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 </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 FreqAI 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><code>bash
+freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates</code></p>
 <p>You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. </p>
 <div class="admonition danger">
 <p class="admonition-title">Not for production</p>
@@ -2193,8 +2193,8 @@
 <p><strong>Inferencing</strong> - the process of feeding a trained model new unseen 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 FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. 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>
+<p><code>bash
+pip install -r requirements-freqai.txt</code></p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Catboost will not be installed on low-powered arm devices (raspberry), since it does not provide wheels for this platform.</p>
@@ -2209,15 +2209,15 @@
 <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>
-<span class="w">    </span><span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.21105/joss.04864}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://doi.org/10.21105/joss.04864}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2022}</span><span class="p">,</span><span class="w"> </span><span class="na">publisher</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{The Open Journal}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">volume</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{7}</span><span class="p">,</span><span class="w"> </span><span class="na">number</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{80}</span><span class="p">,</span><span class="w"> </span><span class="na">pages</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{4864}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts}</span><span class="p">,</span>
-<span class="w">    </span><span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Journal of Open Source Software}</span><span class="w"> </span><span class="p">}</span><span class="w"> </span>
-</code></pre></div>
+<p><code>bibtex
+@article{Caulk2022, 
+    doi = {10.21105/joss.04864},
+    url = {https://doi.org/10.21105/joss.04864},
+    year = {2022}, publisher = {The Open Journal},
+    volume = {7}, number = {80}, pages = {4864},
+    author = {Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt},
+    title = {FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts},
+    journal = {Journal of Open Source Software} }</code></p>
 <h2 id="common-pitfalls">Common pitfalls<a class="headerlink" href="#common-pitfalls" title="Permanent link">&para;</a></h2>
 <p>FreqAI cannot be combined with dynamic <code>VolumePairlists</code> (or any pairlist filter that adds and removes pairs dynamically).
 This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,