385 lines
16 KiB
Markdown
385 lines
16 KiB
Markdown
# 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
|
|
search will burn all your CPU cores, make your laptop sound like a fighter jet
|
|
and still take a long time.
|
|
|
|
!!! Bug
|
|
Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
|
|
|
|
## Prepare Hyperopting
|
|
|
|
Before we start digging into Hyperopt, we recommend you to take a look at
|
|
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py)
|
|
|
|
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 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
|
|
* Add custom loss-function `hyperopt_loss_custom` (Details below)
|
|
|
|
### 1. Install a Custom Hyperopt File
|
|
|
|
Put your hyperopt file into the directory `user_data/hyperopts`.
|
|
|
|
Let assume you want a hyperopt file `awesome_hyperopt.py`:
|
|
Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
|
|
|
|
### 2. Configure your Guards and Triggers
|
|
|
|
There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
|
|
|
|
- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
|
|
- Inside `populate_buy_trend()` - applying the parameters.
|
|
|
|
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
|
|
|
|
1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
|
|
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
|
|
|
|
Hyperoptimization will, for each eval round, pick one trigger and possibly
|
|
multiple guards. The constructed strategy will be something like
|
|
"*buy exactly when close price touches lower bollinger band, BUT only if
|
|
ADX > 10*".
|
|
|
|
If you have updated the buy strategy, ie. changed the contents of
|
|
`populate_buy_trend()` method you have to update the `guards` and
|
|
`triggers` hyperopts must use.
|
|
|
|
#### Sell optimization
|
|
|
|
Similar to the buy-signal above, sell-signals can also be optimized.
|
|
Place the corresponding settings into the following methods
|
|
|
|
* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
|
|
* Inside `populate_sell_trend()` - applying the parameters.
|
|
|
|
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-`.
|
|
|
|
#### Using ticker-interval as part of the Strategy
|
|
|
|
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
|
|
In the case of the linked sample-value this would be `SampleHyperOpts.ticker_interval`.
|
|
|
|
## Solving a Mystery
|
|
|
|
Let's say you are curious: should you use MACD crossings or lower Bollinger
|
|
Bands to trigger your buys. And you also wonder should you use RSI or ADX to
|
|
help with those buy decisions. If you decide to use RSI or ADX, which values
|
|
should I use for them? So let's use hyperparameter optimization to solve this
|
|
mystery.
|
|
|
|
We will start by defining a search space:
|
|
|
|
```python
|
|
def indicator_space() -> List[Dimension]:
|
|
"""
|
|
Define your Hyperopt space for searching strategy parameters
|
|
"""
|
|
return [
|
|
Integer(20, 40, name='adx-value'),
|
|
Integer(20, 40, name='rsi-value'),
|
|
Categorical([True, False], name='adx-enabled'),
|
|
Categorical([True, False], name='rsi-enabled'),
|
|
Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
|
|
]
|
|
```
|
|
|
|
Above definition says: I have five parameters I want you to randomly combine
|
|
to find the best combination. Two of them are integer values (`adx-value`
|
|
and `rsi-value`) and I want you test in the range of values 20 to 40.
|
|
Then we have three category variables. First two are either `True` or `False`.
|
|
We use these to either enable or disable the ADX and RSI guards. The last
|
|
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:
|
|
|
|
``` python
|
|
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
|
conditions = []
|
|
# GUARDS AND TRENDS
|
|
if 'adx-enabled' in params and params['adx-enabled']:
|
|
conditions.append(dataframe['adx'] > params['adx-value'])
|
|
if 'rsi-enabled' in params and params['rsi-enabled']:
|
|
conditions.append(dataframe['rsi'] < params['rsi-value'])
|
|
|
|
# TRIGGERS
|
|
if 'trigger' in params:
|
|
if params['trigger'] == 'bb_lower':
|
|
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
|
|
if params['trigger'] == 'macd_cross_signal':
|
|
conditions.append(qtpylib.crossed_above(
|
|
dataframe['macd'], dataframe['macdsignal']
|
|
))
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
reduce(lambda x, y: x & y, conditions),
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
return populate_buy_trend
|
|
```
|
|
|
|
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
|
|
with different value combinations. It will then use the given historical data and make
|
|
buys based on the buy signals generated with the above function and based on the results
|
|
it will end with telling you which paramter combination produced the best profits.
|
|
|
|
The search for best parameters starts with a few random combinations and then uses a
|
|
regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
|
|
that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
|
|
|
|
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
|
|
When you want to test an indicator that isn't used by the bot currently, remember to
|
|
add it to the `populate_indicators()` method in `hyperopt.py`.
|
|
|
|
## Loss-functions
|
|
|
|
Each hyperparameter tuning requires a target. This is usually defined as a loss function, which get's closer to 0 for increasing values.
|
|
|
|
FreqTrade uses a default loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
|
|
|
|
A different version this can be used by using the `--hyperopt-loss <Class-name>` argument.
|
|
This class should be in it's own file within the `user_data/hyperopts/` directory.
|
|
|
|
### Using a custom loss function
|
|
|
|
To use a custom loss Class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt class.
|
|
For the sample below, you then need to add the command line parameter `--hyperoptloss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
|
|
|
|
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation.
|
|
|
|
``` python
|
|
TARGET_TRADES = 600
|
|
EXPECTED_MAX_PROFIT = 3.0
|
|
MAX_ACCEPTED_TRADE_DURATION = 300
|
|
|
|
class SuperDuperHyperOptLoss(IHyperOptLoss):
|
|
"""
|
|
Defines the default loss function for hyperopt
|
|
"""
|
|
|
|
@staticmethod
|
|
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
|
min_date: datetime, max_date: datetime,
|
|
*args, **kwargs) -> float:
|
|
"""
|
|
Objective function, returns smaller number for better results
|
|
This is the legacy algorithm (used until now in freqtrade).
|
|
Weights are distributed as follows:
|
|
* 0.4 to trade duration
|
|
* 0.25: Avoiding trade loss
|
|
"""
|
|
total_profit = results.profit_percent.sum()
|
|
trade_duration = results.trade_duration.mean()
|
|
|
|
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
|
|
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
|
|
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
|
|
result = trade_loss + profit_loss + duration_loss
|
|
return result
|
|
```
|
|
|
|
Currently, the arguments are:
|
|
|
|
* `results`: DataFrame containing the result
|
|
* `trade_count`: Amount of trades (identical to `len(results)`)
|
|
* `min_date`: Start date of the hyperopting TimeFrame
|
|
* `min_date`: End date of the hyperopting TimeFrame
|
|
|
|
This function needs to return a floating point number (`float`). The smaller that number, the better is the result. The parameters and balancing for this are up to you.
|
|
|
|
!!! Note
|
|
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
|
|
|
|
!!! Note
|
|
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
|
|
|
|
## Execute Hyperopt
|
|
|
|
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
|
|
freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
|
|
```
|
|
|
|
Use `<hyperoptname>` as the name of the custom hyperopt used.
|
|
|
|
The `-e` flag will set how many evaluations hyperopt will do. We recommend
|
|
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
|
|
|
|
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:
|
|
|
|
```bash
|
|
freqtrade 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.
|
|
Or maybe you just want to optimize your stoploss or roi table for that awesome
|
|
new buy strategy you have.
|
|
|
|
Legal values are:
|
|
|
|
* `all`: optimize everything
|
|
* `buy`: just search for a new buy strategy
|
|
* `sell`: just search for a new sell strategy
|
|
* `roi`: just optimize the minimal profit table for your strategy
|
|
* `stoploss`: search for the best stoploss value
|
|
* space-separated list of any of the above values for example `--spaces roi stoploss`
|
|
|
|
### Position stacking and disabling max market positions
|
|
|
|
In some situations, you may need to run Hyperopt (and Backtesting) with the
|
|
`--eps`/`--enable-position-staking` and `--dmmp`/`--disable-max-market-positions` arguments.
|
|
|
|
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
|
|
open trade is allowed for every traded pair. The total number of trades open for all pairs
|
|
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
|
|
some potential trades to be hidden (or masked) by previosly open trades.
|
|
|
|
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
|
|
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
|
|
during Hyperopt/Backtesting (which is equal to setting `max_open_trades` to a very high
|
|
number).
|
|
|
|
!!! Note
|
|
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.
|
|
|
|
You can also enable position stacking in the configuration file by explicitly setting
|
|
`"position_stacking"=true`.
|
|
|
|
## Understand the Hyperopt Result
|
|
|
|
Once Hyperopt is completed you can use the result to create a new strategy.
|
|
Given the following result from hyperopt:
|
|
|
|
```
|
|
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'}
|
|
```
|
|
|
|
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`)
|
|
|
|
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:
|
|
|
|
```
|
|
(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[
|
|
(
|
|
(dataframe['rsi'] < 29.0) & # rsi-value
|
|
dataframe['close'] < dataframe['bb_lowerband'] # trigger
|
|
),
|
|
'buy'] = 1
|
|
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 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.
|
|
|
|
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
|
|
|
|
## Next Step
|
|
|
|
Now you have a perfect bot and want to control it from Telegram. Your
|
|
next step is to learn the [Telegram usage](telegram-usage.md).
|