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<p>Parameters created this way will not show up in the <code>list-strategies</code> parameter count.</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>You can define your own optuna sampler for Hyperopt by implementing <code>generate_estimator()</code> in the Hyperopt subclass.</p>
<div class="highlight"><pre><span></span><code><span class="k">class</span><span class="w"> </span><span class="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">generate_estimator</span><span class="p">(</span><span class="n">dimensions</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="s1">&#39;Dimension&#39;</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">&quot;RF&quot;</span>
</code></pre></div>
<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>
<div class="highlight"><pre><span></span><code><span class="k">class</span><span class="w"> </span><span class="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">generate_estimator</span><span class="p">(</span><span class="n">dimensions</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="s1">&#39;Dimension&#39;</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">skopt.learning</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExtraTreesRegressor</span>
<span class="c1"># Corresponds to &quot;ET&quot; - but allows additional parameters.</span>
<span class="k">return</span> <span class="n">ExtraTreesRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
</code></pre></div>
<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>
<div class="highlight"><pre><span></span><code><span class="k">class</span><span class="w"> </span><span class="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">generate_estimator</span><span class="p">(</span><span class="n">dimensions</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="s1">&#39;Dimension&#39;</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">skopt.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">cook_estimator</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">skopt.learning.gaussian_process.kernels</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">Matern</span><span class="p">,</span> <span class="n">ConstantKernel</span><span class="p">)</span>
<span class="n">kernel_bounds</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
<span class="n">kernel</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">ConstantKernel</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">kernel_bounds</span><span class="p">)</span> <span class="o">*</span>
<span class="n">Matern</span><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)),</span> <span class="n">length_scale_bounds</span><span class="o">=</span><span class="p">[</span><span class="n">kernel_bounds</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">],</span> <span class="n">nu</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">kernel</span> <span class="o">+=</span> <span class="p">(</span>
<span class="n">ConstantKernel</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">kernel_bounds</span><span class="p">)</span> <span class="o">*</span>
<span class="n">Matern</span><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)),</span> <span class="n">length_scale_bounds</span><span class="o">=</span><span class="p">[</span><span class="n">kernel_bounds</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">],</span> <span class="n">nu</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">cook_estimator</span><span class="p">(</span><span class="s2">&quot;GP&quot;</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="n">dimensions</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">n_restarts_optimizer</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="s2">&quot;NSGAIIISampler&quot;</span>
</code></pre></div>
<p>Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "CmaEsSampler", "NSGAIIISampler", "QMCSampler" (Details can be found in the <a href="https://optuna.readthedocs.io/en/stable/reference/samplers/index.html">optuna-samplers documentation</a>), or "an instance of a class that inherits from <code>optuna.samplers.BaseSampler</code>".</p>
<p>Some research will be necessary to find additional Samplers (from optunahub) for example.</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>
If you're unsure about this, best use one of the Defaults (<code>"NSGAIIISampler"</code> has proven to be the most versatile) without further parameters.</p>
</div>
<details class="example">
<summary>Using <code>AutoSampler</code> from Optunahub</summary>
<p><a href="https://hub.optuna.org/samplers/auto_sampler/">AutoSampler docs</a></p>
<p>Install the necessary dependencies
<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>optunahub<span class="w"> </span>cmaes<span class="w"> </span>torch<span class="w"> </span>scipy
</code></pre></div>
Implement <code>generate_estimator()</code> in your strategy</p>
<div class="highlight"><pre><span></span><code><span class="c1"># ...</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">freqtrade.strategy.interface</span><span class="w"> </span><span class="kn">import</span> <span class="n">IStrategy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">List</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">optunahub</span>
<span class="c1"># ... </span>
<span class="k">class</span><span class="w"> </span><span class="nc">my_strategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">generate_estimator</span><span class="p">(</span><span class="n">dimensions</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;Dimension&quot;</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">if</span> <span class="s2">&quot;random_state&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">return</span> <span class="n">optunahub</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="s2">&quot;samplers/auto_sampler&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">AutoSampler</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;random_state&quot;</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">optunahub</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="s2">&quot;samplers/auto_sampler&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">AutoSampler</span><span class="p">()</span>
</code></pre></div>
<p>Obviously the same approach will work for all other Samplers optuna supports.</p>
</details>
<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>

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<h3 id="why-does-freqtrade-not-have-gpu-support">Why does freqtrade not have GPU support?<a class="headerlink" href="#why-does-freqtrade-not-have-gpu-support" title="Permanent link">&para;</a></h3>
<p>First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.</p>
<p>For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn.
Their statement about GPU support is <a href="https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support">pretty clear</a>.</p>
<p>GPU's also are only good at crunching numbers (floating point operations).
<p>GPU's are only good at crunching numbers (floating point operations).
For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting).
As such, GPU's are not too well suited for most parts of hyperopt.</p>
<p>The benefit of using GPU would therefore be pretty slim - and will not justify the complexity introduced by trying to add GPU support.</p>

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<h1 id="hyperopt">Hyperopt<a class="headerlink" href="#hyperopt" title="Permanent link">&para;</a></h1>
<p>This page explains how to tune your strategy by finding the optimal
parameters, a process called hyperparameter optimization. The bot uses algorithms included in the <code>scikit-optimize</code> package to accomplish this.
parameters, a process called hyperparameter optimization. The bot uses algorithms included in the <code>optuna</code> 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.</p>
<p>In general, the search for best parameters starts with a few random combinations (see <a href="#reproducible-results">below</a> for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the <a href="#loss-functions">loss function</a>.</p>
<p>In general, the search for best parameters starts with a few random combinations (see <a href="#reproducible-results">below</a> for more details) and then uses one of optuna's sampler algorithms (currently NSGAIIISampler) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the <a href="#loss-functions">loss function</a>.</p>
<p>Hyperopt requires historic data to be available, just as backtesting does (hyperopt runs backtesting many times with different parameters).
To learn how to get data for the pairs and exchange you're interested in, head over to the <a href="../data-download/">Data Downloading</a> section of the documentation.</p>
<div class="admonition bug">

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