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REST API & FreqUI
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Backtesting
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Hyperopt
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FreqAI
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FreqAI
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Introduction
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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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Reinforcement Learning
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Short / Leverage
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Data Analysis
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Strategy analysis
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Advanced Post-installation Tasks
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Trade Object
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Lookahead analysis
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Recursive analysis
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<h1 id="advanced-strategies">Advanced Strategies<a class="headerlink" href="#advanced-strategies" title="Permanent link">&para;</a></h1>
<p>This page explains some advanced concepts available for strategies.
If you're just getting started, please familiarize yourself with the <a href="../bot-basics/">Freqtrade basics</a> and methods described in <a href="../strategy-customization/">Strategy Customization</a> first.</p>
<p>The call sequence of the methods described here is covered under <a href="../bot-basics/#bot-execution-logic">bot execution logic</a>. Those docs are also helpful in deciding which method is most suitable for your customisation needs.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Callback methods should <em>only</em> be implemented if a strategy uses them.</p>
</div>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>Start off with a strategy template containing all available callback methods by running <code>freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced</code></p>
</div>
<h2 id="storing-information">Storing information<a class="headerlink" href="#storing-information" title="Permanent link">&para;</a></h2>
<p>Storing information can be accomplished by creating a new dictionary within the strategy class.</p>
<p>The name of the variable can be chosen at will, but should be prefixed with <code>custom_</code> to avoid naming collisions with predefined strategy variables.</p>
<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">AwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="c1"># Create custom dictionary</span>
<span class="n">custom_info</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">def</span> <span class="nf">populate_indicators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="c1"># Check if the entry already exists</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;pair&quot;</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_info</span><span class="p">:</span>
<span class="c1"># Create empty entry for this pair</span>
<span class="bp">self</span><span class="o">.</span><span class="n">custom_info</span><span class="p">[</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;pair&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">if</span> <span class="s2">&quot;crosstime&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_info</span><span class="p">[</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;pair&quot;</span><span class="p">]]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">custom_info</span><span class="p">[</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;pair&quot;</span><span class="p">]][</span><span class="s2">&quot;crosstime&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">custom_info</span><span class="p">[</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;pair&quot;</span><span class="p">]][</span><span class="s2">&quot;crosstime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
</code></pre></div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.</p>
</div>
<h2 id="dataframe-access">Dataframe access<a class="headerlink" href="#dataframe-access" title="Permanent link">&para;</a></h2>
<p>You may access dataframe in various strategy functions by querying it from dataprovider.</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">freqtrade.exchange</span> <span class="kn">import</span> <span class="n">timeframe_to_prev_date</span>
<span class="k">class</span> <span class="nc">AwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">confirm_trade_exit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pair</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">trade</span><span class="p">:</span> <span class="s1">&#39;Trade&#39;</span><span class="p">,</span> <span class="n">order_type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">amount</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">rate</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">time_in_force</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">exit_reason</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">current_time</span><span class="p">:</span> <span class="s1">&#39;datetime&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="c1"># Obtain pair dataframe.</span>
<span class="n">dataframe</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dp</span><span class="o">.</span><span class="n">get_analyzed_dataframe</span><span class="p">(</span><span class="n">pair</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">timeframe</span><span class="p">)</span>
<span class="c1"># Obtain last available candle. Do not use current_time to look up latest candle, because </span>
<span class="c1"># current_time points to current incomplete candle whose data is not available.</span>
<span class="n">last_candle</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="c1"># &lt;...&gt;</span>
<span class="c1"># In dry/live runs trade open date will not match candle open date therefore it must be </span>
<span class="c1"># rounded.</span>
<span class="n">trade_date</span> <span class="o">=</span> <span class="n">timeframe_to_prev_date</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeframe</span><span class="p">,</span> <span class="n">trade</span><span class="o">.</span><span class="n">open_date_utc</span><span class="p">)</span>
<span class="c1"># Look up trade candle.</span>
<span class="n">trade_candle</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;date&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">trade_date</span><span class="p">]</span>
<span class="c1"># trade_candle may be empty for trades that just opened as it is still incomplete.</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">trade_candle</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
<span class="n">trade_candle</span> <span class="o">=</span> <span class="n">trade_candle</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="c1"># &lt;...&gt;</span>
</code></pre></div>
<div class="admonition warning">
<p class="admonition-title">Using .iloc[-1]</p>
<p>You can use <code>.iloc[-1]</code> here because <code>get_analyzed_dataframe()</code> only returns candles that backtesting is allowed to see.
This will not work in <code>populate_*</code> methods, so make sure to not use <code>.iloc[]</code> in that area.
Also, this will only work starting with version 2021.5.</p>
</div>
<hr />
<h2 id="enter-tag">Enter Tag<a class="headerlink" href="#enter-tag" title="Permanent link">&para;</a></h2>
<p>When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access your buy signal on <code>custom_exit</code></p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">populate_entry_trend</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="n">dataframe</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
<span class="p">(</span>
<span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;rsi&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">35</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;volume&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
<span class="p">),</span>
<span class="p">[</span><span class="s1">&#39;enter_long&#39;</span><span class="p">,</span> <span class="s1">&#39;enter_tag&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;buy_signal_rsi&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dataframe</span>
<span class="k">def</span> <span class="nf">custom_exit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pair</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">trade</span><span class="p">:</span> <span class="n">Trade</span><span class="p">,</span> <span class="n">current_time</span><span class="p">:</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">current_rate</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">current_profit</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">dataframe</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dp</span><span class="o">.</span><span class="n">get_analyzed_dataframe</span><span class="p">(</span><span class="n">pair</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">timeframe</span><span class="p">)</span>
<span class="n">last_candle</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="k">if</span> <span class="n">trade</span><span class="o">.</span><span class="n">enter_tag</span> <span class="o">==</span> <span class="s1">&#39;buy_signal_rsi&#39;</span> <span class="ow">and</span> <span class="n">last_candle</span><span class="p">[</span><span class="s1">&#39;rsi&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">80</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">&#39;sell_signal_rsi&#39;</span>
<span class="k">return</span> <span class="kc">None</span>
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>enter_tag</code> is limited to 100 characters, remaining data will be truncated.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>There is only one <code>enter_tag</code> column, which is used for both long and short trades.
As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.</p>
</div>
<h2 id="exit-tag">Exit tag<a class="headerlink" href="#exit-tag" title="Permanent link">&para;</a></h2>
<p>Similar to <a href="#buy-tag">Buy Tagging</a>, you can also specify a sell tag.</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">populate_exit_trend</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataframe</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="n">dataframe</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
<span class="p">(</span>
<span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;rsi&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">70</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s1">&#39;volume&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
<span class="p">),</span>
<span class="p">[</span><span class="s1">&#39;exit_long&#39;</span><span class="p">,</span> <span class="s1">&#39;exit_tag&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;exit_rsi&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dataframe</span>
</code></pre></div>
<p>The provided exit-tag is then used as sell-reason - and shown as such in backtest results.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>exit_reason</code> is limited to 100 characters, remaining data will be truncated.</p>
</div>
<h2 id="strategy-version">Strategy version<a class="headerlink" href="#strategy-version" title="Permanent link">&para;</a></h2>
<p>You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">version</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns version of the strategy.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="s2">&quot;1.1&quot;</span>
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.</p>
</div>
<h2 id="derived-strategies">Derived strategies<a class="headerlink" href="#derived-strategies" title="Permanent link">&para;</a></h2>
<p>The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:</p>
<div class="highlight"><span class="filename">user_data/strategies/myawesomestrategy.py</span><pre><span></span><code><span class="k">class</span> <span class="nc">MyAwesomeStrategy</span><span class="p">(</span><span class="n">IStrategy</span><span class="p">):</span>
<span class="o">...</span>
<span class="n">stoploss</span> <span class="o">=</span> <span class="mf">0.13</span>
<span class="n">trailing_stop</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1"># All other attributes and methods are here as they</span>
<span class="c1"># should be in any custom strategy...</span>
<span class="o">...</span>
</code></pre></div>
<div class="highlight"><span class="filename">user_data/strategies/MyAwesomeStrategy2.py</span><pre><span></span><code><span class="kn">from</span> <span class="nn">myawesomestrategy</span> <span class="kn">import</span> <span class="n">MyAwesomeStrategy</span>
<span class="k">class</span> <span class="nc">MyAwesomeStrategy2</span><span class="p">(</span><span class="n">MyAwesomeStrategy</span><span class="p">):</span>
<span class="c1"># Override something</span>
<span class="n">stoploss</span> <span class="o">=</span> <span class="mf">0.08</span>
<span class="n">trailing_stop</span> <span class="o">=</span> <span class="kc">True</span>
</code></pre></div>
<p>Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.</p>
<p>While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.</p>
<h2 id="embedding-strategies">Embedding Strategies<a class="headerlink" href="#embedding-strategies" title="Permanent link">&para;</a></h2>
<p>Freqtrade provides you with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.</p>
<h3 id="encoding-a-string-as-base64">Encoding a string as BASE64<a class="headerlink" href="#encoding-a-string-as-base64" title="Permanent link">&para;</a></h3>
<p>This is a quick example, how to generate the BASE64 string in python</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">base64</span> <span class="kn">import</span> <span class="n">urlsafe_b64encode</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">content</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">content</span> <span class="o">=</span> <span class="n">urlsafe_b64encode</span><span class="p">(</span><span class="n">content</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
</code></pre></div>
<p>The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following</p>
<div class="highlight"><pre><span></span><code><span class="nt">&quot;strategy&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;NameOfStrategy:BASE64String&quot;</span>
</code></pre></div>
<p>Please ensure that 'NameOfStrategy' is identical to the strategy name!</p>
<h2 id="performance-warning">Performance warning<a class="headerlink" href="#performance-warning" title="Permanent link">&para;</a></h2>
<p>When executing a strategy, one can sometimes be greeted by the following in the logs</p>
<blockquote>
<p>PerformanceWarning: DataFrame is highly fragmented.</p>
</blockquote>
<p>This is a warning from <a href="https://github.com/pandas-dev/pandas"><code>pandas</code></a> and as the warning continues to say:
use <code>pd.concat(axis=1)</code>.
This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).</p>
<p>For example:</p>
<div class="highlight"><pre><span></span><code><span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">buy_ema_short</span><span class="o">.</span><span class="n">range</span><span class="p">:</span>
<span class="n">dataframe</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;ema_short_</span><span class="si">{</span><span class="n">val</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ta</span><span class="o">.</span><span class="n">EMA</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">timeperiod</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
</code></pre></div>
<p>should be rewritten to</p>
<div class="highlight"><pre><span></span><code><span class="n">frames</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataframe</span><span class="p">]</span>
<span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">buy_ema_short</span><span class="o">.</span><span class="n">range</span><span class="p">:</span>
<span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="sa">f</span><span class="s1">&#39;ema_short_</span><span class="si">{</span><span class="n">val</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">:</span> <span class="n">ta</span><span class="o">.</span><span class="n">EMA</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">timeperiod</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
<span class="p">}))</span>
<span class="c1"># Combine all dataframes, and reassign the original dataframe column</span>
<span class="n">dataframe</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">frames</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</code></pre></div>
<p>Freqtrade does however also counter this by running <code>dataframe.copy()</code> on the dataframe right after the <code>populate_indicators()</code> method - so performance implications of this should be low to non-existant.</p>
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