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docs: remove scikit-optimize references from docs

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Matthias
2025-04-23 19:17:43 +02:00
parent 8a9b31eccd
commit 8af9875d45
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docs/advanced-hyperopt.md
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@@ -175,7 +175,6 @@ Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "C
Some research will be necessary to find additional Samplers (from optunahub) for example. Some research will be necessary to find additional Samplers (from optunahub) for example.
!!! Note !!! Note
While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used. While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (`"NSGAIIISampler"` has proven to be the most versatile) without further parameters. If you're unsure about this, best use one of the Defaults (`"NSGAIIISampler"` has proven to be the most versatile) without further parameters.
docs/faq.md
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@@ -219,10 +219,7 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations. First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
The GPU improvements would only apply to pandas-native calculations - or ones written by yourself. The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.
For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn. GPU's are only good at crunching numbers (floating point operations).
Their statement about GPU support is [pretty clear](https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support).
GPU's also are only good at crunching numbers (floating point operations).
For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting). For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting).
As such, GPU's are not too well suited for most parts of hyperopt. As such, GPU's are not too well suited for most parts of hyperopt.