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improve hyperopt docs

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Matthias 2019-01-06 14:47:53 +01:00
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docs/hyperopt.md
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@ -16,6 +16,22 @@ an example hyperopt file located into [user_data/hyperopts/](https://github.com/
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. 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.
### Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required.
* fill `populate_indicators` - probably a copy from your strategy
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimzation
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimzation
* fill `roi_space` - for ROI optimization
* fill `generate_roi_table` - for ROI optimization (if you need more than 3 entries)
* fill `stoploss_space` - stoploss optimization
* Optional but reccomended
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
### 1. Install a Custom Hyperopt File ### 1. Install a Custom Hyperopt File
Put your hyperopt file into the folder`user_data/hyperopts`. Put your hyperopt file into the folder`user_data/hyperopts`.
@ -49,11 +65,11 @@ If you have updated the buy strategy, ie. changed the contents of
Similar to the buy-signal above, sell-signals can also be optimized. Similar to the buy-signal above, sell-signals can also be optimized.
Place the corresponding settings into the following methods Place the corresponding settings into the following methods
- Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing. * Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
- Inside `populate_sell_trend()` - applying the parameters. * Inside `populate_sell_trend()` - applying the parameters.
The configuration and rules are the same than for buy signals. The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with sell-. To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
## Solving a Mystery ## Solving a Mystery
@ -65,7 +81,7 @@ mystery.
We will start by defining a search space: We will start by defining a search space:
``` ```python
def indicator_space() -> List[Dimension]: def indicator_space() -> List[Dimension]:
""" """
Define your Hyperopt space for searching strategy parameters Define your Hyperopt space for searching strategy parameters
@ -88,7 +104,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
So let's write the buy strategy using these values: So let's write the buy strategy using these values:
``` ``` python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame: def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = [] conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
@ -136,7 +152,7 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss. We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash ```bash
python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000 python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000 --spaces all
``` ```
Use `<hyperoptname>` as the name of the custom hyperopt used. Use `<hyperoptname>` as the name of the custom hyperopt used.
@ -144,6 +160,11 @@ Use `<hyperoptname>` as the name of 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
running at least several thousand evaluations. running at least several thousand evaluations.
The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Warning
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.
### Execute Hyperopt with Different Ticker-Data Source ### Execute Hyperopt with Different Ticker-Data Source
If you would like to hyperopt parameters using an alternate ticker data that If you would like to hyperopt parameters using an alternate ticker data that
@ -186,7 +207,11 @@ Given the following result from hyperopt:
Best result: Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values: with values:
{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower'} { 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
``` ```
You should understand this result like: You should understand this result like:
@ -226,9 +251,24 @@ If you are optimizing ROI, you're result will look as follows and include a ROI
Best result: Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values: with values:
{'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} { '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
}
ROI table: ROI table:
{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0} { 0: 0.10674752302642071,
21: 0.09158372701087236,
78: 0.03634636907306948,
118: 0}
``` ```
This would translate to the following ROI table: This would translate to the following ROI table: