Supported Indicators

Alpha

Introduction

In financial analysis, the Alpha indicator is used to measure the performance of an investment (such as a stock or ETF) relative to a benchmark index, often representing the broader market. Alpha indicates the excess return of the investment compared to the return of the benchmark index. The S P 500 index is frequently used as a benchmark in Alpha calculations to represent the overall market performance. Alpha is an essential tool for investors to understand the idiosyncratic returns of their investment that aren't caused by movement in the underlying benchmark.

To view the implementation of this indicator, see the LEAN GitHub repository.

Using A Indicator

To create an automatic indicators for Alpha, call the A helper method from the QCAlgorithm class. The A method creates a Alpha object, hooks it up for automatic updates, and returns it so you can used it in your algorithm. In most cases, you should call the helper method in the Initializeinitialize method.

public class AlphaAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Symbol _reference;
    private Alpha _a;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _reference = AddEquity("QQQ", Resolution.Daily).Symbol;
        _a = A(_symbol, reference, 20);
    }

    public override void OnData(Slice data)
    {
        if (_a.IsReady)
        {
            // The current value of _a is represented by itself (_a)
            // or _a.Current.Value
            Plot("Alpha", "a", _a);
            
        }
    }
}
class AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self.reference = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._a = self.a(self._symbol, reference, 20)

    def on_data(self, slice: Slice) -> None:
        if self._a.is_ready:
            # The current value of self._a is represented by self._a.current.value
            self.plot("Alpha", "a", self._a.current.value)
            

The following reference table describes the A method:

a(target, reference, alpha_period=1, beta_period=252, resolution=None, risk_free_rate=None, selector=None)[source]

Adds a tag to the algorithm

Parameters:
  • target (Symbol) — The target symbol whose Alpha value we want
  • reference (Symbol) — The reference symbol to compare with the target symbol
  • alpha_period (int, optional) — The period of the Alpha indicator
  • beta_period (int, optional) — The period of the Beta indicator
  • resolution (Resolution, optional) — The resolution
  • risk_free_rate (float, optional) — The risk free rate
  • selector (Callable[IBaseData, IBaseDataBar], optional) — Selects a value from the BaseData to send into the indicator, if null defaults to casting the input value to a TradeBar
Returns:

The Alpha indicator for the given parameters

Return type:

Alpha

A(target, reference, alphaPeriod=1, betaPeriod=252, resolution=None, riskFreeRate=None, selector=None)[source]

Adds a tag to the algorithm

Parameters:
  • target (Symbol) — The target symbol whose Alpha value we want
  • reference (Symbol) — The reference symbol to compare with the target symbol
  • alphaPeriod (Int32, optional) — The period of the Alpha indicator
  • betaPeriod (Int32, optional) — The period of the Beta indicator
  • resolution (Resolution, optional) — The resolution
  • riskFreeRate (decimal, optional) — The risk free rate
  • selector (Func<IBaseData, IBaseDataBar>, optional) — Selects a value from the BaseData to send into the indicator, if null defaults to casting the input value to a TradeBar
Returns:

The Alpha indicator for the given parameters

Return type:

Alpha

If you don't provide a resolution, it defaults to the security resolution. If you provide a resolution, it must be greater than or equal to the resolution of the security. For instance, if you subscribe to hourly data for a security, you should update its indicator with data that spans 1 hour or longer.

For more information about the selector argument, see Alternative Price Fields.

For more information about plotting indicators, see Plotting Indicators.

You can manually create a Alpha indicator, so it doesn't automatically update. Manual indicators let you update their values with any data you choose.

Updating your indicator manually enables you to control when the indicator is updated and what data you use to update it. To manually update the indicator, call the Updateupdate method with a TradeBar or QuoteBar. The indicator will only be ready after you prime it with enough data.

public class AlphaAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Symbol _reference;
    private Alpha _a;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _reference = AddEquity("QQQ", Resolution.Daily).Symbol;
        _a = new Alpha("", _symbol, reference, 20);
    }

    public override void OnData(Slice data)
    {
        if (data.Bars.TryGetValue(_symbol, out var bar))
        {      
            _a.Update(bar);
        }
        if (data.Bars.TryGetValue(_reference, out bar))
        {      
            _a.Update(bar);
        }
   
        if (_a.IsReady)
        {
            // The current value of _a is represented by itself (_a)
            // or _a.Current.Value
            Plot("Alpha", "a", _a);
            
        }
    }
}
class AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self.reference = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._a = Alpha("", self._symbol, reference, 20)

    def on_data(self, slice: Slice) -> None:
        bar = slice.bars.get(self._symbol)
        if bar:
            self._a.update(bar)
        bar = slice.bars.get(self.referece)
        if bar:
            self._a.update(bar)
        if self._a.is_ready:
            # The current value of self._a is represented by self._a.current.value
            self.plot("Alpha", "a", self._a.current.value)
            

To register a manual indicator for automatic updates with the security data, call the RegisterIndicatorregister_indicator method.

public class AlphaAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Symbol _reference;
    private Alpha _a;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _reference = AddEquity("QQQ", Resolution.Daily).Symbol;
        _a = new Alpha("", _symbol, reference, 20);
        RegisterIndicator(_symbol, _a, Resolution.Daily);
        RegisterIndicator(reference, _a, Resolution.Daily);
    }

    public override void OnData(Slice data)
    {
        if (_a.IsReady)
        {
            // The current value of _a is represented by itself (_a)
            // or _a.Current.Value
            Plot("Alpha", "a", _a);
            
        }
    }
}
class AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self.reference = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._a = Alpha("", self._symbol, reference, 20)
        self.register_indicator(self._symbol, self._a, Resolution.DAILY)
        self.register_indicator(reference, self._a, Resolution.DAILY)

    def on_data(self, slice: Slice) -> None:
        if self._a.is_ready:
            # The current value of self._a is represented by self._a.current.value
            self.plot("Alpha", "a", self._a.current.value)
            

The following reference table describes the Alpha constructor:

Alpha

class QuantConnect.Indicators.Alpha[source]

In financial analysis, the Alpha indicator is used to measure the performance of an investment (such as a stock or ETF) relative to a benchmark index, often representing the broader market. Alpha indicates the excess return of the investment compared to the return of the benchmark index. The S P 500 index is frequently used as a benchmark in Alpha calculations to represent the overall market performance. Alpha is an essential tool for investors to understand the idiosyncratic returns of their investment that aren't caused by movement in the underlying benchmark.

get_enumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

reset()

Resets this indicator to its initial state

to_detailed_string()

Provides a more detailed string of this indicator in the form of {Name} - {Value}

Return type:

str

update(time, value)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • time (datetime)
  • value (float)
Return type:

bool

update(input)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • input (IBaseData)
Return type:

bool

property consolidators

The data consolidators associated with this indicator if any

Returns:

The data consolidators associated with this indicator if any

Return type:

ISet[IDataConsolidator]

property current

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property is_ready

Gets a flag indicating when the indicator is ready and fully initialized

Returns:

Gets a flag indicating when the indicator is ready and fully initialized

Return type:

bool

property item

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Returns:

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Return type:

IndicatorDataPoint

property name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

str

property previous

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property samples

Gets the number of samples processed by this indicator

Returns:

Gets the number of samples processed by this indicator

Return type:

int

property warm_up_period

Required period, in data points, for the indicator to be ready and fully initialized.

Returns:

Required period, in data points, for the indicator to be ready and fully initialized.

Return type:

int

property window

A rolling window keeping a history of the indicator values of a given period

Returns:

A rolling window keeping a history of the indicator values of a given period

Return type:

RollingWindow[IndicatorDataPoint]

Alpha

class QuantConnect.Indicators.Alpha[source]

In financial analysis, the Alpha indicator is used to measure the performance of an investment (such as a stock or ETF) relative to a benchmark index, often representing the broader market. Alpha indicates the excess return of the investment compared to the return of the benchmark index. The S P 500 index is frequently used as a benchmark in Alpha calculations to represent the overall market performance. Alpha is an essential tool for investors to understand the idiosyncratic returns of their investment that aren't caused by movement in the underlying benchmark.

GetEnumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

Reset()

Resets this indicator to its initial state

ToDetailedString()

Provides a more detailed string of this indicator in the form of {Name} - {Value}

Return type:

String

Update(time, value)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • time (DateTime)
  • value (decimal)
Return type:

Boolean

Update(input)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • input (IBaseData)
Return type:

Boolean

property Consolidators

The data consolidators associated with this indicator if any

Returns:

The data consolidators associated with this indicator if any

Return type:

ISet<IDataConsolidator>

property Current

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property IsReady

Gets a flag indicating when the indicator is ready and fully initialized

Returns:

Gets a flag indicating when the indicator is ready and fully initialized

Return type:

bool

property Name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

string

property Previous

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property Samples

Gets the number of samples processed by this indicator

Returns:

Gets the number of samples processed by this indicator

Return type:

int

property WarmUpPeriod

Required period, in data points, for the indicator to be ready and fully initialized.

Returns:

Required period, in data points, for the indicator to be ready and fully initialized.

Return type:

Int32

property Window

A rolling window keeping a history of the indicator values of a given period

Returns:

A rolling window keeping a history of the indicator values of a given period

Return type:

RollingWindow<IndicatorDataPoint>

property [System.Int32]

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Returns:

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Return type:

IndicatorDataPoint

Visualization

The following image shows plot values of selected properties of Alpha using the plotly library.

Alpha line plot.

Indicator History

To obtain the historical data of the Alpha, call the IndicatorHistoryself.indicator_history method. Like ordinary historical data requests, the indicator history takes arguments of symbol(s), time period, (optional) resolution, and (optional) a selector function, with an extra argument of the indicator instance itself. It returns a DataDictionary<IndicatorDataPoints>pandas.DataFrame object containing the historical data of the indicator.

The time period argument can be expressed as:

  • an int of the number of indicator data points requesting
  • a TimeSpantimedelta object as the time period needed from the current time
  • two DateTimedatetime objects as the start and end time of the indicator history needed
public class AlphaAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Symbol _reference;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _reference = AddEquity("QQQ", Resolution.Daily).Symbol;
        var a = A(_symbol, reference, 20);
        var multipleSymbolsCountIndicatorHistory = IndicatorHistory(a, new[] { _symbol, _reference }, 100, Resolution.Minute);
        var multipleSymbolsTimeSpanIndicatorHistory = IndicatorHistory(a, new[] { _symbol, _reference }, TimeSpan.FromDays(10), Resolution.Minute);
        var multipleSymbolsTimePeriodIndicatorHistory = IndicatorHistory(a, new[] { _symbol, _reference }, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
    }
}
class AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self.reference = self.add_equity("QQQ", Resolution.DAILY).symbol
        a = self.a(self._symbol, reference, 20)
        multiple_symbols_count_indicator_history = self.indicator_history(a, [self._symbol, self.reference], 100, Resolution.MINUTE)
        multiple_symbols_timedelta_indicator_history = self.indicator_history(a, [self._symbol, self.reference], timedelta(days=10), Resolution.MINUTE)
        multiple_symbols_time_period_indicator_history = self.indicator_history(a, [self._symbol, self.reference], datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)

You may also provide the historical data instead of the time argument to generate indicator history with respect to the time and values of the historical data provided.

var history = History(new[] { _symbol, _reference }, 100, Resolution.Minute);
var historyIndicatorHistory = IndicatorHistory(a, history);
history = self.history([self._symbol, self.reference], 100, Resolution.MINUTE)
history_indicator_history = self.indicator_history(a, history)

The default indicator historical data will be calculated using the Value property of each BaseData object iterated. You can assign custom calculation on the value being processed through a selector function argument.

var indicatorHistory = IndicatorHistory(a, 100, Resolution.Minute, (bar) => ((TradeBar)bar).High);
indicator_history = self.indicator_history(a, 100, Resolution.Minute, lambda bar: bar.High)

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