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Remove startup-candles after populating buy/sell signals

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closes #5242
This commit is contained in:
Matthias 2021-07-13 19:36:15 +02:00
parent adef5d89f3
commit 365479f5e0
4 changed files with 21 additions and 17 deletions
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27
freqtrade/optimize/backtesting.py
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@ -15,7 +15,7 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.converter import trim_dataframe, trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import BacktestState, SellType
from freqtrade.exceptions import DependencyException, OperationalException
@ -116,6 +116,9 @@ class Backtesting:
self.wallets = Wallets(self.config, self.exchange, log=False)
self.timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
@ -154,14 +157,11 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
timerange=self.timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
@ -174,11 +174,11 @@ class Backtesting:
f'({(max_date - min_date).days} days).')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
self.progress.set_new_value(1)
return data, timerange
return data, self.timerange
def prepare_backtest(self, enable_protections):
"""
@ -223,7 +223,9 @@ class Backtesting:
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# Trim startup period from analyzed dataframe
df_analyzed = trim_dataframe(df_analyzed, self.timerange,
startup_candles=self.required_startup)
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
@ -537,14 +539,15 @@ class Backtesting:
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed:
if not preprocessed_tmp:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
# Use preprocessed_tmp for date generation (the trimmed dataframe).
# Backtesting will re-trim the dataframes after buy/sell signal generation.
min_date, max_date = history.get_timerange(preprocessed_tmp)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days).')

7
freqtrade/optimize/hyperopt.py
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@ -378,16 +378,15 @@ class Hyperopt:
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
# Trim startup period from analyzed dataframe to get correct dates for output.
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))

1
tests/optimize/test_backtest_detail.py
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@ -575,6 +575,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
frame = _build_backtest_dataframe(data.data)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.required_startup = 0
backtesting.strategy.advise_buy = lambda a, m: frame
backtesting.strategy.advise_sell = lambda a, m: frame
backtesting.strategy.use_custom_stoploss = data.use_custom_stoploss

1
tests/optimize/test_backtesting.py
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@ -727,6 +727,7 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
pair='UNITTEST/BTC', datadir=testdatadir)
default_conf['timeframe'] = '1m'
backtesting = Backtesting(default_conf)
backtesting.required_startup = 0
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = _trend_alternate # Override
backtesting.strategy.advise_sell = _trend_alternate # Override