tests: more backtesting testing (#496)
* tests: more backtesting testing * tests: hyperopt * tests: document kludge * tests: improve test_dataframe_correct_length * tests: remove remarks
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kryofly
Janne Sinivirta
parent
53b1f7ac4d
commit
12a19e400f
@ -186,14 +186,15 @@ def start(args):
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data = {}
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pairs = config['exchange']['pair_whitelist']
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logger.info('Using stake_currency: %s ...', config['stake_currency'])
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logger.info('Using stake_amount: %s ...', config['stake_amount'])
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if args.live:
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logger.info('Downloading data for all pairs in whitelist ...')
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for pair in pairs:
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data[pair] = exchange.get_ticker_history(pair, strategy.ticker_interval)
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else:
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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logger.info('Using stake_currency: %s ...', config['stake_currency'])
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logger.info('Using stake_amount: %s ...', config['stake_amount'])
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timerange = misc.parse_timerange(args.timerange)
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data = optimize.load_data(args.datadir,
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@ -261,6 +261,7 @@ def ticker_history_without_bv():
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]
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# FIX: Perhaps change result fixture to use BTC_UNITEST instead?
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@pytest.fixture
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def result():
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with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
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@ -1,9 +1,10 @@
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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103
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import random
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import logging
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import math
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from unittest.mock import MagicMock
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import pandas as pd
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import numpy as np
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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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@ -18,6 +19,70 @@ def trim_dictlist(dict_list, num):
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return new
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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=8, 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,
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pair='BTC_UNITEST',
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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 {'stake_amount': 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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'record': record}
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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(strategy, 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 = strategy.populate_buy_trend
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old_sell = strategy.populate_sell_trend
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strategy.populate_buy_trend = fun # Override
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strategy.populate_sell_trend = fun # Override
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results = backtest(backtest_conf)
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strategy.populate_buy_trend = old_buy # restore override
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strategy.populate_sell_trend = old_sell # restore override
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return results
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def test_generate_text_table():
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results = pd.DataFrame(
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{
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@ -127,21 +192,90 @@ def simple_backtest(config, contour, num_results):
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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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# Test backtest using offline data (testdata directory)
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def test_backtest2(default_conf, mocker, default_strategy):
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def test_backtest_ticks(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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ticks = [1, 5]
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fun = default_strategy.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(default_strategy, fun, backtest_conf)
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assert not results.empty
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def test_backtest_clash_buy_sell(default_conf, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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buy_value = 1
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(default_strategy, fun, backtest_conf)
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assert results.empty
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def test_backtest_only_sell(default_conf, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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buy_value = 0
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(default_strategy, fun, backtest_conf)
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assert results.empty
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def test_backtest_alternate_buy_sell(default_conf, mocker, default_strategy):
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
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results = _run_backtest_1(default_strategy, _trend_alternate,
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backtest_conf)
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assert len(results) == 3
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def test_backtest_record(default_conf, mocker, default_strategy):
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names = []
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records = []
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mocker.patch.dict('freqtrade.main._CONF', default_conf)
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mocker.patch('freqtrade.misc.file_dump_json',
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new=lambda n, r: (names.append(n), records.append(r)))
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backtest_conf = _make_backtest_conf(
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conf=default_conf,
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pair='BTC_UNITEST',
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record="trades"
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)
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results = _run_backtest_1(default_strategy, _trend_alternate,
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backtest_conf)
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assert len(results) == 3
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# Assert file_dump_json was only called once
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assert names == ['backtest-result.json']
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records = records[0]
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# Ensure records are of correct type
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assert len(records) == 3
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# ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
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# Below follows just a typecheck of the schema/type of trade-records
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oix = None
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for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
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assert pair == 'BTC_UNITEST'
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isinstance(profit, float)
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# FIX: buy/sell should be converted to ints
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isinstance(date_buy, str)
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isinstance(date_sell, str)
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isinstance(buy_index, pd._libs.tslib.Timestamp)
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if oix:
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assert buy_index > oix
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oix = buy_index
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assert dur > 0
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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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@ -191,3 +325,29 @@ def test_backtest_start(default_conf, mocker, caplog):
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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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def test_backtest_start_live(default_strategy, default_conf, mocker, caplog):
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caplog.set_level(logging.INFO)
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default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
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mocker.patch('freqtrade.exchange.get_ticker_history',
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new=lambda n, i: _load_pair_as_ticks(n, i))
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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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args = MagicMock()
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args.ticker_interval = 1
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args.level = 10
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args.live = True
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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 2017-11-14T19:32:00+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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@ -1,9 +1,15 @@
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# pragma pylint: disable=missing-docstring,W0212,C0103
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import logging
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from unittest.mock import MagicMock
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import pandas as pd
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from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
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log_results, save_trials, read_trials, generate_roi_table
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import freqtrade.optimize.hyperopt as hyperopt
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def test_loss_calculation_prefer_correct_trade_count():
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correct = calculate_loss(1, TARGET_TRADES, 20)
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@ -250,3 +256,26 @@ def test_roi_table_generation():
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'roi_p3': 3,
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}
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assert generate_roi_table(params) == {'0': 6, '15': 3, '25': 1, '30': 0}
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# test log_trials_result
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# test buy_strategy_generator def populate_buy_trend
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# test optimizer if 'ro_t1' in params
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def test_format_results():
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trades = [('BTC_ETH', 2, 2, 123),
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('BTC_LTC', 1, 1, 123),
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('BTC_XRP', -1, -2, -246)]
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labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
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df = pd.DataFrame.from_records(trades, columns=labels)
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x = hyperopt.format_results(df)
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assert x.find(' 66.67%')
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def test_signal_handler(mocker):
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m = MagicMock()
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mocker.patch('sys.exit', m)
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mocker.patch('freqtrade.optimize.hyperopt.save_trials', m)
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mocker.patch('freqtrade.optimize.hyperopt.log_trials_result', m)
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hyperopt.signal_handler(9, None)
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assert m.call_count == 3
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@ -19,8 +19,8 @@ def test_dataframe_correct_columns(result):
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def test_dataframe_correct_length(result):
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# no idea what this check truly does - should we just remove it?
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assert len(result.index) == 14397
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dataframe = parse_ticker_dataframe(result)
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assert len(result.index) == len(dataframe.index)
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def test_populates_buy_trend(result):
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