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<h1 id="strategy-analysis-example">Strategy analysis example<a class="headerlink" href="#strategy-analysis-example" title="Permanent link">&para;</a></h1>
<p>Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.</p>
<h2 id="setup">Setup<a class="headerlink" href="#setup" title="Permanent link">&para;</a></h2>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="c1"># Customize these according to your needs.</span>
<span class="c1"># Define some constants</span>
<span class="n">ticker_interval</span> <span class="o">=</span> <span class="s2">&quot;5m&quot;</span>
<span class="c1"># Name of the strategy class</span>
<span class="n">strategy_name</span> <span class="o">=</span> <span class="s1">&#39;SampleStrategy&#39;</span>
<span class="c1"># Path to user data</span>
<span class="n">user_data_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s1">&#39;user_data&#39;</span><span class="p">)</span>
<span class="c1"># Location of the strategy</span>
<span class="n">strategy_location</span> <span class="o">=</span> <span class="n">user_data_dir</span> <span class="o">/</span> <span class="s1">&#39;strategies&#39;</span>
<span class="c1"># Location of the data</span>
<span class="n">data_location</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">user_data_dir</span><span class="p">,</span> <span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="s1">&#39;binance&#39;</span><span class="p">)</span>
<span class="c1"># Pair to analyze - Only use one pair here</span>
<span class="n">pair</span> <span class="o">=</span> <span class="s2">&quot;BTC_USDT&quot;</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="c1"># Load data using values set above</span>
<span class="kn">from</span> <span class="nn">freqtrade.data.history</span> <span class="kn">import</span> <span class="n">load_pair_history</span>
<span class="n">candles</span> <span class="o">=</span> <span class="n">load_pair_history</span><span class="p">(</span><span class="n">datadir</span><span class="o">=</span><span class="n">data_location</span><span class="p">,</span>
<span class="n">ticker_interval</span><span class="o">=</span><span class="n">ticker_interval</span><span class="p">,</span>
<span class="n">pair</span><span class="o">=</span><span class="n">pair</span><span class="p">)</span>
<span class="c1"># Confirm success</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Loaded &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">candles</span><span class="p">))</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot; rows of data for </span><span class="si">{</span><span class="n">pair</span><span class="si">}</span><span class="s2"> from </span><span class="si">{</span><span class="n">data_location</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">candles</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</code></pre></div>
<h2 id="load-and-run-strategy">Load and run strategy<a class="headerlink" href="#load-and-run-strategy" title="Permanent link">&para;</a></h2>
<ul>
<li>Rerun each time the strategy file is changed</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># Load strategy using values set above</span>
<span class="kn">from</span> <span class="nn">freqtrade.resolvers</span> <span class="kn">import</span> <span class="n">StrategyResolver</span>
<span class="n">strategy</span> <span class="o">=</span> <span class="n">StrategyResolver</span><span class="p">({</span><span class="s1">&#39;strategy&#39;</span><span class="p">:</span> <span class="n">strategy_name</span><span class="p">,</span>
<span class="s1">&#39;user_data_dir&#39;</span><span class="p">:</span> <span class="n">user_data_dir</span><span class="p">,</span>
<span class="s1">&#39;strategy_path&#39;</span><span class="p">:</span> <span class="n">strategy_location</span><span class="p">})</span><span class="o">.</span><span class="n">strategy</span>
<span class="c1"># Generate buy/sell signals using strategy</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">strategy</span><span class="o">.</span><span class="n">analyze_ticker</span><span class="p">(</span><span class="n">candles</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;pair&#39;</span><span class="p">:</span> <span class="n">pair</span><span class="p">})</span>
<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
</code></pre></div>
<h3 id="display-the-trade-details">Display the trade details<a class="headerlink" href="#display-the-trade-details" title="Permanent link">&para;</a></h3>
<ul>
<li>Note that using <code>data.head()</code> would also work, however most indicators have some "startup" data at the top of the dataframe.</li>
<li>Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in <code>crossed*()</code> functions with completely different units</li>
<li>Comparison with full backtest
* having 200 buy signals as output for one pair from <code>analyze_ticker()</code> does not necessarily mean that 200 trades will be made during backtesting.
* Assuming you use only one condition such as, <code>df['rsi'] &lt; 30</code> as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns &gt; 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available. </li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># Report results</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Generated </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;buy&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2"> buy signals&quot;</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;date&#39;</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
</code></pre></div>
<h2 id="load-existing-objects-into-a-jupyter-notebook">Load existing objects into a Jupyter notebook<a class="headerlink" href="#load-existing-objects-into-a-jupyter-notebook" title="Permanent link">&para;</a></h2>
<p>The following cells assume that you have already generated data using the cli.<br />
They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.</p>
<h3 id="load-backtest-results-to-pandas-dataframe">Load backtest results to pandas dataframe<a class="headerlink" href="#load-backtest-results-to-pandas-dataframe" title="Permanent link">&para;</a></h3>
<p>Analyze a trades dataframe (also used below for plotting)</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="c1"># Load backtest results</span>
<span class="n">trades</span> <span class="o">=</span> <span class="n">load_backtest_data</span><span class="p">(</span><span class="n">user_data_dir</span> <span class="o">/</span> <span class="s2">&quot;backtest_results/backtest-result.json&quot;</span><span class="p">)</span>
<span class="c1"># Show value-counts per pair</span>
<span class="n">trades</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>
<h3 id="load-live-trading-results-into-a-pandas-dataframe">Load live trading results into a pandas dataframe<a class="headerlink" href="#load-live-trading-results-into-a-pandas-dataframe" title="Permanent link">&para;</a></h3>
<p>In case you did already some trading and want to analyze your performance</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="c1"># Fetch trades from database</span>
<span class="n">trades</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="c1"># Display results</span>
<span class="n">trades</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>
<h2 id="plot-results">Plot results<a class="headerlink" href="#plot-results" title="Permanent link">&para;</a></h2>
<p>Freqtrade offers interactive plotting capabilities based on plotly.</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">freqtrade.plot.plotting</span> <span class="kn">import</span> <span class="n">generate_candlestick_graph</span>
<span class="c1"># Limit graph period to keep plotly quick and reactive</span>
<span class="n">data_red</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;2019-06-01&#39;</span><span class="p">:</span><span class="s1">&#39;2019-06-10&#39;</span><span class="p">]</span>
<span class="c1"># Generate candlestick graph</span>
<span class="n">graph</span> <span class="o">=</span> <span class="n">generate_candlestick_graph</span><span class="p">(</span><span class="n">pair</span><span class="o">=</span><span class="n">pair</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">data_red</span><span class="p">,</span>
<span class="n">trades</span><span class="o">=</span><span class="n">trades</span><span class="p">,</span>
<span class="n">indicators1</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sma20&#39;</span><span class="p">,</span> <span class="s1">&#39;ema50&#39;</span><span class="p">,</span> <span class="s1">&#39;ema55&#39;</span><span class="p">],</span>
<span class="n">indicators2</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;rsi&#39;</span><span class="p">,</span> <span class="s1">&#39;macd&#39;</span><span class="p">,</span> <span class="s1">&#39;macdsignal&#39;</span><span class="p">,</span> <span class="s1">&#39;macdhist&#39;</span><span class="p">]</span>
<span class="p">)</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="c1"># Show graph inline</span>
<span class="c1"># graph.show()</span>
<span class="c1"># Render graph in a seperate window</span>
<span class="n">graph</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">renderer</span><span class="o">=</span><span class="s2">&quot;browser&quot;</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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