improve hyperopt docs
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Matthias
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@ -16,6 +16,22 @@ an example hyperopt file located into [user_data/hyperopts/](https://github.com/
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Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
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### Checklist on all tasks / possibilities in hyperopt
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Depending on the space you want to optimize, only some of the below are required.
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* fill `populate_indicators` - probably a copy from your strategy
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* fill `buy_strategy_generator` - for buy signal optimization
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* fill `indicator_space` - for buy signal optimzation
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* fill `sell_strategy_generator` - for sell signal optimization
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* fill `sell_indicator_space` - for sell signal optimzation
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* fill `roi_space` - for ROI optimization
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* fill `generate_roi_table` - for ROI optimization (if you need more than 3 entries)
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* fill `stoploss_space` - stoploss optimization
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* Optional but reccomended
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* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
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* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
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### 1. Install a Custom Hyperopt File
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Put your hyperopt file into the folder`user_data/hyperopts`.
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@ -49,11 +65,11 @@ If you have updated the buy strategy, ie. changed the contents of
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Similar to the buy-signal above, sell-signals can also be optimized.
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Place the corresponding settings into the following methods
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- Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
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- Inside `populate_sell_trend()` - applying the parameters.
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* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
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* Inside `populate_sell_trend()` - applying the parameters.
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The configuration and rules are the same than for buy signals.
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To avoid naming collisions in the search-space, please prefix all sell-spaces with sell-.
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To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
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## Solving a Mystery
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@ -65,7 +81,7 @@ mystery.
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We will start by defining a search space:
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```
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```python
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching strategy parameters
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@ -88,7 +104,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
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So let's write the buy strategy using these values:
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```
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``` python
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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@ -136,7 +152,7 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
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We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
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```bash
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python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
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python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000 --spaces all
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```
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Use `<hyperoptname>` as the name of the custom hyperopt used.
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@ -144,6 +160,11 @@ Use `<hyperoptname>` as the name of 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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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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!!! 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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### Execute Hyperopt with Different Ticker-Data Source
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If you would like to hyperopt parameters using an alternate ticker data that
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@ -186,7 +207,11 @@ Given the following result from hyperopt:
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Best result:
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135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
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with values:
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{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower'}
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{ 'adx-value': 44,
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'rsi-value': 29,
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'adx-enabled': False,
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'rsi-enabled': True,
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'trigger': 'bb_lower'}
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```
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You should understand this result like:
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@ -226,9 +251,24 @@ If you are optimizing ROI, you're result will look as follows and include a ROI
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Best result:
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135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
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with values:
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{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower', 'roi_t1': 40, 'roi_t2': 57, 'roi_t3': 21, 'roi_p1': 0.03634636907306948, 'roi_p2': 0.055237357937802885, 'roi_p3': 0.015163796015548354, 'stoploss': -0.37996664668703606}
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{ 'adx-value': 44,
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'rsi-value': 29,
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'adx-enabled': false,
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'rsi-enabled': True,
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'trigger': 'bb_lower',
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'roi_t1': 40,
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'roi_t2': 57,
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'roi_t3': 21,
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'roi_p1': 0.03634636907306948,
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'roi_p2': 0.055237357937802885,
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'roi_p3': 0.015163796015548354,
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'stoploss': -0.37996664668703606
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}
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ROI table:
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{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0}
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{ 0: 0.10674752302642071,
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21: 0.09158372701087236,
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78: 0.03634636907306948,
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118: 0}
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```
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This would translate to the following ROI table:
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