Sf Market Fit

sf-market-fit

"Best strategy of the year" and "best strategy this week" are not the same thing. This skill picks strategies that are winning in the current market, not strategies with a fat 90-day backtest that has since gone cold.

Signal

For every candidate, compute annualized returns over three windows:

ann_7  = pct7Days  × (365/7)
ann_30 = pct30Days × (365/30)
ann_90 = pct90Days × (365/90)

Then label:

• accelerating — ann_7 > ann_30 > ann_90 and 7d positive (best fit)
• steady — recent windows positive and not collapsing
• fading — 7d down sharply vs 30d (recent regime hostile)
• stale — 30d non-positive or PF below 1.1 (not a current edge)

Only accelerating and steady make the cut. fading is rejected here but surfaced by [sf-edge-watch](../sf-edge-watch/SKILL.md).

When to use

• "What strategies are working this week?"
• "Find me strategies that fit the current market"
• "Which edges are accelerating?"

How to run

python sf-market-fit/fit.py \
  [--top N]                # default 10
  [--prescreen N]          # default 150
  [--min-pf X]             # default 1.2
  [--min-trades N]         # default 30
  [--pair CSV] [--timeframe CSV]
  [--include-fading]       # surface fading/stale too, with labels
  [--json]

Output

Ranked table with score, label (accelerating/steady/fading/stale), and the 3 recency windows so the user can see what changed.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: daviddme
• Source: daviddme/claude-skills
• License: MIT

Install and usage instructions live in the source repository linked above.