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Setup
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Load and run strategy
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Display the trade details
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Load existing objects into a Jupyter notebook
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Load backtest results to pandas dataframe
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Plotting daily profit / equity line
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Load live trading results into a pandas dataframe
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Analyze the loaded trades for trade parallelism
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Plot results
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Plot average profit per trade as distribution graph
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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.
The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.</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="kn">from</span> <span class="nn">freqtrade.configuration</span> <span class="kn">import</span> <span class="n">Configuration</span>
<span class="c1"># Customize these according to your needs.</span>
<span class="c1"># Initialize empty configuration object</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">Configuration</span><span class="o">.</span><span class="n">from_files</span><span class="p">([])</span>
<span class="c1"># Optionally (recommended), use existing configuration file</span>
<span class="c1"># config = Configuration.from_files([&quot;config.json&quot;])</span>
<span class="c1"># Define some constants</span>
<span class="n">config</span><span class="p">[</span><span class="s2">&quot;timeframe&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;5m&quot;</span>
<span class="c1"># Name of the strategy class</span>
<span class="n">config</span><span class="p">[</span><span class="s2">&quot;strategy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;SampleStrategy&quot;</span>
<span class="c1"># Location of the data</span>
<span class="n">data_location</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;datadir&#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="kn">from</span> <span class="nn">freqtrade.enums</span> <span class="kn">import</span> <span class="n">CandleType</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">timeframe</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;timeframe&quot;</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_format</span> <span class="o">=</span> <span class="s2">&quot;hdf5&quot;</span><span class="p">,</span>
<span class="n">candle_type</span><span class="o">=</span><span class="n">CandleType</span><span class="o">.</span><span class="n">SPOT</span><span class="p">,</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="kn">from</span> <span class="nn">freqtrade.data.dataprovider</span> <span class="kn">import</span> <span class="n">DataProvider</span>
<span class="n">strategy</span> <span class="o">=</span> <span class="n">StrategyResolver</span><span class="o">.</span><span class="n">load_strategy</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
<span class="n">strategy</span><span class="o">.</span><span class="n">dp</span> <span class="o">=</span> <span class="n">DataProvider</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</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;enter_long&#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"> entry 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="p">,</span> <span class="n">load_backtest_stats</span>
<span class="c1"># if backtest_dir points to a directory, it&#39;ll automatically load the last backtest file.</span>
<span class="n">backtest_dir</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;user_data_dir&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="s2">&quot;backtest_results&quot;</span>
<span class="c1"># backtest_dir can also point to a specific file </span>
<span class="c1"># backtest_dir = config[&quot;user_data_dir&quot;] / &quot;backtest_results/backtest-result-2020-07-01_20-04-22.json&quot;</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="c1"># You can get the full backtest statistics by using the following command.</span>
<span class="c1"># This contains all information used to generate the backtest result.</span>
<span class="n">stats</span> <span class="o">=</span> <span class="n">load_backtest_stats</span><span class="p">(</span><span class="n">backtest_dir</span><span class="p">)</span>
<span class="n">strategy</span> <span class="o">=</span> <span class="s1">&#39;SampleStrategy&#39;</span>
<span class="c1"># All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.</span>
<span class="c1"># Example usages:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;results_per_pair&#39;</span><span class="p">])</span>
<span class="c1"># Get pairlist used for this backtest</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;pairlist&#39;</span><span class="p">])</span>
<span class="c1"># Get market change (average change of all pairs from start to end of the backtest period)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;market_change&#39;</span><span class="p">])</span>
<span class="c1"># Maximum drawdown ()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;max_drawdown&#39;</span><span class="p">])</span>
<span class="c1"># Maximum drawdown start and end</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;drawdown_start&#39;</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">][</span><span class="s1">&#39;drawdown_end&#39;</span><span class="p">])</span>
<span class="c1"># Get strategy comparison (only relevant if multiple strategies were compared)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy_comparison&#39;</span><span class="p">])</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="c1"># Load backtested trades as dataframe</span>
<span class="n">trades</span> <span class="o">=</span> <span class="n">load_backtest_data</span><span class="p">(</span><span class="n">backtest_dir</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;exit_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="plotting-daily-profit-equity-line">Plotting daily profit / equity line<a class="headerlink" href="#plotting-daily-profit-equity-line" title="Permanent link">&para;</a></h2>
<div class="highlight"><pre><span></span><code><span class="c1"># Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)</span>
<span class="kn">from</span> <span class="nn">freqtrade.configuration</span> <span class="kn">import</span> <span class="n">Configuration</span>
<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="p">,</span> <span class="n">load_backtest_stats</span>
<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="c1"># strategy = &#39;SampleStrategy&#39;</span>
<span class="c1"># config = Configuration.from_files([&quot;user_data/config.json&quot;])</span>
<span class="c1"># backtest_dir = config[&quot;user_data_dir&quot;] / &quot;backtest_results&quot;</span>
<span class="n">stats</span> <span class="o">=</span> <span class="n">load_backtest_stats</span><span class="p">(</span><span class="n">backtest_dir</span><span class="p">)</span>
<span class="n">strategy_stats</span> <span class="o">=</span> <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;strategy&#39;</span><span class="p">][</span><span class="n">strategy</span><span class="p">]</span>
<span class="n">dates</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">profits</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">date_profit</span> <span class="ow">in</span> <span class="n">strategy_stats</span><span class="p">[</span><span class="s1">&#39;daily_profit&#39;</span><span class="p">]:</span>
<span class="n">dates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">date_profit</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">profits</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">date_profit</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">equity</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">equity_daily</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">daily_profit</span> <span class="ow">in</span> <span class="n">profits</span><span class="p">:</span>
<span class="n">equity_daily</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">equity</span><span class="p">)</span>
<span class="n">equity</span> <span class="o">+=</span> <span class="nb">float</span><span class="p">(</span><span class="n">daily_profit</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;dates&#39;</span><span class="p">:</span> <span class="n">dates</span><span class="p">,</span><span class="s1">&#39;equity_daily&#39;</span><span class="p">:</span> <span class="n">equity_daily</span><span class="p">})</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;dates&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;equity_daily&quot;</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">show</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;exit_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="analyze-the-loaded-trades-for-trade-parallelism">Analyze the loaded trades for trade parallelism<a class="headerlink" href="#analyze-the-loaded-trades-for-trade-parallelism" title="Permanent link">&para;</a></h2>
<p>This can be useful to find the best <code>max_open_trades</code> parameter, when used with backtesting in conjunction with <code>--disable-max-market-positions</code>.</p>
<p><code>analyze_trade_parallelism()</code> returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.</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">analyze_trade_parallelism</span>
<span class="c1"># Analyze the above</span>
<span class="n">parallel_trades</span> <span class="o">=</span> <span class="n">analyze_trade_parallelism</span><span class="p">(</span><span class="n">trades</span><span class="p">,</span> <span class="s1">&#39;5m&#39;</span><span class="p">)</span>
<span class="n">parallel_trades</span><span class="o">.</span><span class="n">plot</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="c1"># Filter trades to one pair</span>
<span class="n">trades_red</span> <span class="o">=</span> <span class="n">trades</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">trades</span><span class="p">[</span><span class="s1">&#39;pair&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">pair</span><span class="p">]</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_red</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>
<h2 id="plot-average-profit-per-trade-as-distribution-graph">Plot average profit per trade as distribution graph<a class="headerlink" href="#plot-average-profit-per-trade-as-distribution-graph" title="Permanent link">&para;</a></h2>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">plotly.figure_factory</span> <span class="k">as</span> <span class="nn">ff</span>
<span class="n">hist_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">trades</span><span class="o">.</span><span class="n">profit_ratio</span><span class="p">]</span>
<span class="n">group_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;profit_ratio&#39;</span><span class="p">]</span> <span class="c1"># name of the dataset</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">ff</span><span class="o">.</span><span class="n">create_distplot</span><span class="p">(</span><span class="n">hist_data</span><span class="p">,</span> <span class="n">group_labels</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">show</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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