Code generated by Alternative-Datasets-Code-Generator.py

03 Writing Algorithms/14 Datasets/02 QuantConnect/10 US Futures Security Master/01 Introduction.html

@@ -1,4 +1,4 @@
-<p>The US Futures Security Master dataset by QuantConnect provides mapping reference data for the most liquid contracts of the CME Group exchanges, calculated with popular rolling techniques. The data covers 162 root Future contracts, starts in 2012, and is delivered on a daily frequency with a zip file with all the contract mappings. This dataset is created by daily processing of the US historical Future chains.</p>
+<p>The US Futures Security Master dataset by QuantConnect provides mapping reference data for the most liquid contracts of the CME Group exchanges, calculated with popular rolling techniques. The data covers 162 root Future contracts, starts in May 2009, and is delivered on a daily frequency with a zip file with all the contract mappings. This dataset is created by daily processing of the US historical Future chains.</p>
 
 <p>This dataset, paired with the US Futures dataset, supports the following rolling techniques: forward panama canal, backwards panama canal, and backwards ratio. You can set the specific rolling date to occur on the last trading day, first day month, or the day when the contract with the greatest open interest changes.</p>
 

03 Writing Algorithms/14 Datasets/02 QuantConnect/10 US Futures Security Master/04 Data Summary.html

@@ -3,7 +3,7 @@
 <table class="table qc-table"><thead><tr><th>Property</th>
             <th>Value</th>
         </tr></thead><tbody><tr><td>Start Date</td>
-            <td>January 2012</td>
+            <td>May 2009</td>
         </tr><tr><td>Asset Coverage</td>
             <td>162 Liquid Futures</td>
         </tr><tr><td>Data Density</td>

03 Writing Algorithms/14 Datasets/03 AlgoSeek/04 US Futures/11 Historical Data.html

@@ -75,4 +75,4 @@ <h4>Historical Data In Research</h4>
 var expiries = contracts.Select(x =&gt; x.ID.Date).Distinct().ToList();</pre>
 </div>
 
-<p>To get historical data for a single US Futures contract or the continuous Futures contract, call the <b class="csharp">History</b><b class="python">history</b> method like you would in an algorithm but on the <b>QuantBook</b> object. For more information about historical data in the Research Environment, see <a href="/docs/v2/research-environment/datasets/futures">Futures</a>.</p>
+<p>To get historical data for a single US Futures contract or the continuous Futures contract, call the <b class="csharp">History</b><b class="python">history</b> method like you would in an algorithm but on the <b>QuantBook</b> object. For more information about historical data in the Research Environment, see <a href="/docs/v2/research-environment/datasets/futures">Futures</a>.</p>

03 Writing Algorithms/14 Datasets/04 Morningstar/01 US Fundamental Data/06 Data Point Attributes.html

@@ -10,7 +10,7 @@ <h4>MorningstarSectorCode Enumeration</h4>
     <pre class="csharp">filteredFundamentals = fundamental.Where(x =&gt;
     x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology);</pre>
     <pre class="python">filtered_fundamentals = [x for x in fundamental
-    if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]</pre>
+    if x.asset_classification.morningstar_sector_pre == MorningstarSectorCode.TECHNOLOGY]</pre>
 </div>
 <p>The <b>MorningstarSectorCode</b> enumeration has the following members:</p>
 <div data-tree="QuantConnect.Data.Fundamental.MorningstarSectorCode"></div>
@@ -20,9 +20,9 @@ <h4>MorningstarIndustryGroupCode Enumeration</h4>
 <p>Industry groups are clusters of related industries that tie together.  To access the industry group of an Equity, use the <b>MorningstarIndustryGroupCode</b> property.</p>
 <div class="section-example-container">
     <pre class="csharp">filteredFundamentals = fundamental.Where(x =&gt;
-    x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.ApplicationSoftware);</pre>
+    x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.Software);</pre>
     <pre class="python">filtered_fundamentals = [x for x in fundamental
-    if x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.ApplicationSoftware]</pre>
+    if x.asset_classification.morningstar_industry_group_pre == MorningstarIndustryGroupCode.SOFTWARE]</pre>
 </div>
 <p>The <b>MorningstarIndustryGroupCode</b> enumeration has the following members:</p>
 <div data-tree="QuantConnect.Data.Fundamental.MorningstarIndustryGroupCode"></div>
@@ -34,7 +34,7 @@ <h4>MorningstarIndustryCode Enumeration</h4>
     <pre class="csharp">filteredFundamentals = fundamental.Where(x =&gt;
     x.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.SoftwareApplication);</pre>
     <pre class="python">filtered_fundamentals = [x for x in fundamental
-    if x.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.SoftwareApplication]</pre>
+    if x.asset_classification.morningstar_industry_pre == MorningstarIndustryCode.SOFTWARE_APPLICATION]</pre>
 </div>
 <p>The <b>MorningstarIndustryCode</b> enumeration has the following members:</p>
 <div data-tree="QuantConnect.Data.Fundamental.MorningstarIndustryCode"></div>
@@ -46,7 +46,7 @@ <h4>Exchange ID Values</h4>
     <pre class="csharp">filteredFundamentals = fundamental.Where(x =&gt;
     x.CompanyReference.PrimaryExchangeID == "NAS");</pre>
     <pre class="python">filtered_fundamentals = [x for x in fundamental
-    if x.CompanyReference.PrimaryExchangeID == "NAS"]</pre>
+    if x.company_reference.primary_exchange_id == "NAS"]</pre>
 </div>
 <p>The exchanges are represented by the following string values:</p>
 <table class="table qc-table table-reflow"><thead><tr><th>String Representation</th><th>Exchange</th></tr></thead><tbody><tr><td>NYS</td><td>New York Stock Exchange (NYSE)</td></tr><tr><td>NAS</td><td>NASDAQ</td></tr><tr><td>ASE</td><td>American Stock Exchange (AMEX)</td></tr><tr><td>TSE</td><td>Tokyo Stock Exchange</td></tr><tr><td>AMS</td><td>Amsterdam Internet Exchange</td></tr><tr><td>SGO</td><td>Santiago Stock Exchange</td></tr><tr><td>XMAD</td><td>Madrid Stock Exchange</td></tr><tr><td>ASX</td><td>Australian Securities Exchange</td></tr><tr><td>BVMF</td><td>B3 (stock exchange)</td></tr><tr><td>LON</td><td>London Stock Exchange</td></tr><tr><td>TKS</td><td>Istanbul Stock Exchange Settlement and Custody Bank</td></tr><tr><td>SHG</td><td>Shanghai Exchange</td></tr><tr><td>LIM</td><td>Lima Stock Exchange</td></tr><tr><td>FRA</td><td>Frankfurt Stock Exchange</td></tr><tr><td>JSE</td><td>Johannesburg Stock Exchange</td></tr><tr><td>MIL</td><td>Milan Stock Exchange</td></tr><tr><td>TAE</td><td>Tel Aviv Stock Exchange</td></tr><tr><td>STO</td><td>Stockholm Stock Exchange</td></tr><tr><td>ETR</td><td>Deutsche Boerse Xetra Core</td></tr><tr><td>PAR</td><td>Paris Stock Exchange</td></tr><tr><td>BUE</td><td>Buenos Aires Stock Exchange</td></tr><tr><td>KRX</td><td>Korea Exchange</td></tr><tr><td>SWX</td><td>SIX Swiss Exchange</td></tr><tr><td>PINX</td><td>Pink Sheets (OTC)</td></tr><tr><td>CSE</td><td>Canadian Securities Exchange</td></tr><tr><td>PHS</td><td>Philippine Stock Exchange</td></tr><tr><td>MEX</td><td>Mexican Stock Exchange</td></tr><tr><td>TAI</td><td>Taiwan Stock Exchange</td></tr><tr><td>IDX</td><td>Indonesia Stock Exchange</td></tr><tr><td>OSL</td><td>Oslo Stock Exchange</td></tr><tr><td>BOG</td><td>Colombia Stock Exchange</td></tr><tr><td>NSE</td><td>National Stock Exchange of India</td></tr><tr><td>HEL</td><td>Nasdaq Helsinki</td></tr><tr><td>MISX</td><td>Moscow Exchange</td></tr><tr><td>HKG</td><td>Hong Kong Stock Exchange</td></tr><tr><td>IST</td><td>Istanbul Stock Exchange</td></tr><tr><td>BOM</td><td>Bombay Stock Exchange</td></tr><tr><td>TSX</td><td>Toronto Stock Exchange</td></tr><tr><td>BRU</td><td>Brussels Stock Exchange</td></tr><tr><td>BATS</td><td>BATS Global Markets</td></tr><tr><td>ARCX</td><td>NYSE Arca</td></tr><tr><td>GREY</td><td>Grey Market (OTC)</td></tr><tr><td>DUS</td><td>Dusseldorf Stock Exchange</td></tr><tr><td>BER</td><td>Berlin Stock Exchange</td></tr><tr><td>ROCO</td><td>Taipei Exchange</td></tr><tr><td>CNQ</td><td>Canadian Trading and Quotation System Inc.</td></tr><tr><td>BSP</td><td>Bangko Sentral ng Pilipinas</td></tr><tr><td>NEOE</td><td>NEO Exchange</td></tr></tbody></table>

03 Writing Algorithms/14 Datasets/04 Morningstar/01 US Fundamental Data/08 Accessing Data.html

@@ -2,7 +2,7 @@
 
 <div class="section-example-container">
 <pre class="python">def fundamental_function(self, fundamental: List[Fundamental]) -&gt; List[Symbol]:
-    return [f.symbol for f in fundamental if f.price &gt; 10 f.valuation_ratios.pe_ratios &gt; 30]</pre>
+    return [f.symbol for f in fundamental if f.price &gt; 10 and f.valuation_ratios.pe_ratio &gt; 30]</pre>
 <pre class="csharp">public IEnumerable&lt;Symbol&gt; FundamentalFunction(IEnumerable&lt;Fundamental&gt; fundamental)
 {
     return fundamental.Where(f =&gt; f.Price &gt; 10 &amp;&amp; f.ValuationRatios.PERatios &gt; 30).Select(f =&gt; f.Symbol);

03 Writing Algorithms/14 Datasets/04 Morningstar/01 US Fundamental Data/09 Historical Data.html

@@ -51,7 +51,8 @@ <h4>Historical Data in the Research Environment</h4>
 
 # DataFrame of a single Fundamental attribute
 history = qb.get_fundamental(symbol, "ValuationRatios.pe_ratios", datetime(2021, 1, 1), datetime(2021, 7, 1))</pre>
-<pre class="csharp">var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);foreach (var fundamentals in universeHistory)
+<pre class="csharp">var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var fundamentals in universeHistory)
 {
     foreach (Fundamental fundamental in fundamentals)
     {
@@ -62,4 +63,4 @@ <h4>Historical Data in the Research Environment</h4>
 var history = qb.GetFundamental(symbol, "ValuationRatios.PERatios", new DateTime(2021, 1, 1), new DateTime(2021, 7, 1));</pre>
 </div>
 
-<p>For more information about historical US Equity fundamental data, see <a href="/docs/v2/research-environment/datasets/equity-fundamental-data">Equity Fundamental Data</a>.</p>
+<p>For more information about historical US Equity fundamental data, see <a href="/docs/v2/research-environment/datasets/equity-fundamental-data">Equity Fundamental Data</a>.</p>

03 Writing Algorithms/14 Datasets/05 TickData/01 US Cash Indices/03 Live Data.html

@@ -0,0 +1 @@
+<p>The US Cash Indices dataset does not provide a live data feed. To receive US Cash Indices' data in your live algorothm, you must add a brokerage or a third-party data provider (see <a href="/docs/v2/cloud-platform/datasets">Cloud Platform > Datasets</a>).</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/02 Binance Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/03 Binance US Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/04 Bitfinex Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/06 Bybit Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/07 Coinbase Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/06 CoinAPI/08 Kraken Crypto Price Data/12 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/09 Brain/01 Brain Language Metrics on Company Filings/11 Universe History.html

@@ -44,4 +44,4 @@ <h4>Historical Universe Data in Research</h4>
         print(f"{language_metrics.symbol} sentiment at {language_metrics.end_time}: {language_metrics.report_sentiment.sentiment}")</pre>
 </div>
 
-<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>
+<p>You can call the <b class="csharp">History</b><b class="python">history</b> method in Research.</p>

03 Writing Algorithms/14 Datasets/09 Brain/02 Brain ML Stock Ranking/11 Universe History.html

@@ -12,7 +12,11 @@ <h4>Historical Universe Data in Algorithms</h4>
         Log($"{rank.Symbol} 2-day rank at {rank.EndTime}: {rank.Rank2Days}");
     }
 }</pre>
-     <pre class="python">universe_history = self.history(self._universe, 30, Resolution.DAILY)
+     <pre class="python"># DataFrame example where the columns are the BrainStockRankingUniverse attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of BrainStockRankingUniverse objects: 
+universe_history = self.history(self._universe, 30, Resolution.DAILY)
 for (_, time), ranks in universe_history.items():
     for rank in ranks:
         self.log(f"{rank.symbol} 2-day rank at {rank.end_time}: {rank.rank2_days}")</pre>
@@ -30,7 +34,11 @@ <h4>Historical Universe Data in Research</h4>
         Console.WriteLine($"{rank.Symbol} 2-day rank at {rank.EndTime}: {rank.Rank2Days}");
     }
 }</pre>
-     <pre class="python">universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+     <pre class="python"># DataFrame example where the columns are the BrainStockRankingUniverse attributes: 
+history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of BrainStockRankingUniverse objects: 
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
 for (_, time), ranks in universe_history.items():
     for rank in ranks:
         print(f"{rank.symbol} 2-day rank at {rank.end_time}: {rank.rank2_days}")</pre>

03 Writing Algorithms/14 Datasets/09 Brain/03 Brain Sentiment Indicator/11 Universe History.html

@@ -12,7 +12,11 @@ <h4>Historical Universe Data in Algorithms</h4>
         Log($"{sentiment.Symbol} 7-day sentiment at {sentiment.EndTime}: {sentiment.Sentiment7Days}");
     }
 }</pre>
-     <pre class="python">universe_history = self.history(self._universe, 30, Resolution.DAILY)
+     <pre class="python"># DataFrame example where the columns are the BrainSentimentIndicatorUniverse attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of BrainSentimentIndicatorUniverse objects: 
+universe_history = self.history(self._universe, 30, Resolution.DAILY)
 for (_, time), sentiments in universe_history.items():
     for sentiment in sentiments:
         self.log(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentiment7_days}")</pre>
@@ -30,7 +34,11 @@ <h4>Historical Universe Data in Research</h4>
         Console.WriteLine($"{sentiment.Symbol} 7-day sentiment at {sentiment.EndTime}: {sentiment.Sentiment7Days}");
     }
 }</pre>
-     <pre class="python">universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+     <pre class="python"># DataFrame example where the columns are the BrainSentimentIndicatorUniverse attributes: 
+history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of BrainSentimentIndicatorUniverse objects: 
+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>

03 Writing Algorithms/14 Datasets/11 CoinGecko/01 Crypto Market Cap/12 Universe History.html

@@ -12,7 +12,11 @@ <h4>Historical Universe Data in Algorithms</h4>
         Log($"{coin.Symbol.Value} market cap at {coin.EndTime}: {coin.MarketCap}");
     }
 }</pre>
-     <pre class="python">universe_history = self.history(self._universe, 30, Resolution.DAILY)
+     <pre class="python"># DataFrame example where the columns are the CoinGeckoUniverse attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of CoinGeckoUniverse objects: 
+universe_history = self.history(self._universe, 30, Resolution.DAILY)
 for (_, time), coins in universe_history.items():
     for coin in coins:
         self.log(f"{coin.symbol.value} market cap at {coin.end_time}: {coin.market_cap}")</pre>
@@ -30,7 +34,11 @@ <h4>Historical Universe Data in Research</h4>
         Console.WriteLine($"{coin.Symbol.Value} market cap at {coin.EndTime}: {coin.MarketCap}");
     }
 }</pre>
-     <pre class="python">universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+     <pre class="python"># DataFrame example where the columns are the CoinGeckoUniverse attributes: 
+history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of CoinGeckoUniverse objects: 
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
 for (_, time), coins in universe_history.items():
     for coin in coins:
         print(f"{coin.symbol.value} market cap at {coin.end_time}: {coin.market_cap}")</pre>

03 Writing Algorithms/14 Datasets/13 EOD Historical Data/01 Economic Events/01 Introduction.html

@@ -0,0 +1,3 @@
+<p>The Economic Events dataset, provided by EODHD, offers daily alerts for major economic events or announcements of global markets within the upcoming 7 days, with estimation and previous record if available. The data starts in January 2019, and is delivered on a daily frequency.</p>
+
+<p>For more information about the Economic Events dataset, including CLI commands and pricing, see the <a href="/datasets/eodhd-economic-events">dataset listing</a>.<p>

03 Writing Algorithms/14 Datasets/13 EOD Historical Data/01 Economic Events/02 About the Provider.html

@@ -0,0 +1 @@
+<p>EODHD was a France financial data provider founded in April 2015. They focus on providing clean financial data, including stock prices, splits, dividends, fundamentals, macroeconomy indicators, technical indicators, and alternative data sources, through 24/7 API seamlessly.</p>

03 Writing Algorithms/14 Datasets/13 EOD Historical Data/01 Economic Events/03 Getting Started.html

@@ -0,0 +1,7 @@
+<p>The following snippet demonstrates how to request data from the Economic Events dataset:
+</p><div class="section-example-container">
+<pre class="python">self.add_data(EODHDEconomicEvents, Country.UNITED_STATES)
+self.add_data(EODHDEconomicEvents, EODHD.Events.UnitedStates.CPI) </pre>
+<pre class="csharp">AddData&lt;EODHDEconomicEvents&gt;(Country.UnitedStates);
+AddData&lt;EODHDEconomicEvents&gt;(EODHD.Events.UnitedStates.Cpi);</pre>
+</div>

03 Writing Algorithms/14 Datasets/20 Quiver Quantitative/07 Wikipedia Page Views/04 Data Summary.html → 03 Writing Algorithms/14 Datasets/13 EOD Historical Data/01 Economic Events/04 Data Summary.html

@@ -3,9 +3,7 @@
 <table class="table qc-table"><thead><tr><th>Property</th>
             <th>Value</th>
         </tr></thead><tbody><tr><td>Start Date</td>
-            <td>October 2016</td>
-        </tr><tr><td>Asset Coverage</td>
-            <td>1,300 US Equities</td>
+            <td>January 2019</td>
         </tr><tr><td>Data Density</td>
             <td>Sparse</td>
         </tr><tr><td>Resolution</td>