Update auto-generated code

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 Bybit Crypto Future Margin Rate Data/08 Remove Subscriptions.html

@@ -6,4 +6,4 @@
 <pre class="csharp">RemoveSecurity(_symbol);</pre>
 </div>
 
-<p>The<code class="csharp">RemoveSecurity</code><code class="python">remove_security</code> method cancels your open orders for the security and liquidates your holdings in the <a href="/docs/v2/writing-algorithms/securities/asset-classes/crypto/holdings#03-Virtual-Pairs">virtual pair</a>.</p>
+<p>The <code class="csharp">RemoveSecurity</code><code class="python">remove_security</code> method cancels your open orders for the security and liquidates your holdings in the <a href="/docs/v2/writing-algorithms/securities/asset-classes/crypto/holdings#03-Virtual-Pairs">virtual pair</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/17 US Equities Short Availability/98 Example Applications.html

@@ -17,7 +17,6 @@ <h4>Classic Algorithm Example</h4>
         self.set_cash(1000)
         # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
         self.settings.seed_initial_prices = True
-        self.add_security_initializer(self._custom_security_initializer)
 
         self.equity = self.add_equity("GME")
 
@@ -115,7 +114,6 @@ <h4>Framework Algorithm Example</h4>
         self.set_cash(1000)
         # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
         self.settings.seed_initial_prices = True
-        self.add_security_initializer(self._custom_security_initializer)
 
         self.set_universe_selection(ManualUniverseSelectionModel(
             [Symbol.create("GME", SecurityType.EQUITY, Market.USA)]))           

03 Writing Algorithms/14 Datasets/03 AlgoSeek/02 US Equity Options/98 Example Applications.html

@@ -130,7 +130,7 @@ <h4>Framework Algorithm Example</h4>
         # Daily update with the select_option_chain_symbols function
         super().__init__(timedelta(1), self.select_option_chain_symbols)
 
-    def select_option_chain_symbols(self, utcTime: datetime) -&gt; List[Symbol]:
+    def select_option_chain_symbols(self, utc_time: datetime) -&gt; List[Symbol]:
         # Always select only GOOG options as our focus
         return [ Symbol.create("GOOG", SecurityType.OPTION, Market.USA) ]
 

03 Writing Algorithms/14 Datasets/08 Brain/02 Brain ML Stock Ranking/98 Example Applications.html

@@ -29,7 +29,7 @@ <h4>Classic Algorithm Example</h4>
             dataset_symbol = self.add_data(BrainStockRanking2Day, equity).symbol
             self.equity_by_dataset_symbol[dataset_symbol] = equity
             # Request historical ranking data to inspect subscription coverage.
-            history = self.history(dataset_symbol, 365, Resolution.DAILY)
+            history = self.history(dataset_symbol, 252, Resolution.DAILY)
             self.debug(f"We got {len(history)} items from our history request for {equity}")
 
     def on_data(self, data: Slice) -&gt; None:
@@ -156,7 +156,7 @@ <h4>Framework Algorithm Example</h4>
             # Request data for the two-day estimated relative ranking.
             security.brain_stock_ranking_2_day = algorithm.add_data(BrainStockRanking2Day, security, Resolution.DAILY)
             # Historical data.
-            history = algorithm.history(security.brain_stock_ranking_2_day, 365, Resolution.DAILY)
+            history = algorithm.history(security.brain_stock_ranking_2_day, 252, Resolution.DAILY)
             algorithm.debug(f"We got {len(history)} items from our history request for {security.brain_stock_ranking_2_day.symbol}")
         for security in changes.removed_securities:
             # Unsubscribe from the Brain ML Ranking feed for this security to release resources.
@@ -231,7 +231,7 @@ <h4>Framework Algorithm Example</h4>
             var brainStockRanking2Day = algorithm.AddData&lt;BrainStockRanking2Day&gt;(security.Symbol, Resolution.Daily);
             security.Set("BrainStockRanking2Day", brainStockRanking2Day);
             // Historical data.
-            var history = algorithm.History&lt;BrainStockRanking2Day&gt;(brainStockRanking2Day.Symbol, 365, Resolution.Daily);
+            var history = algorithm.History&lt;BrainStockRanking2Day&gt;(brainStockRanking2Day.Symbol, 252, Resolution.Daily);
             algorithm.Debug($"We got {history.Count()} items from our history request for {brainStockRanking2Day}");
         }
         foreach (var security in changes.RemovedSecurities)

03 Writing Algorithms/14 Datasets/08 Brain/03 Brain Sentiment Indicator/08 Universe Selection.html

@@ -6,8 +6,8 @@
 
 def universe_selection(self, alt_coarse: List[BrainSentimentIndicatorUniverse]) -> List[Symbol]:
     return [d.symbol for d in alt_coarse \
-                if d.total_article_mentions7_days > 0 \
-                and d.sentiment7_days]</pre>
+                if d.total_article_mentions_7_days &gt; 0 \
+                and d.sentiment_7_days]</pre>
 <pre class="csharp">private Universe _universe;
 public override void Initialize()
 {

03 Writing Algorithms/14 Datasets/08 Brain/03 Brain Sentiment Indicator/09 Universe History.html

@@ -19,7 +19,7 @@ <h4>Historical Universe Data in Algorithms</h4>
 universe_history = self.history(self._universe, 30, Resolution.DAILY)
 for (_, time), sentiments in universe_history.items():
     for sentiment in sentiments:
-        self.log(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentiment7_days}")</pre>
+        self.log(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentiment_7_days}")</pre>
 </div>
 
 <h4>Historical Universe Data in Research</h4>
@@ -41,7 +41,7 @@ <h4>Historical Universe Data in Research</h4>
 universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
 for (_, time), sentiments in universe_history.items():
     for sentiment in sentiments:
-        print(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentiment7_days}")</pre>
+        print(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentiment_7_days}")</pre>
 </div>
 
 <p>You can call the <code class="csharp">History</code><code class="python">history</code> method in Research.</p>

03 Writing Algorithms/14 Datasets/08 Brain/03 Brain Sentiment Indicator/98 Example Applications.html

@@ -24,7 +24,7 @@ <h4>Classic Algorithm Example</h4>
         # Request the processed longer term sentiment score data for sentiment trading.
         self.aapl = self.add_equity("AAPL", Resolution.DAILY)
         self.dataset_symbol = self.add_data(BrainSentimentIndicator30Day, self.aapl).symbol
-        history = self.history(self.dataset_symbol, 100, Resolution.DAILY)
+        history = self.history(self.dataset_symbol, 252, Resolution.DAILY)
         self.debug(f"We got {len(history)} items from our history request for {self.dataset_symbol}")
         if history.empty:
             return
@@ -65,7 +65,7 @@ <h4>Classic Algorithm Example</h4>
         // Request the processed longer term sentiment score data for sentiment trading.
         _aapl = AddEquity("AAPL", Resolution.Daily);
         _datasetSymbol = AddData&lt;BrainSentimentIndicator30Day&gt;(_aapl.Symbol).Symbol;
-        var history = History&lt;BrainSentimentIndicator30Day&gt;(_datasetSymbol, 100, Resolution.Daily);
+        var history = History&lt;BrainSentimentIndicator30Day&gt;(_datasetSymbol, 252, Resolution.Daily);
         Debug($"We got {history.Count()} items from our history request for {_datasetSymbol}");
         // Warm up historical sentiment values so the signal is immediately tradable.
         var previousSentimentValues = history.Select(x =&gt; x.Sentiment);
@@ -147,7 +147,7 @@ <h4>Framework Algorithm Example</h4>
             security.latest_sentiment_value = data_point.sentiment
             # Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity
             if security.price and target_direction == InsightDirection.UP and not security.invested:
-                insights.append(Insight.price(security, timedelta(100), target_direction))
+                insights.append(Insight.price(security, timedelta(252), target_direction))
         return insights
 
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -&gt; None:
@@ -157,7 +157,7 @@ <h4>Framework Algorithm Example</h4>
             security.brain_sentiment_data = dataset.symbol
             security.latest_sentiment_value = None
             # Historical data
-            history = algorithm.history(dataset, 100, Resolution.DAILY)
+            history = algorithm.history(dataset, 252, Resolution.DAILY)
             if not history.empty:
                 # Warm up historical sentiment values, cache for comparing last sentiment score to trade
                 previous_sentiment_values = history.loc[security.brain_sentiment_data].sentiment.values
@@ -216,7 +216,7 @@ <h4>Framework Algorithm Example</h4>
             // Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity
             if (security.Price != 0 &amp;&amp; targetDirection == InsightDirection.Up &amp;&amp; !security.Holdings.Invested)
             {
-                insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(100), targetDirection));
+                insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(252), targetDirection));
             }
         }
         return insights;
@@ -231,7 +231,7 @@ <h4>Framework Algorithm Example</h4>
             security.Set("BrainSentimentData", dataset.Symbol);
             security.Set("LatestSentimentValue", null);
             // Historical data
-            var history = algorithm.History&lt;BrainSentimentIndicator30Day&gt;(dataset.Symbol, 100, Resolution.Daily).ToList();
+            var history = algorithm.History&lt;BrainSentimentIndicator30Day&gt;(dataset.Symbol, 252, Resolution.Daily).ToList();
             if (history.Count() != 0)
             {
                 // Warm up historical sentiment values, cache for comparing last sentiment score to trade

03 Writing Algorithms/14 Datasets/08 Brain/04 Brain Wikipedia Page Views/05 Requesting Data.html

@@ -1,13 +1,13 @@
 <p> To add Brain Wikipedia Page Views Indicator data to your algorithm, call the <code class="csharp">AddData</code><code class="python">add_data</code> method. Save a reference to the dataset <code>Symbol</code> so you can access the data later in your algorithm.
-<div class="section-example-container"><pre class="python">class BrainWikipediaPageViews(QCAlgorithm):     
+<div class="section-example-container"><pre class="python">class BrainWikipediaPageViewsAlgorithm(QCAlgorithm):     
     def initialize(self) -&gt; None: 	 
         self.set_start_date(2021, 1, 1)
         self.set_end_date(2021, 7, 8)
         self.set_cash(100000)
         self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol 
         self.dataset_symbol = self.add_data(BrainWikipediaPageViews, self.aapl).symbol</pre> 
 
-<pre class="csharp">public class BrainWikipediaPageViews: QCAlgorithm  
+<pre class="csharp">public class BrainWikipediaPageViewsAlgorithm: QCAlgorithm  
 {  
     private Symbol _symbol;  
     public override void Initialize()  

03 Writing Algorithms/14 Datasets/08 Brain/04 Brain Wikipedia Page Views/06 Accessing Data.html

@@ -3,7 +3,7 @@
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:  
     if slice.contains_key(self.dataset_symbol):  
         data_point = slice[self.dataset_symbol]  
-        self.log(f"{self.dataset_symbol} sentiments at {slice.time}: {data_point.NumberViews1}, {data_point.Buzz1}"</pre> 
+        self.log(f"{self.dataset_symbol} sentiments at {slice.time}: {data_point.number_views_1}, {data_point.buzz_1}")</pre> 
 
 <pre class="csharp">public override void OnData(Slice slice)  
 {  
@@ -18,7 +18,7 @@
 <div class="section-example-container"> 
 	<pre class="python">def on_data(self, slice: Slice) -&gt; None:  
     for dataset_symbol, data_point in slice.get(BrainWikipediaPageViews).items(): 
-         self.log(f"{dataset_symbol} metric at {slice.time}: {data_point.NumberViews1} , {data_point.Buzz1}")</pre> 
+         self.log(f"{dataset_symbol} metric at {slice.time}: {data_point.number_views_1} , {data_point.buzz_1}")</pre> 
 
 <pre class="csharp">public override void OnData(Slice slice)  
 {  

03 Writing Algorithms/14 Datasets/08 Brain/04 Brain Wikipedia Page Views/07 Historical Data.html

@@ -1,5 +1,5 @@
 <p> To get historical Brain Wikipedia Page Views data, call the <code class="csharp">History</code><code class="python">history</code> method with the dataset <code>Symbol</code>. If there is no data in the period you request, the history result is empty.</p>
 <div class="section-example-container"> 
-<pre class="python">history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY) </pre> 
-<pre class="csharp">var history = History&lt;BrainWikipediaPageViews&gt;(_datasetSymbol, 100, Resolution.Daily); </pre> 
+<pre class="python">history_df = self.history(self.dataset_symbol, 252, Resolution.DAILY) </pre> 
+<pre class="csharp">var history = History&lt;BrainWikipediaPageViews&gt;(_datasetSymbol, 252, Resolution.Daily); </pre> 
 </div>

03 Writing Algorithms/14 Datasets/08 Brain/04 Brain Wikipedia Page Views/98 Example Applications.html

@@ -29,7 +29,7 @@ <h4>Classic Algorithm Example</h4>
             equity.dataset_symbol = self.add_data(BrainWikipediaPageViews, equity.symbol).symbol 
 
             # Historical data 
-            history = self.history(equity.dataset_symbol, 365, Resolution.DAILY) 
+            history = self.history(equity.dataset_symbol, 252, Resolution.DAILY) 
             self.debug(f"We got {len(history)} items from our history request for {equity.symbol}") 
 
     def on_data(self, slice: Slice) -&gt; None: 
@@ -40,10 +40,10 @@ <h4>Classic Algorithm Example</h4>
 
         scores = {}
 
-        for point in points.Values: 
+        for point in points.values(): 
             # Example composite signal: 
-            number_of_views = point.NumberViews1 if point.NumberViews1 else 0
-            buzz1day = point.Buzz1 if point.Buzz1 else 0
+            number_of_views = point.number_views_1 if point.number_views_1 else 0
+            buzz1day = point.buzz_1 if point.buzz_1 else 0
 
             symbol = point.symbol.underlying
             scores[symbol] = buzz1day * math.sqrt(number_of_views)
@@ -85,7 +85,7 @@ <h4>Classic Algorithm Example</h4>
             var datasetSymbol = AddData&lt;BrainWikipediaPageViews&gt;(symbol).Symbol;
 
             // Historical data (request dataset symbol) 
-            var history = History(datasetSymbol, 365, Resolution.Daily); 
+            var history = History(datasetSymbol, 252, Resolution.Daily); 
             Debug($"We got {history.Count()} items from our history request for {symbol}"); 
         } 
     } 

03 Writing Algorithms/14 Datasets/09 Bureau of Labor Statistics/01 US Bureau of Labor Statistics (BLS)/07 Historical Data.html

@@ -1,4 +1,4 @@
-<p>To get historical BLS data, call the <code>History</code> method with the dataset <code>Symbol</code>. If there is no data in the period you request, the history result is empty.</p>
+<p>To get historical BLS data, call the <code class="csharp">History</code><code class="python">history</code> method with the dataset <code>Symbol</code>. If there is no data in the period you request, the history result is empty.</p>
 
 <div class="section-example-container">
 <pre class="python"># DataFrame

03 Writing Algorithms/14 Datasets/09 Bureau of Labor Statistics/01 US Bureau of Labor Statistics (BLS)/08 Remove Subscriptions.html

@@ -1,4 +1,4 @@
-<p>To remove your subscription to BLS data, call the <code>RemoveSecurity</code> method.</p>
+<p>To remove your subscription to BLS data, call the <code class="csharp">RemoveSecurity</code><code class="python">remove_security</code> method.</p>
 
 <div class="section-example-container">
 <pre class="python">self.remove_security(self._cpi)</pre>

03 Writing Algorithms/14 Datasets/11 EOD Historical Data/02 Macroeconomics Indicators/98 Example Applications.html

@@ -16,10 +16,10 @@ <h4>Classic Algorithm Example</h4>
 class EODHDMacroIndicatorsAlgorithm(QCAlgorithm):
 
     def initialize(self):
-        self.set_start_date(2024, 9, 1)
+        self.set_start_date(2024, 1, 1)
         self.set_end_date(2024, 12, 31)
         self.equity_symbol = self.add_equity("SPY", Resolution.DAILY).symbol
-
+    
         ticker = EODHD.MacroIndicators.UnitedStates.GDP_GROWTH_ANNUAL
         self.dataset_symbol = self.add_data(EODHDMacroIndicators, ticker).symbol
 
@@ -34,7 +34,7 @@ <h4>Classic Algorithm Example</h4>
 
     public override void Initialize()
     {
-        SetStartDate(2024, 9, 1);
+        SetStartDate(2024, 1, 1);
         SetEndDate(2024, 12, 31);
         _equitySymbol = AddEquity("SPY", Resolution.Daily).Symbol;
         var ticker = EODHD.MacroIndicators.UnitedStates.GdpGrowthAnnual;
@@ -61,7 +61,7 @@ <h4>Framework Algorithm Example</h4>
 class EODHDMacroIndicatorsAlgorithm(QCAlgorithm):
 
    def initialize(self) -&gt; None:
-        self.set_start_date(2024, 9, 1)
+        self.set_start_date(2024, 1, 1)
         self.set_end_date(2024, 12, 31)
         # Use market ETF as a vehicle to trade.
         symbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
@@ -98,7 +98,7 @@ <h4>Framework Algorithm Example</h4>
 {
     public override void Initialize()
     {
-        SetStartDate(2024, 9, 1);
+        SetStartDate(2024, 1, 1);
         SetEndDate(2024, 12, 31);
         // Use market ETF as a vehicle to trade.
         var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);

03 Writing Algorithms/14 Datasets/11 EOD Historical Data/03 Upcoming Dividends/09 Universe History.html

@@ -0,0 +1,39 @@
+<p>You can get historical universe data in an algorithm and in the Research Environment.</p>
+
+<h4>Historical Universe Data in Algorithms</h4>
+<p>To get historical universe data in an algorithm, call the <code class="csharp">History</code><code class="python">history</code> method with the <code>Universe</code> object and the lookback period. If there is no data in the period you request, the history result is empty.</p>
+
+<div class="section-example-container">
+        <pre class="csharp">var universeHistory = History(_universe, 30, Resolution.Daily);
+foreach (var dividends in universeHistory)
+{
+    foreach (EODHDUpcomingDividends dividend in dividends)
+    {
+        Log($"{dividend.Symbol} dividend on {dividend.DividendDate}: {dividend.dividend}");
+    }
+}</pre>
+     <pre class="python"># DataFrame example where the columns are the EODHDUpcomingDividends attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of EODHDUpcomingDividends objects: 
+universe_history = self.history(self._universe, 30, Resolution.DAILY)
+for (_, time), dividends in universe_history.items():
+    for dividend in dividends:
+        self.log(f"{dividend.symbol} dividend on {dividend.dividend_date}: {dividend.dividend}")</pre>
+</div>
+
+<h4>Historical Universe Data in Research</h4>
+<p>To get historical universe data in research, call the <code class="csharp">History</code><code class="python">history</code> method with the <code>Universe</code> object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.</p>
+
+<div class="section-example-container">
+        <pre class="csharp">var universeHistory = qb.History(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var dividends in universeHistory)
+{
+    foreach (EODHDUpcomingDividends dividend in dividends)
+    {
+        Console.WriteLine($"{dividend.Symbol} dividend on {dividend.DividendDate}: {dividend.dividend}");
+    }
+}</pre>
+     <pre class="python"># DataFrame example where the columns are the EODHDUpcomingDividends attributes: 
+history = qb.history(universe, qb.time-timedelta(30), qb.time, flatten=True)</pre>
+</div>

03 Writing Algorithms/14 Datasets/11 EOD Historical Data/04 Upcoming Earnings/07 Accessing Data.html → 03 Writing Algorithms/14 Datasets/11 EOD Historical Data/04 Upcoming Earnings/06 Accessing Data.html

@@ -3,8 +3,8 @@
 <div class="section-example-container">
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:
     upcomings_earnings_for_symbol = slice.get(EODHDUpcomingEarnings).get(self._symbol)
-        if upcomings_earnings_for_symbol:
-            self.log(f"{self._symbol} will report earnings at {upcomings_earnings_for_symbol.report_date} {upcomings_earnings_for_symbol.report_time} with estimated EPS {upcomings_earnings_for_symbol.estimate}")</pre>
+    if upcomings_earnings_for_symbol:
+        self.log(f"{self._symbol} will report earnings at {upcomings_earnings_for_symbol.report_date} {upcomings_earnings_for_symbol.report_time} with estimated EPS {upcomings_earnings_for_symbol.estimate}")</pre>
 <pre class="csharp">public override void OnData(Slice slice)
 {
     var upcomingEarnings = slice.Get&lt;EODHDUpcomingEarnings&gt;();

03 Writing Algorithms/14 Datasets/11 EOD Historical Data/04 Upcoming Earnings/08 Historical Data.html → 03 Writing Algorithms/14 Datasets/11 EOD Historical Data/04 Upcoming Earnings/07 Historical Data.html

@@ -1,4 +1,4 @@
-<p>To get historical Upcoming Earnings for the universe, call the <code class="csharp">History</code><code class="python">history</code> method with the type <code>EODHDUpcomingEarning</code>.</p>
+<p>To get historical Upcoming Earnings for the universe, call the <code class="csharp">History</code><code class="python">history</code> method with the type <code>EODHDUpcomingEarnings</code>.</p>
 
 <div class="section-example-container">
 <pre class="python">history = self.history(EODHDUpcomingEarnings, timedelta(100), Resolution.DAILY)</pre>