Security Trading Analytics

  An Introduction to Retrieving Historical Security Prices with GOOGLEFINANCE and STOCKHISTORY Historical price data plays a central role in security trading analytics. Most trading and analytical models rely on past prices to generate signals about when to buy or sell. With historical prices available, analyst can backtest strategies, evaluate return patterns, and measure volatility across different timeframes. Whether you are calculating exponential moving averages with different period lengths, comparing moving averages to daily or weekly closes, generating MACD lines and histograms, or computing Relative Strength indicators, historical prices are the underlying data source. Historical data across different asset classes and time horizons also helps reveal broader market behavior. Long ‑ term price series allow analysts to estimate growth rates for various asset categories, while cross ‑ asset comparisons can highlight regime shifts and test how well strategies hold up under cha...

  Backtesting an Automated Buy-Sell Model Based on Smoothing Factors for SPY and SPXL Buy-Sell models are among my favorite analytical techniques because they simulate what self-directed and institutional traders do on a regular basis – namely, buy and sell financial securities.  A buy-sell model can evaluate different strategies for profitably buying and selling securities. Over the past several months, this blog introduced and examined variations of a model for buying and selling financial securities based on exponentially smoothed security close prices.  Simple exponential smoothing and exponential moving averages are two different approaches available for computing exponentially weighted historical values of an underlying set of time series values – like ticker prices.  This post relies on securities prices smoothed by a combination of simple exponential smoothing and exponential moving averages.  The model evaluated in the post compares unsmoothed historica...