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Freqtrade
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Home
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<a href="../docker_quickstart/" class="md-nav__link">
<span class="md-ellipsis">
Quickstart with Docker
</span>
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Installation
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Installation
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Linux/MacOS/Raspberry
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Windows
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Freqtrade Basics
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Configuration
</span>
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Strategy Customization
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Strategy Callbacks
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Stoploss
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Plugins
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Start the bot
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Control the bot
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Control the bot
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Telegram
</span>
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<a href="../freq-ui/" class="md-nav__link">
<span class="md-ellipsis">
freqUI
</span>
</a>
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REST API
</span>
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Web Hook
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<li class="md-nav__item">
<a href="../data-download/" class="md-nav__link">
<span class="md-ellipsis">
Data Downloading
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../backtesting/" class="md-nav__link">
<span class="md-ellipsis">
Backtesting
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../hyperopt/" class="md-nav__link">
<span class="md-ellipsis">
Hyperopt
</span>
</a>
</li>
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FreqAI
</span>
<span class="md-nav__icon md-icon"></span>
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FreqAI
</label>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="../freqai/" class="md-nav__link">
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Introduction
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-configuration/" class="md-nav__link">
<span class="md-ellipsis">
Configuration
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-parameter-table/" class="md-nav__link">
<span class="md-ellipsis">
Parameter table
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-feature-engineering/" class="md-nav__link">
<span class="md-ellipsis">
Feature engineering
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-running/" class="md-nav__link">
<span class="md-ellipsis">
Running FreqAI
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-reinforcement-learning/" class="md-nav__link">
<span class="md-ellipsis">
Reinforcement Learning
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../freqai-developers/" class="md-nav__link">
<span class="md-ellipsis">
Developer guide
</span>
</a>
</li>
</ul>
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<li class="md-nav__item">
<a href="../leverage/" class="md-nav__link">
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Short / Leverage
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../utils/" class="md-nav__link">
<span class="md-ellipsis">
Utility Sub-commands
</span>
</a>
</li>
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<a href="../plotting/" class="md-nav__link">
<span class="md-ellipsis">
Plotting
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../exchanges/" class="md-nav__link">
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Exchange-specific Notes
</span>
</a>
</li>
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Data Analysis
</span>
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Data Analysis
</label>
<ul class="md-nav__list" data-md-scrollfix>
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<a href="../data-analysis/" class="md-nav__link">
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Jupyter Notebooks
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../strategy_analysis_example/" class="md-nav__link">
<span class="md-ellipsis">
Strategy analysis
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../advanced-backtesting/" class="md-nav__link">
<span class="md-ellipsis">
Backtest analysis
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Advanced Topics
</span>
<span class="md-nav__icon md-icon"></span>
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Advanced Topics
</label>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="../advanced-setup/" class="md-nav__link">
<span class="md-ellipsis">
Advanced Post-installation Tasks
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../trade-object/" class="md-nav__link">
<span class="md-ellipsis">
Trade Object
</span>
</a>
</li>
<li class="md-nav__item">
<a href="../lookahead-analysis/" class="md-nav__link">
<span class="md-ellipsis">
Lookahead analysis
</span>
</a>
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<h1 id="orderflow-data">Orderflow data<a class="headerlink" href="#orderflow-data" title="Permanent link">&para;</a></h1>
<p>This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.</p>
<div class="admonition warning">
<p class="admonition-title">Experimental Feature</p>
<p>The orderflow feature is currently in beta and may be subject to changes in future releases. Please report any issues or feedback on the <a href="https://github.com/freqtrade/freqtrade/issues">Freqtrade GitHub repository</a>.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Performance</p>
<p>Orderflow requires raw trades data. This data is rather large, and can cause a slow initial startup, when freqtrade needs to download the trades data for the last X candles. Additionally, enabling this feature will cause increased memory usage. Please ensure to have sufficient resources available.</p>
</div>
<h2 id="getting-started">Getting Started<a class="headerlink" href="#getting-started" title="Permanent link">&para;</a></h2>
<h3 id="enable-public-trades">Enable Public Trades<a class="headerlink" href="#enable-public-trades" title="Permanent link">&para;</a></h3>
<p>In your <code>config.json</code> file, set the <code>use_public_trades</code> option to true under the <code>exchange</code> section.</p>
<div class="highlight"><pre><span></span><code><span class="nt">&quot;exchange&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="err">...</span>
<span class="w"> </span><span class="nt">&quot;use_public_trades&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="p">}</span>
</code></pre></div>
<h3 id="configure-orderflow-processing">Configure Orderflow Processing<a class="headerlink" href="#configure-orderflow-processing" title="Permanent link">&para;</a></h3>
<p>Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:</p>
<ul>
<li><code>cache_size</code>: How many previous orderflow candles are saved into cache instead of calculated every new candle</li>
<li><code>max_candles</code>: Filter how many candles would you like to get trades data for.</li>
<li><code>scale</code>: This controls the price bin size for the footprint chart.</li>
<li><code>stacked_imbalance_range</code>: Defines the minimum consecutive imbalanced price levels required for consideration.</li>
<li><code>imbalance_volume</code>: Filters out imbalances with volume below this threshold.</li>
<li><code>imbalance_ratio</code>: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="nt">&quot;orderflow&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;cache_size&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1000</span><span class="p">,</span><span class="w"> </span>
<span class="w"> </span><span class="nt">&quot;max_candles&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1500</span><span class="p">,</span><span class="w"> </span>
<span class="w"> </span><span class="nt">&quot;scale&quot;</span><span class="p">:</span><span class="w"> </span><span class="mf">0.5</span><span class="p">,</span><span class="w"> </span>
<span class="w"> </span><span class="nt">&quot;stacked_imbalance_range&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="c1">// needs at least this amount of imbalance next to each other</span>
<span class="w"> </span><span class="nt">&quot;imbalance_volume&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="c1">// filters out below</span>
<span class="w"> </span><span class="nt">&quot;imbalance_ratio&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="c1">// filters out ratio lower than</span>
<span class="w"> </span><span class="p">},</span>
</code></pre></div>
<h2 id="downloading-trade-data-for-backtesting">Downloading Trade Data for Backtesting<a class="headerlink" href="#downloading-trade-data-for-backtesting" title="Permanent link">&para;</a></h2>
<p>To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.</p>
<div class="highlight"><pre><span></span><code>freqtrade<span class="w"> </span>download-data<span class="w"> </span>-p<span class="w"> </span>BTC/USDT:USDT<span class="w"> </span>--timerange<span class="w"> </span><span class="m">20230101</span>-<span class="w"> </span>--trading-mode<span class="w"> </span>futures<span class="w"> </span>--timeframes<span class="w"> </span>5m<span class="w"> </span>--dl-trades
</code></pre></div>
<div class="admonition warning">
<p class="admonition-title">Data availability</p>
<p>Not all exchanges provide public trade data. For supported exchanges, freqtrade will warn you if public trade data is not available if you start downloading data with the <code>--dl-trades</code> flag.</p>
</div>
<h2 id="accessing-orderflow-data">Accessing Orderflow Data<a class="headerlink" href="#accessing-orderflow-data" title="Permanent link">&para;</a></h2>
<p>Once activated, several new columns become available in your dataframe:</p>
<div class="highlight"><pre><span></span><code><span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;trades&quot;</span><span class="p">]</span> <span class="c1"># Contains information about each individual trade.</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;orderflow&quot;</span><span class="p">]</span> <span class="c1"># Represents a footprint chart dict (see below)</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;imbalances&quot;</span><span class="p">]</span> <span class="c1"># Contains information about imbalances in the order flow.</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;bid&quot;</span><span class="p">]</span> <span class="c1"># Total bid volume </span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;ask&quot;</span><span class="p">]</span> <span class="c1"># Total ask volume</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;delta&quot;</span><span class="p">]</span> <span class="c1"># Difference between ask and bid volume.</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;min_delta&quot;</span><span class="p">]</span> <span class="c1"># Minimum delta within the candle</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;max_delta&quot;</span><span class="p">]</span> <span class="c1"># Maximum delta within the candle</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;total_trades&quot;</span><span class="p">]</span> <span class="c1"># Total number of trades</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;stacked_imbalances_bid&quot;</span><span class="p">]</span> <span class="c1"># Price level of stacked bid imbalance </span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;stacked_imbalances_ask&quot;</span><span class="p">]</span> <span class="c1"># Price level of stacked ask imbalance </span>
</code></pre></div>
<p>You can access these columns in your strategy code for further analysis. Here's an example:</p>
<div class="highlight"><pre><span></span><code><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"># Calculating cumulative delta</span>
<span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;cum_delta&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cumulative_delta</span><span class="p">(</span><span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;delta&quot;</span><span class="p">])</span>
<span class="c1"># Accessing total trades</span>
<span class="n">total_trades</span> <span class="o">=</span> <span class="n">dataframe</span><span class="p">[</span><span class="s2">&quot;total_trades&quot;</span><span class="p">]</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">cumulative_delta</span><span class="p">(</span><span class="n">delta</span><span class="p">:</span> <span class="n">Series</span><span class="p">):</span>
<span class="n">cumdelta</span> <span class="o">=</span> <span class="n">delta</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">cumdelta</span>
</code></pre></div>
<h3 id="footprint-chart-dataframeorderflow">Footprint chart (<code>dataframe["orderflow"]</code>)<a class="headerlink" href="#footprint-chart-dataframeorderflow" title="Permanent link">&para;</a></h3>
<p>This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The <code>scale</code> parameter in your configuration determines the price bin size for this representation</p>
<p>The <code>orderflow</code> column contains a dict with the following structure:</p>
<div class="highlight"><pre><span></span><code><span class="go">{</span>
<span class="go"> &quot;price&quot;: {</span>
<span class="go"> &quot;bid_amount&quot;: 0.0,</span>
<span class="go"> &quot;ask_amount&quot;: 0.0,</span>
<span class="go"> &quot;bid&quot;: 0,</span>
<span class="go"> &quot;ask&quot;: 0,</span>
<span class="go"> &quot;delta&quot;: 0.0,</span>
<span class="go"> &quot;total_volume&quot;: 0.0,</span>
<span class="go"> &quot;total_trades&quot;: 0</span>
<span class="go"> }</span>
<span class="go">}</span>
</code></pre></div>
<h4 id="orderflow-column-explanation">Orderflow column explanation<a class="headerlink" href="#orderflow-column-explanation" title="Permanent link">&para;</a></h4>
<ul>
<li>key: Price bin - binned at <code>scale</code> intervals</li>
<li><code>bid_amount</code>: Total volume bought at each price level.</li>
<li><code>ask_amount</code>: Total volume sold at each price level.</li>
<li><code>bid</code>: Number of buy orders at each price level.</li>
<li><code>ask</code>: Number of sell orders at each price level.</li>
<li><code>delta</code>: Difference between ask and bid volume at each price level.</li>
<li><code>total_volume</code>: Total volume (ask amount + bid amount) at each price level.</li>
<li><code>total_trades</code>: Total number of trades (ask + bid) at each price level.</li>
</ul>
<p>By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.</p>
<h3 id="raw-trades-data-dataframetrades">Raw trades data (<code>dataframe["trades"]</code>)<a class="headerlink" href="#raw-trades-data-dataframetrades" title="Permanent link">&para;</a></h3>
<p>List with the individual trades that occurred during the candle. This data can be used for more granular analysis of order flow dynamics.</p>
<p>Each individual entry contains a dict with the following keys:</p>
<ul>
<li><code>timestamp</code>: Timestamp of the trade.</li>
<li><code>date</code>: Date of the trade.</li>
<li><code>price</code>: Price of the trade.</li>
<li><code>amount</code>: Volume of the trade.</li>
<li><code>side</code>: Buy or sell.</li>
<li><code>id</code>: Unique identifier for the trade.</li>
<li><code>cost</code>: Total cost of the trade (price * amount).</li>
</ul>
<h3 id="imbalances-dataframeimbalances">Imbalances (<code>dataframe["imbalances"]</code>)<a class="headerlink" href="#imbalances-dataframeimbalances" title="Permanent link">&para;</a></h3>
<p>This column provides a dict with information about imbalances in the order flow. An imbalance occurs when there is a significant difference between the ask and bid volume at a given price level.</p>
<p>Each row looks as follows - with price as index, and the corresponding bid and ask imbalance values as columns</p>
<div class="highlight"><pre><span></span><code><span class="go">{</span>
<span class="go"> &quot;price&quot;: {</span>
<span class="go"> &quot;bid_imbalance&quot;: False,</span>
<span class="go"> &quot;ask_imbalance&quot;: False</span>
<span class="go"> }</span>
<span class="go">}</span>
</code></pre></div>
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