def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
strategy.wallets = self.wallets
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed_tmp:
raise OperationalException(
"No data left after adjusting for startup candles.")
# 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).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def _normalize_attributes(strategy: IStrategy) -> IStrategy:
"""
Normalize attributes to have the correct type.
"""
# Sort and apply type conversions
if hasattr(strategy, 'minimal_roi'):
strategy.minimal_roi = dict(
sorted({
int(key): value
for (key, value) in strategy.minimal_roi.items()
}.items(),
key=lambda t: t[0]))
if hasattr(strategy, 'stoploss'):
strategy.stoploss = float(strategy.stoploss)
return strategy
def _normalize_attributes(strategy: IStrategy) -> IStrategy:
"""
Normalize attributes to have the correct type.
"""
# Assign deprecated variable - to not break users code relying on this.
if hasattr(strategy, 'timeframe'):
strategy.ticker_interval = strategy.timeframe
# Sort and apply type conversions
if hasattr(strategy, 'minimal_roi'):
strategy.minimal_roi = dict(sorted(
{int(key): value for (key, value) in strategy.minimal_roi.items()}.items(),
key=lambda t: t[0]))
if hasattr(strategy, 'stoploss'):
strategy.stoploss = float(strategy.stoploss)
return strategy
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any],
timerange: TimeRange):
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start,
supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(
df, timerange, startup_candles=self.required_startup)
min_date, max_date = history.get_timerange(preprocessed)
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)..')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = {
'results':
results,
'config':
self.strategy.config,
'locks':
PairLocks.get_all_locks(),
'final_balance':
self.wallets.get_total(self.strategy.config['stake_currency']),
'backtest_start_time':
int(backtest_start_time.timestamp()),
'backtest_end_time':
int(backtest_end_time.timestamp()),
}
return min_date, max_date
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair in list(preprocessed):
df = preprocessed[pair]
df = trim_dataframe(df, timerange, startup_candles=self.required_startup)
if len(df) > 0:
preprocessed[pair] = df
else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, '
f'skipping.')
del preprocessed[pair]
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
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).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(conf)
def validate_strategy(strategy: IStrategy) -> IStrategy:
if strategy.config.get('trading_mode',
TradingMode.SPOT) != TradingMode.SPOT:
# Require new method
warn_deprecated_setting(strategy, 'sell_profit_only',
'exit_profit_only', True)
warn_deprecated_setting(strategy, 'sell_profit_offset',
'exit_profit_offset', True)
warn_deprecated_setting(strategy, 'use_sell_signal',
'use_exit_signal', True)
warn_deprecated_setting(strategy, 'ignore_roi_if_buy_signal',
'ignore_roi_if_entry_signal', True)
if not check_override(strategy, IStrategy, 'populate_entry_trend'):
raise OperationalException(
"`populate_entry_trend` must be implemented.")
if not check_override(strategy, IStrategy, 'populate_exit_trend'):
raise OperationalException(
"`populate_exit_trend` must be implemented.")
if check_override(strategy, IStrategy, 'check_buy_timeout'):
raise OperationalException(
"Please migrate your implementation "
"of `check_buy_timeout` to `check_entry_timeout`.")
if check_override(strategy, IStrategy, 'check_sell_timeout'):
raise OperationalException(
"Please migrate your implementation "
"of `check_sell_timeout` to `check_exit_timeout`.")
if check_override(strategy, IStrategy, 'custom_sell'):
raise OperationalException(
"Please migrate your implementation of `custom_sell` to `custom_exit`."
)
else:
# TODO: Implementing one of the following methods should show a deprecation warning
# buy_trend and sell_trend, custom_sell
warn_deprecated_setting(strategy, 'sell_profit_only',
'exit_profit_only')
warn_deprecated_setting(strategy, 'sell_profit_offset',
'exit_profit_offset')
warn_deprecated_setting(strategy, 'use_sell_signal',
'use_exit_signal')
warn_deprecated_setting(strategy, 'ignore_roi_if_buy_signal',
'ignore_roi_if_entry_signal')
if (not check_override(strategy, IStrategy, 'populate_buy_trend')
and not check_override(strategy, IStrategy,
'populate_entry_trend')):
raise OperationalException(
"`populate_entry_trend` or `populate_buy_trend` must be implemented."
)
if (not check_override(strategy, IStrategy, 'populate_sell_trend')
and not check_override(strategy, IStrategy,
'populate_exit_trend')):
raise OperationalException(
"`populate_exit_trend` or `populate_sell_trend` must be implemented."
)
strategy._populate_fun_len = len(
getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(
getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(
getfullargspec(strategy.populate_sell_trend).args)
if any(x == 2 for x in [
strategy._populate_fun_len, strategy._buy_fun_len,
strategy._sell_fun_len
]):
strategy.INTERFACE_VERSION = 1
return strategy
