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Merge pull request #2094 from hroff-1902/hyperopt-roi-stoploss

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Simplify custom hyperopts -- no need to copy ugly methods in every custom implementation
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Matthias
2019-08-10 15:49:52 +02:00
committed by GitHub
parent 29619ccf1c 15cf5ac2d7
commit 74e583a612
5 changed files with 367 additions and 108 deletions
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docs/hyperopt.md
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@@ -18,19 +18,24 @@ Configuring hyperopt is similar to writing your own strategy, and many tasks wil
### Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required.
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 recommended
* 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
Optional, but recommended:
* 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
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
### 1. Install a Custom Hyperopt File
@@ -345,7 +350,7 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
### Understand Hyperopt ROI results
If you are optimizing ROI, you're result will look as follows and include a ROI table.
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
```
Best result:
@@ -376,6 +381,41 @@ minimal_roi = {
}
```
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps) with the values that can vary in the following ranges:
| # | minutes | ROI percentage |
|---|---|---|
| 1 | always 0 | 0.03...0.31 |
| 2 | 10...40 | 0.02...0.11 |
| 3 | 20...100 | 0.01...0.04 |
| 4 | 30...220 | always 0 |
This structure of the ROI table is sufficient in most cases. Override the `roi_space()` method defining the ranges desired if you need components of the ROI tables to vary in other ranges.
Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization in these methods if you need a different structure of the ROI table or other amount of rows (steps) in the ROI tables.
### Understand Hyperopt Stoploss results
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
Stoploss: -0.37996664668703606
```
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.5...-0.02, which is sufficient in most cases.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization.
### Validate backtesting results
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.