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-      FreqAI position in open-source machine learning landscape
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 <p>FreqAI 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 signals. In general, FreqAI aims to be a sandbox for easily deploying robust machine learning libraries on real-time data (<a href="#freqai-position-in-open-source-machine-learning-landscape">details</a>).</p>
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 <p class="admonition-title">Note</p>
-<p>FreqAI is, and always will be, a not-for-profit, open-source project. FreqAI does <em>not</em> have a crypto token, FreqAI does <em>not</em> sell signals, and FreqAI does not have a domain besides the present <a href="https://www.freqtrade.io/en/latest/freqai/">freqtrade documentation</a>.</p>
+<p>FreqAI is, and always will be, a not-for-profit, open source project. FreqAI does <em>not</em> have a crypto token, FreqAI does <em>not</em> sell signals, and FreqAI does not have a domain besides the present <a href="https://www.freqtrade.io/en/latest/freqai/">freqtrade documentation</a>.</p>
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 <p>Features include:</p>
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 <p class="admonition-title">docker-compose-freqai.yml</p>
 <p>We do provide an explicit docker-compose file for this in <code>docker/docker-compose-freqai.yml</code> - which can be used via <code>docker compose -f docker/docker-compose-freqai.yml run ...</code> - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.</p>
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-<h3 id="freqai-position-in-open-source-machine-learning-landscape">FreqAI position in open-source machine learning landscape<a class="headerlink" href="#freqai-position-in-open-source-machine-learning-landscape" title="Permanent link">&para;</a></h3>
-<p>Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. <code>scikit-learn</code>) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. <a href="#freqai"><code>FreqAI</code></a> aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The <code>FreqAI</code> framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the <code>FreqAI</code> sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. </p>
+<h3 id="freqai-position-in-open-source-machine-learning-landscape">FreqAI position in open source machine learning landscape<a class="headerlink" href="#freqai-position-in-open-source-machine-learning-landscape" title="Permanent link">&para;</a></h3>
+<p>Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. <code>scikit-learn</code>) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. <a href="#freqai"><code>FreqAI</code></a> aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The <code>FreqAI</code> framework is effectively a sandbox for the rich world of open source machine learning libraries. Inside the <code>FreqAI</code> sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. </p>
 <h3 id="citing-freqai">Citing FreqAI<a class="headerlink" href="#citing-freqai" title="Permanent link">&para;</a></h3>
 <p>FreqAI is <a href="https://joss.theoj.org/papers/10.21105/joss.04864">published in the Journal of Open Source Software</a>. If you find FreqAI useful in your research, please use the following citation:</p>
 <div class="highlight"><pre><span></span><code><span class="nc">@article</span><span class="p">{</span><span class="nl">Caulk2022</span><span class="p">,</span><span class="w"> </span>