base, # But replace O,H,L,C
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'H': 1 - x * base + 0.0001,
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'L': 1 - x * base - 0.0001,
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'C': 1 - x * base} for x in range(0, datalen)]}
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if what == 'sine':
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hz = 0.1 # frequency
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return {'BTC_UNITEST':
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[{'T': pair[x]['T'], # Keep old dates
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'V': pair[x]['V'], # Keep old volume
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'BV': pair[x]['BV'], # keep too
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# But replace O,H,L,C
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'O': math.sin(x * hz) / 1000 + base,
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'H': math.sin(x * hz) / 1000 + base + 0.0001,
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'L': math.sin(x * hz) / 1000 + base - 0.0001,
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'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
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return data
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def simple_backtest(config, contour, num_results) -> None:
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backtesting = _BACKTESTING
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data = load_data_test(contour)
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processed = backtesting.tickerdata_to_dataframe(data)
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assert isinstance(processed, dict)
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results = backtesting.backtest(
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{
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'stake_amount': config['stake_amount'],
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'processed': processed,
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'max_open_trades': 1,
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'realistic': True
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}
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)
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# results :: <class 'pandas.core.frame.DataFrame'>
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assert len(results) == num_results
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def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
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tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
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pairdata = {'BTC_UNITEST': tickerdata}
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return pairdata
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# use for mock freqtrade.exchange.get_ticker_history'
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def _load_pair_as_ticks(pair, tickfreq):
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ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
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ticks = trim_dictlist(ticks, -200)
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return ticks[pair]
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# FIX: fixturize this?
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def _make_backtest_conf(conf=None, pair='BTC_UNITEST', record=None):
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data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
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data = trim_dictlist(data, -200)
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return {
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'stake_amount': conf['stake_amount'],
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'processed': _BACKTESTING.tickerdata_to_dataframe(data),
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'max_open_trades': 10,
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'realistic': True,
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'record': record
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}
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def _trend(signals, buy_value, sell_value):
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n = len(signals['low'])
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buy = np.zeros(n)
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sell = np.zeros(n)
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for i in range(0, len(signals['buy'])):
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if random.random() > 0.5: # Both buy and sell signals at same timeframe
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buy[i] = buy_value
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sell[i] = sell_value
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signals['buy'] = buy
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signals['sell'] = sell
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return signals
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def _trend_alternate(dataframe=None):
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signals = dataframe
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low = signals['low']
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n = len(low)
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buy = np.zeros(n)
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sell = np.zeros(n)
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for i in range(0, len(buy)):
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if i % 2 == 0:
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buy[i] = 1
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else:
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sell[i] = 1
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signals['buy'] = buy
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signals['sell'] = sell
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return dataframe
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def _run_backtest_1(fun, backtest_conf):
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# strategy is a global (hidden as a singleton), so we
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# emulate strategy being pure, by override/restore here
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# if we dont do this, the override in strategy will carry over
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# to other tests
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old_buy = _BACKTESTING.populate_buy_trend
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old_sell = _BACKTESTING.populate_sell_trend
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_BACKTESTING.populate_buy_trend = fun # Override
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_BACKTESTING.populate_sell_trend = fun # Override
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results = _BACKTESTING.backtest(backtest_conf)
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_BACKTESTING.populate_buy_trend = old_buy # restore override
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_BACKTESTING.populate_sell_trend = old_sell # restore override
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return results
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# Unit tests
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def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
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"""
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Test setup_configuration() function
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"""
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mocker.patch('freqtrade.configuration.open', mocker.mock_open(
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read_data=json.dumps(default_conf)
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))
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args = [
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'--config', 'config.json',
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'--strategy', 'default_strategy',
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'backtesting'
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]
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config = setup_configuration(get_args(args))
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assert 'max_open_trades' in config
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assert 'stake_currency' in config
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assert 'stake_amount' in config
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assert 'exchange' in config
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assert 'pair_whitelist' in config['exchange']
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assert 'datadir' in config
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assert log_has(
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'Parameter --datadir detected: {} ...'.format(config['datadir']),
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caplog.record_tuples
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)
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assert 'ticker_interval' in config
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assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
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assert 'live' not in config
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assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
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assert 'realistic_simulation' not in config
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assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
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assert 'refresh_pairs' not in config
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assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
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assert 'timerange' not in config
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assert 'export' not in config
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def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
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"""
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Test setup_configuration() function
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"""
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mocker.patch('freqtrade.configuration.open', mocker.mock_open(
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read_data=json.dumps(default_conf)
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))
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args = [
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'--config', 'config.json',
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'--strategy', 'default_strategy',
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'--datadir', '/foo/bar',
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'backtesting',
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'--ticker-interval', '1',
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'--live',
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'--realistic-simulation',
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'--refresh-pairs-cached',
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'--timerange', ':100',
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'--export', '/bar/foo'
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]
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config = setup_configuration(get_args(args))
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assert 'max_open_trades' in config
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assert 'stake_currency' in config
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assert 'stake_amount' in config
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assert 'exchange' in config
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assert 'pair_whitelist' in config['exchange']
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assert 'datadir' in config
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assert log_has(
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'Parameter --datadir detected: {} ...'.format(config['datadir']),
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caplog.record_tuples
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)
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assert 'ticker_interval' in config
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assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
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assert log_has(
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'Using ticker_interval: 1 ...',
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caplog.record_tuples
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)
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assert 'live' in config
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assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
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assert 'realistic_simulation'in config
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assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
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assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)
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assert 'refresh_pairs'in config
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assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
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assert 'timerange' in config
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assert log_has(
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'Parameter --timerange detected: {} ...'.format(config['timerange']),
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caplog.record_tuples
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)
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assert 'export' in config
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assert log_has(
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'Parameter --export detected: {} ...'.format(config['export']),
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caplog.record_tuples
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)
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def test_start(mocker, default_conf, caplog) -> None:
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"""
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Test start() function
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"""
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start_mock = MagicMock()
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mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
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mocker.patch('freqtrade.configuration.open', mocker.mock_open(
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read_data=json.dumps(default_conf)
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))
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args = [
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'--config', 'config.json',
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'--strategy', 'default_strategy',
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'backtesting'
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]
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args = get_args(args)
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start(args)
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assert log_has(
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'Starting freqtrade in Backtesting mode',
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caplog.record_tuples
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)
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assert start_mock.call_count == 1
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def test_backtesting__init__(mocker, default_conf) -> None:
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"""
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Test Backtesting.__init__() method
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"""
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init_mock = MagicMock()
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mocker.patch('freqtrade.optimize.backtesting.Backtesting._init', init_mock)
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backtesting = Backtesting(default_conf)
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assert backtesting.config == default_conf
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assert backtesting.analyze is None
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assert backtesting.ticker_interval is None
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assert backtesting.tickerdata_to_dataframe is None
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assert backtesting.populate_buy_trend is None
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assert backtesting.populate_sell_trend is None
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assert init_mock.call_count == 1
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def test_backtesting_init(default_conf) -> None:
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"""
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Test Backtesting._init() method
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"""
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backtesting = Backtesting(default_conf)
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assert backtesting.config == default_conf
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assert isinstance(backtesting.analyze, Analyze)
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assert backtesting.ticker_interval == 5
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assert callable(backtesting.tickerdata_to_dataframe)
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assert callable(backtesting.populate_buy_trend)
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assert callable(backtesting.populate_sell_trend)
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def test_tickerdata_to_dataframe(default_conf) -> None:
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"""
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Test Backtesting.tickerdata_to_dataframe() method
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"""
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timerange = ((None, 'line'), None, -100)
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tick = optimize.load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
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tickerlist = {'BTC_UNITEST': tick}
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backtesting = _BACKTESTING
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data = backtesting.tickerdata_to_dataframe(tickerlist)
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assert len(data['BTC_UNITEST']) == 100
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# Load Analyze to compare the result between Backtesting function and Analyze are the same
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analyze = Analyze(default_conf)
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data2 = analyze.tickerdata_to_dataframe(tickerlist)
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assert data['BTC_UNITEST'].equals(data2['BTC_UNITEST'])
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def test_get_timeframe() -> None:
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"""
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Test Backtesting.get_timeframe() method
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"""
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backtesting = _BACKTESTING
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data = backtesting.tickerdata_to_dataframe(
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optimize.load_data(
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None,
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ticker_interval=1,
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pairs=['BTC_UNITEST']
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)
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)
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min_date, max_date = backtesting.get_timeframe(data)
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assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
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assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
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def test_generate_text_table():
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"""
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Test Backtesting.generate_text_table() method
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"""
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backtesting = _BACKTESTING
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results = pd.DataFrame(
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{
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'currency': ['BTC_ETH', 'BTC_ETH'],
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'profit_percent': [0.1, 0.2],
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'profit_BTC': [0.2, 0.4],
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'duration': [10, 30],
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'profit': [2, 0],
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'loss': [0, 0]
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}
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)
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result_str = (
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'pair buy count avg profit % '
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'total profit BTC avg duration profit loss\n'
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'------- ----------- -------------- '
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'------------------ -------------- -------- ------\n'
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'BTC_ETH 2 15.00 '
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'0.60000000 20.0 2 0\n'
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'TOTAL 2 15.00 '
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'0.60000000 20.0 2 0'
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)
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assert backtesting._generate_text_table(data={'BTC_ETH': {}}, results=results) == result_str
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def test_backtesting_start(default_conf, mocker, caplog) -> None:
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"""
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Test Backtesting.start() method
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"""
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def get_timeframe(input1, input2):
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return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
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mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
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mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
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mocker.patch('freqtrade.exchange.get_ticker_history')
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mocker.patch.multiple(
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'freqtrade.optimize.backtesting.Backtesting',
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backtest=MagicMock(),
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_generate_text_table=MagicMock(return_value='1'),
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get_timeframe=get_timeframe,
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)
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conf = deepcopy(default_conf)
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conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
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conf['ticker_interval'] = 1
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conf['live'] = False
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conf['datadir'] = None
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conf['export'] = None
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conf['timerange'] = '-100'
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backtesting = Backtesting(conf)
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backtesting.start()
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# check the logs, that will contain the backtest result
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exists = [
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'Using local backtesting data (using whitelist in given config) ...',
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'Using stake_currency: BTC ...',
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'Using stake_amount: 0.001 ...',
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'Measuring data from 2017-11-14T21:17:00+00:00 '
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'up to 2017-11-14T22:59:00+00:00 (0 days)..'
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]
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for line in exists:
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assert log_has(line, caplog.record_tuples)
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def test_backtest(default_conf) -> None:
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"""
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Test Backtesting.backtest() method
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"""
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backtesting = _BACKTESTING
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data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
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data = trim_dictlist(data, -200)
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results = backtesting.backtest(
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{
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'stake_amount': default_conf['stake_amount'],
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'processed': backtesting.tickerdata_to_dataframe(data),
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'max_open_trades': 10,
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'realistic': True
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}
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)
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assert not results.empty
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def test_backtest_1min_ticker_interval(default_conf) -> None:
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"""
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Test Backtesting.backtest() method with 1 min ticker
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"""
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backtesting = _BACKTESTING
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# Run a backtesting for an exiting 5min ticker_interval
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data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
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data = trim_dictlist(data, -200)
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results = backtesting.backtest(
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{
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'stake_amount': default_conf['stake_amount'],
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'processed': backtesting.tickerdata_to_dataframe(data),
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'max_open_trades': 1,
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'realistic': True
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}
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)
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assert not results.empty
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def test_processed() -> None:
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"""
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Test Backtesting.backtest() method with offline data
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"""
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backtesting = _BACKTESTING
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dict_of_tickerrows = load_data_test('raise')
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dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows)
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dataframe = dataframes['BTC_UNITEST']
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cols = dataframe.columns
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# assert the dataframe got some of the indicator columns
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for col in ['close', 'high', 'low', 'open', 'date',
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'ema50', 'ao', 'macd', 'plus_dm']:
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assert col in cols
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def test_backtest_pricecontours(default_conf) -> None:
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tests = [['raise', 17], ['lower', 0], ['sine', 17]]
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for [contour, numres] in tests:
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simple_backtest(default_conf, contour, numres)
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# Test backtest using offline data (testdata directory)
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def test_backtest_ticks(default_conf):
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ticks = [1, 5]
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fun = _BACKTESTING.populate_buy_trend
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for tick in ticks:
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(fun, backtest_conf)
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assert not results.empty
|
|
|
|
|
|
def test_backtest_clash_buy_sell(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(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(default_conf):
|
|
backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
|
|
results = _run_backtest_1(_trend_alternate, backtest_conf)
|
|
assert len(results) == 3
|
|
|
|
|
|
def test_backtest_record(default_conf, mocker):
|
|
names = []
|
|
records = []
|
|
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='BTC_UNITEST',
|
|
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
|
|
# ('BTC_UNITEST', 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 == 'BTC_UNITEST'
|
|
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(default_conf, mocker, caplog):
|
|
default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
|
|
mocker.patch('freqtrade.exchange.get_ticker_history',
|
|
new=lambda n, i: _load_pair_as_ticks(n, i))
|
|
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 = 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', '1',
|
|
'--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: 1 ...',
|
|
'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)
|