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Matthias
2024-02-07 07:13:41 +01:00
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docs/strategy-advanced.md
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# Advanced Strategies
This page explains some advanced concepts available for strategies.
If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first.
If you're just getting started, please familiarize yourself with the [Freqtrade basics](bot-basics.md) and methods described in [Strategy Customization](strategy-customization.md) first.
[Freqtrade basics](bot-basics.md) describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs.
The call sequence of the methods described here is covered under [bot execution logic](bot-basics.md#bot-execution-logic). Those docs are also helpful in deciding which method is most suitable for your customisation needs.
!!! Note
All callback methods described below should only be implemented in a strategy if they are actually used.
Callback methods should *only* be implemented if a strategy uses them.
!!! Tip
You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
Start off with a strategy template containing all available callback methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
## Storing information (Non-Persistent)
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
The name of the variable can be chosen at will, but should be prefixed with `custom_` to avoid naming collisions with predefined strategy variables.
```python
class AwesomeStrategy(IStrategy):
@@ -148,7 +148,7 @@ class AwesomeStrategy(IStrategy):
## Enter Tag
When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access you buy signal on `custom_exit`
Then you can access your buy signal on `custom_exit`
```python
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -174,6 +174,12 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
!!! Note
`enter_tag` is limited to 100 characters, remaining data will be truncated.
!!! Warning
There is only one `enter_tag` column, which is used for both long and short trades.
As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
## Exit tag
Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
@@ -289,8 +295,8 @@ for val in self.buy_ema_short.range:
f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
}))
# Append columns to existing dataframe
merged_frame = pd.concat(frames, axis=1)
# Combine all dataframes, and reassign the original dataframe column
dataframe = pd.concat(frames, axis=1)
```
Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.