Somewhere around 800,000 data series live inside the FRED database, and the vast majority of traders have never opened it. That is a genuine missed opportunity, because several of those series move in ways that consistently precede market turns.
The ones worth paying attention to fall into a few buckets. Credit spreads, specifically the ICE BofA US High Yield Option-Adjusted Spread (BAMLH0A0HY2), tend to widen before equity selloffs. When high yield spreads start climbing while the S&P sits near highs, something is usually breaking underneath the surface. The lead time varies, but the signal has been consistent across multiple cycles.
Initial jobless claims are another underappreciated series. The four-week moving average smooths out noise, and when it starts trending higher after a sustained decline, the labor market is rolling over. Markets tend to price this in slowly, which creates a window where the data is available but not yet reflected in prices.
M2 money supply growth rate is particularly relevant for crypto. Bitcoin has shown a strong historical correlation with M2 year-over-year changes, usually with a lag of a few months. When M2 is expanding, risk assets tend to benefit. When it contracts, the pressure eventually shows up in prices.
The 10-year minus 2-year Treasury spread (T10Y2Y) gets plenty of attention for recession signaling, but fewer people track the 10-year minus 3-month spread (T10Y3M), which has a slightly better track record. Both matter, but the second one has been more reliable historically.
Consumer credit data (TOTALSL) reveals how stretched households are getting. When revolving credit growth accelerates while personal savings rates decline, consumers are borrowing to maintain spending. That is sustainable for a while but not forever, and the reversal tends to coincide with market weakness.
The practical approach is not to use any single FRED series as a timing tool. Instead, combine several into a composite dashboard. When multiple series are deteriorating simultaneously, the probability of a market downturn increases meaningfully. When they are all stable or improving, the backdrop favors risk-on positioning.
One underrated feature of FRED is the API access. You can pull data programmatically and build automated monitoring. Rather than checking these manually, set up a system that alerts you when key series cross important thresholds. The data is free. The edge comes from actually using it consistently.
The lag between when FRED data shifts and when markets react is where the opportunity lives. It is not a crystal ball, but it is a structured way to evaluate the macro environment that most retail participants completely ignore.