freqtrade_origin/freqtrade/tests/optimize/test_backtesting.py

# 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, trim_tickerlist
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(default_conf['stake_amount'],
                       optimize.preprocess(data), 10, 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(default_conf['stake_amount'],
                       optimize.preprocess(data), 1, True)
    assert not results.empty


def load_data_test(what):
    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
    data = trim_tickerlist(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(config['stake_amount'], processed, 1, 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(default_conf['stake_amount'],
                       optimize.preprocess(data), 10, 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_tickerlist(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.timerange = None  # 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 ...']
    for line in exists:
        assert ('freqtrade.optimize.backtesting',
                logging.INFO,
                line) in caplog.record_tuples