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@@ -2105,11 +2105,11 @@ It's also currently not been tested with freqAI - and combining these two featur
<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>
<p><code>json
"exchange": {
...
"use_public_trades": true,
}</code></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>
@@ -2120,65 +2120,64 @@ It's also currently not been tested with freqAI - and combining these two featur
<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>
<p><code>json
"orderflow": {
"cache_size": 1000,
"max_candles": 1500,
"scale": 0.5,
"stacked_imbalance_range": 3, // needs at least this amount of imbalance next to each other
"imbalance_volume": 1, // filters out below
"imbalance_ratio": 3 // filters out ratio lower than
},</code></p>
<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>
<p><code>bash
freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades</code></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>
<p>``` python</p>
<p>dataframe["trades"] # Contains information about each individual trade.
dataframe["orderflow"] # Represents a footprint chart dict (see below)
dataframe["imbalances"] # Contains information about imbalances in the order flow.
dataframe["bid"] # Total bid volume
dataframe["ask"] # Total ask volume
dataframe["delta"] # Difference between ask and bid volume.
dataframe["min_delta"] # Minimum delta within the candle
dataframe["max_delta"] # Maximum delta within the candle
dataframe["total_trades"] # Total number of trades
dataframe["stacked_imbalances_bid"] # List of price levels of stacked bid imbalance range beginnings
dataframe["stacked_imbalances_ask"] # List of price levels of stacked ask imbalance range beginnings
```</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"># List of price levels of stacked bid imbalance range beginnings</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"># List of price levels of stacked ask imbalance range beginnings</span>
</code></pre></div>
<p>You can access these columns in your strategy code for further analysis. Here's an example:</p>
<p>``` python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -&gt; DataFrame:
# Calculating cumulative delta
dataframe["cum_delta"] = cumulative_delta(dataframe["delta"])
# Accessing total trades
total_trades = dataframe["total_trades"]
...</p>
<p>def cumulative_delta(delta: Series):
cumdelta = delta.cumsum()
return cumdelta</p>
<p>```</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span><span class="w"> </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="w"> </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>
<p><code>output
{
"price": {
"bid_amount": 0.0,
"ask_amount": 0.0,
"bid": 0,
"ask": 0,
"delta": 0.0,
"total_volume": 0.0,
"total_trades": 0
}
}</code></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>
@@ -2206,13 +2205,13 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -&gt; DataFr
<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>
<p><code>output
{
"price": {
"bid_imbalance": False,
"ask_imbalance": False
}
}</code></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>