stable/freqtrade/tests/optimize/test_backtesting.py
2018-01-11 19:14:11 +01:00

188 lines
7.4 KiB
Python

# pragma pylint: disable=missing-docstring,W0212
import logging
import math
import pandas as pd
from unittest.mock import MagicMock
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 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', 5))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
def test_get_timeframe():
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_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'])
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_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'])
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 1,
'realistic': True})
assert not results.empty
def trim_dictlist(dl, num):
new = {}
for pair, pair_data in dl.items():
new[pair] = pair_data[num:]
return new
def load_data_test(what):
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -100)
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 on offline data
# loaded by freqdata/optimize/__init__.py::load_data()
def test_backtest2(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty
def test_processed(default_conf, mocker):
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):
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):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1)
pairdata = {'BTC_UNITEST': tickerdata}
return trim_dictlist(pairdata, -100)
def test_backtest_start(default_conf, mocker, caplog):
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
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 ...']
for line in exists:
assert ('freqtrade.optimize.backtesting',
logging.INFO,
line) in caplog.record_tuples