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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, a process called hyperparameter optimization. The bot uses several parameters, a process called hyperparameter optimization. The bot uses several
algorithms included in the `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.
*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133) !!! Bug
Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
## Table of Contents
- [Prepare your Hyperopt](#prepare-hyperopt)
- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
- [Solving a Mystery](#solving-a-mystery)
- [Adding New Indicators](#adding-new-indicators)
- [Execute Hyperopt](#execute-hyperopt)
- [Understand the hyperopt result](#understand-the-hyperopt-result)
## Prepare Hyperopting ## Prepare Hyperopting
Before we start digging in Hyperopt, we recommend you to take a look at Before we start digging in Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py) an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
### 1. Install a Custom Hyperopt File ### 1. Install a Custom Hyperopt File
This is very simple. Put your hyperopt file into the folder This is very simple. Put your hyperopt file into the folder
`user_data/hyperopts`. `user_data/hyperopts`.
Let assume you want a hyperopt file `awesome_hyperopt.py`: Let assume you want a hyperopt file `awesome_hyperopt.py`:<br/>
1. Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py` Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
### 2. Configure your Guards and Triggers ### 2. Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a There are two places you need to change in your hyperopt file to add a
new buy hyperopt for testing: new buy hyperopt for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251). - Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223). - Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
There you have two different types of indicators: 1. `guards` and 2. `triggers`. There you have two different types 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
"*buy exactly when close price touches lower bollinger band, BUT only if "*buy exactly when close price touches lower bollinger band, BUT only if
ADX > 10*". ADX > 10*".
If you have updated the buy strategy, ie. changed the contents of If you have updated the buy strategy, ie. changed the contents of
`populate_buy_trend()` method you have to update the `guards` and `populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must use. `triggers` hyperopts must use.
## Solving a Mystery ## Solving a Mystery
Let's say you are curious: should you use MACD crossings or lower Bollinger Let's say you are curious: should you use MACD crossings or lower Bollinger
Bands to trigger your buys. And you also wonder should you use RSI or ADX to Bands to trigger your buys. And you also wonder should you use RSI or ADX to
help with those buy decisions. If you decide to use RSI or ADX, which values help with those buy decisions. If you decide to use RSI or ADX, which values
should I use for them? So let's use hyperparameter optimization to solve this should I use for them? So let's use hyperparameter optimization to solve this
mystery. mystery.
We will start by defining a search space: We will start by defining a search space:
@ -77,8 +69,8 @@ We will start by defining a search space:
] ]
``` ```
Above definition says: I have five parameters I want you to randomly combine Above definition says: I have five parameters I want you to randomly combine
to find the best combination. Two of them are integer values (`adx-value` to find the best combination. Two of them are integer values (`adx-value`
and `rsi-value`) and I want you test in the range of values 20 to 40. and `rsi-value`) and I want you test in the range of values 20 to 40.
Then we have three category variables. First two are either `True` or `False`. Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards. The last We use these to either enable or disable the ADX and RSI guards. The last
@ -117,12 +109,12 @@ 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.
The search for best parameters starts with a few random combinations and then uses a 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 regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
that minimizes the value of the objective function `calculate_loss` 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. 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 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`. add it to the `populate_indicators()` method in `hyperopt.py`.
## Execute Hyperopt ## Execute Hyperopt
@ -136,7 +128,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000 python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
``` ```
Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
(only required for generating sells) and the custom hyperopt used. (only required for generating sells) and the custom hyperopt used.
The `-e` flag will set how many evaluations hyperopt will do. We recommend The `-e` flag will set how many evaluations hyperopt will do. We recommend
@ -161,9 +153,9 @@ python3 ./freqtrade/main.py hyperopt --timerange -200
### Running Hyperopt with Smaller Search Space ### Running Hyperopt with Smaller Search Space
Use the `--spaces` argument to limit the search space used by hyperopt. Use the `--spaces` argument to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm. might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome Or maybe you just want to optimize your stoploss or roi table for that awesome
new buy strategy you have. new buy strategy you have.
Legal values are: Legal values are:
@ -189,19 +181,19 @@ with values:
You should understand this result like: You should understand this result like:
- The buy trigger that worked best was `bb_lower`. - The buy trigger that worked best was `bb_lower`.
- You should not use ADX because `adx-enabled: False`) - You should not use ADX because `adx-enabled: False`)
- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`) - You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
You have to look inside your strategy file into `buy_strategy_generator()` You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to. method, what those values match to.
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block: So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
``` ```
(dataframe['rsi'] < 29.0) (dataframe['rsi'] < 29.0)
``` ```
Translating your whole hyperopt result as the new buy-signal Translating your whole hyperopt result as the new buy-signal
would then look like: would then look like:
```python ```python
@ -245,7 +237,7 @@ Once the optimized strategy has been implemented into your strategy, you should
To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`. To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283). This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283).
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality. Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
## Next Step ## Next Step