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Merge pull request #1290 from freqtrade/fix/backtest_toomanyopen

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fix backtesting not respecting max_open_trades
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Matthias 2018-11-30 19:17:09 +01:00 committed by GitHub
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6 changed files with 159 additions and 49 deletions
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39
freqtrade/optimize/backtesting.py
View File

@ -66,6 +66,7 @@ class Backtesting(object):
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -86,6 +87,8 @@ class Backtesting(object):
"""
self.strategy = strategy
self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
@ -280,8 +283,13 @@ class Backtesting(object):
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['end_date']
trades = []
trade_count_lock: Dict = {}
ticker: Dict = {}
pairs = []
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
@ -296,15 +304,28 @@ class Backtesting(object):
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
ticker[pair] = [x for x in ticker_data.itertuples()]
pairs.append(pair)
lock_pair_until: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
index = 0
# Loop timerange and test per pair
while tmp < end_date:
# print(f"time: {tmp}")
for i, pair in enumerate(ticker):
try:
row = ticker[pair][index]
except IndexError:
# missing Data for one pair ...
# Warnings for this are shown by `validate_backtest_data`
continue
lock_pair_until = None
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 not position_stacking:
if lock_pair_until is not None and row.date <= lock_pair_until:
if pair in lock_pair_until and row.date <= lock_pair_until[pair]:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
@ -313,17 +334,19 @@ class Backtesting(object):
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
trade_count_lock, args)
if trade_entry:
lock_pair_until = trade_entry.close_time
lock_pair_until[pair] = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
lock_pair_until[pair] = end_date
tmp += timedelta(minutes=self.ticker_interval_mins)
index += 1
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@ -390,6 +413,8 @@ class Backtesting(object):
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
'start_date': min_date,
'end_date': max_date,
}
)

5
freqtrade/optimize/hyperopt.py
View File

@ -20,7 +20,7 @@ from skopt.space import Dimension
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
from freqtrade.optimize import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.resolvers import HyperOptResolver
@ -167,11 +167,14 @@ class Hyperopt(Backtesting):
self.strategy.stoploss = params['stoploss']
processed = load(TICKERDATA_PICKLE)
min_date, max_date = get_timeframe(processed)
results = self.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': processed,
'position_stacking': self.config.get('position_stacking', True),
'start_date': min_date,
'end_date': max_date,
}
)
result_explanation = self.format_results(results)

7
freqtrade/tests/optimize/__init__.py
View File

@ -4,9 +4,10 @@ import arrow
from pandas import DataFrame
from freqtrade.strategy.interface import SellType
from freqtrade.constants import TICKER_INTERVAL_MINUTES
ticker_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
tests_ticker_interval = "1h"
class BTrade(NamedTuple):
@ -30,8 +31,8 @@ class BTContainer(NamedTuple):
def _get_frame_time_from_offset(offset):
return ticker_start_time.shift(
minutes=(offset * ticker_interval_in_minute)).datetime.replace(tzinfo=None)
return ticker_start_time.shift(minutes=(offset * TICKER_INTERVAL_MINUTES[tests_ticker_interval])
).datetime.replace(tzinfo=None)
def _build_backtest_dataframe(ticker_with_signals):

20
freqtrade/tests/optimize/test_backtest_detail.py
View File

@ -6,10 +6,11 @@ from pandas import DataFrame
import pytest
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.strategy.interface import SellType
from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
_get_frame_time_from_offset)
_get_frame_time_from_offset, tests_ticker_interval)
from freqtrade.tests.conftest import patch_exchange
@ -147,6 +148,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
"""
default_conf["stoploss"] = data.stop_loss
default_conf["minimal_roi"] = {"0": data.roi}
default_conf['ticker_interval'] = tests_ticker_interval
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
patch_exchange(mocker)
frame = _build_backtest_dataframe(data.data)
@ -158,29 +160,21 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
pair = 'UNITTEST/BTC'
# Dummy data as we mock the analyze functions
data_processed = {pair: DataFrame()}
min_date, max_date = get_timeframe({pair: frame})
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'start_date': min_date,
'end_date': max_date,
}
)
print(results.T)
assert len(results) == len(data.trades)
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
# if data.sell_r == SellType.STOP_LOSS:
# assert log_has("Stop loss hit.", caplog.record_tuples)
# else:
# assert not log_has("Stop loss hit.", caplog.record_tuples)
# log_test = (f'Force_selling still open trade UNITTEST/BTC with '
# f'{results.iloc[-1].profit_percent} perc - {results.iloc[-1].profit_abs}')
# if data.sell_r == SellType.FORCE_SELL:
# assert log_has(log_test,
# caplog.record_tuples)
# else:
# assert not log_has(log_test,
# caplog.record_tuples)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.sell_reason == trade.sell_reason

130
freqtrade/tests/optimize/test_backtesting.py
View File

@ -13,6 +13,7 @@ from arrow import Arrow
from freqtrade import DependencyException, constants, optimize
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import (Backtesting, setup_configuration,
start)
from freqtrade.tests.conftest import log_has, patch_exchange
@ -86,17 +87,21 @@ def load_data_test(what):
def simple_backtest(config, contour, num_results, mocker) -> None:
patch_exchange(mocker)
config['ticker_interval'] = '1m'
backtesting = Backtesting(config)
data = load_data_test(contour)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
{
'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
# results :: <class 'pandas.core.frame.DataFrame'>
@ -123,12 +128,16 @@ def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
data = trim_dictlist(data, -201)
patch_exchange(mocker)
backtesting = Backtesting(conf)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
return {
'stake_amount': conf['stake_amount'],
'processed': backtesting.strategy.tickerdata_to_dataframe(data),
'processed': processed,
'max_open_trades': 10,
'position_stacking': False,
'record': record
'record': record,
'start_date': min_date,
'end_date': max_date,
}
@ -449,7 +458,7 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
)
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
default_conf['ticker_interval'] = "1m"
default_conf['ticker_interval'] = '1m'
default_conf['live'] = False
default_conf['datadir'] = None
default_conf['export'] = None
@ -505,12 +514,15 @@ def test_backtest(default_conf, fee, mocker) -> None:
data = optimize.load_data(None, ticker_interval='5m', pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
assert not results.empty
@ -554,12 +566,16 @@ def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.strategy.tickerdata_to_dataframe(data),
'processed': processed,
'max_open_trades': 1,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
assert not results.empty
@ -583,25 +599,13 @@ def test_processed(default_conf, mocker) -> None:
def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
tests = [['raise', 18], ['lower', 0], ['sine', 19]]
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
for [contour, numres] in tests:
simple_backtest(default_conf, contour, numres, mocker)
# Test backtest using offline data (testdata directory)
def test_backtest_ticks(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
ticks = [1, 5]
fun = Backtesting(default_conf).advise_buy
for _ in ticks:
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert not results.empty
def test_backtest_clash_buy_sell(mocker, default_conf):
# Override the default buy trend function in our default_strategy
def fun(dataframe=None, pair=None):
@ -636,14 +640,92 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch('freqtrade.optimize.backtesting.file_dump_json', MagicMock())
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '1m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate # Override
backtesting.advise_sell = _trend_alternate # Override
results = backtesting.backtest(backtest_conf)
backtesting._store_backtest_result("test_.json", results)
assert len(results) == 4
# 200 candles in backtest data
# won't buy on first (shifted by 1)
# 100 buys signals
assert len(results) == 99
# One trade was force-closed at the end
assert len(results.loc[results.open_at_end]) == 1
assert len(results.loc[results.open_at_end]) == 0
def test_backtest_multi_pair(default_conf, fee, mocker):
def evaluate_result_multi(results, freq, max_open_trades):
# Find overlapping trades by expanding each trade once per period
# and then counting overlaps
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)
"""
multi = 8
dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
dataframe['buy'] = dataframe['buy'].shift(-4)
dataframe['sell'] = dataframe['sell'].shift(-4)
return dataframe
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
data = optimize.load_data(None, ticker_interval='5m', pairs=pairs)
data = trim_dictlist(data, -500)
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '5m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate_hold # Override
backtesting.advise_sell = _trend_alternate_hold # Override
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 3,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
# Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5min', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5min', 3)) == 0
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
assert len(evaluate_result_multi(results, '5min', 1)) == 0
def test_backtest_record(default_conf, fee, mocker):

7
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -1,11 +1,12 @@
# pragma pylint: disable=missing-docstring,W0212,C0103
from datetime import datetime
import os
from unittest.mock import MagicMock
import pandas as pd
import pytest
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.optimize import load_tickerdata_file
from freqtrade.optimize.hyperopt import Hyperopt, start
from freqtrade.resolvers import StrategyResolver
from freqtrade.tests.conftest import log_has, patch_exchange
@ -293,6 +294,10 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'freqtrade.optimize.hyperopt.Hyperopt.backtest',
MagicMock(return_value=backtest_result)
)
mocker.patch(
'freqtrade.optimize.hyperopt.get_timeframe',
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())