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Merge branch 'feat/short' into pr/samgermain/5378

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Matthias
2021-09-17 19:24:47 +02:00
parent b752516f65 d680fdf33a
commit 4d558879e9
99 changed files with 1075 additions and 2863 deletions
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25
freqtrade/strategy/interface.py
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@@ -168,7 +168,7 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
Check buy 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,
It is called whenever a limit entry 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.
@@ -178,7 +178,7 @@ 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 buy/short-order is cancelled.
:return bool: When True is returned, then the entry order is cancelled.
"""
return False
@@ -213,7 +213,7 @@ 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/short order.
Called right before placing a entry order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
@@ -237,7 +237,7 @@ 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/exit_short order.
Called right before placing a regular exit order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
@@ -412,7 +412,7 @@ 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/short to happen on an old signal.
of 2 seconds for an entry order 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.
@@ -428,7 +428,7 @@ 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/short signal to it
add several TA indicators and entry order 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
@@ -547,7 +547,9 @@ class IStrategy(ABC, HyperStrategyMixin):
dataframe: DataFrame,
) -> Tuple[Optional[DataFrame], Optional[arrow.Arrow]]:
"""
Get the latest candle. Used only during real mode
Calculates current signal based based on the entry order or exit order
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.
@@ -672,7 +674,7 @@ class IStrategy(ABC, HyperStrategyMixin):
low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluates if one of the conditions required to trigger a sell/exit_short
This function evaluates if one of the conditions required to trigger an exit order
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
@@ -860,10 +862,11 @@ class IStrategy(ABC, HyperStrategyMixin):
Does not run advise_buy or advise_sell!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
Has positive effects on memory usage for whatever reason - also when
using only one strategy.
"""
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair})
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -884,7 +887,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy/short signal for the given dataframe
Based on TA indicators, populates the entry order signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the
@@ -907,7 +910,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell/exit_short signal for the given dataframe
Based on TA indicators, populates the exit order signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the