OnlyFans Trader

This is a remixable template.
The default signal requires no external API — it uses keyword-derived demographic multipliers, jurisdiction-specific regulatory priors, and date-based earnings seasonality applied on top of standard conviction sizing.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your signal provides the alpha.

Strategy Overview

OnlyFans is one of the fastest-growing and most widely covered platforms in prediction markets — yet it is systematically mispriced in three distinct and exploitable ways.

1. Celebrity pipeline bias — Retail prices "will [celebrity] join OnlyFans?" based on general fame level, treating it as a rare or tabloid-worthy event. The actual base rate by demographic category tells a completely different story: reality TV contestants join at ~40%, Instagram models at ~30%, athletes at ~20%. Markets on reality TV show participants routinely sit at 15–20% — 2–3x below the historical base rate. We buy YES aggressively. Conversely, A-list Hollywood actors are priced at 15–25% when the true base rate is ~5% — tabloid speculation inflates prices above reality. We fade those.

2. Regulatory theater — Every 6–12 months a government announces it will "crack down on" or "ban" OnlyFans. These markets are priced by retail on political rhetoric. Legislative reality is different: the average time from "announced review" to enforceable legislation is 3–7 years, court injunctions routinely block enforcement, and VPN workarounds make practical bans largely unenforceable. Of 40+ announced restrictions since 2020, fewer than 3 resulted in enforceable law within 12 months. We systematically buy NO on ban markets.

3. Creator earnings seasonality — OnlyFans revenue is strongly seasonal, driven by the gift economy. The two weeks before Valentine's Day (February) is the single highest-revenue period for most creators. Black Friday, Christmas, and New Year also drive subscription spikes. Summer months show 10–20% lower-than-average activity. Earnings milestone markets that don't account for this seasonal structure are systematically off.

The Core Insight: Three Different Market Types, Three Different Edges

This skill classifies each market before trading it:

Market type	Example	Edge	Direction
INLINECODE0	"Will [celebrity] join OnlyFans?"	Pipeline base-rate × season	Standard YES/NO
INLINECODE1

"Will OnlyFans be banned in [country]?" | Regulatory theater fade | Lean NO |
| earnings | "Will [creator] earn $X by [date]?" | Earnings seasonality | YES in peak, NO in trough |

Signal Logic

Step 1 — Market classification (_classify_market)

Keywords route each market to a type:

• - ban, banned, restrict, blocked, shutdown, illegal, law → INLINECODE11
• INLINECODE12, joining, create, start, sign up, open → INLINECODE18
• INLINECODE19, revenue, million, subscriber, ipo, valuation → INLINECODE25

Step 2 — Type-specific bias multiplier

Celebrity pipeline multiplier (join markets only):

Parsed from question keywords — no external lookup needed.

Demographic	Signals	Historical base rate	Multiplier
Reality TV contestant	"bachelor", "love island", "big brother", "real housewives", "survivor", "too hot to handle", "love is blind", "90 day fiance" + 20 more	~40%	1.35x
Influencer / model

"influencer", "instagram model", "tiktok", "playboy", "fitness model" | ~30% | 1.20x |
| Athlete / sports personality | "nfl", "nba", "ufc", "footballer", "wrestler" | ~20% | 1.10x |
| Musician (pop/hip-hop) | "rapper", "singer", "pop star", "r&b" | ~15% | 1.00x |
| A-list actor | "oscar winner", "hollywood actor", "movie star", "emmy" | ~5% (but often priced at 15–25%) | 0.75x (fade) |
| Unknown | — | — | 1.00x |

Regulatory theater multiplier (ban markets only):

Jurisdiction determines how aggressively we fade the YES side:

Jurisdiction	Reason	Multiplier
US / federal	First Amendment constraints make platform bans near-impossible	0.90x
UK / Britain

Age verification laws exist, but full bans face strong opposition | 0.95x |
| EU / Europe | GDPR enforcement risk, not platform bans | 0.90x |
| Australia | eSafety Office has powers but full bans historically blocked by courts | 0.85x |
| Authoritarian (China, Russia, UAE, Saudi, Iran) | Real risk but still overpriced for speed | 1.10x |
| Generic | — | 1.00x |

Earnings seasonality multiplier (earnings + join markets):

Period	Driver	Multiplier
November – February	Holiday gifting, Valentine's Day ramp-up	1.20x
March – April

Post-peak normalization | 1.00x |
| May – October | Summer trough — lower subscription activity | 0.85x |

Step 3 — Combined bias

CODEBLOCK0

Combined Examples (TODAY: March 22, 2026)

Market	Type	Pipeline	Season	Regulatory	Final bias
"Will Love Island contestant X join OnlyFans?" — March	join	1.35x	1.00x	—	1.35x
"Will [Oscar-winner] join OnlyFans?" — December

join | 0.75x | 1.20x | — | 0.90x | | "Will OnlyFans be banned in the US?" | ban | — | — | 0.90x | 0.90x (lean NO) | | "Will [creator] earn $1M by January?" — November | earnings | — | 1.20x | — | 1.20x | | "Will [creator] reach 100k subscribers by August?" | earnings | — | 0.85x | — | 0.85x |

How Sizing Works

With defaults (YESTHRESHOLD=38%, NOTHRESHOLD=62%, MINTRADE=$5, MAXPOSITION=$30):

Reality TV contestant join market — March (1.35x bias):

Price p	Conviction	Biased conviction	Size
38%	0%	0%	$5 floor
25%

34% | 46% | $14 |
| 15% | 61% | 82% | $25 |
| 5% | 87% | 100% capped | $30 |

Keywords Monitored

CODEBLOCK1

Remix Signal Ideas

• - Social media signal detection: Wire a Twitter/X search for "[celebrity name] onlyfans" mention spikes — a 5x increase in mentions in 48 hours is a strong leading indicator that a market priced at 15% should reprice to 35–50%; the public announcement typically follows within 2–4 weeks of the social media buildup
• Reality TV show calendar: Build a lookup of reality TV show air dates — contestants historically announce OnlyFans accounts 2–8 weeks post-finale when media attention is highest; a market resolving 3 months after a season finale that's priced at 20% for a female contestant is almost certainly underpriced
• Legislative calendar: For ban markets, wire in legislative calendar data (congress.gov for US, parliament.uk for UK) — if no bill has been introduced or assigned to committee, the ban market for "within 12 months" should be ≤5%; if a bill has passed committee, the multiplier should flip from fade to back
• Valentine's Day countdown: Hardcode an additional spike multiplier for earnings markets where the resolution date lands within 7 days of February 14 — this single week drives ~8–12% of annual creator revenue; markets systematically undervalue this concentration
• Creator tier classification: Extend _classify_market to distinguish top-100 creators (known, named in question) from mid-tier — top creators have more predictable earnings trajectories; mid-tier has higher variance that the market tends to underprice on both tails

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

Scenario	Mode	Financial risk
INLINECODE29	Paper (sim)	None
Cron / automaton

Paper (sim) | None |
| python trader.py --live | Live (polymarket) | Real USDC |

INLINECODE31 and cron: null — nothing runs automatically until you configure it in Simmer UI.

Required Credentials

Variable	Required	Notes
INLINECODE33	Yes	Trading authority. Treat as high-value credential.

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Variable	Default	Purpose
INLINECODE36	INLINECODE37	Max USDC per trade (reached at 100% conviction)
INLINECODE38

3000 | Min market volume — lower bar since OnlyFans markets tend to be smaller |
| SIMMER_MAX_SPREAD | 0.08 | Max bid-ask spread (8%) |
| SIMMER_MIN_DAYS | 3 | Min days until resolution |
| SIMMER_MAX_POSITIONS | 8 | Max concurrent open positions |
| SIMMER_YES_THRESHOLD | 0.38 | Buy YES if market price ≤ this value |
| SIMMER_NO_THRESHOLD | 0.62 | Buy NO if market price ≥ this value |
| SIMMER_MIN_TRADE | 5 | Floor for any trade (min USDC regardless of conviction) |

Dependency

INLINECODE52 by Simmer Markets (SpartanLabsXyz)

• - PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk