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# Hyperopt
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This page explains how to tune your strategy by finding the optimal
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parameters, a process called hyperparameter optimization. The bot uses several
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This page explains how to tune your strategy by finding the optimal
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parameters, a process called hyperparameter optimization. The bot uses several
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algorithms included in the `scikit-optimize` package to accomplish this. The
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search will burn all your CPU cores, make your laptop sound like a fighter jet
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and still take a long time.
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*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
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## Table of Contents
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- [Prepare your Hyperopt](#prepare-hyperopt)
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- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
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- [Solving a Mystery](#solving-a-mystery)
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- [Adding New Indicators](#adding-new-indicators)
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- [Execute Hyperopt](#execute-hyperopt)
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- [Understand the hyperopt result](#understand-the-hyperopt-result)
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!!! Bug
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Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
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## Prepare Hyperopting
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Before we start digging in Hyperopt, we recommend you to take a look at
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Before we start digging in Hyperopt, we recommend you to take a look at
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an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
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### 1. Install a Custom Hyperopt File
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This is very simple. Put your hyperopt file into the folder
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This is very simple. Put your hyperopt file into the folder
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`user_data/hyperopts`.
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Let assume you want a hyperopt file `awesome_hyperopt.py`:
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1. Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
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Let assume you want a hyperopt file `awesome_hyperopt.py`:<br/>
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Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
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### 2. Configure your Guards and Triggers
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There are two places you need to change in your hyperopt file to add a
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There are two places you need to change in your hyperopt file to add a
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new buy hyperopt for testing:
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- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
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- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
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There you have two different types of indicators: 1. `guards` and 2. `triggers`.
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1. Guards are conditions like "never buy if ADX < 10", or never buy if
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1. Guards are conditions like "never buy if ADX < 10", or never buy if
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current price is over EMA10.
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2. Triggers are ones that actually trigger buy in specific moment, like
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"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
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2. Triggers are ones that actually trigger buy in specific moment, like
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"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
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bollinger band".
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Hyperoptimization will, for each eval round, pick one trigger and possibly
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multiple guards. The constructed strategy will be something like
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"*buy exactly when close price touches lower bollinger band, BUT only if
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Hyperoptimization will, for each eval round, pick one trigger and possibly
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multiple guards. The constructed strategy will be something like
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"*buy exactly when close price touches lower bollinger band, BUT only if
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ADX > 10*".
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If you have updated the buy strategy, ie. changed the contents of
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`populate_buy_trend()` method you have to update the `guards` and
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`populate_buy_trend()` method you have to update the `guards` and
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`triggers` hyperopts must use.
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## Solving a Mystery
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Let's say you are curious: should you use MACD crossings or lower Bollinger
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Bands to trigger your buys. And you also wonder should you use RSI or ADX to
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help with those buy decisions. If you decide to use RSI or ADX, which values
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should I use for them? So let's use hyperparameter optimization to solve this
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Let's say you are curious: should you use MACD crossings or lower Bollinger
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Bands to trigger your buys. And you also wonder should you use RSI or ADX to
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help with those buy decisions. If you decide to use RSI or ADX, which values
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should I use for them? So let's use hyperparameter optimization to solve this
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mystery.
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We will start by defining a search space:
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@ -77,8 +69,8 @@ We will start by defining a search space:
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]
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```
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Above definition says: I have five parameters I want you to randomly combine
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to find the best combination. Two of them are integer values (`adx-value`
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Above definition says: I have five parameters I want you to randomly combine
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to find the best combination. Two of them are integer values (`adx-value`
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and `rsi-value`) and I want you test in the range of values 20 to 40.
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Then we have three category variables. First two are either `True` or `False`.
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We use these to either enable or disable the ADX and RSI guards. The last
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@ -117,12 +109,12 @@ with different value combinations. It will then use the given historical data an
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buys based on the buy signals generated with the above function and based on the results
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it will end with telling you which paramter combination produced the best profits.
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The search for best parameters starts with a few random combinations and then uses a
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The search for best parameters starts with a few random combinations and then uses a
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regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
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that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
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The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
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When you want to test an indicator that isn't used by the bot currently, remember to
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When you want to test an indicator that isn't used by the bot currently, remember to
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add it to the `populate_indicators()` method in `hyperopt.py`.
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## Execute Hyperopt
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@ -136,7 +128,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
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python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
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```
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Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
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Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
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(only required for generating sells) and the custom hyperopt used.
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The `-e` flag will set how many evaluations hyperopt will do. We recommend
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@ -161,9 +153,9 @@ python3 ./freqtrade/main.py hyperopt --timerange -200
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### Running Hyperopt with Smaller Search Space
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Use the `--spaces` argument to limit the search space used by hyperopt.
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Letting Hyperopt optimize everything is a huuuuge search space. Often it
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might make more sense to start by just searching for initial buy algorithm.
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Or maybe you just want to optimize your stoploss or roi table for that awesome
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Letting Hyperopt optimize everything is a huuuuge search space. Often it
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might make more sense to start by just searching for initial buy algorithm.
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Or maybe you just want to optimize your stoploss or roi table for that awesome
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new buy strategy you have.
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Legal values are:
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@ -189,19 +181,19 @@ with values:
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You should understand this result like:
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- The buy trigger that worked best was `bb_lower`.
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- You should not use ADX because `adx-enabled: False`)
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- You should not use ADX because `adx-enabled: False`)
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- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
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You have to look inside your strategy file into `buy_strategy_generator()`
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You have to look inside your strategy file into `buy_strategy_generator()`
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method, what those values match to.
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So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
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```
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(dataframe['rsi'] < 29.0)
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```
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Translating your whole hyperopt result as the new buy-signal
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Translating your whole hyperopt result as the new buy-signal
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would then look like:
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```python
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@ -245,7 +237,7 @@ Once the optimized strategy has been implemented into your strategy, you should
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To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
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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).
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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.
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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.
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## Next Step
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