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Matthias
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@ -213,19 +213,22 @@ to find optimal parameter values for your stategy.
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```
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usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
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[--max_open_trades MAX_OPEN_TRADES]
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[--stake_amount STAKE_AMOUNT] [-r]
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[--customhyperopt NAME] [--eps] [--dmmp] [-e INT]
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[-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
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[--print-all] [-j JOBS]
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[--max_open_trades INT]
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[--stake_amount STAKE_AMOUNT] [-r]
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[--customhyperopt NAME] [--eps] [-e INT]
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[-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
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[--dmmp] [--print-all] [-j JOBS]
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[--random-state INT] [--min-trades INT] [--continue]
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[--hyperopt-loss-class NAME]
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optional arguments:
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-h, --help show this help message and exit
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-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
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Specify ticker interval (1m, 5m, 30m, 1h, 1d).
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Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
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`1d`).
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--timerange TIMERANGE
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Specify what timerange of data to use.
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--max_open_trades MAX_OPEN_TRADES
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--max_open_trades INT
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Specify max_open_trades to use.
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--stake_amount STAKE_AMOUNT
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Specify stake_amount.
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@ -235,18 +238,18 @@ optional arguments:
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run your optimization commands with up-to-date data.
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--customhyperopt NAME
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Specify hyperopt class name (default:
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DefaultHyperOpts).
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`DefaultHyperOpts`).
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--eps, --enable-position-stacking
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Allow buying the same pair multiple times (position
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stacking).
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-e INT, --epochs INT Specify number of epochs (default: 100).
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-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
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Specify which parameters to hyperopt. Space-separated
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list. Default: `all`.
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--dmmp, --disable-max-market-positions
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Disable applying `max_open_trades` during backtest
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(same as setting `max_open_trades` to a very high
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number).
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-e INT, --epochs INT Specify number of epochs (default: 100).
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-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
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Specify which parameters to hyperopt. Space separate
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list. Default: all.
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--print-all Print all results, not only the best ones.
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-j JOBS, --job-workers JOBS
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The number of concurrently running jobs for
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@ -254,6 +257,19 @@ optional arguments:
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(default), all CPUs are used, for -2, all CPUs but one
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are used, etc. If 1 is given, no parallel computing
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code is used at all.
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--random-state INT Set random state to some positive integer for
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reproducible hyperopt results.
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--min-trades INT Set minimal desired number of trades for evaluations
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in the hyperopt optimization path (default: 1).
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--continue Continue hyperopt from previous runs. By default,
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temporary files will be removed and hyperopt will
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start from scratch.
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--hyperopt-loss-class NAME
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Specify the class name of the hyperopt loss function
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class (IHyperOptLoss). Different functions can
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generate completely different results, since the
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target for optimization is different. (default:
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`DefaultHyperOptLoss`).
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```
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## Edge commands
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@ -144,7 +144,7 @@ it will end with telling you which paramter combination produced the best profit
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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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that minimizes the value of the [loss function](#loss-functions).
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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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@ -152,17 +152,19 @@ add it to the `populate_indicators()` method in `hyperopt.py`.
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## Loss-functions
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Each hyperparameter tuning requires a target. This is usually defined as a loss function, which get's closer to 0 for increasing values.
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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.
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FreqTrade uses a default loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
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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.
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A different version this can be used by using the `--hyperopt-loss <Class-name>` argument.
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A different version this can be used by using the `--hyperopt-loss-class <Class-name>` argument.
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This class should be in it's own file within the `user_data/hyperopts/` directory.
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### Using a custom loss function
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Currently, the following loss-functions are builtin: `SharpeHyperOptLoss` and `DefaultHyperOptLoss`.
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To use a custom loss Class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
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For the sample below, you then need to add the command line parameter `--hyperoptloss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
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### Creating and using a custom loss function
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To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
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For the sample below, you then need to add the command line parameter `--hyperopt-loss-class SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
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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)
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@ -209,7 +211,7 @@ Currently, the arguments are:
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* `min_date`: Start date of the hyperopting TimeFrame
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* `min_date`: End date of the hyperopting TimeFrame
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This function needs to return a floating point number (`float`). The smaller that number, the better is the result. The parameters and balancing for this are up to you.
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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.
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!!! Note
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This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
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@ -220,7 +222,7 @@ This function needs to return a floating point number (`float`). The smaller tha
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## Execute Hyperopt
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Once you have updated your hyperopt configuration you can run it.
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Because hyperopt tries a lot of combinations to find the best parameters it will take time you will have the result (more than 30 mins).
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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.
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We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
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@ -235,8 +237,11 @@ running at least several thousand evaluations.
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The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
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!!! Note
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By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
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!!! Warning
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When switching parameters or changing configuration options, the file `user_data/hyperopt_results.pickle` should be removed. It's used to be able to continue interrupted calculations, but does not detect changes to settings or the hyperopt file.
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When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
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### Execute Hyperopt with Different Ticker-Data Source
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@ -246,12 +251,11 @@ use data from directory `user_data/data`.
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### Running Hyperopt with Smaller Testset
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Use the `--timerange` argument to change how much of the testset
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you want to use. The last N ticks/timeframes will be used.
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Example:
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Use the `--timerange` argument to change how much of the testset you want to use.
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To use one month of data, use the following parameter:
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```bash
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freqtrade hyperopt --timerange -200
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freqtrade hyperopt --timerange 20180401-20180501
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```
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### Running Hyperopt with Smaller Search Space
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@ -319,7 +323,7 @@ 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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``` python
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(dataframe['rsi'] < 29.0)
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```
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@ -232,7 +232,8 @@ AVAILABLE_CLI_OPTIONS = {
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),
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"hyperopt_loss": Arg(
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'--hyperopt-loss-class',
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help='Specify hyperopt loss class name. Can generate completely different results, '
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help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
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'Different functions can generate completely different results, '
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'since the target for optimization is different. (default: `%(default)s`).',
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metavar='NAME',
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default=constants.DEFAULT_HYPEROPT_LOSS,
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