# Sf Best Pick

> Pick the best StrategyFactory strategies using a multi-factor composite score (profit factor + sharpe + drawdown + recency + sample size). Use whenever the user asks "what's the best strategy on SF / pick me the top N / find me bangers / which strategies should I deploy". Returns a ranked shortlist with per-factor breakdown so the agent can explain WHY each pick made it. Read-only.

- Skill: `daviddme/sf-best-pick` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add daviddme/sf-best-pick`
- Raw SKILL.md: https://api.skillmd.com/api/skills/daviddme/sf-best-pick/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: daviddme (https://skillmd.com/u/daviddme)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/daviddme/sf-best-pick

---


# sf-best-pick

The headline strategy picker. Pulls a wide HOT slice with sane quality
filters, scores every candidate across seven factors, and returns the top
N with a per-factor breakdown so you can explain on stream exactly why
each one made the cut.

## The score (each factor 0..1, then weighted)

| Factor          | Weight | Floor / Target                      |
| --------------- | ------ | ----------------------------------- |
| Profit factor   | 25%    | 1.0 → 2.5                           |
| Sharpe          | 20%    | 0.5 → 2.5                           |
| Win rate        | 10%    | 40% → 70%                           |
| Net profit %    | 15%    | 10% → 100%                          |
| Drawdown        | 15%    | inverted: 30% (worst) → 8% (best)   |
| Recency (30d)   | 10%    | 0% → 15%                            |
| Trades          | 5%     | 50 → 500 (sample size)              |

Higher is better. A score of 0.8+ is exceptional; 0.6–0.8 is strong;
below 0.5 means at least one factor is dragging hard.

## When to use

- "Pick me the top 5 strategies on StrategyFactory"
- "Which strategies should I deploy this week?"
- "Find me three bangers I can run on Toobit"

## How to run

```
python sf-best-pick/pick.py \
  [--top N]                       # default 5
  [--prescreen N]                 # how many HOT to score (default 100)
  [--min-profit-factor X]         # quality floor before scoring (default 1.3)
  [--min-trades N]                # sample-size floor (default 50)
  [--max-drawdown %]              # default 25
  [--pair CSV] [--timeframe CSV]
  [--explain]                     # multi-line "why" per pick
  [--json]
```

## Output

Table with rank, name, pair, timeframe, all the metrics, and the composite
score. `--explain` adds a paragraph per pick: which factors lifted it,
which dragged.

## Notes

- Server-side filters are used as a quality floor; the real ranking happens
  client-side so the agent can adjust weights freely.
- For market-regime-aware picking, use [`sf-market-fit`](../sf-market-fit/SKILL.md).
- For deployment after picking, use [`sf-deploy`](../sf-deploy/SKILL.md).