factor-exposure

name	factor-exposure
description	Use when the user asks "is this alpha or just beta?", "what am I actually exposed to?", "factor analysis", "am I just riding the market?", or any variation about distinguishing skill from market exposure. Also use proactively when a group shows strong returns while the broad market is also up.

Factor Exposure

A group in Upward Trend with strong returns may just be riding the broad market. Factor regression separates genuine alpha (the return you earned beyond what the market explains) from beta (your exposure to market movements). If alpha is zero after controlling for beta, the Livermore signals are not adding edge in that group.

The script

scripts/factor.py regresses each leader's returns against SPY.

./run .claude/skills/factor-exposure/scripts/factor.py ~/.lafmm/data/semis
./run .claude/skills/factor-exposure/scripts/factor.py ~/.lafmm/data/energy --json

Options:

• --benchmark PATH — ticker directory for the benchmark. Defaults to data/us-indices/SPY (auto-discovered). Override for non-US markets or sector-relative analysis.
• --json — machine-readable output.

Interpreting the output

Alpha (annualized): excess return not explained by market exposure. Positive alpha means the group outperforms what its beta alone would predict. Reported as a percentage per year.

Alpha t-statistic: statistical significance of the alpha estimate. |t| > 1.96 is significant at 95% confidence (marked **). |t| > 2.58 is significant at 99% confidence (marked ***). If not significant, you cannot distinguish the alpha from zero.

Beta: sensitivity to market moves. Beta 1.0 means the stock moves 1:1 with the market. Beta 1.5 means it moves 50% more than the market in both directions. A "high return" from a beta-2.0 stock in a rising market is not alpha. It's leveraged beta.

R-squared: fraction of the stock's return variance explained by the market. R² of 0.80 means 80% of the stock's moves are market-driven. Only the remaining 20% is stock-specific.

Requirements

Both the group and the benchmark (SPY) need overlapping price history. The regression needs at least 30 overlapping dates to produce meaningful results. 60+ is preferable. The us-indices group provides SPY data by default.

When to use this

• When a group is performing well and you want to know if it's genuine edge or just market correlation.
• When comparing two groups with similar returns. The one with higher alpha and lower beta has the real edge.
• Before sizing a position based on validate-signal results. A signal with high Sharpe but pure beta exposure is not worth the same as one with genuine alpha.
• When the user's portfolio seems concentrated. If all groups have beta > 1.5, the portfolio is effectively leveraged to the market regardless of how many "sectors" it holds.

Limitations

This is a single-factor model (market only). Adding sector ETFs as additional factors would give a cleaner decomposition but requires sector-specific benchmark data. The current implementation supports additional factors via the library API (factor_regression(strategy, market, factors=[sector_returns])) but the CLI wrapper uses market only.

Alpha is backward-looking. A stock that showed alpha over the last 90 days may not show it over the next 90. Use rolling_alpha from the library to check whether alpha is stable or decaying.