Merge commit '1134c81aad049d4357c8f299ffc801218f3d9574' into feature/objectify
This commit is contained in:
Gerald Lonlas
commit
38510d4b03
@ -172,19 +172,17 @@ class Analyze(object):
|
||||
:return True if bot should sell at current rate
|
||||
"""
|
||||
current_profit = trade.calc_profit_percent(current_rate)
|
||||
if self.strategy.stoploss is not None and current_profit < float(self.strategy.stoploss):
|
||||
if self.strategy.stoploss is not None and current_profit < self.strategy.stoploss:
|
||||
self.logger.debug('Stop loss hit.')
|
||||
return True
|
||||
|
||||
# Check if time matches and current rate is above threshold
|
||||
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
|
||||
for duration_string, threshold in self.strategy.minimal_roi.items():
|
||||
duration = float(duration_string)
|
||||
if time_diff > duration and current_profit > threshold:
|
||||
return True
|
||||
|
||||
if time_diff < duration:
|
||||
for duration, threshold in self.strategy.minimal_roi.items():
|
||||
if time_diff <= duration:
|
||||
return False
|
||||
if current_profit > threshold:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
@ -67,5 +67,5 @@ def file_dump_json(filename, data) -> None:
|
||||
:param data: JSON Data to save
|
||||
:return:
|
||||
"""
|
||||
with open(filename, 'w') as file:
|
||||
json.dump(data, file)
|
||||
with open(filename, 'w') as fp:
|
||||
json.dump(data, fp, default=str)
|
||||
|
||||
@ -102,43 +102,35 @@ class Backtesting(object):
|
||||
])
|
||||
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
|
||||
|
||||
def _get_sell_trade_entry(self, pair, row, buy_subset, ticker, trade_count_lock, args):
|
||||
def _get_sell_trade_entry(self, pair, buy_row, partial_ticker, trade_count_lock, args):
|
||||
stake_amount = args['stake_amount']
|
||||
max_open_trades = args.get('max_open_trades', 0)
|
||||
trade = Trade(
|
||||
open_rate=row.close,
|
||||
open_date=row.Index,
|
||||
open_rate=buy_row.close,
|
||||
open_date=buy_row.date,
|
||||
stake_amount=stake_amount,
|
||||
amount=stake_amount / row.open,
|
||||
amount=stake_amount / buy_row.open,
|
||||
fee=exchange.get_fee()
|
||||
)
|
||||
|
||||
# calculate win/lose forwards from buy point
|
||||
sell_subset = ticker[ticker.index > row.Index][['close', 'sell', 'buy']]
|
||||
for row2 in sell_subset.itertuples(index=True):
|
||||
for sell_row in partial_ticker:
|
||||
if max_open_trades > 0:
|
||||
# Increase trade_count_lock for every iteration
|
||||
trade_count_lock[row2.Index] = trade_count_lock.get(row2.Index, 0) + 1
|
||||
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
|
||||
|
||||
buy_signal = row2.buy
|
||||
if(
|
||||
self.analyze.should_sell(
|
||||
trade=trade,
|
||||
rate=row2.close,
|
||||
date=row2.Index,
|
||||
buy=buy_signal,
|
||||
sell=row2.sell
|
||||
)
|
||||
):
|
||||
buy_signal = sell_row.buy
|
||||
if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
|
||||
sell_row.sell):
|
||||
return \
|
||||
row2, \
|
||||
sell_row, \
|
||||
(
|
||||
pair,
|
||||
trade.calc_profit_percent(rate=row2.close),
|
||||
trade.calc_profit(rate=row2.close),
|
||||
(row2.Index - row.Index).seconds // 60
|
||||
),\
|
||||
row2.Index
|
||||
trade.calc_profit_percent(rate=sell_row.close),
|
||||
trade.calc_profit(rate=sell_row.close),
|
||||
(sell_row.date - buy_row.date).seconds // 60
|
||||
), \
|
||||
sell_row.date
|
||||
return None
|
||||
|
||||
def backtest(self, args) -> DataFrame:
|
||||
@ -159,6 +151,7 @@ class Backtesting(object):
|
||||
stoploss: use stoploss
|
||||
:return: DataFrame
|
||||
"""
|
||||
headers = ['date', 'buy', 'open', 'close', 'sell']
|
||||
processed = args['processed']
|
||||
max_open_trades = args.get('max_open_trades', 0)
|
||||
realistic = args.get('realistic', True)
|
||||
@ -167,37 +160,28 @@ class Backtesting(object):
|
||||
trades = []
|
||||
trade_count_lock = {}
|
||||
for pair, pair_data in processed.items():
|
||||
pair_data['buy'], pair_data['sell'] = 0, 0
|
||||
ticker = self.populate_sell_trend(
|
||||
self.populate_buy_trend(pair_data)
|
||||
)
|
||||
if 'date' in ticker:
|
||||
ticker.set_index('date', inplace=True)
|
||||
# for each buy point
|
||||
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
|
||||
|
||||
ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
|
||||
ticker = [x for x in ticker_data.itertuples()]
|
||||
|
||||
lock_pair_until = None
|
||||
headers = ['buy', 'open', 'close', 'sell']
|
||||
buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers]
|
||||
for row in buy_subset.itertuples(index=True):
|
||||
for index, row in enumerate(ticker):
|
||||
if row.buy == 0 or row.sell == 1:
|
||||
continue # skip rows where no buy signal or that would immediately sell off
|
||||
|
||||
if realistic:
|
||||
if lock_pair_until is not None and row.Index <= lock_pair_until:
|
||||
if lock_pair_until is not None and row.date <= lock_pair_until:
|
||||
continue
|
||||
if max_open_trades > 0:
|
||||
# Check if max_open_trades has already been reached for the given date
|
||||
if not trade_count_lock.get(row.Index, 0) < max_open_trades:
|
||||
if not trade_count_lock.get(row.date, 0) < max_open_trades:
|
||||
continue
|
||||
|
||||
if max_open_trades > 0:
|
||||
# Increase lock
|
||||
trade_count_lock[row.Index] = trade_count_lock.get(row.Index, 0) + 1
|
||||
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
|
||||
|
||||
ret = self._get_sell_trade_entry(
|
||||
pair=pair,
|
||||
row=row,
|
||||
buy_subset=buy_subset,
|
||||
ticker=ticker,
|
||||
trade_count_lock=trade_count_lock,
|
||||
args=args
|
||||
)
|
||||
ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
|
||||
trade_count_lock, args)
|
||||
|
||||
if ret:
|
||||
row2, trade_entry, next_date = ret
|
||||
@ -208,9 +192,9 @@ class Backtesting(object):
|
||||
# record a tuple of pair, current_profit_percent,
|
||||
# entry-date, duration
|
||||
records.append((pair, trade_entry[1],
|
||||
row.Index.strftime('%s'),
|
||||
row2.Index.strftime('%s'),
|
||||
row.Index, trade_entry[3]))
|
||||
row.date.strftime('%s'),
|
||||
row2.date.strftime('%s'),
|
||||
row.date, trade_entry[3]))
|
||||
# For now export inside backtest(), maybe change so that backtest()
|
||||
# returns a tuple like: (dataframe, records, logs, etc)
|
||||
if record and record.find('trades') >= 0:
|
||||
@ -226,6 +210,8 @@ class Backtesting(object):
|
||||
"""
|
||||
data = {}
|
||||
pairs = self.config['exchange']['pair_whitelist']
|
||||
self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
||||
self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
|
||||
|
||||
if self.config.get('live'):
|
||||
self.logger.info('Downloading data for all pairs in whitelist ...')
|
||||
@ -233,8 +219,6 @@ class Backtesting(object):
|
||||
data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
|
||||
else:
|
||||
self.logger.info('Using local backtesting data (using whitelist in given config) ...')
|
||||
self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
||||
self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
|
||||
|
||||
timerange = Arguments.parse_timerange(self.config.get('timerange'))
|
||||
data = optimize.load_data(
|
||||
|
||||
@ -240,15 +240,15 @@ class Hyperopt(Backtesting):
|
||||
return trade_loss + profit_loss + duration_loss
|
||||
|
||||
@staticmethod
|
||||
def generate_roi_table(params) -> Dict[str, float]:
|
||||
def generate_roi_table(params) -> Dict[int, float]:
|
||||
"""
|
||||
Generate the ROI table thqt will be used by Hyperopt
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table["0"] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[str(params['roi_t3'])] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[str(params['roi_t3'] + params['roi_t2'])] = params['roi_p1']
|
||||
roi_table[str(params['roi_t3'] + params['roi_t2'] + params['roi_t1'])] = 0
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
|
||||
@ -58,11 +58,11 @@ class Strategy(object):
|
||||
|
||||
# Minimal ROI designed for the strategy
|
||||
self.minimal_roi = OrderedDict(sorted(
|
||||
self.custom_strategy.minimal_roi.items(),
|
||||
key=lambda tuple: float(tuple[0]))) # sort after converting to number
|
||||
{int(key): value for (key, value) in self.custom_strategy.minimal_roi.items()}.items(),
|
||||
key=lambda tuple: tuple[0])) # sort after converting to number
|
||||
|
||||
# Optimal stoploss designed for the strategy
|
||||
self.stoploss = self.custom_strategy.stoploss
|
||||
self.stoploss = float(self.custom_strategy.stoploss)
|
||||
|
||||
self.ticker_interval = self.custom_strategy.ticker_interval
|
||||
|
||||
|
||||
@ -285,6 +285,7 @@ def ticker_history_without_bv():
|
||||
]
|
||||
|
||||
|
||||
# FIX: Perhaps change result fixture to use BTC_UNITEST instead?
|
||||
@pytest.fixture
|
||||
def result():
|
||||
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
|
||||
|
||||
@ -1,12 +1,14 @@
|
||||
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
|
||||
|
||||
import json
|
||||
import random
|
||||
import math
|
||||
from typing import List
|
||||
from copy import deepcopy
|
||||
from unittest.mock import MagicMock
|
||||
from arrow import Arrow
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from freqtrade import optimize
|
||||
from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
|
||||
from freqtrade.arguments import Arguments
|
||||
@ -96,6 +98,70 @@ def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False,
|
||||
return pairdata
|
||||
|
||||
|
||||
# use for mock freqtrade.exchange.get_ticker_history'
|
||||
def _load_pair_as_ticks(pair, tickfreq):
|
||||
ticks = optimize.load_data(None, ticker_interval=tickfreq, 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': _BACKTESTING.tickerdata_to_dataframe(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(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 = _BACKTESTING.populate_buy_trend
|
||||
old_sell = _BACKTESTING.populate_sell_trend
|
||||
_BACKTESTING.populate_buy_trend = fun # Override
|
||||
_BACKTESTING.populate_sell_trend = fun # Override
|
||||
results = _BACKTESTING.backtest(backtest_conf)
|
||||
_BACKTESTING.populate_buy_trend = old_buy # restore override
|
||||
_BACKTESTING.populate_sell_trend = old_sell # restore override
|
||||
return results
|
||||
|
||||
|
||||
# Unit tests
|
||||
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
|
||||
"""
|
||||
@ -418,3 +484,125 @@ def test_backtest_pricecontours(default_conf) -> None:
|
||||
tests = [['raise', 17], ['lower', 0], ['sine', 17]]
|
||||
for [contour, numres] in tests:
|
||||
simple_backtest(default_conf, contour, numres)
|
||||
|
||||
|
||||
# Test backtest using offline data (testdata directory)
|
||||
def test_backtest_ticks(default_conf):
|
||||
ticks = [1, 5]
|
||||
fun = _BACKTESTING.populate_buy_trend
|
||||
for tick in ticks:
|
||||
backtest_conf = _make_backtest_conf(conf=default_conf)
|
||||
results = _run_backtest_1(fun, backtest_conf)
|
||||
assert not results.empty
|
||||
|
||||
|
||||
def test_backtest_clash_buy_sell(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(fun, backtest_conf)
|
||||
assert results.empty
|
||||
|
||||
|
||||
def test_backtest_only_sell(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(fun, backtest_conf)
|
||||
assert results.empty
|
||||
|
||||
|
||||
def test_backtest_alternate_buy_sell(default_conf):
|
||||
backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
|
||||
results = _run_backtest_1(_trend_alternate, backtest_conf)
|
||||
assert len(results) == 3
|
||||
|
||||
|
||||
def test_backtest_record(default_conf, mocker):
|
||||
names = []
|
||||
records = []
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.backtesting.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(_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_backtest_start_live(default_conf, mocker, caplog):
|
||||
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('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
|
||||
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
|
||||
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
|
||||
read_data=json.dumps(default_conf)
|
||||
))
|
||||
|
||||
args = MagicMock()
|
||||
args.ticker_interval = 1
|
||||
args.level = 10
|
||||
args.live = True
|
||||
args.datadir = None
|
||||
args.export = None
|
||||
args.strategy = 'default_strategy'
|
||||
args.timerange = '-100' # needed due to MagicMock malleability
|
||||
|
||||
args = [
|
||||
'--config', 'config.json',
|
||||
'--strategy', 'default_strategy',
|
||||
'backtesting',
|
||||
'--ticker-interval', '1',
|
||||
'--live',
|
||||
'--timerange', '-100'
|
||||
]
|
||||
args = get_args(args)
|
||||
start(args)
|
||||
# check the logs, that will contain the backtest result
|
||||
exists = [
|
||||
'Parameter -i/--ticker-interval detected ...',
|
||||
'Using ticker_interval: 1 ...',
|
||||
'Parameter -l/--live detected ...',
|
||||
'Using max_open_trades: 1 ...',
|
||||
'Parameter --timerange detected: -100 ..',
|
||||
'Parameter --datadir detected: freqtrade/tests/testdata ...',
|
||||
'Using stake_currency: BTC ...',
|
||||
'Using stake_amount: 0.001 ...',
|
||||
'Downloading data for all pairs in whitelist ...',
|
||||
'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:
|
||||
tt.log_has(line, caplog.record_tuples)
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
import os
|
||||
from copy import deepcopy
|
||||
from unittest.mock import MagicMock
|
||||
import pandas as pd
|
||||
from freqtrade.optimize.hyperopt import Hyperopt
|
||||
import freqtrade.tests.conftest as tt # test tools
|
||||
|
||||
@ -157,7 +158,7 @@ def test_fmin_best_results(mocker, default_conf, caplog) -> None:
|
||||
'"uptrend_long_ema": {\n "enabled": true\n },',
|
||||
'"uptrend_short_ema": {\n "enabled": false\n },',
|
||||
'"uptrend_sma": {\n "enabled": false\n }',
|
||||
'ROI table:\n{\'0\': 6.0, \'3.0\': 3.0, \'5.0\': 1.0, \'6.0\': 0}',
|
||||
'ROI table:\n{0: 6.0, 3.0: 3.0, 5.0: 1.0, 6.0: 0}',
|
||||
'Best Result:\nfoo'
|
||||
]
|
||||
for line in exists:
|
||||
@ -275,7 +276,7 @@ def test_roi_table_generation() -> None:
|
||||
}
|
||||
|
||||
hyperopt = _HYPEROPT
|
||||
assert hyperopt.generate_roi_table(params) == {'0': 6, '15': 3, '25': 1, '30': 0}
|
||||
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
|
||||
|
||||
|
||||
def test_start_calls_fmin(mocker, default_conf) -> None:
|
||||
@ -319,3 +320,36 @@ def test_start_uses_mongotrials(mocker, default_conf) -> None:
|
||||
hyperopt.start()
|
||||
mock_mongotrials.assert_called_once()
|
||||
mock_fmin.assert_called_once()
|
||||
|
||||
|
||||
# test log_trials_result
|
||||
# test buy_strategy_generator def populate_buy_trend
|
||||
# test optimizer if 'ro_t1' in params
|
||||
|
||||
def test_format_results():
|
||||
"""
|
||||
Test Hyperopt.format_results()
|
||||
"""
|
||||
trades = [
|
||||
('BTC_ETH', 2, 2, 123),
|
||||
('BTC_LTC', 1, 1, 123),
|
||||
('BTC_XRP', -1, -2, -246)
|
||||
]
|
||||
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
|
||||
df = pd.DataFrame.from_records(trades, columns=labels)
|
||||
x = Hyperopt.format_results(df)
|
||||
assert x.find(' 66.67%')
|
||||
|
||||
|
||||
def test_signal_handler(mocker):
|
||||
"""
|
||||
Test Hyperopt.signal_handler()
|
||||
"""
|
||||
m = MagicMock()
|
||||
mocker.patch('sys.exit', m)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.save_trials', m)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.log_trials_result', m)
|
||||
|
||||
hyperopt = _HYPEROPT
|
||||
hyperopt.signal_handler(9, None)
|
||||
assert m.call_count == 3
|
||||
|
||||
@ -55,7 +55,7 @@ def test_strategy(result):
|
||||
strategy = Strategy({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.04
|
||||
assert strategy.minimal_roi[0] == 0.04
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'stoploss')
|
||||
assert strategy.stoploss == -0.10
|
||||
@ -83,7 +83,7 @@ def test_strategy_override_minimal_roi(caplog):
|
||||
strategy = Strategy(config)
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.5
|
||||
assert strategy.minimal_roi[0] == 0.5
|
||||
assert ('freqtrade.strategy.strategy',
|
||||
logging.INFO,
|
||||
'Override strategy \'minimal_roi\' with value in config file.'
|
||||
@ -136,8 +136,8 @@ def test_strategy_singleton():
|
||||
strategy1 = Strategy({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy1.custom_strategy, 'minimal_roi')
|
||||
assert strategy1.minimal_roi['0'] == 0.04
|
||||
assert strategy1.minimal_roi[0] == 0.04
|
||||
|
||||
strategy2 = Strategy()
|
||||
assert hasattr(strategy2.custom_strategy, 'minimal_roi')
|
||||
assert strategy2.minimal_roi['0'] == 0.04
|
||||
assert strategy2.minimal_roi[0] == 0.04
|
||||
|
||||
@ -43,6 +43,11 @@ def test_analyze_object() -> None:
|
||||
assert hasattr(Analyze, 'min_roi_reached')
|
||||
|
||||
|
||||
def test_dataframe_correct_length(result):
|
||||
dataframe = Analyze.parse_ticker_dataframe(result)
|
||||
assert len(result.index) == len(dataframe.index)
|
||||
|
||||
|
||||
def test_dataframe_correct_columns(result):
|
||||
assert result.columns.tolist() == \
|
||||
['close', 'high', 'low', 'open', 'date', 'volume']
|
||||
|
||||
Loading…
Reference in New Issue
Block a user