Running VWAP Calculation Using Reactive Extensions for .NET

Reactive Framework Labs

This article is one from the “Processing Financial Data with Microsoft Reactive Framework (Reactive Extensions for .NET)” series of articles that demonstrate some basic applications of Rx framework to financial data. This article shows how to calculate a running VWAP (volume weighted average price over a defined window of time). Other articles in the series are:

• Detecting daily Hi/Lo prices
• Throttling high-frequency data feed
• Data feed failover

Visual Studio 2008 solution containing full source code for all examples can be downloaded here.

Calculating VWAP/Sliding Window Implementation

We will use this simple “Trade” type to represent a trade event. To simplify things we will only consider Price and Volume:

class Trade
{
    public double Price { get; set; }
    public int Volume { get; set; }
}

The following “TradeWithVWAP” type wraps underlying trade and attaches running VWAP value to it. It is used for calculation output:

class TradeWithVWAP
{
    public Trade Trade { get; set; }
    public double VWAP { get; set; }
}

Our main module starts with a simple market data generator that produces infinite stream of random prices ($30 to $33 range) at random volumes (1,000 to 100,000 shares) at a random frequency (0 to 3 seconds between trades). BuildVwapFeed functions takes generated trade stream and converts it to another stream with running VWAP attached to every trade. And finally we simply subscribe to VWAP-wrapped feed and output the results.

Data Feed Failover Using Reactive Extensions for .NET

Reactive Framework Labs

This article is one from the “Processing Financial Data with Microsoft Reactive Framework (Reactive Extensions for .NET)” series of articles that demonstrate some basic applications of Rx framework to financial data streams. This article demonstrates automatic and transparent failover from primary data feed to a backup one implemented in terms of Microsoft Reactive Framework. Other articles in the series are:

• Detecting daily Hi/Lo prices
• Throttling high-frequency data feed
• Running VWAP calculation

Visual Studio 2008 solution containing full source code for all examples in this article can be downloaded here.

Failover Implementation

The effect of automatic failover is implemented using the “Catch” operator built into the Rx framework.  The “Catch” operator simply repeats the first stream until it fails. When the first stream fails, exception is swallowed and the second stream is subscribed to and repeated. You can chain any number of streams this way. Here is a code sample that uses “Catch” to switch from some primary feed to a backup:

Throttling High-Frequency Data Feed Using Reactive Extensions for .NET

Reactive Framework Labs

This article is one from the “Processing Financial Data with Microsoft Reactive Framework (Reactive Extensions for .NET)” series of articles that demonstrate some basic applications of Rx framework to financial data. This article shows how to throttle your incoming feed to a desired frequency. Other articles in the series are:

• Detecting daily Hi/Lo prices
• Data feed failover
• Running VWAP calculation

Visual Studio 2008 solution containing full source code for all examples can be downloaded here.

Throttling Implementation

The key operator here is “Sample”. Sampling at a desired rate effectively returns your a throttled stream. My intuition was that if underlying feed does not produce a value during the sampling interval, “Sample” will repeat the most recent available value when sampling is due. But it doesn’t and this makes it a perfect fit for throttling. Note that there is also an operator called “Throttle” but its name is misleading and its behavior most likely will not match your definition of throttling. The way implemented it will not emit any values at all if underlying feed produces faster than throttling interval, so it looks more like “Choke” to me.

Detecting Running High/Low Prices Using Reactive Extensions for .NET

Reactive Framework Labs

This article is one from the “Processing Financial Data with Microsoft Reactive Framework (Reactive Extensions for .NET)” series of articles that demonstrate some basic applications of Rx framework to financial data. This article focuses on detecting running high/low values (like daily max and min) from a market data feed. Other articles in the series are:

• Throttling high-frequency data feed
• Data feed failover
• Running VWAP calculation

Visual Studio 2008 solution containing full source code for all examples can be downloaded here.

Detecting Running High/Low Prices

The key operators in this implementation are “Do” and “Where”, both built into the Reactive Framework. The first one is used to produce side effect of recording the running High or Low value, and the second is used to filter incoming values that do not produce newest High or Low:

Processing Financial Data with Microsoft Reactive Framework (Reactive Extensions for .NET)

Reactive Framework Labs

I have recently discovered Microsoft Reactive Framework (thanks to spontaneous introduction at New York .Net Meetup Group) and have been playing with it a lot trying to apply it to many financial problems I’ve coded over the last years. I am truly amazed with the technology and the power it unleashes for processing and analyzing streaming financial data (like quotes, market data feeds, trade executions, etc.). Seamless integration with LINQ combined with very powerful support for composition makes self coded CEP in .Net a low hanging fruit. In the following series of articles I am going to demonstrate sample Rx-based implementations for some commonly occurring tasks in finance, including:

• Detecting daily Hi/Lo prices
• Throttling high-frequency data feed
• Data feed failover
• Running VWAP calculation

Visual Studio 2008 solution containing full source code for all examples can be downloaded here.

Advance Your Composite Application MVVM to the Next Level – Use Attributes to Automatically Populate Your View Model with Service Calls

Abstract

MVVM is a proven design pattern in WFP/Silverlight world. Its use helps achieve separation of concerns, frees UI from any logic to enhance testing, and generally makes the code easier to understand and maintain. In many LOB multi-tier applications MVVM is extended to include a presenter class. Presenter usually controls the view model, receives necessary services from dependency injection container and has some logic to populate the view model with business entities and/or reference data and send any changes back. The resulting code is usually pretty clean, easy to maintain and test. This article argues though that most of the code in such presenters to populate the view models is also very mechanical, routine, boring and can be avoided all together by introducing small attribute-based view model framework to declaratively populate view models from service calls and avoid extra coding, and, thus, avoid writing extra mechanical, routine, and boring tests.

If you prefer the “code first” approach VS 2008 solution can be downloaded here: MVVMAttributes.zip