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