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@@ -639,6 +639,167 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
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Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
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!!! Note
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Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
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This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
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is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `sell_reason` in
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`confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
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`current_profit < open_relative_stop`.
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### *stoploss_from_absolute()*
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In some situations it may be confusing to deal with stops relative to current rate. Instead, you may define a stoploss level using an absolute price.
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??? Example "Returning a stoploss using absolute price from the custom stoploss function"
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If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
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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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class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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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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current_rate: float, current_profit: float, **kwargs) -> float:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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candle = dataframe.iloc[-1].squeeze()
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return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
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```
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### *@informative()*
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``` python
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def informative(timeframe: str, asset: str = '',
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fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
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ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
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"""
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A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
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define informative indicators.
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Example usage:
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@informative('1h')
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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:param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
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:param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
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current pair.
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:param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
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specified, defaults to:
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* {base}_{quote}_{column}_{timeframe} if asset is specified.
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* {column}_{timeframe} if asset is not specified.
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Format string supports these format variables:
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* {asset} - full name of the asset, for example 'BTC/USDT'.
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* {base} - base currency in lower case, for example 'eth'.
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* {BASE} - same as {base}, except in upper case.
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* {quote} - quote currency in lower case, for example 'usdt'.
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* {QUOTE} - same as {quote}, except in upper case.
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* {column} - name of dataframe column.
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* {timeframe} - timeframe of informative dataframe.
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:param ffill: ffill dataframe after merging informative pair.
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"""
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```
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In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation,
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not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method.
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When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter)
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for more information.
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??? Example "Fast and easy way to define informative pairs"
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Most of the time we do not need power and flexibility offered by `merge_informative_pair()`, therefore we can use a decorator to quickly define informative 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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from freqtrade.strategy import IStrategy, informative
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class AwesomeStrategy(IStrategy):
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# This method is not required.
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# def informative_pairs(self): ...
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# Define informative upper timeframe for each pair. Decorators can be stacked on same
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# method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
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@informative('30m')
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@informative('1h')
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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# Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
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# 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
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# instead of hardcoding actual stake currency. Available in populate_indicators and other
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# methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
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@informative('1h', 'BTC/{stake}')
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def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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# Define BTC/ETH informative pair. You must specify quote currency if it is different from
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# stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
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@informative('1h', 'ETH/BTC')
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def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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# Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
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# column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
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# formatting. Available in populate_indicators and other methods as 'rsi_upper'.
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@informative('1h', 'BTC/{stake}', '{column}')
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def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Strategy timeframe indicators for current pair.
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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# Informative pairs are available in this method.
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dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
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return dataframe
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```
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!!! Note
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Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs
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manually as described [in the DataProvider section](#complete-data-provider-sample).
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!!! Note
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Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
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``` python
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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stake = self.config['stake_currency']
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dataframe.loc[
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(
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(dataframe[f'btc_{stake}_rsi_1h'] < 35)
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&
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(dataframe['volume'] > 0)
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),
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['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
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return dataframe
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```
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Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
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!!! Warning "Duplicate method names"
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Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method)
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will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
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created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
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## Additional data (Wallets)
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Sam Germain