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update api ref and indicator ref pages
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LouisSzeto committed Aug 29, 2024
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"110": "",
"111": "",
"112": "",
"113": ""
"113": "",
"114": ""
}
}
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<!-- Code generated by indicator_reference_code_generator.py -->
<p>This indicator represents the Vortex Indicator, which identifies the start and continuation of market trends. It includes components that capture positive (upward) and negative (downward) trend movements. This indicator compares the ranges within the current period to previous periods to calculate upward and downward movement trends.</p>
<p>To view the implementation of this indicator, see the <a rel="nofollow" target="_blank" href="https://github.com/QuantConnect/Lean/tree/master/Indicators/Vortex.cs">LEAN GitHub repository</a>.</p>
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<!-- Code generated by indicator_reference_code_generator.py -->
<?
$hasReference = false;
$hasAutomaticIndicatorHelper = true;
$helperPrefix = '';
$typeName = 'Vortex';
$helperName = 'Vortex';
$helperArguments = 'symbol';
$properties = array("PlusVortex","MinusVortex");
$pyProperties = array("plus_vortex","minus_vortex");
$otherProperties = array();
$otherPyProperties = array();
$updateParameterType = 'a <code>TradeBar</code> or <code>QuoteBar</code>';
$constructorArguments = '';
$updateParameterValue = 'bar';
$hasMovingAverageTypeParameter = False;
$constructorBox = 'vortex';
$isOptionIndicator = false;
include(DOCS_RESOURCES."/indicators/using-indicator.php");
?>
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<!-- Code generated by indicator_reference_code_generator.py -->
<?
$typeName = "Vortex";
$imageSource = "https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/vortex.png";
include(DOCS_RESOURCES."/indicators/visualization.php");
?>
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<!-- Code generated by indicator_reference_code_generator.py -->
<?
$helperPrefix = '';
$typeName = 'Vortex';
$helperName = 'Vortex';
$helperArguments = 'symbol';
$hasReference = false;
$isOptionIndicator = false;
$csharpProperties = array("PlusVortex","MinusVortex");
$pythonProperties = array("plus_vortex","minus_vortex");
include(DOCS_RESOURCES."/indicators/indicator-history.php");
?>
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{
"type": "metadata",
"values": {
"description": "This indicator represents the Vortex Indicator, which identifies the start and continuation of market trends. It includes compo...",
"keywords": "vortex",
"og:type": "website",
"og:description": "This indicator represents the Vortex Indicator, which identifies the start and continuation of market trends. It includes compo...",
"og:title": "Vortex - Using Indicators on QuantConnect.com",
"og:site_name": "Vortex - Using Indicators on QuantConnect.com",
"og:image": "https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/vortex.png"
}
}
1 change: 1 addition & 0 deletions 03 Writing Algorithms/98 API Reference/08 Indicators.php
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<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-v.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-vidya.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-vp.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-vtx.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-vwap.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-vwma.html"); ?>
<? include(DOCS_RESOURCES."/qcalgorithm-api/qcalgorithm-warm-up-indicator.html"); ?>
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<h4 id="AutoRegressiveIntegratedMovingAverage">AutoRegressiveIntegratedMovingAverage</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.AutoRegressiveIntegratedMovingAverage</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+AutoRegressiveIntegratedMovingAverage&type=code">[source]</a>
</div>
<p>An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing <a href="#_diffOrder">_diffOrder</a> times): <para> Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ + Σᵢθᵢεₜ₋ᵢ </para> where the first sum has an upper limit of <a href="#_arOrder">_arOrder</a> and the second <a href="#_maOrder">_maOrder</a>.</p>
<p>An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing _diffOrder times): <para> Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ + Σᵢθᵢεₜ₋ᵢ </para> where the first sum has an upper limit of _arOrder and the second _maOrder.</p>
<div class="subsection-content">
<div class="code-snippet"><code>get_enumerator()</code></div>
<div class="subsection-content">
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<h4 id="AutoRegressiveIntegratedMovingAverage">AutoRegressiveIntegratedMovingAverage</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.AutoRegressiveIntegratedMovingAverage</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+AutoRegressiveIntegratedMovingAverage&type=code">[source]</a>
</div>
<p>An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing <a href="#_diffOrder">_diffOrder</a> times): <para> Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ + Σᵢθᵢεₜ₋ᵢ </para> where the first sum has an upper limit of <a href="#_arOrder">_arOrder</a> and the second <a href="#_maOrder">_maOrder</a>.</p>
<p>An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing _diffOrder times): <para> Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ + Σᵢθᵢεₜ₋ᵢ </para> where the first sum has an upper limit of _arOrder and the second _maOrder.</p>
<div class="subsection-content">
<div class="code-snippet"><code>GetEnumerator()</code></div>
<div class="subsection-content">
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2 changes: 1 addition & 1 deletion Resources/indicators/constructors/implied-volatility.html
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<div class="subsection-header">Parameters:</div>
<ul class="subsection-content">
<li>
<span class="arg-name">function</span> (<span class="object-type">PyObject | Func[Decimal, Decimal, Decimal]</span>)
<span class="arg-name">function</span> (<span class="object-type">Func[Decimal, Decimal, Decimal] | PyObject</span>)
</li>
</ul>

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<h4 id="McClellanSummationIndex">McClellanSummationIndex</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.McClellanSummationIndex</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+McClellanSummationIndex&type=code">[source]</a>
</div>
<p>The McClellan Summation Index (MSI) is a market breadth indicator that is based on the rolling average of difference between the number of advancing and declining issues on a stock exchange. It is generally considered as is a long-term version of the <a href="#McClellanOscillator">McClellanOscillator</a></p>
<p>The McClellan Summation Index (MSI) is a market breadth indicator that is based on the rolling average of difference between the number of advancing and declining issues on a stock exchange. It is generally considered as is a long-term version of the McClellanOscillator</p>
<div class="subsection-content">
<div class="code-snippet"><code>get_enumerator()</code></div>
<div class="subsection-content">
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<h4 id="McClellanSummationIndex">McClellanSummationIndex</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.McClellanSummationIndex</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+McClellanSummationIndex&type=code">[source]</a>
</div>
<p>The McClellan Summation Index (MSI) is a market breadth indicator that is based on the rolling average of difference between the number of advancing and declining issues on a stock exchange. It is generally considered as is a long-term version of the <a href="#McClellanOscillator">McClellanOscillator</a></p>
<p>The McClellan Summation Index (MSI) is a market breadth indicator that is based on the rolling average of difference between the number of advancing and declining issues on a stock exchange. It is generally considered as is a long-term version of the McClellanOscillator</p>
<div class="subsection-content">
<div class="code-snippet"><code>GetEnumerator()</code></div>
<div class="subsection-content">
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12 changes: 6 additions & 6 deletions Resources/indicators/constructors/pivot-points-high-low.html
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Expand Up @@ -10,7 +10,7 @@ <h4 id="PivotPointsHighLow">PivotPointsHighLow</h4>


<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>get_enumerator()</code></div>
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<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>get_low_pivot_points_array()</code></div>
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<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>reset()</code></div>
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<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>GetEnumerator()</code></div>
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<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>GetLowPivotPointsArray()</code></div>
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<div class="subsection-header">Return type:</div>
<p class="subsection-content"><a href="/docs/v2/writing-algorithms/api-reference#PivotPoint">PivotPoint[]</a></p>
<p class="subsection-content">PivotPoint[]</p>
</div>

<div class="code-snippet"><code>Reset()</code></div>
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4 changes: 2 additions & 2 deletions Resources/indicators/constructors/regression-channel.html
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<h4 id="RegressionChannel">RegressionChannel</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.RegressionChannel</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+RegressionChannel&type=code">[source]</a>
</div>
<p>The Regression Channel indicator extends the <a href="#LeastSquaresMovingAverage">LeastSquaresMovingAverage</a> with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html</p>
<p>The Regression Channel indicator extends the LeastSquaresMovingAverage with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html</p>
<div class="subsection-content">
<div class="code-snippet"><code>get_enumerator()</code></div>
<div class="subsection-content">
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<h4 id="RegressionChannel">RegressionChannel</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.RegressionChannel</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+RegressionChannel&type=code">[source]</a>
</div>
<p>The Regression Channel indicator extends the <a href="#LeastSquaresMovingAverage">LeastSquaresMovingAverage</a> with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html</p>
<p>The Regression Channel indicator extends the LeastSquaresMovingAverage with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html</p>
<div class="subsection-content">
<div class="code-snippet"><code>GetEnumerator()</code></div>
<div class="subsection-content">
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4 changes: 2 additions & 2 deletions Resources/indicators/constructors/sortino-ratio.html
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<h4 id="SortinoRatio">SortinoRatio</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.SortinoRatio</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+SortinoRatio&type=code">[source]</a>
</div>
<p>Calculation of the Sortino Ratio, a modification of the <a href="#SharpeRatio">SharpeRatio</a>. Reference: https://www.cmegroup.com/education/files/rr-sortino-a-sharper-ratio.pdf Formula: S(x) = (R - T) / TDD Where: S(x) - Sortino ratio of x R - the average period return T - the target or required rate of return for the investment strategy under consideration. In Sortino’s early work, T was originally known as the minimum acceptable return, or MAR. In his more recent work, MAR is now referred to as the Desired Target Return. TDD - the target downside deviation. <a href="#TargetDownsideDeviation">TargetDownsideDeviation</a></p>
<p>Calculation of the Sortino Ratio, a modification of the SharpeRatio. Reference: https://www.cmegroup.com/education/files/rr-sortino-a-sharper-ratio.pdf Formula: S(x) = (R - T) / TDD Where: S(x) - Sortino ratio of x R - the average period return T - the target or required rate of return for the investment strategy under consideration. In Sortino’s early work, T was originally known as the minimum acceptable return, or MAR. In his more recent work, MAR is now referred to as the Desired Target Return. TDD - the target downside deviation. TargetDownsideDeviation</p>
<div class="subsection-content">
<div class="code-snippet"><code>get_enumerator()</code></div>
<div class="subsection-content">
Expand Down Expand Up @@ -165,7 +165,7 @@ <h4 id="SortinoRatio">SortinoRatio</h4>
<h4 id="SortinoRatio">SortinoRatio</h4>
<div class="code-snippet"><span class="object-type">class</span> <code>QuantConnect.Indicators.SortinoRatio</code><a class="code-source" href="https://github.com/search?q=repo%3AQuantConnect%2FLean+SortinoRatio&type=code">[source]</a>
</div>
<p>Calculation of the Sortino Ratio, a modification of the <a href="#SharpeRatio">SharpeRatio</a>. Reference: https://www.cmegroup.com/education/files/rr-sortino-a-sharper-ratio.pdf Formula: S(x) = (R - T) / TDD Where: S(x) - Sortino ratio of x R - the average period return T - the target or required rate of return for the investment strategy under consideration. In Sortino’s early work, T was originally known as the minimum acceptable return, or MAR. In his more recent work, MAR is now referred to as the Desired Target Return. TDD - the target downside deviation. <a href="#TargetDownsideDeviation">TargetDownsideDeviation</a></p>
<p>Calculation of the Sortino Ratio, a modification of the SharpeRatio. Reference: https://www.cmegroup.com/education/files/rr-sortino-a-sharper-ratio.pdf Formula: S(x) = (R - T) / TDD Where: S(x) - Sortino ratio of x R - the average period return T - the target or required rate of return for the investment strategy under consideration. In Sortino’s early work, T was originally known as the minimum acceptable return, or MAR. In his more recent work, MAR is now referred to as the Desired Target Return. TDD - the target downside deviation. TargetDownsideDeviation</p>
<div class="subsection-content">
<div class="code-snippet"><code>GetEnumerator()</code></div>
<div class="subsection-content">
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