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({
'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 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 _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, row[OPEN_IDX], -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(
self.strategy.custom_stake_amount, default_retval=stake_amount)(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
current_rate=row[OPEN_IDX],
proposed_stake=stake_amount,
min_stake=min_stake_amount,
max_stake=max_stake_amount)
stake_amount = self.wallets._validate_stake_amount(
pair, stake_amount, min_stake_amount)
if not stake_amount:
return None
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=row[OPEN_IDX],
time_in_force=time_in_force,
current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
# Enter trade
has_buy_tag = len(row) >= BUY_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
exchange='backtesting',
)
return trade
return None
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 analyze_pair(self, pair: str) -> None:
"""
Fetch data for this pair from dataprovider and analyze.
Stores the dataframe into the dataprovider.
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
:param pair: Pair to analyze.
"""
if not self.dp:
raise OperationalException("DataProvider not found.")
dataframe = self.dp.ohlcv(pair, self.timeframe)
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(self._analyze_ticker_internal,
message="")(dataframe, {
'pair': pair
})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(
f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}"
)
return
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force):
return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
# Enter trade
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type.value
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_type.value):
return None
trade.close(closerate, show_msg=False)
return trade
return None
def test_strategy_safe_wrapper_error(caplog, error):
def failing_method():
raise error('This is an error.')
def working_method(argumentpassedin):
return argumentpassedin
with pytest.raises(StrategyError, match=r'This is an error.'):
strategy_safe_wrapper(failing_method, message='DeadBeef')()
assert log_has_re(r'DeadBeef.*', caplog)
ret = strategy_safe_wrapper(failing_method,
message='DeadBeef',
default_retval=True)()
assert isinstance(ret, bool)
assert ret
def test_strategy_safe_wrapper(value):
def working_method(argumentpassedin):
return argumentpassedin
ret = strategy_safe_wrapper(working_method, message='DeadBeef')(value)
assert type(ret) == type(value)
assert ret == value
def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:param dataframe: Dataframe to analyze
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
# Explicitly convert to arrow object to ensure the below comparison does not fail
latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
interval_minutes = timeframe_to_minutes(interval)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if latest_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - latest_date).seconds // 60
)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
def _get_sell_trade_entry_for_candle(
self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(
trade,
sell_row[OPEN_IDX], # type: ignore
sell_candle_time,
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_candle_time
trade_dur = int(
(trade.close_date_utc - trade.open_date_utc).total_seconds() //
60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit,
default_retval=True)(
pair=trade.pair,
trade=trade,
order_type='limit',
amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
if (len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
trade.close(closerate, show_msg=False)
return trade
return None
def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
) -> LocalTrade:
current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
max_stake = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
if pos_trade is not None:
self.wallets.update()
return pos_trade
return trade
def ft_check_timed_out(self, side: str, trade: Trade, order: Dict,
current_time: datetime) -> bool:
"""
FT Internal method.
Check if timeout is active, and if the order is still open and timed out
"""
timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime
if timeout is not None:
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
timeout_kwargs = {timeout_unit: -timeout}
timeout_threshold = current_time + timedelta(**timeout_kwargs)
timedout = (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold)
if timedout:
return True
time_method = self.check_sell_timeout if order['side'] == 'sell' else self.check_buy_timeout
return strategy_safe_wrapper(time_method,
default_retval=False)(
pair=trade.pair, trade=trade, order=order,
current_time=current_time)
def _get_sell_trade_entry_for_candle(
self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(
trade,
sell_row[OPEN_IDX], # type: ignore
sell_candle_time,
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_candle_time
trade_dur = int(
(trade.close_date_utc - trade.open_date_utc).total_seconds() //
60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
# call the custom exit price,with default value as previous closerate
current_profit = trade.calc_profit_ratio(closerate)
if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
# Custom exit pricing only for sell-signals
closerate = strategy_safe_wrapper(
self.strategy.custom_exit_price,
default_retval=closerate)(pair=trade.pair,
trade=trade,
current_time=sell_row[DATE_IDX],
proposed_rate=closerate,
current_profit=current_profit)
# Use the maximum between close_rate and low as we cannot sell outside of a candle.
closerate = min(max(closerate, sell_row[LOW_IDX]),
sell_row[HIGH_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit,
default_retval=True)(
pair=trade.pair,
trade=trade,
order_type='limit',
amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
if (len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
trade.close(closerate, show_msg=False)
return trade
return None
def _enter_trade(
self,
pair: str,
row: Tuple,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
# let's call the custom entry price, using the open price as default price
propose_rate = strategy_safe_wrapper(
self.strategy.custom_entry_price, default_retval=row[OPEN_IDX])(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
proposed_rate=row[OPEN_IDX]) # default value is the open rate
# Move rate to within the candle's low/high rate
propose_rate = min(max(propose_rate, row[LOW_IDX]), row[HIGH_IDX])
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
pos_adjust = trade is not None
if not pos_adjust:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return trade
stake_amount = strategy_safe_wrapper(
self.strategy.custom_stake_amount,
default_retval=stake_amount)(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
current_rate=propose_rate,
proposed_stake=stake_amount,
min_stake=min_stake_amount,
max_stake=max_stake_amount)
stake_amount = self.wallets.validate_stake_amount(
pair, stake_amount, min_stake_amount)
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
return trade
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not pos_adjust:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=propose_rate,
time_in_force=time_in_force,
current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
amount = round(stake_amount / propose_rate, 8)
if trade is None:
# Enter trade
has_buy_tag = len(row) >= BUY_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=propose_rate,
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=amount,
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
exchange='backtesting',
orders=[])
order = Order(ft_is_open=False,
ft_pair=trade.pair,
symbol=trade.pair,
ft_order_side="buy",
side="buy",
order_type="market",
status="closed",
price=propose_rate,
average=propose_rate,
amount=amount,
filled=amount,
cost=stake_amount + trade.fee_open)
trade.orders.append(order)
if pos_adjust:
trade.recalc_trade_from_orders()
return trade
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
# let's call the custom entry price, using the open price as default price
propose_rate = strategy_safe_wrapper(
self.strategy.custom_entry_price, default_retval=row[OPEN_IDX])(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
proposed_rate=row[OPEN_IDX]) # default value is the open rate
# Move rate to within the candle's low/high rate
propose_rate = min(max(propose_rate, row[LOW_IDX]), row[HIGH_IDX])
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(
self.strategy.custom_stake_amount, default_retval=stake_amount)(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
current_rate=propose_rate,
proposed_stake=stake_amount,
min_stake=min_stake_amount,
max_stake=max_stake_amount)
stake_amount = self.wallets.validate_stake_amount(
pair, stake_amount, min_stake_amount)
if not stake_amount:
return None
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=propose_rate,
time_in_force=time_in_force,
current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
# Enter trade
has_buy_tag = len(row) >= BUY_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=propose_rate,
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=round(stake_amount / propose_rate, 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
exchange='backtesting',
)
return trade
return None
def stop_loss_reached(self,
dataframe: DataFrame,
current_rate: float,
trade: Trade,
current_time: datetime,
current_profit: float,
force_stoploss: float,
high: float = None) -> SellCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
:param current_profit: current profit as ratio
"""
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
if self.use_custom_stoploss:
stop_loss_value = strategy_safe_wrapper(
self.custom_stoploss,
default_retval=None)(pair=trade.pair,
trade=trade,
current_time=current_time,
current_rate=current_rate,
current_profit=current_profit,
dataframe=dataframe)
# Sanity check - error cases will return None
if stop_loss_value:
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")
trade.adjust_stop_loss(current_rate, stop_loss_value)
else:
logger.warning(
"CustomStoploss function did not return valid stoploss")
if self.trailing_stop:
# trailing stoploss handling
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
high_profit = current_profit if not high else trade.calc_profit_ratio(
high)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached
and high_profit < sl_offset):
# Specific handling for trailing_stop_positive
if self.trailing_stop_positive is not None and high_profit > sl_offset:
stop_loss_value = self.trailing_stop_positive
logger.debug(
f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.4f}%"
)
trade.adjust_stop_loss(high or current_rate, stop_loss_value)
# evaluate if the stoploss was hit if stoploss is not on exchange
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
# regular stoploss handling.
if ((trade.stop_loss >= current_rate)
and (not self.order_types.get('stoploss_on_exchange')
or self.config['dry_run'])):
sell_type = SellType.STOP_LOSS
# If initial stoploss is not the same as current one then it is trailing.
if trade.initial_stop_loss != trade.stop_loss:
sell_type = SellType.TRAILING_STOP_LOSS
logger.debug(
f"{trade.pair} - HIT STOP: current price at {current_rate:.6f}, "
f"stoploss is {trade.stop_loss:.6f}, "
f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
f"trade opened at {trade.open_rate:.6f}")
logger.debug(
f"{trade.pair} - Trailing stop saved "
f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
return SellCheckTuple(sell_type=sell_type)
return SellCheckTuple(sell_type=SellType.NONE)
def should_sell(self,
dataframe: DataFrame,
trade: Trade,
rate: float,
date: datetime,
buy: bool,
sell: bool,
low: float = None,
high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluates if one of the conditions required to trigger a sell
has been reached, which can either be a stop-loss, ROI or sell-signal.
:param low: Only used during backtesting to simulate stoploss
:param high: Only used during backtesting, to simulate ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be sold, False otherwise
"""
# Set current rate to low for backtesting sell
current_rate = low or rate
current_profit = trade.calc_profit_ratio(current_rate)
trade.adjust_min_max_rates(high or current_rate)
stoplossflag = self.stop_loss_reached(dataframe=dataframe,
current_rate=current_rate,
trade=trade,
current_time=date,
current_profit=current_profit,
force_stoploss=force_stoploss,
high=high)
# Set current rate to high for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_ratio(current_rate)
ask_strategy = self.config.get('ask_strategy', {})
# if buy signal and ignore_roi is set, we don't need to evaluate min_roi.
roi_reached = (
not (buy and ask_strategy.get('ignore_roi_if_buy_signal', False))
and self.min_roi_reached(
trade=trade, current_profit=current_profit, current_time=date))
sell_signal = SellType.NONE
custom_reason = ''
if (ask_strategy.get('sell_profit_only', False) and
current_profit <= ask_strategy.get('sell_profit_offset', 0)):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
pass
elif ask_strategy.get('use_sell_signal', True) and not buy:
if sell:
sell_signal = SellType.SELL_SIGNAL
else:
custom_reason = strategy_safe_wrapper(
self.custom_sell,
default_retval=False)(pair=trade.pair,
trade=trade,
current_time=date,
current_rate=current_rate,
current_profit=current_profit,
dataframe=dataframe)
if custom_reason:
sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
logger.warning(
f'Custom sell reason returned from custom_sell is too '
f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
f'characters.')
custom_reason = custom_reason[:
CUSTOM_SELL_MAX_LENGTH]
else:
custom_reason = None
# TODO: return here if sell-signal should be favored over ROI
# Start evaluations
# Sequence:
# ROI (if not stoploss)
# Sell-signal
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(
f"{trade.pair} - Required profit reached. sell_type=SellType.ROI"
)
return SellCheckTuple(sell_type=SellType.ROI)
if sell_signal != SellType.NONE:
logger.debug(
f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal,
sell_reason=custom_reason)
if stoplossflag.sell_flag:
logger.debug(
f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}"
)
return stoplossflag
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No sell signal.")
return SellCheckTuple(sell_type=SellType.NONE)
def _get_sell_trade_entry_for_candle(
self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
check_adjust_buy = True
if self.strategy.max_entry_position_adjustment > -1:
count_of_buys = trade.nr_of_successful_buys
check_adjust_buy = (
count_of_buys <=
self.strategy.max_entry_position_adjustment)
if check_adjust_buy:
trade = self._get_adjust_trade_entry_for_candle(
trade, sell_row)
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(
trade,
sell_row[OPEN_IDX], # type: ignore
sell_candle_time,
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_candle_time
trade_dur = int(
(trade.close_date_utc - trade.open_date_utc).total_seconds() //
60)
try:
closerate = self._get_close_rate(sell_row, trade, sell,
trade_dur)
except ValueError:
return None
# call the custom exit price,with default value as previous closerate
current_profit = trade.calc_profit_ratio(closerate)
order_type = self.strategy.order_types['sell']
if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
# Custom exit pricing only for sell-signals
if order_type == 'limit':
closerate = strategy_safe_wrapper(
self.strategy.custom_exit_price,
default_retval=closerate)(
pair=trade.pair,
trade=trade,
current_time=sell_candle_time,
proposed_rate=closerate,
current_profit=current_profit)
# We can't place orders lower than current low.
# freqtrade does not support this in live, and the order would fill immediately
closerate = max(closerate, sell_row[LOW_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit,
default_retval=True)(
pair=trade.pair,
trade=trade,
order_type='limit',
amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
if (len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
self.order_id_counter += 1
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
order_date=sell_candle_time,
order_update_date=sell_candle_time,
ft_is_open=True,
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side="sell",
side="sell",
order_type=order_type,
status="open",
price=closerate,
average=closerate,
amount=trade.amount,
filled=0,
remaining=trade.amount,
cost=trade.amount * closerate,
)
trade.orders.append(order)
return trade
return None
def _enter_trade(
self,
pair: str,
row: Tuple,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
# let's call the custom entry price, using the open price as default price
order_type = self.strategy.order_types['buy']
propose_rate = row[OPEN_IDX]
if order_type == 'limit':
propose_rate = strategy_safe_wrapper(
self.strategy.custom_entry_price,
default_retval=row[OPEN_IDX])(
pair=pair,
current_time=current_time,
proposed_rate=propose_rate,
entry_tag=entry_tag) # default value is the open rate
# We can't place orders higher than current high (otherwise it'd be a stop limit buy)
# which freqtrade does not support in live.
propose_rate = min(propose_rate, row[HIGH_IDX])
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
pos_adjust = trade is not None
if not pos_adjust:
try:
stake_amount = self.wallets.get_trade_stake_amount(
pair, None, update=False)
except DependencyException:
return None
stake_amount = strategy_safe_wrapper(
self.strategy.custom_stake_amount,
default_retval=stake_amount)(pair=pair,
current_time=current_time,
current_rate=propose_rate,
proposed_stake=stake_amount,
min_stake=min_stake_amount,
max_stake=max_stake_amount,
entry_tag=entry_tag)
stake_amount = self.wallets.validate_stake_amount(
pair, stake_amount, min_stake_amount)
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
return trade
time_in_force = self.strategy.order_time_in_force['buy']
# Confirm trade entry:
if not pos_adjust:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry,
default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=propose_rate,
time_in_force=time_in_force,
current_time=current_time,
entry_tag=entry_tag):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
self.order_id_counter += 1
amount = round(stake_amount / propose_rate, 8)
if trade is None:
# Enter trade
self.trade_id_counter += 1
trade = LocalTrade(id=self.trade_id_counter,
open_order_id=self.order_id_counter,
pair=pair,
open_rate=propose_rate,
open_rate_requested=propose_rate,
open_date=current_time,
stake_amount=stake_amount,
amount=amount,
amount_requested=amount,
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=entry_tag,
exchange='backtesting',
orders=[])
trade.adjust_stop_loss(trade.open_rate,
self.strategy.stoploss,
initial=True)
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
ft_is_open=True,
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side="buy",
side="buy",
order_type=order_type,
status="open",
order_date=current_time,
order_filled_date=current_time,
order_update_date=current_time,
price=propose_rate,
average=propose_rate,
amount=amount,
filled=0,
remaining=amount,
cost=stake_amount + trade.fee_open,
)
if pos_adjust and self._get_order_filled(order.price, row):
order.close_bt_order(current_time)
else:
trade.open_order_id = str(self.order_id_counter)
trade.orders.append(order)
trade.recalc_trade_from_orders()
return trade
