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...
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2024 and 2025 Watchlists and Signal-to-Noise Ratios Thanks for viewing the Security Trading Analytics blog’s second annual post on watchlists. Last year’s watchlist post introduced watchlists as a means of tracking performance within a calendar year by grouping stocks and ETFs that drove performance within a calendar year, but watching securities can serve many additional roles. For example, this year’s edition of the annual watchlist post presents cross-year performance changes for the tickers within a watchlist. This second annual watchlist post shows how signal‑to‑noise ratios reveal which tickers maintain clear, consistent performance across years — and which ones don’t. A signal-to-noise ratio computed in this post is a means of objectively assessing the strength of a trend amidst normal weekly volatility. This post demonstrates how easy it is to objectively assess signal-to-noise ratios for this use case. The cross-year performance analysis...