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@ -191,7 +191,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
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### Hyperopt execution logic
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Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators.
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Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified.
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Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
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@ -434,7 +434,8 @@ While this strategy is most likely too simple to provide consistent profit, it s
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??? Hint "Performance tip"
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By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter.
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While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values).
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You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space.
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As this also has Performance implications, hyperopt provides `--analyze-per-epoch` - which will move the execution of `populate_indicators()` to the epoch process. This will implicitly also change the `.range` functionality to only return the actually used value.
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You should however try to use space ranges as small as possible.
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## Optimizing protections
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@ -885,6 +886,7 @@ To combat these, you have multiple options:
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* Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
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* Reduce the number of parallel processes (`-j <n>`).
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* Increase the memory of your machine.
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* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality.
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## The objective has been evaluated at this point before.
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