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Merge pull request #3836 from freqtrade/hyperopt_remove_default_loss

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Hyperopt remove default loss
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Matthias 2020-10-07 09:49:25 +02:00 committed by GitHub
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4
.github/workflows/ci.yml vendored
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@ -88,7 +88,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -154,7 +154,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |

2
.travis.yml
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@ -33,7 +33,7 @@ jobs:
- script:
- cp config.json.example config.json
- freqtrade create-userdir --userdir user_data
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
name: hyperopt
- script: flake8
name: flake8

4
docs/advanced-hyperopt.md
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@ -27,9 +27,9 @@ class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
```
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --strategy MyAwesomeStrategy ...
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --strategy MyAwesomeStrategy ...
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
```
## Creating and using a custom loss function

3
docs/bot-usage.md
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@ -356,8 +356,7 @@ optional arguments:
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily, SortinoHyperOptLoss,
SortinoHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
SortinoHyperOptLossDaily.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

2
docs/faq.md
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@ -140,7 +140,7 @@ Since hyperopt uses Bayesian search, running for too many epochs may not produce
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt --hyperop SampleHyperopt --strategy SampleStrategy -e 1000
freqtrade hyperopt --hyperop SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
```
### Why does it take a long time to run hyperopt?

105
docs/hyperopt.md
View File

@ -37,12 +37,20 @@ pip install -r requirements-hyperopt.txt
Before we start digging into Hyperopt, we recommend you to take a look at
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/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.
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar.
The simplest way to get started is to use `freqtrade new-hyperopt --hyperopt AwesomeHyperopt`.
This will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
!!! Tip "About this page"
For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
### Checklist on all tasks / possibilities in hyperopt
The simplest way to get started is to use the following, command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
### Hyperopt checklist
Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
@ -54,17 +62,15 @@ Depending on the space you want to optimize, only some of the below are required
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional - can also be loaded from a strategy:
Optional in hyperopt - can also be loaded from a strategy (recommended):
* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
* 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
!!! Note
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
@ -80,17 +86,20 @@ Rarely you may also need to override:
# Have a working strategy at hand.
freqtrade new-hyperopt --hyperopt EmptyHyperopt
freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
freqtrade hyperopt --hyperopt EmptyHyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
```
### 1. Install a Custom Hyperopt File
### Create a Custom Hyperopt File
Put your hyperopt file into the directory `user_data/hyperopts`.
Let assume you want a hyperopt file `AwesomeHyperopt.py`:
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`
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
### 2. Configure your Guards and Triggers
This command will create a new hyperopt file from a template, allowing you to get started quickly.
### 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:
@ -102,14 +111,16 @@ 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".
!!! Hint "Guards and Triggers"
Technically, there is no difference between Guards and Triggers.
However, this guide will make this distinction to make it clear that signals should not be "sticking".
Sticking signals are signals that are active for multiple candles. This can lead into buying a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
Hyper-optimization will, for each epoch 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
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, i.e. changed the contents of
`populate_buy_trend()` method, you have to update the `guards` and
`triggers` your hyperopt must use correspondingly.
If you have updated the buy strategy, i.e. changed the contents of `populate_buy_trend()` method, you have to update the `guards` and `triggers` your hyperopt must use correspondingly.
#### Sell optimization
@ -126,7 +137,7 @@ To avoid naming collisions in the search-space, please prefix all sell-spaces wi
The Strategy class exposes the timeframe value as the `self.timeframe` attribute.
The same value is available as class-attribute `HyperoptName.timeframe`.
In the case of the linked sample-value this would be `SampleHyperOpt.timeframe`.
In the case of the linked sample-value this would be `AwesomeHyperopt.timeframe`.
## Solving a Mystery
@ -154,7 +165,7 @@ We will start by defining a search space:
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.
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.
@ -192,27 +203,25 @@ So let's write the buy strategy using these values:
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 parameter combination produced the best profits.
Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.
It will use the given historical data and make buys based on the buy signals generated with the above function.
Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
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 your custom hyperopt file.
!!! Note
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 your strategy or hyperopt file.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
A loss function must be specified via the `--hyperopt-loss <Class-name>` argument (or optionally via the configuration under the `"hyperopt_loss"` key).
This class should be in its own file within the `user_data/hyperopts/` directory.
Currently, the following loss functions are builtin:
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
@ -229,19 +238,20 @@ 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.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --strategy <strategyname> -e 500 --spaces all
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` option will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
### Execute Hyperopt with different historical data source
@ -249,7 +259,7 @@ If you would like to hyperopt parameters using an alternate historical data set
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
uses data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
### Running Hyperopt with a smaller test-set
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, to use one month of data, pass the following parameter to the hyperopt call:
@ -263,16 +273,15 @@ freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timeran
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --strategy SampleStrategy --hyperopt SampleHyperopt
freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
```
### Running Hyperopt with Smaller Search Space
Use the `--spaces` option 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.
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:
@ -417,7 +426,9 @@ These ranges should be sufficient in most cases. The minutes in the steps (ROI d
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the `roi_space()` method 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 if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
Override the `roi_space()` method 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 if you need a different structure of the ROI tables or other amount of rows (steps).
A sample for these methods can be found in [sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
### Understand Hyperopt Stoploss results
@ -439,7 +450,7 @@ Stoploss: -0.27996
In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
```
``` python
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.27996
@ -473,7 +484,7 @@ Trailing stop:
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
```
``` python
# Trailing stop
# These attributes will be overridden if the config file contains corresponding values.
trailing_stop = True
@ -498,4 +509,8 @@ After you run Hyperopt for the desired amount of epochs, you can later list all
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.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results don't match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).

4
freqtrade/commands/cli_options.py
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@ -258,10 +258,8 @@ AVAILABLE_CLI_OPTIONS = {
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
'(default: `%(default)s`).',
'SortinoHyperOptLoss, SortinoHyperOptLossDaily.',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,
),
"hyperoptexportfilename": Arg(
'--hyperopt-filename',

1
freqtrade/constants.py
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@ -11,7 +11,6 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'

14
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -7,7 +7,7 @@ import logging
from pathlib import Path
from typing import Dict
from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
from freqtrade.constants import USERPATH_HYPEROPTS
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
@ -70,10 +70,10 @@ class HyperOptLossResolver(IResolver):
:param config: configuration dictionary
"""
# Verify the hyperopt_loss is in the configuration, otherwise fallback to the
# default hyperopt loss
hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
hyperoptloss_name = config.get('hyperopt_loss')
if not hyperoptloss_name:
raise OperationalException("No Hyperopt loss set. Please use `--hyperopt-loss` to "
"specify the Hyperopt-Loss class to use.")
hyperoptloss = HyperOptLossResolver.load_object(hyperoptloss_name,
config, kwargs={},
extra_dir=config.get('hyperopt_path'))
@ -82,8 +82,4 @@ class HyperOptLossResolver(IResolver):
hyperoptloss.__class__.ticker_interval = str(config['timeframe'])
hyperoptloss.__class__.timeframe = str(config['timeframe'])
if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(
f"Found HyperoptLoss class {hyperoptloss_name} does not "
"implement `hyperopt_loss_function`.")
return hyperoptloss

24
tests/optimize/test_hyperopt.py
View File

@ -33,6 +33,7 @@ def hyperopt_conf(default_conf):
hyperconf = deepcopy(default_conf)
hyperconf.update({
'hyperopt': 'DefaultHyperOpt',
'hyperopt_loss': 'DefaultHyperOptLoss',
'hyperopt_path': str(Path(__file__).parent / 'hyperopts'),
'epochs': 1,
'timerange': None,
@ -229,6 +230,13 @@ def test_hyperoptresolver_noname(default_conf):
HyperOptResolver.load_hyperopt(default_conf)
def test_hyperoptlossresolver_noname(default_conf):
with pytest.raises(OperationalException,
match="No Hyperopt loss set. Please use `--hyperopt-loss` to specify "
"the Hyperopt-Loss class to use."):
HyperOptLossResolver.load_hyperoptloss(default_conf)
def test_hyperoptlossresolver(mocker, default_conf) -> None:
hl = DefaultHyperOptLoss
@ -236,6 +244,7 @@ def test_hyperoptlossresolver(mocker, default_conf) -> None:
'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver.load_object',
MagicMock(return_value=hl)
)
default_conf.update({'hyperopt_loss': 'DefaultHyperoptLoss'})
x = HyperOptLossResolver.load_hyperoptloss(default_conf)
assert hasattr(x, "hyperopt_loss_function")
@ -278,6 +287,7 @@ def test_start(mocker, hyperopt_conf, caplog) -> None:
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'DefaultHyperOpt',
'--hyperopt-loss', 'DefaultHyperOptLoss',
'--epochs', '5'
]
pargs = get_args(args)
@ -301,6 +311,7 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'DefaultHyperOpt',
'--hyperopt-loss', 'DefaultHyperOptLoss',
'--epochs', '5'
]
pargs = get_args(args)
@ -318,6 +329,7 @@ def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'DefaultHyperOpt',
'--hyperopt-loss', 'DefaultHyperOptLoss',
'--epochs', '5'
]
pargs = get_args(args)
@ -325,8 +337,8 @@ def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
assert log_has("Another running instance of freqtrade Hyperopt detected.", caplog)
def test_loss_calculation_prefer_correct_trade_count(default_conf, hyperopt_results) -> None:
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
def test_loss_calculation_prefer_correct_trade_count(hyperopt_conf, hyperopt_results) -> None:
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100,
@ -337,11 +349,11 @@ def test_loss_calculation_prefer_correct_trade_count(default_conf, hyperopt_resu
assert under > correct
def test_loss_calculation_prefer_shorter_trades(default_conf, hyperopt_results) -> None:
def test_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results) -> None:
resultsb = hyperopt_results.copy()
resultsb.loc[1, 'trade_duration'] = 20
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
longer = hl.hyperopt_loss_function(hyperopt_results, 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
shorter = hl.hyperopt_loss_function(resultsb, 100,
@ -349,13 +361,13 @@ def test_loss_calculation_prefer_shorter_trades(default_conf, hyperopt_results)
assert shorter < longer
def test_loss_calculation_has_limited_profit(default_conf, hyperopt_results) -> None:
def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, 600,