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en/develop/freqai-configuration/index.html

@@ -1944,7 +1944,7 @@
     <span class="c1"># passed to any single indicator)</span>
     <span class="n">startup_candle_count</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span>
 
-    <span class="k">def</span> <span class="nf">populate_indicators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">populate_indicators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
 
         <span class="c1"># the model will return all labels created by user in `set_freqai_targets()`</span>
         <span class="c1"># (&amp; appended targets), an indication of whether or not the prediction should be accepted,</span>
@@ -1955,7 +1955,7 @@
 
         <span class="k">return</span> <span class="n">dataframe</span>
 
-    <span class="k">def</span> <span class="nf">feature_engineering_expand_all</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">period</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="n">DataFrame</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">feature_engineering_expand_all</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">period</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="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        *Only functional with FreqAI enabled strategies*</span>
 <span class="sd">        This function will automatically expand the defined features on the config defined</span>
@@ -1980,7 +1980,7 @@
 
         <span class="k">return</span> <span class="n">dataframe</span>
 
-    <span class="k">def</span> <span class="nf">feature_engineering_expand_basic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">feature_engineering_expand_basic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        *Only functional with FreqAI enabled strategies*</span>
 <span class="sd">        This function will automatically expand the defined features on the config defined</span>
@@ -2004,7 +2004,7 @@
         <span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%-r</span><span class="s2">aw_price&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;close&quot;</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">dataframe</span>
 
-    <span class="k">def</span> <span class="nf">feature_engineering_standard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">feature_engineering_standard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        *Only functional with FreqAI enabled strategies*</span>
 <span class="sd">        This optional function will be called once with the dataframe of the base timeframe.</span>
@@ -2025,7 +2025,7 @@
         <span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;</span><span class="si">%-ho</span><span class="s2">ur_of_day&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;date&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">hour</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">25</span>
         <span class="k">return</span> <span class="n">dataframe</span>
 
-    <span class="k">def</span> <span class="nf">set_freqai_targets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">set_freqai_targets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</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="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        *Only functional with FreqAI enabled strategies*</span>
 <span class="sd">        Required function to set the targets for the model.</span>
@@ -2151,37 +2151,37 @@ This docker-compose file also contains a (disabled) section to enable GPU resour
 <h3 id="structure">Structure<a class="headerlink" href="#structure" title="Permanent link">&para;</a></h3>
 <h4 id="model">Model<a class="headerlink" href="#model" title="Permanent link">&para;</a></h4>
 <p>You can construct your own Neural Network architecture in PyTorch by simply defining your <code>nn.Module</code> class inside your custom <a href="#using-different-prediction-models"><code>IFreqaiModel</code> file</a> and then using that class in your <code>def train()</code> function. Here is an example of logistic regression model implementation using PyTorch (should be used with nn.BCELoss criterion) for classification tasks.</p>
-<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">LogisticRegression</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+<div class="highlight"><pre><span></span><code><span class="k">class</span><span class="w"> </span><span class="nc">LogisticRegression</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># Define your layers</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="c1"># Define the forward pass</span>
         <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
         <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">out</span>
 
-<span class="k">class</span> <span class="nc">MyCoolPyTorchClassifier</span><span class="p">(</span><span class="n">BasePyTorchClassifier</span><span class="p">):</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">MyCoolPyTorchClassifier</span><span class="p">(</span><span class="n">BasePyTorchClassifier</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This is a custom IFreqaiModel showing how a user might setup their own </span>
 <span class="sd">    custom Neural Network architecture for their training.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">data_convertor</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PyTorchDataConvertor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">data_convertor</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PyTorchDataConvertor</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">DefaultPyTorchDataConvertor</span><span class="p">(</span><span class="n">target_tensor_type</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">freqai_info</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;model_training_parameters&quot;</span><span class="p">,</span> <span class="p">{})</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;learning_rate&quot;</span><span class="p">,</span>  <span class="mf">3e-4</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">model_kwargs</span><span class="p">:</span> <span class="nb">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">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;model_kwargs&quot;</span><span class="p">,</span>  <span class="p">{})</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">trainer_kwargs</span><span class="p">:</span> <span class="nb">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">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;trainer_kwargs&quot;</span><span class="p">,</span>  <span class="p">{})</span>
 
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dictionary</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">dk</span><span class="p">:</span> <span class="n">FreqaiDataKitchen</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="n">Any</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dictionary</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">dk</span><span class="p">:</span> <span class="n">FreqaiDataKitchen</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="n">Any</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        User sets up the training and test data to fit their desired model here</span>
 <span class="sd">        :param data_dictionary: the dictionary holding all data for train, test,</span>
@@ -2229,15 +2229,15 @@ From top to bottom:</p>
 <p><img alt="image" src="../assets/freqai_pytorch-diagram.png" /></p>
 <h4 id="full-example">Full example<a class="headerlink" href="#full-example" title="Permanent link">&para;</a></h4>
 <p>Building a PyTorch regressor using MLP (multilayer perceptron) model, MSELoss criterion, and AdamW optimizer.</p>
-<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">PyTorchMLPRegressor</span><span class="p">(</span><span class="n">BasePyTorchRegressor</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">:</span>
+<div class="highlight"><pre><span></span><code><span class="k">class</span><span class="w"> </span><span class="nc">PyTorchMLPRegressor</span><span class="p">(</span><span class="n">BasePyTorchRegressor</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">freqai_info</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;model_training_parameters&quot;</span><span class="p">,</span> <span class="p">{})</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;learning_rate&quot;</span><span class="p">,</span>  <span class="mf">3e-4</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">model_kwargs</span><span class="p">:</span> <span class="nb">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">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;model_kwargs&quot;</span><span class="p">,</span>  <span class="p">{})</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">trainer_kwargs</span><span class="p">:</span> <span class="nb">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">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;trainer_kwargs&quot;</span><span class="p">,</span>  <span class="p">{})</span>
 
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dictionary</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">dk</span><span class="p">:</span> <span class="n">FreqaiDataKitchen</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="n">Any</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dictionary</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">dk</span><span class="p">:</span> <span class="n">FreqaiDataKitchen</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="n">Any</span><span class="p">:</span>
         <span class="n">n_features</span> <span class="o">=</span> <span class="n">data_dictionary</span><span class="p">[</span><span class="s2">&quot;train_features&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">model</span> <span class="o">=</span> <span class="n">PyTorchMLPModel</span><span class="p">(</span>
             <span class="n">input_dim</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span>
@@ -2265,7 +2265,7 @@ From top to bottom:</p>
 <summary>Setting Class Names for Classifiers</summary>
 <p>When using classifiers, the user must declare the class names (or targets) by overriding the <code>IFreqaiModel.class_names</code> attribute. This is achieved by setting <code>self.freqai.class_names</code> in the FreqAI strategy inside the <code>set_freqai_targets</code> method.</p>
 <p>For example, if you are using a binary classifier to predict price movements as up or down, you can set the class names as follows:
-<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">set_freqai_targets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</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="n">DataFrame</span><span class="p">:</span>
+<div class="highlight"><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">set_freqai_targets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</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="n">DataFrame</span><span class="p">:</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">freqai</span><span class="o">.</span><span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;down&quot;</span><span class="p">,</span> <span class="s2">&quot;up&quot;</span><span class="p">]</span>
     <span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;&amp;s-up_or_down&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;close&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">)</span> <span class="o">&gt;</span>
                                               <span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;close&quot;</span><span class="p">],</span> <span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">)</span>