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Introduction
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Introduction
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Introduction
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Quick start
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General approach
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Important machine learning vocabulary
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Install prerequisites
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Common pitfalls
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Credits
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Configuration
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Parameter table
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Feature engineering
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Running FreqAI
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Developer guide
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Short / Leverage
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Utility Sub-commands
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Plotting
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Exchange-specific Notes
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Data Analysis
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Data Analysis
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Jupyter Notebooks
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Strategy analysis
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Backtest analysis
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Advanced Post-installation Tasks
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Advanced Strategy
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Advanced Hyperopt
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Producer/Consumer mode
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Edge Positioning
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Sandbox Testing
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FAQ
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SQL Cheat-sheet
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Strategy migration
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Updating Freqtrade
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Deprecated Features
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Contributors Guide
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Introduction
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Quick start
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General approach
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<p><img alt="freqai-logo" src="../assets/freqai_doc_logo.svg" /></p>
<h1 id="freqai"><code>FreqAI</code><a class="headerlink" href="#freqai" title="Permanent link">&para;</a></h1>
<h2 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Permanent link">&para;</a></h2>
<p><code>FreqAI</code> is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.</p>
<p>Features include:</p>
<ul>
<li><strong>Self-adaptive retraining</strong> - Retrain models during <a href="../freqai-running/#live-deployments">live deployments</a> to self-adapt to the market in a supervised manner</li>
<li><strong>Rapid feature engineering</strong> - Create large rich <a href="../freqai-feature-engineering/#feature-engineering">feature sets</a> (10k+ features) based on simple user-created strategies</li>
<li><strong>High performance</strong> - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing</li>
<li><strong>Realistic backtesting</strong> - Emulate self-adaptive training on historic data with a <a href="../freqai-running/#backtesting">backtesting module</a> that automates retraining</li>
<li><strong>Extensibility</strong> - The generalized and robust architecture allows for incorporating any <a href="../freqai-configuration/#using-different-prediction-models">machine learning library/method</a> available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network</li>
<li><strong>Smart outlier removal</strong> - Remove outliers from training and prediction data sets using a variety of <a href="../freqai-feature-engineering/#outlier-detection">outlier detection techniques</a></li>
<li><strong>Crash resilience</strong> - Store trained models to disk to make reloading from a crash fast and easy, and <a href="../freqai-running/#purging-old-model-data">purge obsolete files</a> for sustained dry/live runs</li>
<li><strong>Automatic data normalization</strong> - <a href="../freqai-feature-engineering/#feature-normalization">Normalize the data</a> in a smart and statistically safe way</li>
<li><strong>Automatic data download</strong> - Compute timeranges for data downloads and update historic data (in live deployments)</li>
<li><strong>Cleaning of incoming data</strong> - Handle NaNs safely before training and model inferencing</li>
<li><strong>Dimensionality reduction</strong> - Reduce the size of the training data via <a href="../freqai-feature-engineering/#data-dimensionality-reduction-with-principal-component-analysis">Principal Component Analysis</a></li>
<li><strong>Deploying bot fleets</strong> - Set one bot to train models while a fleet of <a href="../freqai-running/#setting-up-a-follower">follower bots</a> inference the models and handle trades</li>
</ul>
<h2 id="quick-start">Quick start<a class="headerlink" href="#quick-start" title="Permanent link">&para;</a></h2>
<p>The easiest way to quickly test <code>FreqAI</code> is to run it in dry mode with the following command:</p>
<div class="highlight"><pre><span></span><code>freqtrade<span class="w"> </span>trade<span class="w"> </span>--config<span class="w"> </span>config_examples/config_freqai.example.json<span class="w"> </span>--strategy<span class="w"> </span>FreqaiExampleStrategy<span class="w"> </span>--freqaimodel<span class="w"> </span>LightGBMRegressor<span class="w"> </span>--strategy-path<span class="w"> </span>freqtrade/templates
</code></pre></div>
<p>You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.</p>
<p>An example strategy, prediction model, and config to use as a starting points can be found in
<code>freqtrade/templates/FreqaiExampleStrategy.py</code>, <code>freqtrade/freqai/prediction_models/LightGBMRegressor.py</code>, and
<code>config_examples/config_freqai.example.json</code>, respectively.</p>
<h2 id="general-approach">General approach<a class="headerlink" href="#general-approach" title="Permanent link">&para;</a></h2>
<p>You provide <code>FreqAI</code> with a set of custom <em>base indicators</em> (the same way as in a <a href="../strategy-customization/">typical Freqtrade strategy</a>) as well as target values (<em>labels</em>). For each pair in the whitelist, <code>FreqAI</code> trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. <code>FreqAI</code> offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, <code>FreqAI</code> can be set to constant retraining in a background thread to keep models as up to date as possible.</p>
<p>An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.</p>
<p><img alt="freqai-algo" src="../assets/freqai_algo.jpg" /></p>
<h3 id="important-machine-learning-vocabulary">Important machine learning vocabulary<a class="headerlink" href="#important-machine-learning-vocabulary" title="Permanent link">&para;</a></h3>
<p><strong>Features</strong> - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In <code>FreqAI</code>, you build a feature data sets from anything you can construct in the strategy.</p>
<p><strong>Labels</strong> - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.</p>
<p><strong>Training</strong> - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found <a href="../freqai-configuration/#using-different-prediction-models">here</a>.</p>
<p><strong>Train data</strong> - a subset of the feature data set that is fed to the model during training. This data directly influences weight connections in the model.</p>
<p><strong>Test data</strong> - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.</p>
<p><strong>Inferencing</strong> - the process of feeding a trained model new data on which it will make a prediction.</p>
<h2 id="install-prerequisites">Install prerequisites<a class="headerlink" href="#install-prerequisites" title="Permanent link">&para;</a></h2>
<p>The normal Freqtrade install process will ask if you wish to install <code>FreqAI</code> dependencies. You should reply "yes" to this question if you wish to use <code>FreqAI</code>. If you did not reply yes, you can manually install these dependencies after the install with:</p>
<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements-freqai.txt
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.</p>
</div>
<h3 id="usage-with-docker">Usage with docker<a class="headerlink" href="#usage-with-docker" title="Permanent link">&para;</a></h3>
<p>If you are using docker, a dedicated tag with <code>FreqAI</code> dependencies is available as <code>:freqai</code>. As such - you can replace the image line in your docker-compose file with <code>image: freqtradeorg/freqtrade:develop_freqai</code>. This image contains the regular <code>FreqAI</code> dependencies. Similar to native installs, Catboost will not be available on ARM based devices.</p>
<h2 id="common-pitfalls">Common pitfalls<a class="headerlink" href="#common-pitfalls" title="Permanent link">&para;</a></h2>
<p><code>FreqAI</code> cannot be combined with dynamic <code>VolumePairlists</code> (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - <code>FreqAI</code> relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. <code>FreqAI</code> stores and appends
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, <code>FreqAI</code> does work with the <code>ShufflePairlist</code> or a <code>VolumePairlist</code> which keeps the total pairlist constant (but reorders the pairs according to volume).</p>
<h2 id="credits">Credits<a class="headerlink" href="#credits" title="Permanent link">&para;</a></h2>
<p><code>FreqAI</code> is developed by a group of individuals who all contribute specific skillsets to the project.</p>
<p>Conception and software development:
Robert Caulk @robcaulk</p>
<p>Theoretical brainstorming and data analysis:
Elin Törnquist @th0rntwig</p>
<p>Code review and software architecture brainstorming:
@xmatthias</p>
<p>Software development:
Wagner Costa @wagnercosta</p>
<p>Beta testing and bug reporting:
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Robert Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau,
Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza</p>
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