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Merge branch 'feat/short' into lev-exchange

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This commit is contained in:
Sam Germain 2021-08-22 20:59:33 -06:00
commit 488d729574
10 changed files with 190 additions and 155 deletions
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4
freqtrade/optimize/backtesting.py
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@ -232,7 +232,9 @@ class Backtesting:
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
df_analyzed = self.strategy.advise_sell( df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy() self.strategy.advise_buy(pair_data, {'pair': pair}),
{'pair': pair}
).copy()
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
df_analyzed = trim_dataframe(df_analyzed, self.timerange, df_analyzed = trim_dataframe(df_analyzed, self.timerange,
startup_candles=self.required_startup) startup_candles=self.required_startup)

20
freqtrade/resolvers/strategy_resolver.py
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@ -193,18 +193,22 @@ class StrategyResolver(IResolver):
# register temp path with the bot # register temp path with the bot
abs_paths.insert(0, temp.resolve()) abs_paths.insert(0, temp.resolve())
strategy = StrategyResolver._load_object(paths=abs_paths, strategy = StrategyResolver._load_object(
object_name=strategy_name, paths=abs_paths,
add_source=True, object_name=strategy_name,
kwargs={'config': config}, add_source=True,
) kwargs={'config': config},
)
if strategy: if strategy:
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args) strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args) strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args) strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
if any(x == 2 for x in [strategy._populate_fun_len, if any(x == 2 for x in [
strategy._buy_fun_len, strategy._populate_fun_len,
strategy._sell_fun_len]): strategy._buy_fun_len,
strategy._sell_fun_len
]):
strategy.INTERFACE_VERSION = 1 strategy.INTERFACE_VERSION = 1
return strategy return strategy

121
freqtrade/strategy/interface.py
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@ -135,7 +135,7 @@ class IStrategy(ABC, HyperStrategyMixin):
@abstractmethod @abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Populate indicators that will be used in the Buy and Sell strategy Populate indicators that will be used in the Buy, Sell, Short, Exit_short strategy
:param dataframe: DataFrame with data from the exchange :param dataframe: DataFrame with data from the exchange
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies :return: a Dataframe with all mandatory indicators for the strategies
@ -164,9 +164,9 @@ class IStrategy(ABC, HyperStrategyMixin):
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
""" """
Check buy timeout function callback. Check buy enter timeout function callback.
This method can be used to override the buy-timeout. This method can be used to override the enter-timeout.
It is called whenever a limit buy order has been created, It is called whenever a limit buy/short order has been created,
and is not yet fully filled. and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this, Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough. so ensure to set these timeouts high enough.
@ -176,16 +176,16 @@ class IStrategy(ABC, HyperStrategyMixin):
:param trade: trade object. :param trade: trade object.
:param order: Order dictionary as returned from CCXT. :param order: Order dictionary as returned from CCXT.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is cancelled. :return bool: When True is returned, then the buy/short-order is cancelled.
""" """
return False return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
""" """
Check sell timeout function callback. Check sell timeout function callback.
This method can be used to override the sell-timeout. This method can be used to override the exit-timeout.
It is called whenever a limit sell order has been created, It is called whenever a (long) limit sell order or (short) limit buy
and is not yet fully filled. has been created, and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this, Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough. so ensure to set these timeouts high enough.
@ -194,7 +194,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param trade: trade object. :param trade: trade object.
:param order: Order dictionary as returned from CCXT. :param order: Order dictionary as returned from CCXT.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is cancelled. :return bool: When True is returned, then the (long)sell/(short)buy-order is cancelled.
""" """
return False return False
@ -210,7 +210,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, **kwargs) -> bool: time_in_force: str, current_time: datetime, **kwargs) -> bool:
""" """
Called right before placing a buy order. Called right before placing a buy/short order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
network requests in this method. network requests in this method.
@ -218,7 +218,7 @@ class IStrategy(ABC, HyperStrategyMixin):
When not implemented by a strategy, returns True (always confirming). When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought. :param pair: Pair that's about to be bought/shorted.
:param order_type: Order type (as configured in order_types). usually limit or market. :param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded. :param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders :param rate: Rate that's going to be used when using limit orders
@ -234,7 +234,7 @@ class IStrategy(ABC, HyperStrategyMixin):
rate: float, time_in_force: str, sell_reason: str, rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool: current_time: datetime, **kwargs) -> bool:
""" """
Called right before placing a regular sell order. Called right before placing a regular sell/exit_short order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
network requests in this method. network requests in this method.
@ -242,18 +242,18 @@ class IStrategy(ABC, HyperStrategyMixin):
When not implemented by a strategy, returns True (always confirming). When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold. :param pair: Pair for trade that's about to be exited.
:param trade: trade object. :param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market. :param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency. :param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders :param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason. :param sell_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell'] 'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime :param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange. :return bool: When True, then the sell-order/exit_short-order is placed on the exchange.
False aborts the process False aborts the process
""" """
return True return True
@ -283,15 +283,15 @@ class IStrategy(ABC, HyperStrategyMixin):
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]: current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
""" """
Custom sell signal logic indicating that specified position should be sold. Returning a Custom exit signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting sell signal on a candle at specified string or True from this method is equal to setting exit signal on a candle at specified
time. This method is not called when sell signal is set. time. This method is not called when exit signal is set.
This method should be overridden to create sell signals that depend on trade parameters. For This method should be overridden to create exit signals that depend on trade parameters. For
example you could implement a sell relative to the candle when the trade was opened, example you could implement an exit relative to the candle when the trade was opened,
or a custom 1:2 risk-reward ROI. or a custom 1:2 risk-reward ROI.
Custom sell reason max length is 64. Exceeding characters will be removed. Custom exit reason max length is 64. Exceeding characters will be removed.
:param pair: Pair that's currently analyzed :param pair: Pair that's currently analyzed
:param trade: trade object. :param trade: trade object.
@ -299,7 +299,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_rate: Rate, calculated based on pricing settings in ask_strategy. :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate. :param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return: To execute sell, return a string with custom sell reason or True. Otherwise return :return: To execute exit, return a string with custom sell reason or True. Otherwise return
None or False. None or False.
""" """
return None return None
@ -371,7 +371,7 @@ class IStrategy(ABC, HyperStrategyMixin):
Checks if a pair is currently locked Checks if a pair is currently locked
The 2nd, optional parameter ensures that locks are applied until the new candle arrives, The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
of 2 seconds for a buy to happen on an old signal. of 2 seconds for a buy/short to happen on an old signal.
:param pair: "Pair to check" :param pair: "Pair to check"
:param candle_date: Date of the last candle. Optional, defaults to current date :param candle_date: Date of the last candle. Optional, defaults to current date
:returns: locking state of the pair in question. :returns: locking state of the pair in question.
@ -387,7 +387,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Parses the given candle (OHLCV) data and returns a populated DataFrame Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it add several TA indicators and buy/short signal to it
:param dataframe: Dataframe containing data from exchange :param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair') :param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added :return: DataFrame of candle (OHLCV) data with indicator data and signals added
@ -502,12 +502,14 @@ class IStrategy(ABC, HyperStrategyMixin):
dataframe: DataFrame dataframe: DataFrame
) -> Tuple[bool, bool, Optional[str]]: ) -> Tuple[bool, bool, Optional[str]]:
""" """
Calculates current signal based based on the buy / sell columns of the dataframe. Calculates current signal based based on the buy/short or sell/exit_short
Used by Bot to get the signal to buy or sell columns of the dataframe.
Used by Bot to get the signal to buy, sell, short, or exit_short
:param pair: pair in format ANT/BTC :param pair: pair in format ANT/BTC
:param timeframe: timeframe to use :param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from. :param dataframe: Analyzed dataframe to get signal from.
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal :return: (Buy, Sell)/(Short, Exit_short) A bool-tuple indicating
(buy/sell)/(short/exit_short) signal
""" """
if not isinstance(dataframe, DataFrame) or dataframe.empty: if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning(f'Empty candle (OHLCV) data for pair {pair}') logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
@ -528,27 +530,34 @@ class IStrategy(ABC, HyperStrategyMixin):
) )
return False, False, None return False, False, None
buy = latest[SignalType.BUY.value] == 1 enter = latest[SignalType.BUY.value] == 1
sell = False exit = False
if SignalType.SELL.value in latest: if SignalType.SELL.value in latest:
sell = latest[SignalType.SELL.value] == 1 exit = latest[SignalType.SELL.value] == 1
buy_tag = latest.get(SignalTagType.BUY_TAG.value, None) buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell)) latest['date'], pair, str(enter), str(exit))
timeframe_seconds = timeframe_to_seconds(timeframe) timeframe_seconds = timeframe_to_seconds(timeframe)
if self.ignore_expired_candle(latest_date=latest_date, if self.ignore_expired_candle(
current_time=datetime.now(timezone.utc), latest_date=latest_date,
timeframe_seconds=timeframe_seconds, current_time=datetime.now(timezone.utc),
buy=buy): timeframe_seconds=timeframe_seconds,
return False, sell, buy_tag enter=enter
return buy, sell, buy_tag ):
return False, exit, buy_tag
return enter, exit, buy_tag
def ignore_expired_candle(self, latest_date: datetime, current_time: datetime, def ignore_expired_candle(
timeframe_seconds: int, buy: bool): self,
if self.ignore_buying_expired_candle_after and buy: latest_date: datetime,
current_time: datetime,
timeframe_seconds: int,
enter: bool
):
if self.ignore_buying_expired_candle_after and enter:
time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds)) time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds))
return time_delta.total_seconds() > self.ignore_buying_expired_candle_after return time_delta.total_seconds() > self.ignore_buying_expired_candle_after
else: else:
@ -558,12 +567,12 @@ class IStrategy(ABC, HyperStrategyMixin):
sell: bool, low: float = None, high: float = None, sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple: force_stoploss: float = 0) -> SellCheckTuple:
""" """
This function evaluates if one of the conditions required to trigger a sell This function evaluates if one of the conditions required to trigger a sell/exit_short
has been reached, which can either be a stop-loss, ROI or sell-signal. has been reached, which can either be a stop-loss, ROI or exit-signal.
:param low: Only used during backtesting to simulate stoploss :param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
:param high: Only used during backtesting, to simulate ROI :param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
:param force_stoploss: Externally provided stoploss :param force_stoploss: Externally provided stoploss
:return: True if trade should be sold, False otherwise :return: True if trade should be exited, False otherwise
""" """
current_rate = rate current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate) current_profit = trade.calc_profit_ratio(current_rate)
@ -578,7 +587,7 @@ class IStrategy(ABC, HyperStrategyMixin):
current_rate = high or rate current_rate = high or rate
current_profit = trade.calc_profit_ratio(current_rate) current_profit = trade.calc_profit_ratio(current_rate)
# if buy signal and ignore_roi is set, we don't need to evaluate min_roi. # if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
roi_reached = (not (buy and self.ignore_roi_if_buy_signal) roi_reached = (not (buy and self.ignore_roi_if_buy_signal)
and self.min_roi_reached(trade=trade, current_profit=current_profit, and self.min_roi_reached(trade=trade, current_profit=current_profit,
current_time=date)) current_time=date))
@ -609,12 +618,12 @@ class IStrategy(ABC, HyperStrategyMixin):
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH] custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else: else:
custom_reason = None custom_reason = None
# TODO: return here if sell-signal should be favored over ROI # TODO: return here if exit-signal should be favored over ROI
# Start evaluations # Start evaluations
# Sequence: # Sequence:
# ROI (if not stoploss) # ROI (if not stoploss)
# Sell-signal # Exit-signal
# Stoploss # Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS: if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI") logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
@ -632,7 +641,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return stoplossflag return stoplossflag
# This one is noisy, commented out... # This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No sell signal.") # logger.debug(f"{trade.pair} - No exit signal.")
return SellCheckTuple(sell_type=SellType.NONE) return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade, def stop_loss_reached(self, current_rate: float, trade: Trade,
@ -641,7 +650,7 @@ class IStrategy(ABC, HyperStrategyMixin):
high: float = None) -> SellCheckTuple: high: float = None) -> SellCheckTuple:
""" """
Based on current profit of the trade and configured (trailing) stoploss, Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not decides to exit or not
:param current_profit: current profit as ratio :param current_profit: current profit as ratio
:param low: Low value of this candle, only set in backtesting :param low: Low value of this candle, only set in backtesting
:param high: High value of this candle, only set in backtesting :param high: High value of this candle, only set in backtesting
@ -746,7 +755,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Populate indicators that will be used in the Buy and Sell strategy Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
This method should not be overridden. This method should not be overridden.
:param dataframe: Dataframe with data from the exchange :param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
@ -762,14 +771,15 @@ class IStrategy(ABC, HyperStrategyMixin):
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Based on TA indicators, populates the buy signal for the given dataframe Based on TA indicators, populates the buy/short signal for the given dataframe
This method should not be overridden. This method should not be overridden.
:param dataframe: DataFrame :param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the :param metadata: Additional information dictionary, with details like the
currently traded pair currently traded pair
:return: DataFrame with buy column :return: DataFrame with buy column
""" """
logger.debug(f"Populating buy signals for pair {metadata.get('pair')}.")
logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
if self._buy_fun_len == 2: if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see " warnings.warn("deprecated - check out the Sample strategy to see "
@ -780,14 +790,15 @@ class IStrategy(ABC, HyperStrategyMixin):
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Based on TA indicators, populates the sell signal for the given dataframe Based on TA indicators, populates the sell/exit_short signal for the given dataframe
This method should not be overridden. This method should not be overridden.
:param dataframe: DataFrame :param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the :param metadata: Additional information dictionary, with details like the
currently traded pair currently traded pair
:return: DataFrame with sell column :return: DataFrame with sell column
""" """
logger.debug(f"Populating sell signals for pair {metadata.get('pair')}.")
logger.debug(f"Populating exit signals for pair {metadata.get('pair')}.")
if self._sell_fun_len == 2: if self._sell_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see " warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning) "the current function headers!", DeprecationWarning)

2
freqtrade/strategy/strategy_helper.py
View File

@ -72,7 +72,7 @@ def stoploss_from_open(open_relative_stop: float, current_profit: float) -> floa
:param open_relative_stop: Desired stop loss percentage relative to open price :param open_relative_stop: Desired stop loss percentage relative to open price
:param current_profit: The current profit percentage :param current_profit: The current profit percentage
:return: Positive stop loss value relative to current price :return: Stop loss value relative to current price
""" """
# formula is undefined for current_profit -1, return maximum value # formula is undefined for current_profit -1, return maximum value

68
freqtrade/templates/sample_hyperopt.py
View File

@ -59,7 +59,7 @@ class SampleHyperOpt(IHyperOpt):
Categorical([True, False], name='fastd-enabled'), Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'), Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'), Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger') Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
] ]
@staticmethod @staticmethod
@ -71,37 +71,39 @@ class SampleHyperOpt(IHyperOpt):
""" """
Buy strategy Hyperopt will build and use. Buy strategy Hyperopt will build and use.
""" """
conditions = [] long_conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']: if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value']) long_conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']: if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value']) long_conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']: if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value']) long_conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']: if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value']) long_conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS # TRIGGERS
if 'trigger' in params: if 'trigger' in params:
if params['trigger'] == 'bb_lower': if params['trigger'] == 'boll':
conditions.append(dataframe['close'] < dataframe['bb_lowerband']) long_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal': if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above( long_conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal'] dataframe['macd'],
dataframe['macdsignal']
)) ))
if params['trigger'] == 'sar_reversal': if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above( long_conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar'] dataframe['close'],
dataframe['sar']
)) ))
# Check that volume is not 0 # Check that volume is not 0
conditions.append(dataframe['volume'] > 0) long_conditions.append(dataframe['volume'] > 0)
if conditions: if long_conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, long_conditions),
'buy'] = 1 'buy'] = 1
return dataframe return dataframe
@ -122,9 +124,11 @@ class SampleHyperOpt(IHyperOpt):
Categorical([True, False], name='sell-fastd-enabled'), Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'), Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'), Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper', Categorical(['sell-boll',
'sell-macd_cross_signal', 'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger') 'sell-sar_reversal'],
name='sell-trigger'
)
] ]
@staticmethod @staticmethod
@ -136,37 +140,39 @@ class SampleHyperOpt(IHyperOpt):
""" """
Sell strategy Hyperopt will build and use. Sell strategy Hyperopt will build and use.
""" """
conditions = [] exit_long_conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']: if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value']) exit_long_conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']: if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value']) exit_long_conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']: if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value']) exit_long_conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']: if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value']) exit_long_conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS # TRIGGERS
if 'sell-trigger' in params: if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper': if params['sell-trigger'] == 'sell-boll':
conditions.append(dataframe['close'] > dataframe['bb_upperband']) exit_long_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal': if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above( exit_long_conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd'] dataframe['macdsignal'],
dataframe['macd']
)) ))
if params['sell-trigger'] == 'sell-sar_reversal': if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above( exit_long_conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close'] dataframe['sar'],
dataframe['close']
)) ))
# Check that volume is not 0 # Check that volume is not 0
conditions.append(dataframe['volume'] > 0) exit_long_conditions.append(dataframe['volume'] > 0)
if conditions: if exit_long_conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, exit_long_conditions),
'sell'] = 1 'sell'] = 1
return dataframe return dataframe

61
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -74,7 +74,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
Categorical([True, False], name='fastd-enabled'), Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'), Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'), Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger') Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
] ]
@staticmethod @staticmethod
@ -86,36 +86,36 @@ class AdvancedSampleHyperOpt(IHyperOpt):
""" """
Buy strategy Hyperopt will build and use Buy strategy Hyperopt will build and use
""" """
conditions = [] long_conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']: if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value']) long_conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']: if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value']) long_conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']: if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value']) long_conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']: if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value']) long_conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS # TRIGGERS
if 'trigger' in params: if 'trigger' in params:
if params['trigger'] == 'bb_lower': if params['trigger'] == 'boll':
conditions.append(dataframe['close'] < dataframe['bb_lowerband']) long_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal': if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above( long_conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal'] dataframe['macd'], dataframe['macdsignal']
)) ))
if params['trigger'] == 'sar_reversal': if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above( long_conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar'] dataframe['close'], dataframe['sar']
)) ))
# Check that volume is not 0 # Check that volume is not 0
conditions.append(dataframe['volume'] > 0) long_conditions.append(dataframe['volume'] > 0)
if conditions: if long_conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, long_conditions),
'buy'] = 1 'buy'] = 1
return dataframe return dataframe
@ -136,9 +136,10 @@ class AdvancedSampleHyperOpt(IHyperOpt):
Categorical([True, False], name='sell-fastd-enabled'), Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'), Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'), Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper', Categorical(['sell-boll',
'sell-macd_cross_signal', 'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger') 'sell-sar_reversal'],
name='sell-trigger')
] ]
@staticmethod @staticmethod
@ -151,36 +152,38 @@ class AdvancedSampleHyperOpt(IHyperOpt):
Sell strategy Hyperopt will build and use Sell strategy Hyperopt will build and use
""" """
# print(params) # print(params)
conditions = [] exit_long_conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']: if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value']) exit_long_conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']: if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value']) exit_long_conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']: if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value']) exit_long_conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']: if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value']) exit_long_conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS # TRIGGERS
if 'sell-trigger' in params: if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper': if params['sell-trigger'] == 'sell-boll':
conditions.append(dataframe['close'] > dataframe['bb_upperband']) exit_long_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal': if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above( exit_long_conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd'] dataframe['macdsignal'],
dataframe['macd']
)) ))
if params['sell-trigger'] == 'sell-sar_reversal': if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above( exit_long_conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close'] dataframe['sar'],
dataframe['close']
)) ))
# Check that volume is not 0 # Check that volume is not 0
conditions.append(dataframe['volume'] > 0) exit_long_conditions.append(dataframe['volume'] > 0)
if conditions: if exit_long_conditions:
dataframe.loc[ dataframe.loc[
reduce(lambda x, y: x & y, conditions), reduce(lambda x, y: x & y, exit_long_conditions),
'sell'] = 1 'sell'] = 1
return dataframe return dataframe

23
tests/optimize/hyperopts/default_hyperopt.py
View File

@ -68,15 +68,17 @@ class DefaultHyperOpt(IHyperOpt):
# TRIGGERS # TRIGGERS
if 'trigger' in params: if 'trigger' in params:
if params['trigger'] == 'bb_lower': if params['trigger'] == 'boll':
conditions.append(dataframe['close'] < dataframe['bb_lowerband']) conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal': if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal'] dataframe['macd'],
dataframe['macdsignal']
)) ))
if params['trigger'] == 'sar_reversal': if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar'] dataframe['close'],
dataframe['sar']
)) ))
if conditions: if conditions:
@ -102,7 +104,7 @@ class DefaultHyperOpt(IHyperOpt):
Categorical([True, False], name='fastd-enabled'), Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'), Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'), Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger') Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
] ]
@staticmethod @staticmethod
@ -128,15 +130,17 @@ class DefaultHyperOpt(IHyperOpt):
# TRIGGERS # TRIGGERS
if 'sell-trigger' in params: if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper': if params['sell-trigger'] == 'sell-boll':
conditions.append(dataframe['close'] > dataframe['bb_upperband']) conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal': if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd'] dataframe['macdsignal'],
dataframe['macd']
)) ))
if params['sell-trigger'] == 'sell-sar_reversal': if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close'] dataframe['sar'],
dataframe['close']
)) ))
if conditions: if conditions:
@ -162,9 +166,10 @@ class DefaultHyperOpt(IHyperOpt):
Categorical([True, False], name='sell-fastd-enabled'), Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'), Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'), Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper', Categorical(['sell-boll',
'sell-macd_cross_signal', 'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger') 'sell-sar_reversal'],
name='sell-trigger')
] ]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

2
tests/strategy/strats/hyperoptable_strategy.py
View File

@ -167,7 +167,7 @@ class HyperoptableStrategy(IStrategy):
Based on TA indicators, populates the sell signal for the given dataframe Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame :param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column :return: DataFrame with sell column
""" """
dataframe.loc[ dataframe.loc[
( (

20
tests/strategy/test_interface.py
View File

@ -156,17 +156,21 @@ def test_ignore_expired_candle(default_conf):
# Add 1 candle length as the "latest date" defines candle open. # Add 1 candle length as the "latest date" defines candle open.
current_time = latest_date + timedelta(seconds=80 + 300) current_time = latest_date + timedelta(seconds=80 + 300)
assert strategy.ignore_expired_candle(latest_date=latest_date, assert strategy.ignore_expired_candle(
current_time=current_time, latest_date=latest_date,
timeframe_seconds=300, current_time=current_time,
buy=True) is True timeframe_seconds=300,
enter=True
) is True
current_time = latest_date + timedelta(seconds=30 + 300) current_time = latest_date + timedelta(seconds=30 + 300)
assert not strategy.ignore_expired_candle(latest_date=latest_date, assert not strategy.ignore_expired_candle(
current_time=current_time, latest_date=latest_date,
timeframe_seconds=300, current_time=current_time,
buy=True) is True timeframe_seconds=300,
enter=True
) is True
def test_assert_df_raise(mocker, caplog, ohlcv_history): def test_assert_df_raise(mocker, caplog, ohlcv_history):

24
tests/strategy/test_strategy_loading.py
View File

@ -382,13 +382,13 @@ def test_call_deprecated_function(result, monkeypatch, default_conf, caplog):
assert isinstance(indicator_df, DataFrame) assert isinstance(indicator_df, DataFrame)
assert 'adx' in indicator_df.columns assert 'adx' in indicator_df.columns
buydf = strategy.advise_buy(result, metadata=metadata) enterdf = strategy.advise_buy(result, metadata=metadata)
assert isinstance(buydf, DataFrame) assert isinstance(enterdf, DataFrame)
assert 'buy' in buydf.columns assert 'buy' in enterdf.columns
selldf = strategy.advise_sell(result, metadata=metadata) exitdf = strategy.advise_sell(result, metadata=metadata)
assert isinstance(selldf, DataFrame) assert isinstance(exitdf, DataFrame)
assert 'sell' in selldf assert 'sell' in exitdf
assert log_has("DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'.", assert log_has("DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'.",
caplog) caplog)
@ -409,10 +409,10 @@ def test_strategy_interface_versioning(result, monkeypatch, default_conf):
assert isinstance(indicator_df, DataFrame) assert isinstance(indicator_df, DataFrame)
assert 'adx' in indicator_df.columns assert 'adx' in indicator_df.columns
buydf = strategy.advise_buy(result, metadata=metadata) enterdf = strategy.advise_buy(result, metadata=metadata)
assert isinstance(buydf, DataFrame) assert isinstance(enterdf, DataFrame)
assert 'buy' in buydf.columns assert 'buy' in enterdf.columns
selldf = strategy.advise_sell(result, metadata=metadata) exitdf = strategy.advise_sell(result, metadata=metadata)
assert isinstance(selldf, DataFrame) assert isinstance(exitdf, DataFrame)
assert 'sell' in selldf assert 'sell' in exitdf