mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-11 02:33:55 +00:00
202 lines
7.9 KiB
Python
202 lines
7.9 KiB
Python
# pragma pylint: disable=missing-docstring,W0212
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import logging
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import math
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import pandas as pd
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import pytest
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from unittest.mock import MagicMock
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from freqtrade import exchange, optimize
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from freqtrade.exchange import Bittrex
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from freqtrade.optimize import preprocess
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from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
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import freqtrade.optimize.backtesting as backtesting
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from freqtrade.strategy.strategy import Strategy
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@pytest.fixture
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def default_strategy():
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strategy = Strategy()
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strategy.init({'strategy': 'default_strategy'})
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return strategy
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def trim_dictlist(dl, num):
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new = {}
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for pair, pair_data in dl.items():
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new[pair] = pair_data[num:]
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return new
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def test_generate_text_table():
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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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print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
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assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
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'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
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'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
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'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
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'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
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def test_get_timeframe(default_strategy):
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data = preprocess(optimize.load_data(
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None, ticker_interval=1, pairs=['BTC_UNITEST']))
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min_date, max_date = 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_backtest(default_strategy, default_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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exchange._API = Bittrex({'key': '', 'secret': ''})
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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 = backtest({'stake_amount': default_conf['stake_amount'],
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'processed': optimize.preprocess(data),
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'max_open_trades': 10,
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'realistic': True})
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assert not results.empty
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def test_backtest_1min_ticker_interval(default_strategy, default_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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exchange._API = Bittrex({'key': '', 'secret': ''})
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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 = backtest({'stake_amount': default_conf['stake_amount'],
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'processed': optimize.preprocess(data),
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'max_open_trades': 1,
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'realistic': True})
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assert not results.empty
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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):
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data = load_data_test(contour)
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processed = optimize.preprocess(data)
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assert isinstance(processed, dict)
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results = backtest({'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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# results :: <class 'pandas.core.frame.DataFrame'>
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assert len(results) == num_results
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# Test backtest on offline data
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# loaded by freqdata/optimize/__init__.py::load_data()
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def test_backtest2(default_conf, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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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 = backtest({'stake_amount': default_conf['stake_amount'],
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'processed': optimize.preprocess(data),
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'max_open_trades': 10,
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'realistic': True})
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assert not results.empty
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def test_processed(default_conf, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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dict_of_tickerrows = load_data_test('raise')
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dataframes = optimize.preprocess(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, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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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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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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def test_backtest_start(default_conf, mocker, caplog):
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default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
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mocker.patch.multiple('freqtrade.optimize',
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load_data=mocked_load_data)
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args = MagicMock()
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args.ticker_interval = 1
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args.level = 10
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args.live = False
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args.datadir = None
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args.export = None
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args.timerange = '-100' # needed due to MagicMock malleability
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backtesting.start(args)
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# check the logs, that will contain the backtest result
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exists = ['Using max_open_trades: 1 ...',
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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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for line in exists:
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assert ('freqtrade.optimize.backtesting',
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logging.INFO,
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line) in caplog.record_tuples
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