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<h1 id="advanced-hyperopt">Advanced Hyperopt<a class="headerlink" href="#advanced-hyperopt" title="Permanent link">&para;</a></h1>
<p>This page explains some advanced Hyperopt topics that may require higher
coding skills and Python knowledge than creation of an ordinal hyperoptimization
class.</p>
<h2 id="creating-and-using-a-custom-loss-function">Creating and using a custom loss function<a class="headerlink" href="#creating-and-using-a-custom-loss-function" title="Permanent link">&para;</a></h2>
<p>To use a custom loss function class, make sure that the function <code>hyperopt_loss_function</code> is defined in your custom hyperopt loss class.
For the sample below, you then need to add the command line parameter <code>--hyperopt-loss SuperDuperHyperOptLoss</code> to your hyperopt call so this function is being used.</p>
<p>A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in <a href="https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py">userdata/hyperopts</a>.</p>
<p>``` python
from datetime import datetime
from typing import Any, Dict</p>
<p>from pandas import DataFrame</p>
<p>from freqtrade.optimize.hyperopt import IHyperOptLoss</p>
<p>TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300</p>
<p>class SuperDuperHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""</p>
<div class="codehilite"><pre><span></span><code>@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args, **kwargs) -&gt; float:
&quot;&quot;&quot;
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
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
&quot;&quot;&quot;
total_profit = results[&#39;profit_ratio&#39;].sum()
trade_duration = results[&#39;trade_duration&#39;].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
</code></pre></div>
<p>```</p>
<p>Currently, the arguments are:</p>
<ul>
<li><code>results</code>: DataFrame containing the resulting trades.
The following columns are available in results (corresponds to the output-file of backtesting when used with <code>--export trades</code>):<br />
<code>pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs</code></li>
<li><code>trade_count</code>: Amount of trades (identical to <code>len(results)</code>)</li>
<li><code>min_date</code>: Start date of the timerange used</li>
<li><code>min_date</code>: End date of the timerange used</li>
<li><code>config</code>: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).</li>
<li><code>processed</code>: Dict of Dataframes with the pair as keys containing the data used for backtesting.</li>
<li><code>backtest_stats</code>: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in <code>generate_strategy_stats()</code> in <code>optimize_reports.py</code>.</li>
</ul>
<p>This function needs to return a floating point number (<code>float</code>). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.</p>
</div>
<div class="admonition note">
<p class="admonition-title"><code>*args</code> and <code>**kwargs</code></p>
<p>Please keep the arguments <code>*args</code> and <code>**kwargs</code> in the interface to allow us to extend this interface in the future.</p>
</div>
<h2 id="overriding-pre-defined-spaces">Overriding pre-defined spaces<a class="headerlink" href="#overriding-pre-defined-spaces" title="Permanent link">&para;</a></h2>
<p>To override a pre-defined space (<code>roi_space</code>, <code>generate_roi_table</code>, <code>stoploss_space</code>, <code>trailing_space</code>), define a nested class called Hyperopt and define the required spaces as follows:</p>
<p>```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
def stoploss_space():
return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]</p>
<div class="codehilite"><pre><span></span><code> # Define custom ROI space
def roi_space() -&gt; List[Dimension]:
return [
Integer(10, 120, name=&#39;roi_t1&#39;),
Integer(10, 60, name=&#39;roi_t2&#39;),
Integer(10, 40, name=&#39;roi_t3&#39;),
SKDecimal(0.01, 0.04, decimals=3, name=&#39;roi_p1&#39;),
SKDecimal(0.01, 0.07, decimals=3, name=&#39;roi_p2&#39;),
SKDecimal(0.01, 0.20, decimals=3, name=&#39;roi_p3&#39;),
]
</code></pre></div>
<p>```</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>All overrides are optional and can be mixed/matched as necessary.</p>
</div>
<h3 id="overriding-base-estimator">Overriding Base estimator<a class="headerlink" href="#overriding-base-estimator" title="Permanent link">&para;</a></h3>
<p>You can define your own estimator for Hyperopt by implementing <code>generate_estimator()</code> in the Hyperopt subclass.</p>
<p>```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
return "RF"</p>
<p>```</p>
<p>Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the <a href="https://scikit-optimize.github.io/">scikit-optimize documentation</a>), or "an instance of a class that inherits from <code>RegressorMixin</code> (from sklearn) and where the <code>predict</code> method has an optional <code>return_std</code> argument, which returns <code>std(Y | x)</code> along with <code>E[Y | x]</code>".</p>
<p>Some research will be necessary to find additional Regressors.</p>
<p>Example for <code>ExtraTreesRegressor</code> ("ET") with additional parameters:</p>
<p>```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.learning import ExtraTreesRegressor
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)</p>
<p>```</p>
<p>The <code>dimensions</code> parameter is the list of <code>skopt.space.Dimension</code> objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the <code>skopt.learning.GaussianProcessRegressor</code> estimator. Here's an example:</p>
<p>```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.utils import cook_estimator
from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
kernel_bounds = (0.0001, 10000)
kernel = (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
)
kernel += (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
)</p>
<div class="codehilite"><pre><span></span><code> return cook_estimator(&quot;GP&quot;, space=dimensions, kernel=kernel, n_restarts_optimizer=2)
</code></pre></div>
<p>```</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (<code>"ET"</code> has proven to be the most versatile) without further parameters.</p>
</div>
<h2 id="space-options">Space options<a class="headerlink" href="#space-options" title="Permanent link">&para;</a></h2>
<p>For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:</p>
<ul>
<li><code>Categorical</code> - Pick from a list of categories (e.g. <code>Categorical(['a', 'b', 'c'], name="cat")</code>)</li>
<li><code>Integer</code> - Pick from a range of whole numbers (e.g. <code>Integer(1, 10, name='rsi')</code>)</li>
<li><code>SKDecimal</code> - Pick from a range of decimal numbers with limited precision (e.g. <code>SKDecimal(0.1, 0.5, decimals=3, name='adx')</code>). <em>Available only with freqtrade</em>.</li>
<li><code>Real</code> - Pick from a range of decimal numbers with full precision (e.g. <code>Real(0.1, 0.5, name='adx')</code></li>
</ul>
<p>You can import all of these from <code>freqtrade.optimize.space</code>, although <code>Categorical</code>, <code>Integer</code> and <code>Real</code> are only aliases for their corresponding scikit-optimize Spaces. <code>SKDecimal</code> is provided by freqtrade for faster optimizations.</p>
<p><code>python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa</code></p>
<div class="admonition hint">
<p class="admonition-title">SKDecimal vs. Real</p>
<p>We recommend to use <code>SKDecimal</code> instead of the <code>Real</code> space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.</p>
<p>Assuming the definition of a rather small space (<code>SKDecimal(0.10, 0.15, decimals=2, name='xxx')</code>) - SKDecimal will have 5 possibilities (<code>[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]</code>).</p>
<p>A corresponding real space <code>Real(0.10, 0.15 name='xxx')</code> on the other hand has an almost unlimited number of possibilities (<code>[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]</code>).</p>
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