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

03 Writing Algorithms/14 Datasets/02 QuantConnect/04 US Equities Short Availability/07 Requesting Data.html

@@ -5,6 +5,7 @@
     def initialize(self) -> None:
         self.set_start_date(2019, 1, 1)
         security = self.add_equity("AAPL")
+        # Set shortable provider as IB
         security.set_shortable_provider(InteractiveBrokersShortableProvider())
         self._symbol = security.symbol</pre>
 
@@ -19,6 +20,7 @@
         {
             SetStartDate(2019, 1, 1);
             var security = AddEquity("AAPL");
+            // Set shortable provider as IB
             security.SetShortableProvider(new InteractiveBrokersShortableProvider());
             _symbol = security.Symbol;
         }

03 Writing Algorithms/14 Datasets/02 QuantConnect/04 US Equities Short Availability/08 Accessing Data.html

@@ -2,12 +2,14 @@
 
 <div class="section-example-container">
     <pre class="csharp">var shortableProvider = Securities[_symbol].ShortableProvider;
+// Get the shortable quantity of the selected symbol at the selected time
 var shortableQuantity = shortableProvider.ShortableQuantity(_symbol, Time);
 
 // Check if there are a certain quantity of shares available
 var quantity = 100;
 var isShortableQuantity = Shortable(_symbol, quantity);</pre>
     <pre class="python">shortable_provider = self.securities[self._symbol].shortable_provider
+# Get the shortable quantity of the selected symbol at the selected time
 shortable_quantity = shortable_provider.shortable_quantity(self._symbol, self.time)
 
 # Check if there are a certain quantity of shares available

03 Writing Algorithms/14 Datasets/02 QuantConnect/05 US Equity Coarse Universe/03 Getting Started.html

@@ -16,19 +16,24 @@
 
 <ul><li>Universe Selection</li></ul><div class="section-example-container">
 <pre class="python">def initialize(self) -&gt; None:
+    # Universe selection
     self._universe = self.add_universe(self.fundamental_filter_function)
 
 def fundamental_filter_function(self, fundamental: List[Fundamental]):
+    # Sort all equities with price above $10 by dollar volume
     sorted_by_dollar_volume = sorted([f for f in fundamental if f.price &gt; 10],
                                 key=lambda f: f.dollar_volume)
+    # Take the top 10
     return [f.symbol for f in sorted_by_dollar_volume[:10]]</pre>
 <pre class="csharp">public override void Initialize()
 {
+    // Universe selection
     _universe = AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
 }
 
 public override List&lt;Symbol&gt; FundamentalFilterFunction(List&lt;Fundamental&gt; fundamental)
 {
+    // Sort all equities with price above $10 by dollar volume, take the top 10
     return (from f in fundamental
             where f.Price &gt; 10
             orderby f.DollarVolume

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/01 Introduction.html

@@ -0,0 +1,9 @@
+<p>
+The US Equity Option Chain Master dataset by QuantConnect lists the available US Equity Options contracts and the current Implied Volatility and Greeks. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a daily update frequency. To create this dataset, we use <a href='https://www.quantconnect.com/research/16977/greeks-and-iv-implementation/p1'>our implementation</a> of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily <a href='/docs/v2/writing-algorithms/securities/asset-classes/equity-options/greeks-and-implied-volatility/indicators'>indicators</a> with mirror Options.
+</p>
+
+<p>This dataset depends on the <a href="/datasets/quantconnect-us-equity-security-master">US Equity Security Master</a> dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes of the underlying security.</p>
+
+<p>This dataset <b>does not</b> contain market data. For market data, see <a href="/datasets/algoseek-us-equity-options">US Equity Options by AlgoSeek</a>.</p>
+
+<p>For more information about the US Equity Option Universe dataset, including CLI commands and pricing, see the <a href="/datasets/quantconnect-us-equity-option-universe">dataset listing</a>.<p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/03 Getting Started.html

@@ -0,0 +1,10 @@
+<p>The following snippet demonstrates how to request data from the US Equity Options dataset:</p>
+
+<div class="section-example-container">
+<pre class="python">option = self.add_option("GOOG")
+self.option_symbol = option.symbol
+option.set_filter(lambda universe: universe.delta(0.4, 0.6))</pre>
+<pre><code class="csharp">var option = AddOption("GOOG");
+_optionSymbol = option.Symbol;
+option.SetFilter(universe => universe.delta(0.4m, 0.6m));</code></pre>
+</div>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/04 Data Summary.html

@@ -0,0 +1,15 @@
+<p>The following table describes the dataset properties:</p>
+
+<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>
+        </tr><tr><td>Asset Coverage</td>
+            <td>4,000 Symbols</td>
+        </tr><tr><td>Data Density</td>
+            <td>Dense</td>
+        </tr><tr><td>Resolution</td>
+            <td>Daily</td>
+        </tr><tr><td>Timezone</td>
+            <td>New York</td>
+        </tr></tbody></table

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/05 Example Applications.html

@@ -0,0 +1,8 @@
+<p>
+The US Equity Options dataset enables you to accurately design Option strategies. Examples include the following strategies:
+</p>
+<ul><li>Buying put Options to hedge against downward price movement in positive Equity positions</li>
+<li>Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value</li>
+</ul>
+<div class='python'><div class='qc-embed-frame' style='display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;'><div class='qc-embed-dummy' style='padding-top: 56.25%;'></div><div class='qc-embed-element' style='position: absolute; top: 0; bottom: 0; left: 0; right: 0;'><iframe class='qc-embed-backtest' src='https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_467152d2f04391913b704e21767f1682.html' style='max-width: calc(100vw - 30px); max-height: 100vw; overflow: hidden;' scrolling='no' width='100%' height='100%'></iframe></div></div></div>
+<div class='csharp'><div class='qc-embed-frame' style='display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;'><div class='qc-embed-dummy' style='padding-top: 56.25%;'></div><div class='qc-embed-element' style='position: absolute; top: 0; bottom: 0; left: 0; right: 0;'><iframe class='qc-embed-backtest' src='https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ac6db075b8e9c9abc7c9d48c55db3871.html' style='max-width: calc(100vw - 30px); max-height: 100vw; overflow: hidden;' scrolling='no' width='100%' height='100%'></iframe></div></div></div><p>For more example algorithms, see <a href='/datasets/algoseek-us-equity-options/examples'>Examples</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/06 Data Point Attributes.html

@@ -0,0 +1,5 @@
+<p>The US Equity Options dataset provides <b>OptionUniverse</b> object.</p>
+
+<h4>OptionUniverse Attributes</h4>
+<p><b>OptionUniverse</b> objects have the following attributes:</p>
+<div data-tree="QuantConnect.Data.UniverseSelection.OptionUniverse"></div>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/07 Supported Assets.html

@@ -0,0 +1 @@
+<p>To view the supported assets in the US Equity Options dataset, see the <a href="/datasets/algoseek-us-equity-options/explorer">Data Explorer</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/08 Requesting Data.html

@@ -0,0 +1,66 @@
+<p>To add US Equity Options data to your algorithm, call the <b class="csharp">AddOption</b><b class="python">add_option</b> method. Save a reference to the Equity Option <b>Symbol</b> so you can access the data later in your algorithm.</p>
+
+<p>To set a contract filter, call the <b class="csharp">SetFilter</b><b class="python">set_filter</b> method of the Option object.</p>
+
+<div class="section-example-container">
+<pre><code class="python">class USEquityOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -&gt; None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        option = self.add_option("GOOG")
+        self.option_symbol = option.symbol
+        # Set our strike/expiry filter for this option chain
+        option.set_filter(self._option_filter)
+
+    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Contracts can be filtered by greeks, implied volatility, open interest:
+        return universe \
+            .delta(0.5, 1.5) \
+            .gamma(0.0001, 0.0006) \
+            .vega(0.01, 1.5) \
+            .theta(-2.0, -0.5) \
+            .rho(0.5, 3.0) \
+            .implied_volatility(1, 3) \
+            .open_interest(100,500)
+</code></pre>
+<pre class="csharp">namespace QuantConnect
+{
+    public class USEquityOptionsDataAlgorithm : QCAlgorithm
+    {
+        private Symbol _optionSymbol;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 6, 1);
+            SetEndDate(2021, 6, 1);
+            SetCash(100000);
+            UniverseSettings.Asynchronous = True;
+            // Requesting data
+            var option = AddOption("GOOG");
+            _optionSymbol = option.Symbol;
+            // Set our strike/expiry filter for this option chain
+            option.SetFilter(OptionFilter);
+        }
+
+        private virtual OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+        {
+            // Contracts can be filtered by greeks, implied volatility, open interest:
+            return universe
+                .Delta(0.5m, 1.5m)
+                .Gamma(0.0001m, 0.0006m)
+                .Vega(0.01m, 1.5m)
+                .Theta(-2.0m, -0.5m)
+                .Rho(0.5m, 3.0m)
+                .ImpliedVolatility(1.0m, 3.0m)
+                .OpenInterest(100m, 500m);
+        }
+    }
+}</pre>
+</div>
+
+<p>The Equity resolution must be less than or equal to the Equity Option resolution. For example, if you set the Equity resolution to minute, then you must set the Equity Option resolution to minute, hour, or daily.</p>
+
+<p>For more information about creating US Equity Option subscriptions, see <a href="/docs/v2/writing-algorithms/securities/asset-classes/equity-options/requesting-data">Requesting Data</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/09 Accessing Data.html

@@ -0,0 +1 @@
+<p>For information about accessing US Equity Options data, see <a href="/docs/v2/writing-algorithms/securities/asset-classes/equity-options/handling-data">Handling Data</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/10 Historical Data.html

@@ -0,0 +1,44 @@
+<p>You can get historical US Index Options data in an algorithm and the Research Environment.</p>
+
+<h4>Historical Data In Algorithms</h4>
+
+<p>To get historical US Index Options data in an algorithm, call the <b class="csharp">History</b><b class="python">history</b> method with the Index Option chain <b>Symbol</b>. 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
+history_df = self.history(self.option_symbol, 3, Resolution.MINUTE)</pre>
+
+<pre><code class="csharp">// TradeBar objects 
+var history = History<OptionUniverse>(option, 3, Resolution.Daily).ToList();</code></pre>
+</div>
+
+<p>For more information about historical data in algorithms, see <a href="/docs/v2/writing-algorithms/historical-data/history-requests">History Requests</a>.</p>
+
+<h4>Historical Data In Research</h4>
+
+<p>To get historical US Index Options data in the Research Environment for an entire Option chain, call the <b class="csharp">OptionHistory</b><b class="python">option_history</b> method with the canonical Option <b>Symbol</b>.</p>
+
+<div class="section-example-container">
+<pre class="python">qb = QuantBook()
+index_symbol = qb.add_index('VIX').symbol
+option = qb.add_index_option(index_symbol) # or qb.add_index_option(index_symbol, "VIXW")
+option.set_filter(-2, 2, 0, 90)
+history = qb.option_history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
+
+all_history = history.get_all_data()
+expiries = history.get_expiry_dates() 
+strikes = history.get_strikes()</pre>
+
+
+<pre class="csharp">var qb = new QuantBook();
+var indexSymbol = qb.AddIndex("VIX").Symbol;
+var option = qb.AddIndexOption(indexSymbol); // or qb.AddIndexOption(indexSymbol, "VIXW");
+option.SetFilter(-2, 2, 0, 90);
+var history = qb.OptionHistory(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
+
+var contracts = history.SelectMany(x =&gt; x.OptionChains.SelectMany(y =&gt; y.Value.Contracts.Keys)).Distinct().ToList();
+var expiries = contracts.Select(x =&gt; x.ID.Date).Distinct().ToList();
+var strikes = contracts.Select(x =&gt; x.ID.StrikePrice).Distinct().ToList();</pre>
+</div>
+
+<p>To get historical data for a single US Index Option 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/index-options">Index Options</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/99 Example Applications.html

@@ -0,0 +1,8 @@
+<p>
+The US Equity Options dataset enables you to accurately design Option strategies. Examples include the following strategies:
+</p>
+<ul><li>Buying put Options to hedge against downward price movement in positive Equity positions</li>
+<li>Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value</li>
+</ul>
+<div class='python'><div class='qc-embed-frame' style='display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;'><div class='qc-embed-dummy' style='padding-top: 56.25%;'></div><div class='qc-embed-element' style='position: absolute; top: 0; bottom: 0; left: 0; right: 0;'><iframe class='qc-embed-backtest' src='https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_467152d2f04391913b704e21767f1682.html' style='max-width: calc(100vw - 30px); max-height: 100vw; overflow: hidden;' scrolling='no' width='100%' height='100%'></iframe></div></div></div>
+<div class='csharp'><div class='qc-embed-frame' style='display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;'><div class='qc-embed-dummy' style='padding-top: 56.25%;'></div><div class='qc-embed-element' style='position: absolute; top: 0; bottom: 0; left: 0; right: 0;'><iframe class='qc-embed-backtest' src='https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ac6db075b8e9c9abc7c9d48c55db3871.html' style='max-width: calc(100vw - 30px); max-height: 100vw; overflow: hidden;' scrolling='no' width='100%' height='100%'></iframe></div></div></div><p>For more example algorithms, see <a href='/datasets/algoseek-us-equity-options/examples'>Examples</a>.</p>

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Option Universe/metadata.json

@@ -0,0 +1,12 @@
+{
+    "type": "metadata",
+    "values": {
+        "description": "US Equity Option Universe dataset from QuantConnect.",
+        "keywords": "data, financial data, alternative dataset",
+        "og:description": "US Equity Option Universe dataset from QuantConnect.",
+        "og:title": "US Equity Option Universe - Documentation QuantConnect.com",
+        "og:type": "website",
+        "og:site_name": "US Equity Option Universe - QuantConnect.com",
+        "og:image": "https://cdn.quantconnect.com/docs/i/writing-algorithms/datasets/quantconnect/us-equity-option-universe.png"
+    }
+}

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Security Master/08 Accessing Splits.html → 03 Writing Algorithms/14 Datasets/02 QuantConnect/07 US Equity Security Master/08 Accessing Splits.html

@@ -2,14 +2,18 @@
 
 <div class="section-example-container">
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:
+    # Check if any splits for the symbol
     if slice.splits.contains_key(self._symbol):
+        # If so, get the mapped split object
         split = slice.splits[self._symbol]
         split_type = {0: "Warning", 1: "SplitOccurred"}.get(split.type)
         self.log(f"Split: {split.symbol}\t{split.split_factor}\t{split.reference_price}\t{split_type}")</pre>
 <pre class="csharp">public override void OnData(Slice slice)
 {
+    // Check if any splits for the symbol
     if (slice.Splits.ContainsKey(_symbol))
     {
+        // If so, get the mapped split object
         var split = slice.Splits[_symbol];
         Log($"Split: {split.Symbol}\t{split.SplitFactor}\t{split.ReferencePrice}\t{split.Type}");
     }

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Security Master/09 Accessing Dividends.html → 03 Writing Algorithms/14 Datasets/02 QuantConnect/07 US Equity Security Master/09 Accessing Dividends.html

@@ -2,13 +2,17 @@
 
 <div class="section-example-container">
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:
+    # Check if any dividend for the symbol
     if slice.dividends.contains_key(self._symbol):
+        # If so, get the mapped dividend object
         dividend = slice.dividends[self._symbol]
         self.log(f'Dividend: {dividend.symbol}\t{dividend.distribution}\t{dividend.reference_price}')</pre>
 <pre class="csharp">public override void OnData(Slice slice)
 {
+    // Check if any dividend for the symbol
     if (slice.Dividends.ContainsKey(_symbol))
     {
+        // If so, get the mapped dividend object
         var dividend = slice.Dividends[_symbol];
         Log($"Dividend: {dividend.Symbol}\t{dividend.Distribution}\t{dividend.ReferencePrice}");
     }

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Security Master/10 Accessing Delistings.html → 03 Writing Algorithms/14 Datasets/02 QuantConnect/07 US Equity Security Master/10 Accessing Delistings.html

@@ -2,14 +2,18 @@
 
 <div class="section-example-container">
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:
+    # Check if any delisting for the symbol
     if slice.delistings.contains_key(self._symbol):
+        # If so, get the mapped delisting object
         delisting = slice.delistings[self._symbol]
         delisting_type = {0: "Warning", 1: "Delisted"}.get(delisting.type)
         self.log(f'Delistings: {delisting_type}')</pre>
 <pre class="csharp">public override void OnData(Slice slice)
 {
+    // Check if any delisting for the symbol
     if (slice.Delistings.ContainsKey(_symbol))
     {
+        // If so, get the mapped delisting object
         var delisting = slice.Delistings[_symbol];
         Log($"Delistings: {delisting.Type}");
     }

03 Writing Algorithms/14 Datasets/02 QuantConnect/06 US Equity Security Master/11 Accessing Symbol Change Events.html → 03 Writing Algorithms/14 Datasets/02 QuantConnect/07 US Equity Security Master/11 Accessing Symbol Change Events.html

@@ -1,14 +1,18 @@
 <p>To get the current Symbol change events, index the <b class="csharp">SymbolChangedEvents</b><b class="python">symbol_changed_events</b> property of the current <b>Slice</b> with the Equity <b>Symbol</b>. Slice objects deliver unique events to your algorithm as they happen, but the <b>Slice</b> may not contain data for your security at every time step. To avoid issues, check if the <b>Slice</b> contains the data you want before you index it.</p>
 <div class="section-example-container">
 <pre class="python">def on_data(self, slice: Slice) -&gt; None:
+    # Check if any symbol change event for the symbol
     if slice.symbol_changed_events.contains_key(self._symbol):
+        # If so, get the mapped SymbolChangeEvent object
         symbol_changed_event = slice.symbol_changed_events[self._symbol]
         self.log(f"Symbol changed: {symbol_changed_event.old_symbol} -&gt; {symbol_changed_event.new_symbol}")
 </pre>
 <pre class="csharp">
 public override void OnData(Slice slice){
+    // Check if any symbol change event for the symbol
     if (slice.SymbolChangedEvents.ContainsKey(_symbol))
     {
+        // If so, get the mapped SymbolChangeEvent object
         var symbolChangedEvent = slice.SymbolChangedEvents[_symbol];
         Log($"Symbol changed: {symbolChangedEvent.OldSymbol} -&gt; {symbolChangedEvent.NewSymbol}");
     }

03 Writing Algorithms/14 Datasets/02 QuantConnect/07 US Equity Security Master/metadata.json

@@ -0,0 +1,12 @@
+{
+    "type": "metadata",
+    "values": {
+        "description": "US Equity Security Master dataset from QuantConnect.",
+        "keywords": "data, financial data, alternative dataset",
+        "og:description": "US Equity Security Master dataset from QuantConnect.",
+        "og:title": "US Equity Security Master - Documentation QuantConnect.com",
+        "og:type": "website",
+        "og:site_name": "US Equity Security Master - QuantConnect.com",
+        "og:image": "https://cdn.quantconnect.com/docs/i/writing-algorithms/datasets/quantconnect/us-equity-security-master.png"
+    }
+}