Add hyperopt ROI documentation, add note on methology for hyperopt
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Matthias
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# Hyperopt
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This page explains how to tune your strategy by finding the optimal
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parameters, a process called hyperparameter optimization. The bot uses several
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algorithms included in the `scikit-optimize` package to accomplish this. The
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@ -8,17 +9,20 @@ and still take a long time.
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*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
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## Table of Contents
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- [Prepare your Hyperopt](#prepare-hyperopt)
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- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
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- [Solving a Mystery](#solving-a-mystery)
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- [Adding New Indicators](#adding-new-indicators)
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- [Execute Hyperopt](#execute-hyperopt)
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- [Understand the hyperopts result](#understand-the-backtesting-result)
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- [Understand the hyperopt result](#understand-the-hyperopt-result)
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## Prepare Hyperopting
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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)
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### Configure your Guards and Triggers
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There are two places you need to change to add a new buy strategy for testing:
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- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L231-L264).
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- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L213-L224)
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@ -113,11 +117,12 @@ When you want to test an indicator that isn't used by the bot currently, remembe
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add it to the `populate_indicators()` method in `hyperopt.py`.
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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 combination to find the best parameters
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it will take time you will have the result (more than 30 mins).
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We strongly recommend to use `screen` to prevent any connection loss.
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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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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 -c config.json hyperopt -e 5000
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```
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@ -126,11 +131,13 @@ 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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### 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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you have on-disk, use the `--datadir PATH` option. Default hyperopt will
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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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@ -140,6 +147,7 @@ python3 ./freqtrade/main.py hyperopt --timerange -200
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```
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### Running Hyperopt with Smaller Search Space
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Use the `--spaces` argument to limit the search space used by hyperopt.
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Letting Hyperopt optimize everything is a huuuuge search space. Often it
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might make more sense to start by just searching for initial buy algorithm.
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@ -154,7 +162,8 @@ Legal values are:
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- `stoploss`: search for the best stoploss value
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- space-separated list of any of the above values for example `--spaces roi stoploss`
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## Understand the Hyperopts Result
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## Understand the Hyperopt Result
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Once Hyperopt is completed you can use the result to create a new strategy.
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Given the following result from hyperopt:
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@ -166,22 +175,24 @@ with values:
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```
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You should understand this result like:
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- The buy trigger that worked best was `bb_lower`.
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- You should not use ADX because `adx-enabled: False`)
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- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
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You have to look inside your strategy file into `buy_strategy_generator()`
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method, what those values match to.
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method, what those values match to.
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So for example you had `rsi-value: 29.0` so we would look
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at `rsi`-block, that translates to the following code block:
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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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(dataframe['rsi'] < 29.0)
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```
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Translating your whole hyperopt result as the new buy-signal
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would then look like:
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```
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```python
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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dataframe.loc[
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(
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@ -192,6 +203,37 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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return dataframe
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```
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### Understand Hyperopt ROI results
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If you are optimizing ROI, you're result will look as follows and include a ROI table.
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```
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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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ROI table:
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{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0}
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```
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This would translate to the following ROI table:
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``` python
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minimal_roi = {
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"118": 0,
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"78": 0.0363463,
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"21": 0.0915,
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"0": 0.106
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}
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```
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### Validate backtest result
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Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
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To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
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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)
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## Next Step
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Now you have a perfect bot and want to control it from Telegram. Your
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next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).
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