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<h1 id="analyzing-bot-data">Analyzing bot data<a class="headerlink" href="#analyzing-bot-data" title="Permanent link">&para;</a></h1>
<p>After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.</p>
<p>A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.</p>
<p>The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.</p>
<h2 id="backtesting">Backtesting<a class="headerlink" href="#backtesting" title="Permanent link">&para;</a></h2>
<p>To analyze your backtest results, you can <a href="#exporting-trades-to-file">export the trades</a>.
You can then load the trades to perform further analysis.</p>
<p>Freqtrade provides the <code>load_backtest_data()</code> helper function to easily load the backtest results, which takes the path to the the backtest-results file as parameter.</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">freqtrade.data.btanalysis</span> <span class="kn">import</span> <span class="n">load_backtest_data</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">load_backtest_data</span><span class="p">(</span><span class="s2">&quot;user_data/backtest-result.json&quot;</span><span class="p">)</span>
<span class="c1"># Show value-counts per pair</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;pair&quot;</span><span class="p">)[</span><span class="s2">&quot;sell_reason&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</code></pre></div>
<p>This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.</p>
<p>If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.</p>
<h2 id="live-data">Live data<a class="headerlink" href="#live-data" title="Permanent link">&para;</a></h2>
<p>To analyze the trades your bot generated, you can load them to a DataFrame as follows:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">freqtrade.data.btanalysis</span> <span class="kn">import</span> <span class="n">load_trades_from_db</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">load_trades_from_db</span><span class="p">(</span><span class="s2">&quot;sqlite:///tradesv3.sqlite&quot;</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;pair&quot;</span><span class="p">)[</span><span class="s2">&quot;sell_reason&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</code></pre></div>
<p>Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.</p>
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