About the Security Trading Analytics Blog This blog aims to empower site visitors who seek examples and demonstrations of quantitative methods for tracking and projecting security prices. Another goal of the blog is to present methods and resources that are practical and useful for individuals who want to become better traders and investors with the help of quant methods. Quantitative methods may include, but are not limited to: · Analysis of historical security prices · Technical analysis of trends and indicators · Models for when to buy and sell securities implemented with: o SQL Server o Google Sheets with the GOOGLEFINANCE Function o Excel with the STOCKHISTORY Function ...
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ETF Price Puzzles: Examples Showing Tiingo’s Raw and Adjusted Series Don’t Always Differ As I was preparing an analysis of a buy‑sell model for several major ETFs, I stumbled across some unexpected puzzles in the price data. Tiingo provides both raw and adjusted price series, but for certain ETFs the two are identical — even across known split dates. This post explains how I discovered these puzzles in selected ETF ticker prices from Tiingo. The post also describes why the puzzles matter, and how you can adjust for them when performing ETF analyses with Tiingo data. Where I Discovered Tiingo ETF Price Puzzles I was analyzing a model based on EMA proper orders for prices when I first noticed ETF price puzzles. Any ETF price series can have multiple EMAs depending on their period lengths. No matter what the period length for an EMA, it is always dependent on its underlying price data. For any trading date, EMAs with shorter period lengths...
A SQL Model for Buying and Selling Tickers Based on Historical Prices from Tiingo and EMAs This Security Trading Analytics blog exists to illustrate analytical approaches for trading securities. This post describes and demonstrates T-SQL code for choosing buy and sell dates for four securities based on raw close prices downloaded with PowerShell and EMA value sets computed in SQL Server. The model chooses to buy a ticker share when the security price concurrently rises above its long-term and short-term price trends. The model chooses to sell an owned ticker share when the security price falls below a short-term price trend that is above the one used to select a buy date. Recognize that the model is just one hypothesis among many for choosing buy and sell dates. Within the context of this post, the main objective of implementing the model is to establish a framework for backtesting the model returns. A follow-up post to this one will illustrat...
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