S&P 500 Daily from FRED (alternative gateway) (Date)
- Rows
- 2,512
- Columns
- 2
Daily S&P 500 index CSV via direct FRED wrapper. Government data, long historical daily series.
AI analysis
Data Analysis: S&P 500 Daily (FRED Wrapper)
1. Dataset Representation and Value
This dataset provides a daily time series of the S&P 500 index, sourced via a wrapper API pointed at FRED (Federal Reserve Economic Data) — a highly authoritative government/institutional data source. The URL structure (ivo-welch.info/cgi-bin/fredwrap?symbol=SP500) indicates this is a lightweight passthrough to FRED's canonical SP500 series rather than a primary source itself, so data freshness and revision history ultimately depend on FRED's update cadence (typically daily, next-business-day). The ~10-year daily span (2016–2026) with 2,610 rows makes this a strong backbone series for correlation studies involving equity market performance against macroeconomic indicators, sector indices, commodities, or alternative datasets (sentiment, weather, policy events, etc.).
2. Data Quality Observations
Overall quality is good but not pristine. Of 2,610 total rows, there are 95 null cells (3.6%), all concentrated in the single value column sp500 — the Date join key has zero nulls and full distinct-value coverage (2,610 distinct dates matching row count, confirming no duplicate dates). The 95 nulls in sp500 almost certainly correspond to market holidays or non-trading days that still appear as calendar rows (weekends/holidays where the index doesn't trade) — this is a common artifact of FRED's daily series construction and should be treated as "no data" rather than a true missing-data problem, but analysts should explicitly filter or forward-fill these before merging with other datasets to avoid introducing false gaps in correlation calculations. Duplicate-row and type-mismatch figures are not yet computed (pending Phase D reconciliation), so those checks should be re-verified before production use.
3. Key Columns and Distributions
The sp500 column (2,508 distinct values across 2,610 rows) shows a wide-ranging distribution consistent with a decade that included COVID-era volatility, a strong bull run, and rate-driven swings: min=2,000.54, max=7,501.24, mean=3,962.42, median=3,861.59, σ=1,380.48. The mean sitting slightly above the median and a positive skew of 0.625 indicates a right-tailed distribution — i.e., more extreme high values than low, consistent with the index's long-term upward drift punctuated by sharp corrections. The interquartile range (Q1=2,779.71, Q3=4,697.74) spans nearly 1,918 points, underscoring substantial variance over the period and suggesting that any correlation analysis should consider log-returns or percentage changes rather than raw levels to avoid spurious trend-driven correlation (non-stationarity is a real risk with a σ this large relative to the median).
4. Recommended Join Key
The Date column is the clear and only sensible join key — it's complete (zero nulls), fully distinct (one row per calendar day), and spans a full decade. For merging with datasets that report on business-day, weekly, or monthly frequencies, use Date with appropriate resampling/aggregation (e.g., last-observation-of-period) rather than expecting exact match rates.
5. Complementary Datasets for Correlation Discovery
Strong pairing candidates include: (a) macro indicators from FRED itself — Fed Funds Rate, CPI/inflation, unemployment rate, 10-year Treasury yield — to study rate-sensitivity and inflation correlation; (b) volatility measures like VIX for risk-on/risk-off regime analysis; (c) commodity prices (oil, gold) for cross-asset correlation; (d) other equity indices (NASDAQ, Russell 2000, international indices like FTSE or Nikkei) for market-comovement studies; and (e) alternative/non-traditional series such as consumer sentiment indices, employment data, or even search-trend/news-sentiment datasets to explore leading-indicator relationships. Given the daily granularity and long history, this dataset is well-suited as an anchor series in a broader correlation-mining pipeline, provided the null trading-day rows are handled and returns (not levels) are used for statistical rigor.
Columns
- Date (date)
- sp500 (decimal)
