Merge pull request #1300 from freqtrade/doc/hyperopt_roi
Add hyperopt ROI documentation, add note on methology for hyperopt
This commit is contained in:
Samuel Husso
GitHub
commit
d733657db5
@ -1,4 +1,5 @@
|
||||
# Hyperopt
|
||||
|
||||
This page explains how to tune your strategy by finding the optimal
|
||||
parameters, a process called hyperparameter optimization. The bot uses several
|
||||
algorithms included in the `scikit-optimize` package to accomplish this. The
|
||||
@ -8,17 +9,20 @@ and still take a long time.
|
||||
*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Prepare your Hyperopt](#prepare-hyperopt)
|
||||
- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
|
||||
- [Solving a Mystery](#solving-a-mystery)
|
||||
- [Adding New Indicators](#adding-new-indicators)
|
||||
- [Execute Hyperopt](#execute-hyperopt)
|
||||
- [Understand the hyperopts result](#understand-the-backtesting-result)
|
||||
- [Understand the hyperopt result](#understand-the-hyperopt-result)
|
||||
|
||||
## Prepare Hyperopting
|
||||
|
||||
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
|
||||
|
||||
### Configure your Guards and Triggers
|
||||
|
||||
There are two places you need to change to add a new buy strategy for testing:
|
||||
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L231-L264).
|
||||
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L213-L224)
|
||||
@ -113,11 +117,12 @@ When you want to test an indicator that isn't used by the bot currently, remembe
|
||||
add it to the `populate_indicators()` method in `hyperopt.py`.
|
||||
|
||||
## Execute Hyperopt
|
||||
Once you have updated your hyperopt configuration you can run it.
|
||||
Because hyperopt tries a lot of combination to find the best parameters
|
||||
it will take time you will have the result (more than 30 mins).
|
||||
|
||||
We strongly recommend to use `screen` to prevent any connection loss.
|
||||
Once you have updated your hyperopt configuration you can run it.
|
||||
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).
|
||||
|
||||
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
|
||||
|
||||
```bash
|
||||
python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
|
||||
```
|
||||
@ -126,11 +131,13 @@ The `-e` flag will set how many evaluations hyperopt will do. We recommend
|
||||
running at least several thousand evaluations.
|
||||
|
||||
### Execute Hyperopt with Different Ticker-Data Source
|
||||
|
||||
If you would like to hyperopt parameters using an alternate ticker data that
|
||||
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
|
||||
use data from directory `user_data/data`.
|
||||
|
||||
### Running Hyperopt with Smaller Testset
|
||||
|
||||
Use the `--timerange` argument to change how much of the testset
|
||||
you want to use. The last N ticks/timeframes will be used.
|
||||
Example:
|
||||
@ -140,6 +147,7 @@ python3 ./freqtrade/main.py hyperopt --timerange -200
|
||||
```
|
||||
|
||||
### Running Hyperopt with Smaller Search Space
|
||||
|
||||
Use the `--spaces` argument to limit the search space used by hyperopt.
|
||||
Letting Hyperopt optimize everything is a huuuuge search space. Often it
|
||||
might make more sense to start by just searching for initial buy algorithm.
|
||||
@ -154,7 +162,8 @@ Legal values are:
|
||||
- `stoploss`: search for the best stoploss value
|
||||
- space-separated list of any of the above values for example `--spaces roi stoploss`
|
||||
|
||||
## Understand the Hyperopts Result
|
||||
## Understand the Hyperopt Result
|
||||
|
||||
Once Hyperopt is completed you can use the result to create a new strategy.
|
||||
Given the following result from hyperopt:
|
||||
|
||||
@ -166,6 +175,7 @@ with values:
|
||||
```
|
||||
|
||||
You should understand this result like:
|
||||
|
||||
- The buy trigger that worked best was `bb_lower`.
|
||||
- You should not use ADX because `adx-enabled: False`)
|
||||
- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
|
||||
@ -173,15 +183,16 @@ You should understand this result like:
|
||||
You have to look inside your strategy file into `buy_strategy_generator()`
|
||||
method, what those values match to.
|
||||
|
||||
So for example you had `rsi-value: 29.0` so we would look
|
||||
at `rsi`-block, that translates to the following code block:
|
||||
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
|
||||
|
||||
```
|
||||
(dataframe['rsi'] < 29.0)
|
||||
```
|
||||
|
||||
Translating your whole hyperopt result as the new buy-signal
|
||||
would then look like:
|
||||
```
|
||||
|
||||
```python
|
||||
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
@ -192,6 +203,39 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
return dataframe
|
||||
```
|
||||
|
||||
### Understand Hyperopt ROI results
|
||||
|
||||
If you are optimizing ROI, you're result will look as follows and include a ROI table.
|
||||
|
||||
```
|
||||
Best result:
|
||||
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
|
||||
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}
|
||||
ROI table:
|
||||
{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0}
|
||||
```
|
||||
|
||||
This would translate to the following ROI table:
|
||||
|
||||
``` python
|
||||
minimal_roi = {
|
||||
"118": 0,
|
||||
"78": 0.0363463,
|
||||
"21": 0.0915,
|
||||
"0": 0.106
|
||||
}
|
||||
```
|
||||
|
||||
### Validate backtest result
|
||||
|
||||
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
|
||||
To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
|
||||
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).
|
||||
|
||||
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.
|
||||
|
||||
## Next Step
|
||||
|
||||
Now you have a perfect bot and want to control it from Telegram. Your
|
||||
next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).
|
||||
|
||||
Loading…
Reference in New Issue
Block a user