Are you interested in implementing a stock trading or investment strategy? Do you want to evaluate which strategies generate the largest returns? In this article, we will provide a high-level overview of the steps involved in implementing and comparing alternative plans for investing in or trading financial securities using SQL Server.
Step 1: Reading Financial Historical Data
The first step in implementing a stock trading or investment strategy is to learn how to read financial historical data about securities and transfer the data to a SQL Server instance. This will allow you to evaluate strategies based on historical data patterns. There are many sources for obtaining stock price data, such as stockbrokers, data vendors, or websites like Stooq and Yahoo Finance.
Step 2: Selecting Tickers for Comparison
Once you have the historical data, you need to decide what you want to compare. For example, you can compare the stock price of one company versus another by selecting historical prices for different tickers. Tickers are abbreviations for the names of financial securities. You can also track major market indexes through exchange-traded funds (ETFs) with tickers like DIA, QQQ, and SPY.
Step 3: Contrasting Returns for Different Tickers
After selecting the tickers, you can contrast the returns for different tickers over a collection of trading days. This can be done by comparing daily prices from the initial public offering (IPO) date through a specific date or by using weekly, monthly, quarterly, or annual periods. You can also use data mining techniques to segment financial securities for comparative performance analyses.
Step 4: Buy-and-Hold Strategy vs. Swing Trading Strategy
When it comes to investing or trading, there are different strategies to consider. A buy-and-hold strategy is typically used by investors who buy a security and hold it for a long period, based on the fundamental properties of the security. On the other hand, traders are more likely to base trading decisions on short-term price trends. They may use indicators like moving averages to determine when to buy or sell a security.
Step 5: Leveraged vs. Unleveraged ETFs
ETFs based on major market indexes can be leveraged or unleveraged. Leveraged ETFs move a ratio of the underlying index on a daily basis, amplifying gains and losses. Unleveraged ETFs track the underlying index without leverage. It’s important to consider the performance and risks associated with leveraged and unleveraged ETFs when implementing a trading strategy.
Step 6: Computing Exponential Moving Averages
Exponential moving averages (EMAs) are commonly used financial indicators for analyzing time series data. EMAs can smooth a series of values over successive periods and help identify trends. They are computed using a weighted average formula that assigns different weights to the current period’s value and the prior period’s moving average. SQL Server provides functions and stored procedures to compute EMAs for financial applications.
Step 7: Evaluating Buy-Sell Strategies
In addition to the buy-and-hold strategy, you can also consider implementing a buy-sell strategy based on EMAs. This strategy involves looking for specific patterns in the EMAs to determine when to buy or sell a security. By tracking the gains and losses across buy-sell cycles, you can evaluate the performance of the strategy. However, it’s important to note that the efficacy of the buy-sell strategy may vary depending on the ticker symbols and timeframes.
By following these steps and leveraging the capabilities of SQL Server, you can implement and compare different stock trading or investment strategies. Whether you prefer a buy-and-hold strategy or a more active trading approach, SQL Server can help you analyze historical data, compute indicators, and evaluate the performance of your strategies.