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docs: remove callback examples imports
This commit is contained in:
Matthias
parent
9408e858cd
commit
b1ae09c003
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@ -198,9 +198,7 @@ Of course, many more things are possible, and all examples can be combined at wi
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To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
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``` python
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# additional imports required
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from datetime import datetime
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -208,7 +206,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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"""
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@ -238,8 +236,7 @@ class AwesomeStrategy(IStrategy):
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Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
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``` python
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -247,7 +244,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -265,8 +262,7 @@ Use the initial stoploss for the first 60 minutes, after this change to 10% trai
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If an additional order fills, set stoploss to -10% below the new `open_rate` ([Averaged across all entries](#position-adjust-calculations)).
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``` python
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -274,7 +270,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -295,8 +291,7 @@ Use a different stoploss depending on the pair.
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In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
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``` python
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from datetime import datetime
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -304,7 +299,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -322,8 +317,7 @@ Use the initial stoploss until the profit is above 4%, then use a trailing stopl
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Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
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``` python
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -331,7 +325,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -355,9 +349,7 @@ Instead of continuously trailing behind the current price, this example sets fix
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* Once profit is > 40% - set stoploss to 25% above open price.
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``` python
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from datetime import datetime
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from freqtrade.persistence import Trade
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from freqtrade.strategy import stoploss_from_open
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -365,7 +357,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -386,6 +378,8 @@ class AwesomeStrategy(IStrategy):
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Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
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``` python
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# Default imports
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class AwesomeStrategy(IStrategy):
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@ -394,7 +388,7 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -431,10 +425,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
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``` python
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from datetime import datetime
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from freqtrade.persistence import Trade
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from freqtrade.strategy import IStrategy, stoploss_from_open
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -442,7 +433,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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@ -475,10 +466,7 @@ The helper function `stoploss_from_absolute()` can be used to convert from an ab
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For futures, we need to adjust the direction (up or down), as well as adjust for leverage, since the [`custom_stoploss`](strategy-callbacks.md#custom-stoploss) callback returns the ["risk for this trade"](stoploss.md#stoploss-and-leverage) - not the relative price movement.
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``` python
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from datetime import datetime
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from freqtrade.persistence import Trade
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from freqtrade.strategy import IStrategy, stoploss_from_absolute, timeframe_to_prev_date
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -488,7 +476,7 @@ The helper function `stoploss_from_absolute()` can be used to convert from an ab
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dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
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return dataframe
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> Optional[float]:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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@ -502,7 +490,6 @@ The helper function `stoploss_from_absolute()` can be used to convert from an ab
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```
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---
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## Custom order price rules
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@ -522,19 +509,18 @@ Each of these methods are called right before placing an order on the exchange.
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### Custom order entry and exit price example
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``` python
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from datetime import datetime, timedelta, timezone
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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# ... populate_* methods
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def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float,
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def custom_entry_price(self, pair: str, trade: Optional[Trade], current_time: datetime, proposed_rate: float,
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entry_tag: Optional[str], side: str, **kwargs) -> float:
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dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
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timeframe=self.timeframe)
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new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
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new_entryprice = dataframe["bollinger_10_lowerband"].iat[-1]
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return new_entryprice
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@ -544,7 +530,7 @@ class AwesomeStrategy(IStrategy):
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dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
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timeframe=self.timeframe)
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new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
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new_exitprice = dataframe["bollinger_10_upperband"].iat[-1]
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return new_exitprice
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@ -581,8 +567,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
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The function must return either `True` (cancel order) or `False` (keep order alive).
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``` python
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade, Order
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -594,7 +579,7 @@ class AwesomeStrategy(IStrategy):
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'exit': 60 * 25
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}
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def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
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def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
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return True
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@ -605,7 +590,7 @@ class AwesomeStrategy(IStrategy):
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return False
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def check_exit_timeout(self, pair: str, trade: Trade, order: 'Order',
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def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
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return True
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@ -622,8 +607,7 @@ class AwesomeStrategy(IStrategy):
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### Custom order timeout example (using additional data)
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``` python
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from datetime import datetime
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from freqtrade.persistence import Trade, Order
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -631,24 +615,24 @@ class AwesomeStrategy(IStrategy):
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# Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
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unfilledtimeout = {
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'entry': 60 * 25,
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'exit': 60 * 25
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"entry": 60 * 25,
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"exit": 60 * 25
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}
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def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
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def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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ob = self.dp.orderbook(pair, 1)
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current_price = ob['bids'][0][0]
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current_price = ob["bids"][0][0]
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# Cancel buy order if price is more than 2% above the order.
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if current_price > order.price * 1.02:
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return True
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return False
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def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
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def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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ob = self.dp.orderbook(pair, 1)
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current_price = ob['asks'][0][0]
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current_price = ob["asks"][0][0]
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# Cancel sell order if price is more than 2% below the order.
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if current_price < order.price * 0.98:
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return True
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@ -667,6 +651,8 @@ This are the last methods that will be called before an order is placed.
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`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
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``` python
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# Default imports
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class AwesomeStrategy(IStrategy):
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# ... populate_* methods
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@ -713,8 +699,7 @@ The exit-reasons (if applicable) will be in the following sequence:
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* `trailing_stop_loss`
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``` python
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from freqtrade.persistence import Trade
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -747,7 +732,7 @@ class AwesomeStrategy(IStrategy):
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:return bool: When True, then the exit-order is placed on the exchange.
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False aborts the process
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"""
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if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
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if exit_reason == "force_exit" and trade.calc_profit_ratio(rate) < 0:
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# Reject force-sells with negative profit
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# This is just a sample, please adjust to your needs
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# (this does not necessarily make sense, assuming you know when you're force-selling)
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@ -813,6 +798,7 @@ Returning a value more than the above (so remaining stake_amount would become ne
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Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
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``` python
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# Default imports
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from freqtrade.persistence import Trade
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from typing import Optional, Tuple, Union
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@ -953,8 +939,7 @@ If the cancellation of the original order fails, then the order will not be repl
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Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
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```python
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from freqtrade.persistence import Trade
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from datetime import timedelta, datetime
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# Default imports
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class AwesomeStrategy(IStrategy):
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@ -985,7 +970,12 @@ class AwesomeStrategy(IStrategy):
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"""
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# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
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if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
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if (
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pair == "BTC/USDT"
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and entry_tag == "long_sma200"
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and side == "long"
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and (current_time - timedelta(minutes=10)) > trade.open_date_utc
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):
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# just cancel the order if it has been filled more than half of the amount
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if order.filled > order.remaining:
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return None
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@ -993,7 +983,7 @@ class AwesomeStrategy(IStrategy):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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current_candle = dataframe.iloc[-1].squeeze()
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# desired price
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return current_candle['sma_200']
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return current_candle["sma_200"]
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# default: maintain existing order
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return current_order_rate
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```
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@ -1008,6 +998,8 @@ Values that are above `max_leverage` will be adjusted to `max_leverage`.
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For markets / exchanges that don't support leverage, this method is ignored.
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``` python
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# Default imports
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class AwesomeStrategy(IStrategy):
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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@ -1038,6 +1030,8 @@ It will be called independent of the order type (entry, exit, stoploss or positi
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Assuming that your strategy needs to store the high value of the candle at trade entry, this is possible with this callback as the following example show.
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``` python
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# Default imports
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class AwesomeStrategy(IStrategy):
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def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
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"""
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