Improve hyperopt docs

part of #7088
This commit is contained in:
Matthias 2022-07-15 18:55:30 +02:00
parent cdc58058d7
commit b657a4df23
2 changed files with 24 additions and 2 deletions

View File

@ -335,6 +335,7 @@ There are four parameter types each suited for different purposes.
## Optimizing an indicator parameter ## Optimizing an indicator parameter
Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy. Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
``` python ``` python
from pandas import DataFrame from pandas import DataFrame
@ -349,6 +350,9 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy): class MyAwesomeStrategy(IStrategy):
stoploss = -0.05 stoploss = -0.05
timeframe = '15m' timeframe = '15m'
minimal_roi = {
"0": 0.10
},
# Define the parameter spaces # Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5) buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50) buy_ema_long = IntParameter(15, 200, default=50)
@ -383,7 +387,7 @@ class MyAwesomeStrategy(IStrategy):
return dataframe return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = [] conditions = []
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}'] dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
)) ))
@ -404,7 +408,7 @@ Using `self.buy_ema_short.range` will return a range object containing all entri
In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`). In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`). By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
Hyperopt itself will then use the selected value to create the buy and sell signals Hyperopt itself will then use the selected value to create the buy and sell signals.
While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters. While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
@ -868,6 +872,22 @@ To combat these, you have multiple options:
* reduce the number of parallel processes (`-j <n>`) * reduce the number of parallel processes (`-j <n>`)
* Increase the memory of your machine * Increase the memory of your machine
## The objective has been evaluated at this point before.
If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
Example:
``` python
buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
# This is the only parameter in the buy space
```
The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
## Show details of Hyperopt results ## Show details of Hyperopt results
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter. After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter.

View File

@ -224,3 +224,5 @@ for val in self.buy_ema_short.range:
# Append columns to existing dataframe # Append columns to existing dataframe
merged_frame = pd.concat(frames, axis=1) merged_frame = 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.