To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
*`generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table or the ranges of the values in the ROI table that differ from default)
*`stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
*`trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stopl parameters in the optimization hyperspace that differ from default)
1. Guards are conditions like "never buy if ADX <10",orneverbuyifcurrentpriceisoverEMA10.
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".
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
### Running Hyperopt using methods from a strategy
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
By default, hyperopt prints colorized results -- epochs with positive profit are printed in the green color. This highlighting helps you find epochs that can be interesting for later analysis. Epochs with zero total profit or with negative profits (losses) are printed in the normal color. If you do not need colorization of results (for instance, when you are redirecting hyperopt output to a file) you can switch colorization off by specifying the `--no-color` option in the command line.
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
In order to use this best ROI table found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `minimal_roi` attribute of your custom strategy:
```
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
0: 0.10674,
21: 0.09158,
78: 0.03634,
118: 0
}
```
As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
```
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.27996
```
As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
```
# Trailing stop
# These attributes will be overridden if the config file contains corresponding values.
If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
