/factor-research

name: factor-research
description: Run a quant-style multi-factor backtest on a defined US equity universe. For momentum, value, quality, and low-vol factors, compute decile spreads, information coefficients with t-stats, IC decay curves at 1M/3M/6M/12M forward horizons, single-name attribution at the long and short tails, and the factor correlation matrix. Emits FactSet/Axioma factor research-style table output a buy-side quant would hand to a PM. First skill to exercise the flat-files foundation: a 5-year x top-500 daily aggregates pull is ~80,000 ticker-days, done via a few day-bucket downloads instead of 80,000 REST calls.

factor-research

You hand over a universe definition and a window. The skill runs a multi-factor IC + decile analysis and emits two output layers from one analysis.

The output drops into a quant strategy meeting unchanged. The structure matches what FactSet's Alpha Testing, Axioma's factor research module, and any internal quant team's signal review document already use.

When to invoke

• A PM asks "what's working in the factor zoo right now"
• A quant analyst is sizing a multi-factor sleeve and needs IC and

correlation evidence

• A researcher is testing whether momentum's IC has decayed in the

current regime

• The user says "run a factor study on the S&P 500", "is value working",

"show me the decile spread for momentum"

What you need

• A universe (defaults to top 500 by current market cap)
• A window (defaults to 2021-06-01 to today)
• `MASSIVE_API_KEY` exported in the environment
• Optional: `MASSIVE_S3_ACCESS_KEY` and `MASSIVE_S3_SECRET_KEY` (generated under

Flat Files in the Massive dashboard) for bulk daily bars. Without them the skill uses REST.

• Stocks Starter plan minimum (flat files included with any paid plan)

Expected runtime

This skill is the first one in the suite where a real run takes meaningful wall-clock time. A default 5-year, top-500 run:

• Cold: ~10-20 minutes (downloads ~1,260 daily aggregate files plus

~500 financials calls plus ~500 ticker-details calls)

• Warm (with on-disk cache of the daily files): under 2 minutes

That's the legitimate cost of universe-wide work. If a quant proposition is wrong, finding out in 20 minutes beats finding out in a six-week production cycle. The same workflow over REST would be 80,000+ calls and require an unlimited paid tier just to complete.

What you get back

Two output layers from one analysis.

**Layer 1: canonical JSON** matching [`output-schema.json`](./output-schema.json). The universe definition (with the survivorship caveat made explicit), the window, per-factor IC at four forward horizons with t-stats and sample sizes, decile spread returns (D10 - D1) annualized, hit rates, factor correlation matrix on signal ranks, the current top-5 and bottom-5 deciles per factor, and the take. UIs, downstream agents, and Python scripts consume this.

**Layer 2: rendered table** in FactSet Alpha Testing / Axioma factor research style. See [`references/rendering.md`](./references/rendering.md). Four blocks: single-factor IC + decay table, long-short decile spread table, factor correlation matrix, current decile membership block, and a mandatory one-paragraph take at the bottom.

UI devs build their own dashboards from the JSON. Claude Code users read the rendered tables.

How it works

1. **Build the universe** per [`references/universe-construction.md`](./references/universe-construction.md). Default: top 500 by current market cap, filtered to names with continuous daily price history across the window. The JSON labels this `current_top500_survivorship_biased` so consumers know that for a true point-in-time backtest you reconstruct the top-500 per month.

2. **Pull the daily aggregates via flat files.** One S3 day-bucket per trading day across the window. Files at `s3://flatfiles/us_stocks_sip/day_aggs_v1/{yyyy}/{mm}/{yyyy-mm-dd}.csv.gz`. Schema is lowercase columns (`ticker`, `volume`, `open`, `close`, `high`, `low`, `window_start`, `transactions`); see [`../massive-flat-files/SKILL.md`](../massive-flat-files/SKILL.md). Parallelized 16 workers; rate-limit-free.

3. **Pull TTM fundamentals via REST** for the value and quality factors. One call per name to `/vX/reference/financials?ticker={T}&timeframe=annual&limit=2`. Returns shareholders' equity (for book value), net income (for ROE), and gross profit / revenue / total assets (for gross profitability and leverage).

4. **Compute factor scores** per [`references/factor-definitions.md`](./references/factor-definitions.md). Momentum is 12M-1M return (skip the most recent month, the academic standard, to avoid mean-reversion contamination). Value is `1 / (P/B)` (price-to-book inverse so higher is cheaper). Quality is ROE. Low-vol is `1 / realized_vol_252d`. Cross-sectional rank within the universe each month. Winsorize raw values at the 1st and 99th percentile before ranking.

5. **Compute information coefficients** per [`references/information-coefficient.md`](./references/information-coefficient.md). Per month, take the Spearman rank correlation between factor score and forward return. Compute for 1M, 3M, 6M, 12M forward horizons. Report mean IC, IC standard error, and the t-stat (mean_IC / IC_se * sqrt(n_months)). IC decay is the table across the four horizons; a healthy alpha factor has positive IC at all horizons but decays gradually.

6. **Compute decile spread returns** per [`references/decile-analysis.md`](./references/decile-analysis.md). Sort the universe into 10 deciles by factor score per month. Equal-weight names within each decile. Compute the long-short spread `D10 - D1` per forward horizon. Annualize. The hit rate is the percentage of months where D10 beats D1 over the 12M horizon.

7. **Compute factor correlation** per [`references/factor-correlation.md`](./references/factor-correlation.md). The correlation ma