Capital Gains (Losses) and Capital Gains Tax

Capital gains are often thought of in the context of profiting from the sale of some stock or other security-based financial product. Capital losses on the other hand, are the opposite (the loss incurred from the sale of stock). It is important to remember however, that capital gains and capital losses can also include other sales such as the sale of a vehicle, the sale of a home, the sale of an antique, etc.

Capital gains tax is paid by sellers (both businesses and consumers) who have profited from the sale of some asset (bonds, stocks in other businesses, company equipment that was sold for profit). 

Capital loss occurs when an asset is sold for less than was purchased. The amount of this sale is usually exempt (deductible) from taxes up to a certain amount.

Reference: https://www.fool.com/retirement/2017/02/15/what-is-the-capital-loss-tax-deduction.aspx

Commodities and Securities Futures

"A futures contract is an agreement to buy or sell an asset at a future date at an agreed-upon price"

Futures markets such as the New York Board of Trade and the Chicago Mercantile Exchange facilitate the trading of futures contracts. Futures trading is often thought of only as raw materials (commodities), however financial products or "securities" are also traded in futures markets:

Commodities: A commodity is a raw material that has value and is more or less in constant demand (think- milk, eggs, pork, beef, chicken, lumber, iron, salt, crude oil, coal, etc.).

Securities (Financial): A security is a financial product such as an interest rate, the price of a stock, the value of some kind of debt like CDOs.

A recent history of returns on commodities futures by year and type

Futures trading is simply buyers betting on the future value of some product from the sellers. In commodities this could be a day trader speculating that the price of oil is about to skyrocket and buying contracts for purchases of oil at a lower price (he/she hopes).

Remember that futures trading is not limited to commodities

In securities futures, an example would be a buyer entering a contractual agreement to purchase some amount of stock for an agreed upon price at some future date. This would be to the buyer's advantage only if the price of the stock price on the future date is higher than the price agreed to in the futures contract.

At the heart of this kind of trading (and one could argue all trading) is the idea of betting for (+) or hedging against (-) the inevitable fluctuation of future value.


Reference: https://finance.zacks.com/futures-vs-commodities-5663.html

Continuous Integration

"Continuous Integration (CI) is a development practice that requires developers to integrate code into a shared repository several times a day. Each check-in is then verified by an automated build, allowing teams to detect problems early."

The idea behind CI is that by having all developers continuously tracking (pulling down changes from)- and incrementally integrating their branch/feature code into- a "master" branch (or some common branch that facilitates CI of all other branches), problems that stem from incompatible or "not easily merge-able" features surface at the first detection of incompatibility, as opposed to at the end of feature branch development when any incompatibilities are magnified, and result in time-consuming redesign efforts to make things merge and interoperate cleanly.

In short, CI is used to nip potential integration problems in the bud.

Dividends

Dividends are a company's optional distribution of (typically) cash to stockholders and provide another way to earn money from investing beyond growing the value of one's portfolio.

A dividend is defined as "a sum of money paid regularly (typically quarterly) by a company to its shareholders out of its profits (or reserves)".

A dividend yield is an expression of the dividend amount relative to the company's current share price. You can calculate the current dividend yield for a given year by dividing the total dividend paid for that year or the following year (or any 12 month period) by the current stock price.

Some companies regularly pay out a cash dividend and can make their stock more attractive by doing so. Johnson Controls (JCI) for instance, has managed to pay a quarterly dividend every year since 1887. They paid a total dividend of $1.04 in 2018 and the stock price as of today is $31.21.

There are two ways to calculate a company's current dividend yield: (1) by using what are called "forward dividends" or (2) by using "trailing dividends". Trailing uses the preceeding 12 months while forward uses the expected payouts in the proceeding 12 months. As of today (1/2/2019) using trailing dividends, or to be more clear- in relation to their 2018 total dividend payout"- JCI's dividend yield was:

$1.04 / $31.21

...or 3.3%.

As you can see from the charts above, General Electric and Honeywell have paid out cash dividends consistently for years. But GE has recently clawed back these payouts dramatically. This is probably due to GE's ongoing restructuring and spin-off efforts.

Reference:

https://www.nasdaq.com/symbol/jci/dividend-history

Collateralized Debt Obligations (CDOs)

Collateralized Debt Obligations are units of packaged debt, sometimes referred to as "Frankenstein debt" which consists of various kinds of debt obligations (auto, home, credit card, student loans, corporate debt, etc.) of various credit ratings (AAA, AA, A, BBB, BB, B, CCC, CC, etc.).

"Originally developed as instruments for the corporate debt markets, after 2002 CDOs became vehicles for refinancing mortgage-backed securities." -Wikipedia

The idea behind this type of investment is that although it contains lots of high-risk debt (that may well default), that risk is offset by the better rated debt in the CDO package.

There are also CDOs known as "CDOs squared". These are also simply packages of variously rated debt, but with an additional layer of abstraction (obfuscation). Instead of various cash-backed assets and other kinds of direct claims on debt in the bundle, CDO^2 consist of pieces or "tranches" of other CDOs.

Additionally, there are Synthetic CDOs and CDSs. A Synthetic CDO is not backed by debt assets but rather derivatives of debt assets known as "Credit Default Swaps" (CDSs), which are basically CDO insurance. The buyer of a CDS makes periodic premium payments in much the same way as premiums for home and auto insurance.

CDSs provide a way for investors to hedge CDO investments. If a credit event (default on a CDO's underlying debt asset) occurs, the buyer of a credit default swap is protected from losses. If no credit event occurs, the seller of the CDS continues to collect the premium payments for the duration of the term of the CDS.

Crazy stuff, huh? Be careful, Wall Street.. Lehman Brothers never saw it coming... 😶

2008 was obviously the wake-up call, trillions in wealth vanished as values crashed to Earth

Price Discrimination

Price Discrimination is the act of selling the same product or service at different prices to different buyers in order to match differing levels of demand. It is used to ensure business from lower demand markets and earn the maximum possible profit from higher demand markets. This can be illustrated in the case of your grandparent or child getting a discount at the movie theater because they tend to have a lower demand than the average moviegoer.

Examples of Price Discrimination

"Price Equilibrium" (PE) is the price point at which a Supply Curve and Demand Curve intersect. Any price charged above the PE will result in more profit (seller surplus) and any price below PE will result in less profit (consumer surplus, missed opportunity by seller). Pricing products and services is done through the process known as Marginal Cost Analysis.

Price discrimination can be quite problematic when it is applied on the basis of ethnicity or socioeconomic status.

Just a matter of risk data? Or a racially biased algorithm used by banks?

Although the discriminatory practice known as "redlining" has been outlawed for over 50 years, banks continue to charge higher mortgage rates to non-white consumers. From the bank's perspective they would argue that it is coincidence and simply reflects the consumer's credit and a higher risk they are taking on. Others would argue that minority loan-seekers are being priced out of the American Dream because of the color of their skin.

Gas stations tend to have higher-than-average prices in low income areas because the customers in these areas have less nearby options, are often less mobile and are in general less discriminating than shoppers in a wealthy suburb who can leverage their environment of more competition, their mobility and in turn be more selective in their consuming habits (which is to say, more likely to sharply increase or decrease demand if a price is not in equilibrium).

Now in some cases price discrimination makes perfect sense. Take for example a hardware store in Arizona and a hardware store in Wisconsin who are both selling snowblowers. The store in Arizona is almost assuredly going to sell their snowblowers for far less as the demand for snowblowers is very low in that area of the country.

But in Wisconsin, there is virtually year-round demand as it snows every year, and so the Wisconsin store is likely to charge much more than the Arizona store. Furthermore, even within Wisconsin, stores will charge less for snowblowers in the summer than in the winter (when demand is higher).

Companies differentiate prices to match demand for different types of consumers

With the exception of 4th degree price discrimination, when a price is different for different types of consumption of the same product or service- it is because demand for that product or service is different among consumers and so companies set the prices accordingly.

TwickrTape

Real-time scrolling Tweets related to financial news alerts and updates. Just enter the Twitter handle you want to see a marquee of live streaming tweets from.

TL;DR - the working app is here: https://twickrtape.azurewebsites.net
..znd here on Google Play Store: https://play.google.com/store/apps/details?id=io.cordova.twicktape

Use button in lower-left to toggle different Twitter handles

The code is a hodgepodge of various references and vestiges of past personal projects. My initial aim was to get something working end-to-end as I envisioned, and I was able to achieve that pretty easily through the Twitter API and .NET.

The process is standard API stuff- first you authenticate and receive a token which you can then subsequently pass to prove that your requests are valid. Then, using simple (REST API) HTTP GET, we get the content (Twitter Timeline information) that we are interested in.

The gist of the code is .NET C# (an ASP.NET Controller method) and listed below:

  public ActionResult GetTwickr(string handle = "business")  
     {  
       // Set your own keys and screen name  
       var oAuthConsumerKey = "XXXXXXXXXXXXX"; // "API key";  
       var oAuthConsumerSecret = "XXXXXXXXXXXXXXXXXXXXX"; // "API secret key";  
       var oAuthUrl = "https://api.twitter.com/oauth2/token";  
       var screenname = "@" + handle; // default Twitter display current status  
       // Authenticate  
       var authHeaderFormat = "Basic {0}";  
       var authHeader = string.Format(authHeaderFormat,  
         Convert.ToBase64String(Encoding.UTF8.GetBytes(Uri.EscapeDataString(oAuthConsumerKey) + ":" +  
         Uri.EscapeDataString((oAuthConsumerSecret)))  
       ));  
       var postBody = "grant_type=client_credentials";  
       HttpWebRequest authRequest = (HttpWebRequest)WebRequest.Create(oAuthUrl);  
       authRequest.Headers.Add("Authorization", authHeader);  
       authRequest.Method = "POST";  
       authRequest.ContentType = "application/x-www-form-urlencoded;charset=UTF-8";  
       authRequest.AutomaticDecompression = DecompressionMethods.GZip | DecompressionMethods.Deflate;  
       using (Stream stream = authRequest.GetRequestStream())  
       {  
         byte[] content = ASCIIEncoding.ASCII.GetBytes(postBody);  
         stream.Write(content, 0, content.Length);  
       }  
       authRequest.Headers.Add("Accept-Encoding", "gzip");  
       WebResponse authResponse = authRequest.GetResponse();  
       // deserialize into an object  
       TwitAuthenticateResponse twitAuthResponse;  
       using (authResponse)  
       {  
         using (var reader = new StreamReader(authResponse.GetResponseStream()))  
         {  
           System.Web.Script.Serialization.JavaScriptSerializer js = new System.Web.Script.Serialization.JavaScriptSerializer();  
           var objectText = reader.ReadToEnd();  
           twitAuthResponse = JsonConvert.DeserializeObject<TwitAuthenticateResponse>(objectText);  
         }  
       }  
       try  
       {  
         // Get timeline info  
         var timelineFormat = "https://api.twitter.com/1.1/statuses/user_timeline.json?screen_name={0}&include_rts=1&exclude_replies=1&count=5";  
         var timelineUrl = string.Format(timelineFormat, screenname);  
         HttpWebRequest timeLineRequest = (HttpWebRequest)WebRequest.Create(timelineUrl);  
         var timelineHeaderFormat = "{0} {1}";  
         timeLineRequest.Headers.Add("Authorization", string.Format(timelineHeaderFormat, twitAuthResponse.token_type, twitAuthResponse.access_token));  
         timeLineRequest.Method = "GET";  
         WebResponse timeLineResponse = timeLineRequest.GetResponse();  
         var timeLineJson = string.Empty;  
         string scrolltxt = string.Empty;  
         using (timeLineResponse)  
         {  
           using (var reader = new StreamReader(timeLineResponse.GetResponseStream()))  
           {  
             timeLineJson = reader.ReadToEnd();  
           }  
         }  
         // deserialize into an object  
         dynamic obj = JsonConvert.DeserializeObject(timeLineJson);  
         foreach (var r in obj.Root)  
         {  
           scrolltxt += " ***** " + r.text;  
         }  
         var model = new LoggedInUserTimelineViewModel { TimelineContent = scrolltxt, Handle = handle };  
         return View("TwickrMain", model);  
       }  
       catch (Exception e) { }  
       return View("TwickrMain");  
     }  

Next, using Newtonsoft,Json JsonConvert() function, we deserialize the JSON response and zero in on the 'text' property of each entity (tweet) in the array of JSON results (tweets).

And finally, using some basic Bootstrap HTML, JavaScript and CSS we are able to wire up a very simple, but effective app that interacts with the Twitter API in real-time.

Reference: https://developer.twitter.com/en/docs/tweets/timelines/api-reference/get-statuses-user_timeline.html

Using PowerShell and youtube-dl to automate grunt work

Earlier this evening I was tasked by my wife to create audio files from some (DRM-free) YouTube content. I knew how to do this via a manual process, however after the 5th manual file conversion I started to remember the youtube-dl Python project and figured there has to be an easier and quicker way to do this file conversion grunt work.

And sure enough... youtube-dl (with some help from PowerShell) does the trick.

Expected input and output

For starters (we are assuming you have already installed Python 3), you will need to have pip install two modules:

 pip install youtube-dl  
 pip install ffmpeg  

Next, create a youtube-uris.txt with the URI of each song you want to convert on separate lines of the file.

Finally, you can use the following PowerShell script to copy the song URIs into a string array which is then iterated through, converting each array item into a playable audio file (mp4), saved in whatever directory you run the script from.

 [string[]]$arrayFromFile = Get-Content -Path 'C:\sand\youtube-dl-sandbox\youtube-uris.txt'  
 foreach ($uri in $arrayFromFile) {  
   youtube-dl -f 140 $uri  
  }  

That's it. This script can be used for automating other manual/repetitive tasks requiring a procedure to be run against every item in a large list.


Reference: https://github.com/rg3/youtube-dl

Generate Secure Machine Key Section for Web.config via PowerShell

Machine Keys are used in ASP.NET for securing machines that are part of a web farm as well as for sharing encrypted application session and state information.

This PowerShell script (function) can be called (once run and saved to your PS session) via,

"PS C:\: Generate-MachineKey"

With the output from this PS function, you can copy and paste to your web.config ie:

 <configuration>  
  <system.web>  
   <machineKey ... />  
  </system.web>  
 </configuration>  

Generate-MachineKey function definition/PS script

 # Generates a <machineKey> element that can be copied + pasted into a Web.config file.  
 function Generate-MachineKey {  
  [CmdletBinding()]  
  param (  
   [ValidateSet("AES", "DES", "3DES")]  
   [string]$decryptionAlgorithm = 'AES',  
   [ValidateSet("MD5", "SHA1", "HMACSHA256", "HMACSHA384", "HMACSHA512")]  
   [string]$validationAlgorithm = 'HMACSHA256'  
  )  
  process {  
   function BinaryToHex {  
     [CmdLetBinding()]  
     param($bytes)  
     process {  
       $builder = new-object System.Text.StringBuilder  
       foreach ($b in $bytes) {  
        $builder = $builder.AppendFormat([System.Globalization.CultureInfo]::InvariantCulture, "{0:X2}", $b)  
       }  
       $builder  
     }  
   }  
   switch ($decryptionAlgorithm) {  
    "AES" { $decryptionObject = new-object System.Security.Cryptography.AesCryptoServiceProvider }  
    "DES" { $decryptionObject = new-object System.Security.Cryptography.DESCryptoServiceProvider }  
    "3DES" { $decryptionObject = new-object System.Security.Cryptography.TripleDESCryptoServiceProvider }  
   }  
   $decryptionObject.GenerateKey()  
   $decryptionKey = BinaryToHex($decryptionObject.Key)  
   $decryptionObject.Dispose()  
   switch ($validationAlgorithm) {  
    "MD5" { $validationObject = new-object System.Security.Cryptography.HMACMD5 }  
    "SHA1" { $validationObject = new-object System.Security.Cryptography.HMACSHA1 }  
    "HMACSHA256" { $validationObject = new-object System.Security.Cryptography.HMACSHA256 }  
    "HMACSHA385" { $validationObject = new-object System.Security.Cryptography.HMACSHA384 }  
    "HMACSHA512" { $validationObject = new-object System.Security.Cryptography.HMACSHA512 }  
   }  
   $validationKey = BinaryToHex($validationObject.Key)  
   $validationObject.Dispose()  
   [string]::Format([System.Globalization.CultureInfo]::InvariantCulture,  
    "<machineKey decryption=`"{0}`" decryptionKey=`"{1}`" validation=`"{2}`" validationKey=`"{3}`" />",  
    $decryptionAlgorithm.ToUpperInvariant(), $decryptionKey,  
    $validationAlgorithm.ToUpperInvariant(), $validationKey)  
  }  
 }  

Reference: https://support.microsoft.com/en-us/help/2915218/resolving-view-state-message-authentication-code-mac-errors#appendixa

Accessing SQL Server Data in R

Importing SQL Server data into R for analysis is pretty straightforward and simple. You will obviously need R installed on your machine. The following R code will connect to your SQL Server database (using R Studio):

 library(RODBC)  
 dbconnection <- odbcDriverConnect('driver={SQL Server};server=.;database=CLARO;trusted_connection=true')  
 initdata <- sqlQuery(dbconnection,paste('SELECT * FROM [CLARO].[dbo].[Fielding];')) 

SELECT data from a SQL Server database output in R Studio

Reference: https://stackoverflow.com/questions/39401230/connect-r-to-a-sql-server-database-engine

Accessing SQL Server Data in Python

So you want to access Microsoft SQL Server from your Python script(s)?

After weeding out some long-abandoned and/or nonworking solutions, I discovered a very simple Python ODBC driver that works with virtually all SQL Servers since MSSQL 2005 called "pyodbc". 

First, you will need to install this MSSQL ODBC (13.1 or 17 should work) component on your machine in addition to installing the pyodbc driver.

Next, get the pyodbc module for Python by running this from Windows command prompt:

pip install pyodbc

Then open up a python shell using 'py' or 'python' and enter the following after editing configuration values to match your development environment:

 import pyodbc  
 cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER=localhost;DATABASE=WideWorldImporters;UID=DemoUser;PWD=123Password')  
 cursor = cnxn.cursor()  
 #Sample of a simple SELECT  
 cursor.execute("SELECT TOP (100) Comments, count(*) FROM WideWorldImporters.Sales.Orders GROUP BY Comments")  
 row = cursor.fetchone()   
 while row:   
   print(row[0] + ': ' + str(row[1]))  
   row = cursor.fetchone()  

Running this code will result in the below if you have configured everything correctly (note this example makes use of the Microsoft SQL Server demo WorldWideImporters database):

Reference: https://docs.microsoft.com/en-us/sql/connect/python/pyodbc/step-3-proof-of-concept-connecting-to-sql-using-pyodbc?view=sql-server-2017

.udl for DbConnection Check

This is a useful method to quickly check SQL credentials and/or RDBMS connectivity if working on a Windows OS. Just create a file in any editor (ie. Notepad) and save it with .udl extension which makes it a Microsoft Data Link file type. Then, right-click and inspect file Properties >> "Connection" tab.

Credit to a former colleague of mine (thanks Gene David!) who showed me how to use this simple but very useful trick.

Reference: https://blogs.msdn.microsoft.com/farukcelik/2007/12/31/basics-first-udl-test/

Short Selling

Broker borrows a share, sells the share high, repurchases share at lower price ($) and returns it.

Short selling stock is the practice by which a broker borrows stock with the hope that the price of that stock will fall so that he or she can sell at a high price, (re)purchase at a lower price, and pocket the difference.

Hypothetically, let's say a trader named Joe firmly believed that Apple, Inc. was about to experience a large drop in share price. To short a single share of Apple stock, Joe would do the following:

1). Borrow a share of APPL from his portfolio, a client portfolio, or a fellow broker

2). Sell the share at the highest a price they can find before a drop (say 1 share of AAPL at current $157.76)

3). Wait for the price to fall (say APPL falls to $102.76), then purchase one share at this lower price

4). Subtract the higher price from the lower price (less fees) and return the borrowed share. 

Joe earns a cool $53 bucks from this scheme as he sold at $157.76 and bought back for just $102.76. After fees of $2.00 this is $157.76 - $102.76 -$2.00 == $53.00.

While the idea of selling something short of true value is often associated with the nefarious case of a stock "short" like this, oftentimes it is a necessity. The market always needs people on both the long end (owners/buyers) and the short end (renters/sellers) for it to work properly.

This is why banks who are on the hook with a property that they cannot sell will ultimately agree to a "short sale" (selling the home for below its fair market value) to recoup at least some of their losses.

A combination of consumer preferences and financial factors determine whether to go long or short on any kind of investment or large financial transaction.

Short selling doesn't always work in the sellers favor