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# Hyperopt
This page explains how to tune your strategy by finding the optimal
parameters, process called hyperparameter optimization. The bot uses several
algorithms included in `scikit-optimize` package to accomplish this. The
This page explains how to tune your strategy by finding the optimal
parameters, a process called hyperparameter optimization. The bot uses several
algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
@ -17,18 +17,17 @@ and still take a long time.
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
### Configure your Guards and Triggers
There are two places you need to change in your strategy file to add a
new buy strategy for testing:
There are two places you need to change to add a new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L218-L229)
and the associated methods `indicator_space`, `roi_space`, `stoploss_space`.
There you have two different type of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or never buy if
current price is over EMA10.
1. Guards are conditions like "never buy if ADX < 10", or "never buy if
current price is over EMA10".
2. Triggers are ones that actually trigger buy in specific moment, like
"buy when EMA5 crosses over EMA10" or buy when close price touches lower
bollinger band.
"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
bollinger band".
Hyperoptimization will, for each eval round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like
@ -103,9 +102,13 @@ with different value combinations. It will then use the given historical data an
buys based on the buy signals generated with the above function and based on the results
it will end with telling you which paramter combination produced the best profits.
### Adding New Indicators
If you want to test an indicator that isn't used by the bot currently,
you need to add it to the `populate_indicators()` method in `hyperopt.py`.
The search for best parameters starts with a few random combinations and then uses a
regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in `hyperopt.py`.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
@ -150,8 +153,8 @@ Legal values are:
- space-separated list of any of the above values for example `--spaces roi stoploss`
## Understand the Hyperopts Result
Once Hyperopt is completed you can use the result to creating a new strategy.
Given following result from hyperopt:
Once Hyperopt is completed you can use the result to create a new strategy.
Given the following result from hyperopt:
```
Best result: