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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...
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A Preliminary Analysis of EMA Period Lengths and Price Action in a Buy-Sell Model This blog introduced on March 26, 2026, an earlier version of the Buy on Proper Order and Sell on Dynamic Stop Loss Orders Buy-Sell model.  Proper order was defined in that post by a relationship between a close price and two EMA values, such as close > ema_21 and ema_21 > ema_100.  In this post, the model dynamically updates upper and lower boundary values based on price action to lock in a portion of accumulated gains or at least constrain the size of a loss associated with a trade.  This post extends the March 26 post by updating the code for implementing the model, diving more deeply into how price action can change model performance, and testing the model over a broader range of tickers examined as well as over longer timeframes. The model examined in this post uses proper order relationships for close prices and exponential moving averages with different period lengths to discove...
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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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