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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>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span>
<span class="kn">from</span> <span class="nn">pandas</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">freqtrade.optimize.hyperopt</span> <span class="kn">import</span> <span class="n">IHyperOptLoss</span>
<span class="n">TARGET_TRADES</span> <span class="o">=</span> <span class="mi">600</span>
<span class="n">EXPECTED_MAX_PROFIT</span> <span class="o">=</span> <span class="mf">3.0</span>
<span class="n">MAX_ACCEPTED_TRADE_DURATION</span> <span class="o">=</span> <span class="mi">300</span>
<span class="k">class</span> <span class="nc">SuperDuperHyperOptLoss</span><span class="p">(</span><span class="n">IHyperOptLoss</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Defines the default loss function for hyperopt</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">hyperopt_loss_function</span><span class="p">(</span><span class="n">results</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">trade_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">min_date</span><span class="p">:</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">max_date</span><span class="p">:</span> <span class="n">datetime</span><span class="p">,</span>
<span class="n">config</span><span class="p">:</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">processed</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">],</span>
<span class="n">backtest_stats</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
<span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Objective function, returns smaller number for better results</span>
<span class="sd"> This is the legacy algorithm (used until now in freqtrade).</span>
<span class="sd"> Weights are distributed as follows:</span>
<span class="sd"> * 0.4 to trade duration</span>
<span class="sd"> * 0.25: Avoiding trade loss</span>
<span class="sd"> * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">total_profit</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;profit_ratio&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">trade_duration</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;trade_duration&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">trade_loss</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">trade_count</span> <span class="o">-</span> <span class="n">TARGET_TRADES</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">10</span> <span class="o">**</span> <span class="mf">5.8</span><span class="p">)</span>
<span class="n">profit_loss</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">total_profit</span> <span class="o">/</span> <span class="n">EXPECTED_MAX_PROFIT</span><span class="p">)</span>
<span class="n">duration_loss</span> <span class="o">=</span> <span class="mf">0.4</span> <span class="o">*</span> <span class="nb">min</span><span class="p">(</span><span class="n">trade_duration</span> <span class="o">/</span> <span class="n">MAX_ACCEPTED_TRADE_DURATION</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">trade_loss</span> <span class="o">+</span> <span class="n">profit_loss</span> <span class="o">+</span> <span class="n">duration_loss</span>
<span class="k">return</span> <span class="n">result</span>
</code></pre></div>
<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, exit_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>
<div class="highlight"><pre><span></span><code><span class="k">class</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="nc">HyperOpt</span><span class="p">:</span>
<span class="c1"># Define a custom stoploss space.</span>
<span class="k">def</span> <span class="nf">stoploss_space</span><span class="p">():</span>
<span class="k">return</span> <span class="p">[</span><span class="n">SKDecimal</span><span class="p">(</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;stoploss&#39;</span><span class="p">)]</span>
<span class="c1"># Define custom ROI space</span>
<span class="k">def</span> <span class="nf">roi_space</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">Dimension</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">[</span>
<span class="n">Integer</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">120</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_t1&#39;</span><span class="p">),</span>
<span class="n">Integer</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_t2&#39;</span><span class="p">),</span>
<span class="n">Integer</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_t3&#39;</span><span class="p">),</span>
<span class="n">SKDecimal</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.04</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_p1&#39;</span><span class="p">),</span>
<span class="n">SKDecimal</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.07</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_p2&#39;</span><span class="p">),</span>
<span class="n">SKDecimal</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.20</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;roi_p3&#39;</span><span class="p">),</span>
<span class="p">]</span>
</code></pre></div>
<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>
<div class="highlight"><pre><span></span><code><span class="k">class</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="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</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="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</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="nn">skopt.learning</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="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">HyperOpt</span><span class="p">:</span>
<span class="k">def</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="nn">skopt.utils</span> <span class="kn">import</span> <span class="n">cook_estimator</span>
<span class="kn">from</span> <span class="nn">skopt.learning.gaussian_process.kernels</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>
</code></pre></div>
<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>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">freqtrade.optimize.space</span> <span class="kn">import</span> <span class="n">Categorical</span><span class="p">,</span> <span class="n">Dimension</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">SKDecimal</span><span class="p">,</span> <span class="n">Real</span> <span class="c1"># noqa</span>
</code></pre></div>
<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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