161 lines
6.2 KiB
Python
161 lines
6.2 KiB
Python
# pragma pylint: disable=missing-docstring,W0212
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import math
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import pandas as pd
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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.backtesting import backtest, generate_text_table, get_timeframe
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# import freqtrade.optimize.backtesting as backtesting
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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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}
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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\n'
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'------- ----------- -------------- ------------------ --------------\n'
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'BTC_ETH 2 15.00 0.60000000 100.0\n'
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'TOTAL 2 15.00 0.60000000 100.0')
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def test_get_timeframe():
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data = optimize.load_data(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_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(ticker_interval=5, pairs=['BTC_ETH'])
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results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True)
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assert not results.empty
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def test_backtest_1min_ticker_interval(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(ticker_interval=1, pairs=['BTC_UNITEST'])
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results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 1, True)
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assert not results.empty
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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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# Can't figure out why -num wont work
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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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data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])
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data = trim_dictlist(data, -100)
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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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'O': math.sin(x*hz) / 1000 + base, # But replace O,H,L,C
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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(config['stake_amount'], processed, 1, 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):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
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results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True)
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assert not results.empty
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def test_processed(default_conf, mocker):
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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):
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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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# Please make this work, the load_config needs to be mocked
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# and cleanups.
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# def test_backtest_start(default_conf, mocker):
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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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# # see https://pypi.python.org/pypi/pytest-mock/
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# # and http://www.voidspace.org.uk/python/mock/patch.html
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# # No usage example of simple function mocking,
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# # and no documentation of side_effect
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# mocker.patch('freqtrade.misc.load_config', new=lambda s, t: {})
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# args = MagicMock()
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# args.level = 10
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# #load_config('foo')
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# backtesting.start(args)
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#
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# Check what sideeffect backtstesting has done.
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# Probably need to capture standard-output and
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# check for the generated report table.
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