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Update 02 Trades.php
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  • 08 Drafts/05 Historical Data/04 Asset Classes/01 US Equities

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08 Drafts/05 Historical Data/04 Asset Classes/01 US Equities/02 Trades.php

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include(DOCS_RESOURCES."/history/tradebars.php");
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?>
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<p class='csharp'>
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To get historical <a href='<?=$dataTypeLink?>'>trade data</a>, call the <code>History&lt;<?=$dataType?>&gt;</code> method with an asset's <code>Symbol</code>.
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</p>
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<p class='python'>
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To get historical <a href='<?=$dataTypeLink?>'>trade data</a>, call the <code>history</code> method with the <code><?=$dataType?></code> type and an asset's <code>Symbol</code>.
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This method returns a DataFrame with columns for the open, high, low, close, and volume.
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</p>
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<div class="section-example-container">
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<pre class="csharp">public class USEquityTradeBarHistoryAlgorithm : QCAlgorithm
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{
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public override void Initialize()
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{
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SetStartDate(2024, 12, 19);
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// Get the Symbol of an asset.
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var symbol = AddEquity("SPY").Symbol;
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// Get the 5 trailing daily <?=$dataType?> objects of the asset.
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var history = History&lt;<?=$dataType?>&gt;(symbol, 5, Resolution.Daily);
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// Iterate through each TradeBar and calculate its dollar volume.
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foreach (var bar in history)
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{
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var t = bar.EndTime;
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var dollarVolume = bar.Close * bar.Volume;
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}
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}
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}</pre>
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<pre class="python">class USEquityTradeBarHistoryAlgorithm(QCAlgorithm):
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def initialize(self) -> None:
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self.set_start_date(2024, 12, 19)
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# Get the Symbol of an asset.
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symbol = self.add_equity('SPY').symbol
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# Get the 5 trailing daily <?=$dataType?> objects of the asset in DataFrame format.
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history = self.history(<?=$dataType?>, symbol, 5, Resolution.DAILY)</pre>
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</div>
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<table border="1" class="dataframe python">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th></th>
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<th>close</th>
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<th>high</th>
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<th>low</th>
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<th>open</th>
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<th>volume</th>
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</tr>
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<tr>
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<th>symbol</th>
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<th>time</th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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<th></th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th rowspan="5" valign="top">SPY</th>
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<th>2024-12-12 16:00:00</th>
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<td>604.33</td>
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<td>607.160</td>
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<td>604.33</td>
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<td>606.64</td>
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<td>24962360.0</td>
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</tr>
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<tr>
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<th>2024-12-13 16:00:00</th>
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<td>604.21</td>
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<td>607.100</td>
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<td>602.82</td>
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<td>606.38</td>
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<td>29250856.0</td>
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</tr>
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<tr>
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<th>2024-12-16 16:00:00</th>
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<td>606.79</td>
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<td>607.775</td>
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<td>605.22</td>
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<td>606.00</td>
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<td>33686372.0</td>
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</tr>
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<tr>
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<th>2024-12-17 16:00:00</th>
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<td>604.29</td>
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<td>605.160</td>
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<td>602.89</td>
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<td>604.22</td>
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<td>38527534.0</td>
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</tr>
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<tr>
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<th>2024-12-18 16:00:00</th>
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<td>586.28</td>
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<td>606.400</td>
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<td>585.89</td>
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<td>604.00</td>
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<td>80642184.0</td>
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</tr>
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</tbody>
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</table>
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<div class="python section-example-container">
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<pre class="python"># Calculate the daily returns.
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daily_returns = history.close.pct_change().iloc[1:]</pre>
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</div>
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<div class="python section-example-container">
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<pre>symbol time
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SPY 2024-12-13 16:00:00 -0.000199
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2024-12-16 16:00:00 0.004270
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2024-12-17 16:00:00 -0.004120
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2024-12-18 16:00:00 -0.029804
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Name: close, dtype: float64</pre>
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</div>
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<p class='python'>
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If you intend to use the data in the DataFrame to create <code><?=$dataType?></code> objects, request that the history request returns the data type you need.
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Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame.
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To get a list of <code><?=$dataType?></code> objects instead of a DataFrame, call the <code>history[<?=$dataType?>]</code> method.
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</p>
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<div class="python section-example-container">
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<pre class="python"># Get the 5 trailing daily <?=$dataType?> objects of an asset in <?=$dataType?> format.
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history = self.history[<?=$dataType?>](symbol, 5, Resolution.DAILY)
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# Iterate through the TradeBar objects and access their volumes.
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for trade_bar in history:
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volume = trade_bar.volume</pre>
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</div>

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