Selected Prior References About Securities Trading Analytics Blog Topics from MSSQLTips.com

The following table lists a selection of my articles on securities trading analytics.  These articles represent some of my best thoughts on securities analytics.  I will be issuing fresh posts that extend the scope of some these articles as well as covering new securities analytics topics.  You are invited to email me with any feedback about content in these articles as well as requests for new content that can help your securities trading or analytics research.

 

Indicator/Title	Short Summary
Relative Strength Index Indicator
Compute the Relative Strength Index for Time Series within SQL Server	The Relative Strength Index (RSI) is widely used by technical analysts to detect the status of security prices as they reverse from oversold towards overbought and back again.  This article clearly explains and demonstrates the steps for computing RSI values (with either Excel or SQL).  Knowing how to compute RSI values will equip you to understand the significance of RSI to making smarter trading decisions.
Using T-SQL and the RSI to Predict When to Buy and Sell Financial Securities	The article assesses the success of buying and selling decisions when those decisions are based on the RSI.  It also shows how to re-define the standard RSI overbought and oversold criteria for even more beneficial trading decisions.  The compound annual growth rate (CAGR) is introduced for measuring model performance. The download for this tip includes sample SQL code for computing RSI.
MACD Indicators
Building a SQL Computational Framework for MACD Indicators	The MACD (Moving Average Convergence Divergence) was introduced about a decade and a half after the RSI indicator.  There are three MACD indicators, which are typically referred to as the MACD line, the Signal line, and the Histogram.  This article introduces the three MACD indicators along with code and sample results for computing each indicator.
Use MACD to Predict When to Buy and Sell Securities with T-SQL	This article drills down further into the application of MACD indicators for making trading decisions.  The article is a beginning-level case study in how to apply the MACD indicators for designating buy and sell dates.  The designated dates are tracked for six tickers: AAPL, GOOGL, MSFT, SPXL, TQQQ, and UDOW.
Mining Stock Price Time Series with MACD in SQL Server	This article summarizes a data mining study for the three MACD indicators.  However, the focus of the mining is to correlate daily differences between MACD line values and Signal line values with ticker closing values.  The article gathers and analyzes data for periods with MACD line values greater than Signal line values relative to ticker close prices for a sample of a ten tickers.  For example, the article investigates whether ticker close prices generally rise when MACD line values are greater than the Signal line values.
Moving Average Indicators
Differences Between Exponential Moving Average and Simple Moving Average in SQL Server	Whenever you are analyzing time series data, such as ticker prices on successive trading days or weeks, it is likely you may be using simple or exponential moving averages. This article introduces the basics of computing these two  types of moving averages with SQL code to answer two questions. ·         Do simple or exponential moving averages more closely match your most recent underlying time series data? ·         Do the exponential moving average values for your most recent time series data depend on the date range over which you calculate exponential moving averages?
Revisiting Time Series Model Performance Assessment with T-SQL	Moving averages have period lengths.  Moving averages with shorter period lengths respond more quickly to changes in the underlying time series values than moving averages with longer period lengths.  This tip investigates a couple of models with cross-overs between moving averages based on different period lengths to assess which model specifies more profitable buy and sell dates.  A correlational analysis performed with Excel confirms the conclusion about one model being better than the other and even confirms the validity of the relationship between the models across a couple of decades of historical data.
Leveraged versus Unleveraged Major Market ETFs
SQL Server Data Mining for Leveraged Versus Unleveraged ETFs as a Long-Term Investment	Many market analysts frequently discourage investing in leveraged ETFs because they are too volatile, which can lead to a loss of capital.  Is this general assertion also true for ETFs based on major market indexes? ·         This tip investigates three pairs of major market ETFs (SPY vs. SPXL, DIA vs. UDOW, and QQQ vs. TQQQ) from their inception through 2022-11-30 ·         See how the compound annual growth rate (CAGR) for the leveraged ETFs compares to the CAGR for unleveraged ETFs
Time Series Data Mining Example with T-SQL when Adding New Data	How stable is the relationship of leveraged to unleveraged major market ETF price performance over time?  This article adds ten months of fresh trading data to the data in the prior article (SQL Server Data Mining for Leveraged Versus Unleveraged ETFs as a Long-Term Investment).  The purpose for adding the fresh data is to confirm what happens, if anything, to the relationships between prices for leveraged ETFs and unleveraged ETFs.  Two analyses are performed to answer the question ·         Compare the differences between pairs of ETF ticker CAGR values before and after adding ten months of fresh data ·         Use correlation coefficients to assess the correspondence for individual ETF CAGRs before and after adding ten months of fresh data