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Quickstart with Docker
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Freqtrade Basics
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Configuration
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Strategy Quickstart
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Strategy Customization
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Strategy Callbacks
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Stoploss
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freqUI
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REST API
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Backtesting
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Hyperopt
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FreqAI
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Introduction
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<h1 id="development">Development<a class="headerlink" href="#development" title="Permanent link">&para;</a></h1>
<h2 id="project-architecture">Project architecture<a class="headerlink" href="#project-architecture" title="Permanent link">&para;</a></h2>
<p>The architecture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc.</p>
<p>The class structure and a detailed algorithmic overview is depicted in the following diagram:</p>
<p><img alt="image" src="../assets/freqai_algorithm-diagram.jpg" /></p>
<p>As shown, there are three distinct objects comprising FreqAI:</p>
<ul>
<li><strong>IFreqaiModel</strong> - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.</li>
<li><strong>FreqaiDataKitchen</strong> - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.</li>
<li><strong>FreqaiDataDrawer</strong> - A singular persistent object containing all the historical predictions, models, and save/load methods.</li>
</ul>
<p>There are a variety of built-in <a href="../freqai-configuration/#using-different-prediction-models">prediction models</a> which inherit directly from <code>IFreqaiModel</code>. Each of these models have full access to all methods in <code>IFreqaiModel</code> and can therefore override any of those functions at will. However, advanced users will likely stick to overriding <code>fit()</code>, <code>train()</code>, <code>predict()</code>, and <code>data_cleaning_train/predict()</code>.</p>
<h2 id="data-handling">Data handling<a class="headerlink" href="#data-handling" title="Permanent link">&para;</a></h2>
<p>FreqAI aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,<code>user_data_dir/models/</code>, which contains all the data associated with the trainings and backtests. The <code>FreqaiDataKitchen()</code> relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.</p>
<h3 id="file-structure">File structure<a class="headerlink" href="#file-structure" title="Permanent link">&para;</a></h3>
<p>The file structure is automatically generated based on the model <code>identifier</code> set in the <a href="../freqai-configuration/#setting-up-the-configuration-file">config</a>. The following structure shows where the data is stored for post processing:</p>
<table>
<thead>
<tr>
<th>Structure</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>config_*.json</code></td>
<td>A copy of the model specific configuration file.</td>
</tr>
<tr>
<td><code>historic_predictions.pkl</code></td>
<td>A file containing all historic predictions generated during the lifetime of the <code>identifier</code> model during live deployment. <code>historic_predictions.pkl</code> is used to reload the model after a crash or a config change. A backup file is always held in case of corruption on the main file. FreqAI <strong>automatically</strong> detects corruption and replaces the corrupted file with the backup.</td>
</tr>
<tr>
<td><code>pair_dictionary.json</code></td>
<td>A file containing the training queue as well as the on disk location of the most recently trained model.</td>
</tr>
<tr>
<td><code>sub-train-*_TIMESTAMP</code></td>
<td>A folder containing all the files associated with a single model, such as: <br></td>
</tr>
<tr>
<td></td>
<td><code>*_metadata.json</code> - Metadata for the model, such as normalization max/min, expected training feature list, etc. <br></td>
</tr>
<tr>
<td></td>
<td><code>*_model.*</code> - The model file saved to disk for reloading from a crash. Can be <code>joblib</code> (typical boosting libs), <code>zip</code> (stable_baselines), <code>hd5</code> (keras type), etc. <br></td>
</tr>
<tr>
<td></td>
<td><code>*_pca_object.pkl</code> - The <a href="../freqai-feature-engineering/#data-dimensionality-reduction-with-principal-component-analysis">Principal component analysis (PCA)</a> transform (if <code>principal_component_analysis: True</code> is set in the config) which will be used to transform unseen prediction features. <br></td>
</tr>
<tr>
<td></td>
<td><code>*_svm_model.pkl</code> - The <a href="../freqai-feature-engineering/#identifying-outliers-using-a-support-vector-machine-svm">Support Vector Machine (SVM)</a> model (if <code>use_SVM_to_remove_outliers: True</code> is set in the config) which is used to detect outliers in unseen prediction features. <br></td>
</tr>
<tr>
<td></td>
<td><code>*_trained_df.pkl</code> - The dataframe containing all the training features used to train the <code>identifier</code> model. This is used for computing the <a href="../freqai-feature-engineering/#identifying-outliers-with-the-dissimilarity-index-di">Dissimilarity Index (DI)</a> and can also be used for post-processing. <br></td>
</tr>
<tr>
<td></td>
<td><code>*_trained_dates.df.pkl</code> - The dates associated with the <code>trained_df.pkl</code>, which is useful for post-processing.</td>
</tr>
</tbody>
</table>
<p>The example file structure would look like this:</p>
<div class="highlight"><pre><span></span><code>├── models
│   └── unique-id
│   ├── config_freqai.example.json
│   ├── historic_predictions.backup.pkl
│   ├── historic_predictions.pkl
│   ├── pair_dictionary.json
│   ├── sub-train-1INCH_1662821319
│   │   ├── cb_1inch_1662821319_metadata.json
│   │   ├── cb_1inch_1662821319_model.joblib
│   │   ├── cb_1inch_1662821319_pca_object.pkl
│   │   ├── cb_1inch_1662821319_svm_model.joblib
│   │   ├── cb_1inch_1662821319_trained_dates_df.pkl
│   │   └── cb_1inch_1662821319_trained_df.pkl
│   ├── sub-train-1INCH_1662821371
│   │   ├── cb_1inch_1662821371_metadata.json
│   │   ├── cb_1inch_1662821371_model.joblib
│   │   ├── cb_1inch_1662821371_pca_object.pkl
│   │   ├── cb_1inch_1662821371_svm_model.joblib
│   │   ├── cb_1inch_1662821371_trained_dates_df.pkl
│   │   └── cb_1inch_1662821371_trained_df.pkl
│   ├── sub-train-ADA_1662821344
│   │   ├── cb_ada_1662821344_metadata.json
│   │   ├── cb_ada_1662821344_model.joblib
│   │   ├── cb_ada_1662821344_pca_object.pkl
│   │   ├── cb_ada_1662821344_svm_model.joblib
│   │   ├── cb_ada_1662821344_trained_dates_df.pkl
│   │   └── cb_ada_1662821344_trained_df.pkl
│   └── sub-train-ADA_1662821399
│   ├── cb_ada_1662821399_metadata.json
│   ├── cb_ada_1662821399_model.joblib
│   ├── cb_ada_1662821399_pca_object.pkl
│   ├── cb_ada_1662821399_svm_model.joblib
│   ├── cb_ada_1662821399_trained_dates_df.pkl
│   └── cb_ada_1662821399_trained_df.pkl
</code></pre></div>
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