adelorenzo
/
stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added short and exit_short to strategy

This commit is contained in:
Sam Germain 2021-08-08 03:38:34 -06:00
parent 98fe3e73de
commit d4a7d2d444
24 changed files with 862 additions and 152 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
freqtrade/edge/edge_positioning.py
View File

@ -167,8 +167,15 @@ class Edge:
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
df_analyzed = self.strategy.advise_exit(
dataframe=self.strategy.advise_enter(
dataframe=pair_data,
metadata={'pair': pair},
is_short=False
),
metadata={'pair': pair},
is_short=False
)[headers].copy()
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)

3
freqtrade/enums/signaltype.py
View File

@ -7,6 +7,8 @@ class SignalType(Enum):
"""
BUY = "buy"
SELL = "sell"
SHORT = "short"
EXIT_SHORT = "exit_short"
class SignalTagType(Enum):
@ -14,3 +16,4 @@ class SignalTagType(Enum):
Enum for signal columns
"""
BUY_TAG = "buy_tag"
SELL_TAG = "sell_tag"

4
freqtrade/optimize/backtesting.py
View File

@ -231,8 +231,8 @@ class Backtesting:
if has_buy_tag:
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
df_analyzed = self.strategy.advise_exit(
self.strategy.advise_enter(pair_data, {'pair': pair}), {'pair': pair}).copy()
# Trim startup period from analyzed dataframe
df_analyzed = trim_dataframe(df_analyzed, self.timerange,
startup_candles=self.required_startup)

7
freqtrade/optimize/hyperopt.py
View File

@ -110,7 +110,7 @@ class Hyperopt:
self.backtesting.strategy.advise_indicators = ( # type: ignore
self.custom_hyperopt.populate_indicators) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.backtesting.strategy.advise_enter = ( # type: ignore
self.custom_hyperopt.populate_buy_trend) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.backtesting.strategy.advise_sell = ( # type: ignore
@ -283,12 +283,13 @@ class Hyperopt:
params_dict = self._get_params_dict(self.dimensions, raw_params)
# Apply parameters
# TODO-lev: These don't take a side, how can I pass is_short=True/False to it
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.backtesting.strategy.advise_enter = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.backtesting.strategy.advise_exit = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if HyperoptTools.has_space(self.config, 'protection'):

1
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -51,6 +51,7 @@ class HyperOptResolver(IResolver):
if not hasattr(hyperopt, 'populate_sell_trend'):
logger.info("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
# TODO-lev: Short equivelents?
return hyperopt

7
freqtrade/resolvers/strategy_resolver.py
View File

@ -202,9 +202,14 @@ class StrategyResolver(IResolver):
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)
strategy._short_fun_len = len(getfullargspec(strategy.populate_short_trend).args)
strategy._exit_short_fun_len = len(
getfullargspec(strategy.populate_exit_short_trend).args)
if any(x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]):
strategy._sell_fun_len,
strategy._short_fun_len,
strategy._exit_short_fun_len]):
strategy.INTERFACE_VERSION = 1
return strategy

2
freqtrade/rpc/api_server/uvicorn_threaded.py
View File

@ -44,5 +44,5 @@ class UvicornServer(uvicorn.Server):
time.sleep(1e-3)
def cleanup(self):
self.should_exit = True
self.should_sell = True
self.thread.join()

2
freqtrade/strategy/hyper.py
View File

@ -22,6 +22,8 @@ from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
# TODO-lev: This file
class BaseParameter(ABC):
"""

203
freqtrade/strategy/interface.py
View File

@ -62,6 +62,8 @@ class IStrategy(ABC, HyperStrategyMixin):
_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
_short_fun_len: int = 0
_exit_short_fun_len: int = 0
_ft_params_from_file: Dict = {}
# associated minimal roi
minimal_roi: Dict
@ -135,7 +137,7 @@ class IStrategy(ABC, HyperStrategyMixin):
@abstractmethod
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 metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
@ -143,7 +145,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return dataframe
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_enter_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@ -153,7 +155,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return dataframe
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@ -164,9 +166,9 @@ class IStrategy(ABC, HyperStrategyMixin):
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
Check buy timeout function callback.
This method can be used to override the buy-timeout.
It is called whenever a limit buy order has been created,
Check enter timeout function callback.
This method can be used to override the enter-timeout.
It is called whenever a limit buy/short order has been created,
and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough.
@ -176,16 +178,16 @@ class IStrategy(ABC, HyperStrategyMixin):
:param trade: trade object.
:param order: Order dictionary as returned from CCXT.
: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
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
Check sell timeout function callback.
This method can be used to override the sell-timeout.
It is called whenever a limit sell order has been created,
and is not yet fully filled.
Check exit timeout function callback.
This method can be used to override the exit-timeout.
It is called whenever a (long) limit sell order or (short) limit buy
has been created, and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough.
@ -194,7 +196,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param trade: trade object.
:param order: Order dictionary as returned from CCXT.
: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
@ -210,7 +212,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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
network requests in this method.
@ -218,7 +220,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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 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
@ -234,7 +236,7 @@ class IStrategy(ABC, HyperStrategyMixin):
rate: float, time_in_force: str, sell_reason: str,
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
network requests in this method.
@ -242,18 +244,18 @@ class IStrategy(ABC, HyperStrategyMixin):
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 order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
: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 sell_reason: Sell reason.
:param sell_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
: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.
: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
"""
return True
@ -283,15 +285,15 @@ class IStrategy(ABC, HyperStrategyMixin):
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
Custom sell 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
time. This method is not called when sell signal is set.
Custom exit signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting exit signal on a candle at specified
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
example you could implement a sell relative to the candle when the trade was opened,
This method should be overridden to create exit signals that depend on trade parameters. For
example you could implement an exit relative to the candle when the trade was opened,
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 trade: trade object.
@ -299,7 +301,7 @@ class IStrategy(ABC, HyperStrategyMixin):
: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 **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.
"""
return None
@ -371,7 +373,7 @@ class IStrategy(ABC, HyperStrategyMixin):
Checks if a pair is currently locked
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
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 candle_date: Date of the last candle. Optional, defaults to current date
:returns: locking state of the pair in question.
@ -387,15 +389,17 @@ class IStrategy(ABC, HyperStrategyMixin):
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> 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 metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
dataframe = self.advise_enter(dataframe, metadata, is_short=False)
dataframe = self.advise_exit(dataframe, metadata, is_short=False)
dataframe = self.advise_enter(dataframe, metadata, is_short=True)
dataframe = self.advise_exit(dataframe, metadata, is_short=True)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -422,7 +426,10 @@ class IStrategy(ABC, HyperStrategyMixin):
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe['buy'] = 0
dataframe['sell'] = 0
dataframe['short'] = 0
dataframe['exit_short'] = 0
dataframe['buy_tag'] = None
dataframe['short_tag'] = None
# Other Defs in strategy that want to be called every loop here
# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
@ -482,6 +489,7 @@ class IStrategy(ABC, HyperStrategyMixin):
if dataframe is None:
message = "No dataframe returned (return statement missing?)."
elif 'buy' not in dataframe:
# TODO-lev: Something?
message = "Buy column not set."
elif df_len != len(dataframe):
message = message_template.format("length")
@ -499,15 +507,18 @@ class IStrategy(ABC, HyperStrategyMixin):
self,
pair: str,
timeframe: str,
dataframe: DataFrame
dataframe: DataFrame,
is_short: bool = False
) -> Tuple[bool, bool, Optional[str]]:
"""
Calculates current signal based based on the buy / sell columns of the dataframe.
Used by Bot to get the signal to buy or sell
Calculates current signal based based on the buy/short or sell/exit_short
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 timeframe: timeframe to use
: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:
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
@ -528,42 +539,49 @@ class IStrategy(ABC, HyperStrategyMixin):
)
return False, False, None
buy = latest[SignalType.BUY.value] == 1
(enter_type, enter_tag) = (
(SignalType.SHORT, SignalTagType.SHORT_TAG)
if is_short else
(SignalType.BUY, SignalTagType.BUY_TAG)
)
exit_type = SignalType.EXIT_SHORT if is_short else SignalType.SELL
sell = False
if SignalType.SELL.value in latest:
sell = latest[SignalType.SELL.value] == 1
enter = latest[enter_type.value] == 1
buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
exit = False
if exit_type.value in latest:
exit = latest[exit_type.value] == 1
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell))
enter_tag_value = latest.get(enter_tag.value, None)
logger.debug(f'trigger: %s (pair=%s) {enter_type.value}=%s {exit_type.value}=%s',
latest['date'], pair, str(enter), str(exit))
timeframe_seconds = timeframe_to_seconds(timeframe)
if self.ignore_expired_candle(latest_date=latest_date,
current_time=datetime.now(timezone.utc),
timeframe_seconds=timeframe_seconds,
buy=buy):
return False, sell, buy_tag
return buy, sell, buy_tag
enter=enter):
return False, exit, enter_tag_value
return enter, exit, enter_tag_value
def ignore_expired_candle(self, latest_date: datetime, current_time: datetime,
timeframe_seconds: int, buy: bool):
if self.ignore_buying_expired_candle_after and buy:
timeframe_seconds: int, enter: bool):
if self.ignore_buying_expired_candle_after and enter:
time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds))
return time_delta.total_seconds() > self.ignore_buying_expired_candle_after
else:
return False
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool, low: float = None, high: float = None,
def should_sell(self, trade: Trade, rate: float, date: datetime, enter: bool,
exit: 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
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 exit-signal.
:param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
:param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
: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_profit = trade.calc_profit_ratio(current_rate)
@ -578,8 +596,8 @@ class IStrategy(ABC, HyperStrategyMixin):
current_rate = high or 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.
roi_reached = (not (buy and self.ignore_roi_if_buy_signal)
# if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
roi_reached = (not (enter and self.ignore_roi_if_buy_signal)
and self.min_roi_reached(trade=trade, current_profit=current_profit,
current_time=date))
@ -592,10 +610,11 @@ class IStrategy(ABC, HyperStrategyMixin):
if (self.sell_profit_only and current_profit <= self.sell_profit_offset):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
pass
elif self.use_sell_signal and not buy:
if sell:
elif self.use_sell_signal and not enter:
if exit:
sell_signal = SellType.SELL_SIGNAL
else:
trade_type = "exit_short" if trade.is_short else "sell"
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)
@ -603,18 +622,18 @@ class IStrategy(ABC, HyperStrategyMixin):
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.')
logger.warning(f'Custom {trade_type} reason returned from '
f'custom_{trade_type} is too long and was trimmed'
f'to {CUSTOM_SELL_MAX_LENGTH} characters.')
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else:
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
# Sequence:
# ROI (if not stoploss)
# Sell-signal
# Exit-signal
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
@ -632,7 +651,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return stoplossflag
# 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)
def stop_loss_reached(self, current_rate: float, trade: Trade,
@ -641,7 +660,7 @@ class IStrategy(ABC, HyperStrategyMixin):
high: float = None) -> SellCheckTuple:
"""
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 low: Low value of this candle, only set in backtesting
:param high: High value of this candle, only set in backtesting
@ -651,7 +670,12 @@ class IStrategy(ABC, HyperStrategyMixin):
# 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 and trade.stop_loss < (low or current_rate):
dir_correct = (
trade.stop_loss < (low or current_rate) and not trade.is_short or
trade.stop_loss > (low or current_rate) and trade.is_short
)
if self.use_custom_stoploss and dir_correct:
stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
)(pair=trade.pair, trade=trade,
current_time=current_time,
@ -735,7 +759,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advise_buy or advise_sell!
Does not run advise_enter or advise_exit!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Has positive effects on memory usage for whatever reason - also when
@ -746,7 +770,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
@ -760,37 +784,60 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
return self.populate_indicators(dataframe, metadata)
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def advise_enter(
self,
dataframe: DataFrame,
metadata: dict,
is_short: bool = False
) -> 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.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the
currently traded pair
:return: DataFrame with buy column
"""
logger.debug(f"Populating buy signals for pair {metadata.get('pair')}.")
(type, fun_len) = (
("short", self._short_fun_len)
if is_short else
("buy", self._buy_fun_len)
)
if self._buy_fun_len == 2:
logger.debug(f"Populating {type} signals for pair {metadata.get('pair')}.")
if fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_buy_trend(dataframe) # type: ignore
return self.populate_enter_trend(dataframe) # type: ignore
else:
return self.populate_buy_trend(dataframe, metadata)
return self.populate_enter_trend(dataframe, metadata)
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def advise_exit(
self,
dataframe: DataFrame,
metadata: dict,
is_short: bool = False
) -> 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.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the
currently traded pair
:return: DataFrame with sell column
"""
logger.debug(f"Populating sell signals for pair {metadata.get('pair')}.")
if self._sell_fun_len == 2:
(type, fun_len) = (
("exit_short", self._exit_short_fun_len)
if is_short else
("sell", self._sell_fun_len)
)
logger.debug(f"Populating {type} signals for pair {metadata.get('pair')}.")
if fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_sell_trend(dataframe) # type: ignore
return self.populate_exit_trend(dataframe) # type: ignore
else:
return self.populate_sell_trend(dataframe, metadata)
return self.populate_exit_trend(dataframe, metadata)

15
freqtrade/strategy/strategy_helper.py
View File

@ -58,7 +58,11 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
return dataframe
def stoploss_from_open(open_relative_stop: float, current_profit: float) -> float:
def stoploss_from_open(
open_relative_stop: float,
current_profit: float,
for_short: bool = False
) -> float:
"""
Given the current profit, and a desired stop loss value relative to the open price,
@ -72,14 +76,17 @@ 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 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
if current_profit == -1:
return 1
stoploss = 1-((1+open_relative_stop)/(1+current_profit))
stoploss = 1-((1+open_relative_stop)/(1+current_profit)) # TODO-lev: Is this right?
# negative stoploss values indicate the requested stop price is higher than the current price
return max(stoploss, 0.0)
if for_short:
return min(stoploss, 0.0)
else:
return max(stoploss, 0.0)

122
freqtrade/templates/sample_hyperopt.py
View File

@ -172,3 +172,125 @@ class SampleHyperOpt(IHyperOpt):
return dataframe
return populate_sell_trend
@staticmethod
def short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the short strategy parameters to be used by Hyperopt.
"""
def populate_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] > params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] > params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] < params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] > params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'short'] = 1
return dataframe
return populate_short_trend
@staticmethod
def short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching short strategy parameters.
"""
return [
Integer(75, 90, name='mfi-value'),
Integer(55, 85, name='fastd-value'),
Integer(50, 80, name='adx-value'),
Integer(60, 80, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_upper', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def exit_short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the exit_short strategy parameters to be used by Hyperopt.
"""
def populate_exit_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Exit_short strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'exit-short-mfi-enabled' in params and params['exit-short-mfi-enabled']:
conditions.append(dataframe['mfi'] < params['exit-short-mfi-value'])
if 'exit-short-fastd-enabled' in params and params['exit-short-fastd-enabled']:
conditions.append(dataframe['fastd'] < params['exit-short-fastd-value'])
if 'exit-short-adx-enabled' in params and params['exit-short-adx-enabled']:
conditions.append(dataframe['adx'] > params['exit-short-adx-value'])
if 'exit-short-rsi-enabled' in params and params['exit-short-rsi-enabled']:
conditions.append(dataframe['rsi'] < params['exit-short-rsi-value'])
# TRIGGERS
if 'exit-short-trigger' in params:
if params['exit-short-trigger'] == 'exit-short-bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['exit-short-trigger'] == 'exit-short-macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macdsignal'], dataframe['macd']
))
if params['exit-short-trigger'] == 'exit-short-sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'exit_short'] = 1
return dataframe
return populate_exit_short_trend
@staticmethod
def exit_short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching exit short strategy parameters.
"""
return [
Integer(1, 25, name='exit_short-mfi-value'),
Integer(1, 50, name='exit_short-fastd-value'),
Integer(1, 50, name='exit_short-adx-value'),
Integer(1, 40, name='exit_short-rsi-value'),
Categorical([True, False], name='exit_short-mfi-enabled'),
Categorical([True, False], name='exit_short-fastd-enabled'),
Categorical([True, False], name='exit_short-adx-enabled'),
Categorical([True, False], name='exit_short-rsi-enabled'),
Categorical(['exit_short-bb_lower',
'exit_short-macd_cross_signal',
'exit_short-sar_reversal'], name='exit_short-trigger')
]

126
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -187,9 +187,132 @@ class AdvancedSampleHyperOpt(IHyperOpt):
return populate_sell_trend
@staticmethod
def short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the short strategy parameters to be used by Hyperopt.
"""
def populate_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] > params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] > params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] < params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] > params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'short'] = 1
return dataframe
return populate_short_trend
@staticmethod
def short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching short strategy parameters.
"""
return [
Integer(75, 90, name='mfi-value'),
Integer(55, 85, name='fastd-value'),
Integer(50, 80, name='adx-value'),
Integer(60, 80, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_upper', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def exit_short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the exit_short strategy parameters to be used by Hyperopt.
"""
def populate_exit_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Exit_short strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'exit-short-mfi-enabled' in params and params['exit-short-mfi-enabled']:
conditions.append(dataframe['mfi'] < params['exit-short-mfi-value'])
if 'exit-short-fastd-enabled' in params and params['exit-short-fastd-enabled']:
conditions.append(dataframe['fastd'] < params['exit-short-fastd-value'])
if 'exit-short-adx-enabled' in params and params['exit-short-adx-enabled']:
conditions.append(dataframe['adx'] > params['exit-short-adx-value'])
if 'exit-short-rsi-enabled' in params and params['exit-short-rsi-enabled']:
conditions.append(dataframe['rsi'] < params['exit-short-rsi-value'])
# TRIGGERS
if 'exit-short-trigger' in params:
if params['exit-short-trigger'] == 'exit-short-bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['exit-short-trigger'] == 'exit-short-macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macdsignal'], dataframe['macd']
))
if params['exit-short-trigger'] == 'exit-short-sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'exit_short'] = 1
return dataframe
return populate_exit_short_trend
@staticmethod
def exit_short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching exit short strategy parameters.
"""
return [
Integer(1, 25, name='exit_short-mfi-value'),
Integer(1, 50, name='exit_short-fastd-value'),
Integer(1, 50, name='exit_short-adx-value'),
Integer(1, 40, name='exit_short-rsi-value'),
Categorical([True, False], name='exit_short-mfi-enabled'),
Categorical([True, False], name='exit_short-fastd-enabled'),
Categorical([True, False], name='exit_short-adx-enabled'),
Categorical([True, False], name='exit_short-rsi-enabled'),
Categorical(['exit_short-bb_lower',
'exit_short-macd_cross_signal',
'exit_short-sar_reversal'], name='exit_short-trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
# TODO-lev?
Generate the ROI table that will be used by Hyperopt
This implementation generates the default legacy Freqtrade ROI tables.
@ -211,6 +334,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
@staticmethod
def roi_space() -> List[Dimension]:
"""
# TODO-lev?
Values to search for each ROI steps
Override it if you need some different ranges for the parameters in the
@ -231,6 +355,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
# TODO-lev?
Stoploss Value to search
Override it if you need some different range for the parameter in the
@ -243,6 +368,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
@staticmethod
def trailing_space() -> List[Dimension]:
"""
# TODO-lev?
Create a trailing stoploss space.
You may override it in your custom Hyperopt class.

41
freqtrade/templates/sample_strategy.py
View File

@ -29,7 +29,7 @@ class SampleStrategy(IStrategy):
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_buy_trend, populate_sell_trend
- the methods: populate_indicators, populate_buy_trend, populate_sell_trend, populate_short_trend, populate_exit_short_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
@ -58,6 +58,8 @@ class SampleStrategy(IStrategy):
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# Optimal timeframe for the strategy.
timeframe = '5m'
@ -373,3 +375,40 @@ class SampleStrategy(IStrategy):
),
'sell'] = 1
return dataframe
def populate_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the short signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with short column
"""
dataframe.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) &
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'short'] = 1
return dataframe
def populate_exit_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit_short signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit_short column
"""
dataframe.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) &
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'exit_short'] = 1
return dataframe

156
tests/optimize/hyperopts/default_hyperopt.py
View File

@ -105,6 +105,66 @@ class DefaultHyperOpt(IHyperOpt):
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the short strategy parameters to be used by Hyperopt.
"""
def populate_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] > params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] > params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] < params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] > params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'short'] = 1
return dataframe
return populate_short_trend
@staticmethod
def short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching short strategy parameters.
"""
return [
Integer(75, 90, name='mfi-value'),
Integer(55, 85, name='fastd-value'),
Integer(50, 80, name='adx-value'),
Integer(60, 80, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_upper', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
@ -148,6 +208,49 @@ class DefaultHyperOpt(IHyperOpt):
return populate_sell_trend
@staticmethod
def exit_short_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the exit_short strategy parameters to be used by Hyperopt.
"""
def populate_exit_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Exit_short strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'exit-short-mfi-enabled' in params and params['exit-short-mfi-enabled']:
conditions.append(dataframe['mfi'] < params['exit-short-mfi-value'])
if 'exit-short-fastd-enabled' in params and params['exit-short-fastd-enabled']:
conditions.append(dataframe['fastd'] < params['exit-short-fastd-value'])
if 'exit-short-adx-enabled' in params and params['exit-short-adx-enabled']:
conditions.append(dataframe['adx'] > params['exit-short-adx-value'])
if 'exit-short-rsi-enabled' in params and params['exit-short-rsi-enabled']:
conditions.append(dataframe['rsi'] < params['exit-short-rsi-value'])
# TRIGGERS
if 'exit-short-trigger' in params:
if params['exit-short-trigger'] == 'exit-short-bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['exit-short-trigger'] == 'exit-short-macd_cross_signal':
conditions.append(qtpylib.crossed_below(
dataframe['macdsignal'], dataframe['macd']
))
if params['exit-short-trigger'] == 'exit-short-sar_reversal':
conditions.append(qtpylib.crossed_below(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'exit_short'] = 1
return dataframe
return populate_exit_short_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
@ -167,6 +270,25 @@ class DefaultHyperOpt(IHyperOpt):
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def exit_short_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching exit short strategy parameters.
"""
return [
Integer(1, 25, name='exit_short-mfi-value'),
Integer(1, 50, name='exit_short-fastd-value'),
Integer(1, 50, name='exit_short-adx-value'),
Integer(1, 40, name='exit_short-rsi-value'),
Categorical([True, False], name='exit_short-mfi-enabled'),
Categorical([True, False], name='exit_short-fastd-enabled'),
Categorical([True, False], name='exit_short-adx-enabled'),
Categorical([True, False], name='exit_short-rsi-enabled'),
Categorical(['exit_short-bb_lower',
'exit_short-macd_cross_signal',
'exit_short-sar_reversal'], name='exit_short-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
@ -200,3 +322,37 @@ class DefaultHyperOpt(IHyperOpt):
'sell'] = 1
return dataframe
def populate_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include short space.
"""
dataframe.loc[
(
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['mfi'] < 84) &
(dataframe['adx'] > 75) &
(dataframe['rsi'] < 79)
),
'buy'] = 1
return dataframe
def populate_exit_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include exit_short space.
"""
dataframe.loc[
(
(qtpylib.crossed_below(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] < 46)
),
'sell'] = 1
return dataframe

4
tests/optimize/test_backtest_detail.py
View File

@ -597,8 +597,8 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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.advise_enter = lambda a, m: frame
backtesting.strategy.advise_exit = lambda a, m: frame
backtesting.strategy.use_custom_stoploss = data.use_custom_stoploss
caplog.set_level(logging.DEBUG)

20
tests/optimize/test_backtesting.py
View File

@ -290,8 +290,8 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
assert backtesting.config == default_conf
assert backtesting.timeframe == '5m'
assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
assert callable(backtesting.strategy.advise_buy)
assert callable(backtesting.strategy.advise_sell)
assert callable(backtesting.strategy.advise_enter)
assert callable(backtesting.strategy.advise_exit)
assert isinstance(backtesting.strategy.dp, DataProvider)
get_fee.assert_called()
assert backtesting.fee == 0.5
@ -700,8 +700,8 @@ def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = fun # Override
backtesting.strategy.advise_sell = fun # Override
backtesting.strategy.advise_enter = fun # Override
backtesting.strategy.advise_exit = fun # Override
result = backtesting.backtest(**backtest_conf)
assert result['results'].empty
@ -716,8 +716,8 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = fun # Override
backtesting.strategy.advise_sell = fun # Override
backtesting.strategy.advise_enter = fun # Override
backtesting.strategy.advise_exit = fun # Override
result = backtesting.backtest(**backtest_conf)
assert result['results'].empty
@ -731,8 +731,8 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
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
backtesting.strategy.advise_enter = _trend_alternate # Override
backtesting.strategy.advise_exit = _trend_alternate # Override
result = backtesting.backtest(**backtest_conf)
# 200 candles in backtest data
# won't buy on first (shifted by 1)
@ -777,8 +777,8 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = _trend_alternate_hold # Override
backtesting.strategy.advise_sell = _trend_alternate_hold # Override
backtesting.strategy.advise_enter = _trend_alternate_hold # Override
backtesting.strategy.advise_exit = _trend_alternate_hold # Override
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)

19
tests/optimize/test_hyperopt.py
View File

@ -25,6 +25,9 @@ from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
from .hyperopts.default_hyperopt import DefaultHyperOpt
# TODO-lev: This file
def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
@ -363,8 +366,8 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
# Should be called for historical candle data
assert dumper.call_count == 1
assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt.backtesting.strategy, "advise_exit")
assert hasattr(hyperopt.backtesting.strategy, "advise_enter")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
@ -822,8 +825,8 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
assert dumper.call_count == 1
assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt.backtesting.strategy, "advise_exit")
assert hasattr(hyperopt.backtesting.strategy, "advise_enter")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
@ -903,8 +906,8 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
assert dumper.called
assert dumper.call_count == 1
assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt.backtesting.strategy, "advise_exit")
assert hasattr(hyperopt.backtesting.strategy, "advise_enter")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
@ -957,8 +960,8 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
assert dumper.called
assert dumper.call_count == 1
assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt.backtesting.strategy, "advise_exit")
assert hasattr(hyperopt.backtesting.strategy, "advise_enter")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")

2
tests/rpc/test_rpc_apiserver.py
View File

@ -264,7 +264,7 @@ def test_api_UvicornServer(mocker):
assert thread_mock.call_count == 1
s.cleanup()
assert s.should_exit is True
assert s.should_sell is True
def test_api_UvicornServer_run(mocker):

45
tests/strategy/strats/default_strategy.py
View File

@ -154,3 +154,48 @@ class DefaultStrategy(IStrategy):
),
'sell'] = 1
return dataframe
def populate_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the short signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with short column
"""
dataframe.loc[
(
(dataframe['rsi'] > 65) &
(dataframe['fastd'] > 65) &
(dataframe['adx'] < 70) &
(dataframe['plus_di'] < 0.5) # TODO-lev: What to do here
) |
(
(dataframe['adx'] < 35) &
(dataframe['plus_di'] < 0.5) # TODO-lev: What to do here
),
'short'] = 1
return dataframe
def populate_exit_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit_short signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit_short column
"""
dataframe.loc[
(
(
(qtpylib.crossed_below(dataframe['rsi'], 30)) |
(qtpylib.crossed_below(dataframe['fastd'], 30))
) &
(dataframe['adx'] < 90) &
(dataframe['minus_di'] < 0) # TODO-lev: what to do here
) |
(
(dataframe['adx'] > 30) &
(dataframe['minus_di'] < 0.5) # TODO-lev: what to do here
),
'exit_short'] = 1
return dataframe

62
tests/strategy/strats/hyperoptable_strategy.py
View File

@ -60,6 +60,15 @@ class HyperoptableStrategy(IStrategy):
'sell_minusdi': 0.4
}
short_params = {
'short_rsi': 65,
}
exit_short_params = {
'exit_short_rsi': 26,
'exit_short_minusdi': 0.6
}
buy_rsi = IntParameter([0, 50], default=30, space='buy')
buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
@ -78,6 +87,12 @@ class HyperoptableStrategy(IStrategy):
})
return prot
short_rsi = IntParameter([50, 100], default=70, space='sell')
short_plusdi = RealParameter(low=0, high=1, default=0.5, space='sell')
exit_short_rsi = IntParameter(low=0, high=50, default=30, space='buy')
exit_short_minusdi = DecimalParameter(low=0, high=1, default=0.4999, decimals=3, space='buy',
load=False)
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
@ -167,7 +182,7 @@ class HyperoptableStrategy(IStrategy):
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
:return: DataFrame with sell column
"""
dataframe.loc[
(
@ -184,3 +199,48 @@ class HyperoptableStrategy(IStrategy):
),
'sell'] = 1
return dataframe
def populate_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the short signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with short column
"""
dataframe.loc[
(
(dataframe['rsi'] > self.short_rsi.value) &
(dataframe['fastd'] > 65) &
(dataframe['adx'] < 70) &
(dataframe['plus_di'] < self.short_plusdi.value)
) |
(
(dataframe['adx'] < 35) &
(dataframe['plus_di'] < self.short_plusdi.value)
),
'short'] = 1
return dataframe
def populate_exit_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit_short signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit_short column
"""
dataframe.loc[
(
(
(qtpylib.crossed_below(dataframe['rsi'], self.exit_short_rsi.value)) |
(qtpylib.crossed_below(dataframe['fastd'], 30))
) &
(dataframe['adx'] < 90) &
(dataframe['minus_di'] < 0) # TODO-lev: What should this be
) |
(
(dataframe['adx'] < 30) &
(dataframe['minus_di'] < self.exit_short_minusdi.value)
),
'exit_short'] = 1
return dataframe

31
tests/strategy/strats/legacy_strategy.py
View File

@ -85,3 +85,34 @@ class TestStrategyLegacy(IStrategy):
),
'sell'] = 1
return dataframe
def populate_short_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] > dataframe['tema'].shift(1)) &
(dataframe['volume'] > 0)
),
'buy'] = 1
return dataframe
def populate_exit_short_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] < dataframe['tema'].shift(1)) &
(dataframe['volume'] > 0)
),
'sell'] = 1
return dataframe

28
tests/strategy/test_default_strategy.py
View File

@ -14,6 +14,8 @@ def test_default_strategy_structure():
assert hasattr(DefaultStrategy, 'populate_indicators')
assert hasattr(DefaultStrategy, 'populate_buy_trend')
assert hasattr(DefaultStrategy, 'populate_sell_trend')
assert hasattr(DefaultStrategy, 'populate_short_trend')
assert hasattr(DefaultStrategy, 'populate_exit_short_trend')
def test_default_strategy(result, fee):
@ -27,6 +29,10 @@ def test_default_strategy(result, fee):
assert type(indicators) is DataFrame
assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame
# TODO-lev: I think these two should be commented out in the strategy by default
# TODO-lev: so they can be tested, but the tests can't really remain
assert type(strategy.populate_short_trend(indicators, metadata)) is DataFrame
assert type(strategy.populate_exit_short_trend(indicators, metadata)) is DataFrame
trade = Trade(
open_rate=19_000,
@ -37,10 +43,28 @@ def test_default_strategy(result, fee):
assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1,
rate=20000, time_in_force='gtc',
current_time=datetime.utcnow()) is True
is_short=False, current_time=datetime.utcnow()) is True
assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=trade, order_type='limit', amount=0.1,
rate=20000, time_in_force='gtc', sell_reason='roi',
current_time=datetime.utcnow()) is True
is_short=False, current_time=datetime.utcnow()) is True
# TODO-lev: Test for shorts?
assert strategy.custom_stoploss(pair='ETH/BTC', trade=trade, current_time=datetime.now(),
current_rate=20_000, current_profit=0.05) == strategy.stoploss
short_trade = Trade(
open_rate=21_000,
amount=0.1,
pair='ETH/BTC',
fee_open=fee.return_value
)
assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1,
rate=20000, time_in_force='gtc',
is_short=True, current_time=datetime.utcnow()) is True
assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=short_trade, order_type='limit',
amount=0.1, rate=20000, time_in_force='gtc',
sell_reason='roi', is_short=True,
current_time=datetime.utcnow()) is True

60
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.
current_time = latest_date + timedelta(seconds=80 + 300)
assert strategy.ignore_expired_candle(latest_date=latest_date,
current_time=current_time,
timeframe_seconds=300,
buy=True) is True
assert strategy.ignore_expired_candle(
latest_date=latest_date,
current_time=current_time,
timeframe_seconds=300,
enter=True
) is True
current_time = latest_date + timedelta(seconds=30 + 300)
assert not strategy.ignore_expired_candle(latest_date=latest_date,
current_time=current_time,
timeframe_seconds=300,
buy=True) is True
assert not strategy.ignore_expired_candle(
latest_date=latest_date,
current_time=current_time,
timeframe_seconds=300,
enter=True
) is True
def test_assert_df_raise(mocker, caplog, ohlcv_history):
@ -478,20 +482,20 @@ def test_custom_sell(default_conf, fee, caplog) -> None:
def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
caplog.set_level(logging.DEBUG)
ind_mock = MagicMock(side_effect=lambda x, meta: x)
buy_mock = MagicMock(side_effect=lambda x, meta: x)
sell_mock = MagicMock(side_effect=lambda x, meta: x)
enter_mock = MagicMock(side_effect=lambda x, meta, is_short: x)
exit_mock = MagicMock(side_effect=lambda x, meta, is_short: x)
mocker.patch.multiple(
'freqtrade.strategy.interface.IStrategy',
advise_indicators=ind_mock,
advise_buy=buy_mock,
advise_sell=sell_mock,
advise_enter=enter_mock,
advise_exit=exit_mock,
)
strategy = DefaultStrategy({})
strategy.analyze_ticker(ohlcv_history, {'pair': 'ETH/BTC'})
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
assert enter_mock.call_count == 2
assert enter_mock.call_count == 2
assert log_has('TA Analysis Launched', caplog)
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
@ -500,8 +504,8 @@ def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
strategy.analyze_ticker(ohlcv_history, {'pair': 'ETH/BTC'})
# No analysis happens as process_only_new_candles is true
assert ind_mock.call_count == 2
assert buy_mock.call_count == 2
assert buy_mock.call_count == 2
assert enter_mock.call_count == 4
assert enter_mock.call_count == 4
assert log_has('TA Analysis Launched', caplog)
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
@ -509,13 +513,13 @@ def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) -> None:
caplog.set_level(logging.DEBUG)
ind_mock = MagicMock(side_effect=lambda x, meta: x)
buy_mock = MagicMock(side_effect=lambda x, meta: x)
sell_mock = MagicMock(side_effect=lambda x, meta: x)
enter_mock = MagicMock(side_effect=lambda x, meta, is_short: x)
exit_mock = MagicMock(side_effect=lambda x, meta, is_short: x)
mocker.patch.multiple(
'freqtrade.strategy.interface.IStrategy',
advise_indicators=ind_mock,
advise_buy=buy_mock,
advise_sell=sell_mock,
advise_enter=enter_mock,
advise_exit=exit_mock,
)
strategy = DefaultStrategy({})
@ -528,8 +532,8 @@ def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) ->
assert 'close' in ret.columns
assert isinstance(ret, DataFrame)
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
assert enter_mock.call_count == 2 # Once for buy, once for short
assert enter_mock.call_count == 2
assert log_has('TA Analysis Launched', caplog)
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
caplog.clear()
@ -537,8 +541,8 @@ def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) ->
ret = strategy._analyze_ticker_internal(ohlcv_history, {'pair': 'ETH/BTC'})
# No analysis happens as process_only_new_candles is true
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
assert enter_mock.call_count == 2
assert enter_mock.call_count == 2
# only skipped analyze adds buy and sell columns, otherwise it's all mocked
assert 'buy' in ret.columns
assert 'sell' in ret.columns
@ -743,10 +747,10 @@ def test_auto_hyperopt_interface(default_conf):
assert strategy.sell_minusdi.value == 0.5
all_params = strategy.detect_all_parameters()
assert isinstance(all_params, dict)
assert len(all_params['buy']) == 2
assert len(all_params['sell']) == 2
# Number of Hyperoptable parameters
assert all_params['count'] == 6
# TODO-lev: Should these be 4,4 and 10?
assert len(all_params['buy']) == 4
assert len(all_params['sell']) == 4
assert all_params['count'] == 10
strategy.__class__.sell_rsi = IntParameter([0, 10], default=5, space='buy')

43
tests/strategy/test_strategy_loading.py
View File

@ -117,12 +117,18 @@ def test_strategy(result, default_conf):
df_indicators = strategy.advise_indicators(result, metadata=metadata)
assert 'adx' in df_indicators
dataframe = strategy.advise_buy(df_indicators, metadata=metadata)
dataframe = strategy.advise_enter(df_indicators, metadata=metadata, is_short=False)
assert 'buy' in dataframe.columns
dataframe = strategy.advise_sell(df_indicators, metadata=metadata)
dataframe = strategy.advise_exit(df_indicators, metadata=metadata, is_short=False)
assert 'sell' in dataframe.columns
dataframe = strategy.advise_enter(df_indicators, metadata=metadata, is_short=True)
assert 'short' in dataframe.columns
dataframe = strategy.advise_exit(df_indicators, metadata=metadata, is_short=True)
assert 'exit_short' in dataframe.columns
def test_strategy_override_minimal_roi(caplog, default_conf):
caplog.set_level(logging.INFO)
@ -218,6 +224,7 @@ def test_strategy_override_process_only_new_candles(caplog, default_conf):
def test_strategy_override_order_types(caplog, default_conf):
caplog.set_level(logging.INFO)
# TODO-lev: Maybe change
order_types = {
'buy': 'market',
'sell': 'limit',
@ -345,7 +352,7 @@ def test_deprecate_populate_indicators(result, default_conf):
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
strategy.advise_buy(indicators, {'pair': 'ETH/BTC'})
strategy.advise_enter(indicators, {'pair': 'ETH/BTC'}, is_short=False) # TODO-lev
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
@ -354,7 +361,7 @@ def test_deprecate_populate_indicators(result, default_conf):
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
strategy.advise_sell(indicators, {'pair': 'ETH_BTC'})
strategy.advise_exit(indicators, {'pair': 'ETH_BTC'}, is_short=False) # TODO-lev
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
@ -374,6 +381,8 @@ def test_call_deprecated_function(result, monkeypatch, default_conf, caplog):
assert strategy._populate_fun_len == 2
assert strategy._buy_fun_len == 2
assert strategy._sell_fun_len == 2
# assert strategy._short_fun_len == 2
# assert strategy._exit_short_fun_len == 2
assert strategy.INTERFACE_VERSION == 1
assert strategy.timeframe == '5m'
assert strategy.ticker_interval == '5m'
@ -382,14 +391,22 @@ def test_call_deprecated_function(result, monkeypatch, default_conf, caplog):
assert isinstance(indicator_df, DataFrame)
assert 'adx' in indicator_df.columns
buydf = strategy.advise_buy(result, metadata=metadata)
buydf = strategy.advise_enter(result, metadata=metadata, is_short=False)
assert isinstance(buydf, DataFrame)
assert 'buy' in buydf.columns
selldf = strategy.advise_sell(result, metadata=metadata)
selldf = strategy.advise_exit(result, metadata=metadata, is_short=False)
assert isinstance(selldf, DataFrame)
assert 'sell' in selldf
# shortdf = strategy.advise_enter(result, metadata=metadata, is_short=True)
# assert isinstance(shortdf, DataFrame)
# assert 'short' in shortdf.columns
# exit_shortdf = strategy.advise_exit(result, metadata=metadata, is_short=True)
# assert isinstance(exit_shortdf, DataFrame)
# assert 'exit_short' in exit_shortdf
assert log_has("DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'.",
caplog)
@ -403,16 +420,26 @@ def test_strategy_interface_versioning(result, monkeypatch, default_conf):
assert strategy._populate_fun_len == 3
assert strategy._buy_fun_len == 3
assert strategy._sell_fun_len == 3
assert strategy._short_fun_len == 3
assert strategy._exit_short_fun_len == 3
assert strategy.INTERFACE_VERSION == 2
indicator_df = strategy.advise_indicators(result, metadata=metadata)
assert isinstance(indicator_df, DataFrame)
assert 'adx' in indicator_df.columns
buydf = strategy.advise_buy(result, metadata=metadata)
buydf = strategy.advise_enter(result, metadata=metadata, is_short=False)
assert isinstance(buydf, DataFrame)
assert 'buy' in buydf.columns
selldf = strategy.advise_sell(result, metadata=metadata)
selldf = strategy.advise_exit(result, metadata=metadata, is_short=False)
assert isinstance(selldf, DataFrame)
assert 'sell' in selldf
shortdf = strategy.advise_enter(result, metadata=metadata, is_short=True)
assert isinstance(shortdf, DataFrame)
assert 'short' in shortdf.columns
exit_shortdf = strategy.advise_exit(result, metadata=metadata, is_short=True)
assert isinstance(exit_shortdf, DataFrame)
assert 'exit_short' in exit_shortdf