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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">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="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="o">.</span><span class="n">profit_percent</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="o">.</span><span class="n">trade_duration</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 result<br />
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_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason</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 hyperopting TimeFrame</li>
<li><code>min_date</code>: End date of the hyperopting TimeFrame</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 iteration - 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">Note</p>
<p>Please keep the arguments <code>*args</code> and <code>**kwargs</code> in the interface to allow us to extend this interface later.</p>
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