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stable/freqtrade/tests/optimize/test_backtesting.py
kryofly 12a19e400f tests: more backtesting testing (#496) 
* tests: more backtesting testing

* tests: hyperopt

* tests: document kludge

* tests: improve test_dataframe_correct_length

* tests: remove remarks
2018-02-08 21:49:43 +02:00

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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103
import random
import logging
import math
from unittest.mock import MagicMock
import pandas as pd
import numpy as np
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
from freqtrade.optimize import preprocess
from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
import freqtrade.optimize.backtesting as backtesting
def trim_dictlist(dict_list, num):
new = {}
for pair, pair_data in dict_list.items():
new[pair] = pair_data[num:]
return new
# use for mock freqtrade.exchange.get_ticker_history'
def _load_pair_as_ticks(pair, tickfreq):
ticks = optimize.load_data(None, ticker_interval=8, pairs=[pair])
ticks = trim_dictlist(ticks, -200)
return ticks[pair]
# FIX: fixturize this?
def _make_backtest_conf(conf=None,
pair='BTC_UNITEST',
record=None):
data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
data = trim_dictlist(data, -200)
return {'stake_amount': conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True,
'record': record}
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):
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
def _run_backtest_1(strategy, fun, backtest_conf):
# strategy is a global (hidden as a singleton), so we
# emulate strategy being pure, by override/restore here
# if we dont do this, the override in strategy will carry over
# to other tests
old_buy = strategy.populate_buy_trend
old_sell = strategy.populate_sell_trend
strategy.populate_buy_trend = fun # Override
strategy.populate_sell_trend = fun # Override
results = backtest(backtest_conf)
strategy.populate_buy_trend = old_buy # restore override
strategy.populate_sell_trend = old_sell # restore override
return results
def test_generate_text_table():
results = pd.DataFrame(
{
'currency': ['BTC_ETH', 'BTC_ETH'],
'profit_percent': [0.1, 0.2],
'profit_BTC': [0.2, 0.4],
'duration': [10, 30],
'profit': [2, 0],
'loss': [0, 0]
}
)
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC'))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC') == (
'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 20.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 20.0 2 0') # noqa
def test_get_timeframe(default_strategy):
data = preprocess(optimize.load_data(
None, ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def test_backtest(default_strategy, default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
data = trim_dictlist(data, -200)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty
def test_backtest_1min_ticker_interval(default_strategy, default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -200)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 1,
'realistic': True})
assert not results.empty
def load_data_test(what):
timerange = ((None, 'line'), None, -100)
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
pair = data['BTC_UNITEST']
datalen = len(pair)
# Depending on the what parameter we now adjust the
# loaded data looks:
# pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
# 'C': 0.123, 'V': 123.123,
# 'T': '2017-11-04T23:02:00', 'BV': 0.123}]
base = 0.001
if what == 'raise':
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': x * base, # But replace O,H,L,C
'H': x * base + 0.0001,
'L': x * base - 0.0001,
'C': x * base} for x in range(0, datalen)]}
if what == 'lower':
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': 1 - x * base, # But replace O,H,L,C
'H': 1 - x * base + 0.0001,
'L': 1 - x * base - 0.0001,
'C': 1 - x * base} for x in range(0, datalen)]}
if what == 'sine':
hz = 0.1 # frequency
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
# But replace O,H,L,C
'O': math.sin(x * hz) / 1000 + base,
'H': math.sin(x * hz) / 1000 + base + 0.0001,
'L': math.sin(x * hz) / 1000 + base - 0.0001,
'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
return data
def simple_backtest(config, contour, num_results):
data = load_data_test(contour)
processed = optimize.preprocess(data)
assert isinstance(processed, dict)
results = backtest({'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'realistic': True})
# results :: <class 'pandas.core.frame.DataFrame'>
assert len(results) == num_results
# Test backtest using offline data (testdata directory)
def test_backtest_ticks(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
ticks = [1, 5]
fun = default_strategy.populate_buy_trend
for tick in ticks:
backtest_conf = _make_backtest_conf(conf=default_conf)
results = _run_backtest_1(default_strategy, fun, backtest_conf)
assert not results.empty
def test_backtest_clash_buy_sell(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', 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(default_strategy, fun, backtest_conf)
assert results.empty
def test_backtest_only_sell(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', 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(default_strategy, fun, backtest_conf)
assert results.empty
def test_backtest_alternate_buy_sell(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
results = _run_backtest_1(default_strategy, _trend_alternate,
backtest_conf)
assert len(results) == 3
def test_backtest_record(default_conf, mocker, default_strategy):
names = []
records = []
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.misc.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(default_strategy, _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_processed(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
dict_of_tickerrows = load_data_test('raise')
dataframes = optimize.preprocess(dict_of_tickerrows)
dataframe = dataframes['BTC_UNITEST']
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, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
tests = [['raise', 17], ['lower', 0], ['sine', 17]]
for [contour, numres] in tests:
simple_backtest(default_conf, contour, numres)
def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
pairdata = {'BTC_UNITEST': tickerdata}
return pairdata
def test_backtest_start(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
mocker.patch.multiple('freqtrade.optimize',
load_data=mocked_load_data)
args = MagicMock()
args.ticker_interval = 1
args.level = 10
args.live = False
args.datadir = None
args.export = None
args.timerange = '-100' # needed due to MagicMock malleability
backtesting.start(args)
# check the logs, that will contain the backtest result
exists = ['Using max_open_trades: 1 ...',
'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 ('freqtrade.optimize.backtesting',
logging.INFO,
line) in caplog.record_tuples
def test_backtest_start_live(default_strategy, default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
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.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
args = MagicMock()
args.ticker_interval = 1
args.level = 10
args.live = True
args.datadir = None
args.export = None
args.timerange = '-100' # needed due to MagicMock malleability
backtesting.start(args)
# check the logs, that will contain the backtest result
exists = ['Using max_open_trades: 1 ...',
'Using stake_amount: 0.001 ...',
'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:
assert ('freqtrade.optimize.backtesting',
logging.INFO,
line) in caplog.record_tuples
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