Music & Entertainment Trader

This is a template.
The default signal is keyword discovery + Spotify Charts API momentum — remix it with Billboard chart position tracking, TikTok trending audio API, Apple Music chart feeds, or social media velocity metrics for artist momentum.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Record labels now monitor Polymarket the way Wall Street monitors stocks — as real-time demand signals for artist momentum. This creates an unusual information flow:

• - Artists/labels with inside momentum push prices UP before numbers confirm
• Retail fans bid on emotional attachment, often overpaying for beloved artists
• Data-driven traders can fade fan-driven overpricing and capture industry-informed flows

This skill trades:

• - Streaming milestones — First-week equivalents, billion-stream thresholds
• Chart performance — Billboard 200 #1, Hot 100 chart positions
• Awards — Grammy nominations/wins, VMAs, AMAs outcomes
• Tour revenue — Gross threshold markets for major arena tours
• Industry deals — Catalog sales, platform launches, licensing deals

Signal Logic

Default Signal: Conviction-Based Sizing with Sentiment Bias

1. 1. Discover active music/entertainment markets on Polymarket
2. Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
3. Apply sentiment_bias() multiplier based on market type and artist category
4. Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAXPOSITION
5. Skip markets with spread > MAXSPREAD or fewer than MIN_DAYS to resolution

Sentiment Bias (built-in, no API required)

Different market types have systematic mispricing patterns in music. sentiment_bias() adjusts conviction based on known retail behavior:

Market type	Bias	Why
Megastar fan markets (Taylor Swift, Beyoncé, BTS)	0.75x	Fan bias inflates YES; emotionally driven, high noise
Awards ceremonies (Grammy, Oscar, VMA)

0.85x | Fan voting + label politics = hard to model reliably |
| Streaming / chart milestones (Spotify, Billboard) | 1.15x | Data available before market reprices — lean in |
| Emerging global genres (Afrobeats, K-pop, Latin) | 1.20x | Systematically underweighted by US-centric retail traders |
| Other | 1.00x | No systematic bias detected |

Example: Afrobeats streaming milestone at 25% → conviction 34% × 1.2x = 41% → $6 position. Same market for a Beyoncé milestone → 34% × 0.75x = 26% → $5 (floor, trade cautiously).

Remix Ideas

• - Spotify Charts API / Chartmetric: Replace market.current_probability with stream velocity-implied probability — trade the divergence between real-time data and market price
• TikTok Trending: Viral audio as leading indicator for streaming momentum (48–72h lag to market)
• Ticketmaster/StubHub: Secondary ticket prices as proxy for tour gross markets
• RIAA certification tracker: Monitor certifications approaching milestone thresholds

Market Categories Tracked

CODEBLOCK0

Risk Parameters

Parameter	Default	Notes
Max position size	$15 USDC	Entertainment markets are retail-driven
Min market volume

$2,000 | Lower bar; community markets matter | | Max bid-ask spread | 15% | Entertainment markets can be illiquid | | Min days to resolution | 7 | Streaming data needs time to settle | | Max open positions | 10 | Diversify across artists and categories |

Behavioral Edge

Fan Bias

Music fans are strongly emotionally attached. For beloved artists (Taylor Swift, BTS), markets consistently overprice YES outcomes by 8–15% vs streaming data expectations. Short-term this means NO positions on fan-favorite markets are structurally profitable.

Recency Momentum

Conversely, artists trending hard on TikTok are underpriced for 48–72 hours before mainstream media coverage. Early entry on breakout markets captures the lag.

Key Data Sources

• - Spotify Charts: https://charts.spotify.com/charts/overview/global
• Billboard API: https://www.billboard.com/charts/
• Chartmetric: https://chartmetric.com/ (paid, powerful)
• RIAA Database: https://www.riaa.com/gold-platinum/

Installation & Setup

CODEBLOCK1

Requires: SIMMER_API_KEY environment variable.

Cron Schedule

Runs every 30 minutes (*/30 * * * *). Chart data updates weekly; streaming data daily. No need for tight polling.

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only execute when --live is passed explicitly.

Scenario	Mode	Financial risk
INLINECODE8	Paper (sim)	None
Cron / automaton

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

The automaton cron is set to null — it does not run on a schedule until you configure it in the Simmer UI. autostart: false means it won't start automatically on install.

Required Credentials

Variable	Required	Notes
INLINECODE12	Yes	Trading authority — keep this credential private. Do not place a live-capable key in any environment where automated code could call --live.

Tunables (Risk Parameters)

All risk parameters are declared in clawhub.json as tunables and adjustable from the Simmer UI without code changes. They use SIMMER_-prefixed env vars so apply_skill_config() can load them securely.

Variable	Default	Purpose
INLINECODE18	INLINECODE19	Max USDC per trade (reached at 100% conviction)
INLINECODE20

2000 | Min market volume filter (USD) |
| SIMMER_MAX_SPREAD | 0.15 | Max bid-ask spread (0.15 = 15%) |
| SIMMER_MIN_DAYS | 7 | Min days until market resolves |
| SIMMER_MAX_POSITIONS | 10 | Max concurrent open positions |
| SIMMER_YES_THRESHOLD | 0.38 | Buy YES if market price ≤ this value |
| SIMMER_NO_THRESHOLD | 0.62 | Sell NO if market price ≥ this value |
| SIMMER_MIN_TRADE | 5 | Floor for any trade (min USDC regardless of conviction) |

Dependency

INLINECODE34 is published on PyPI by Simmer Markets.

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

Review the source before providing live credentials if you require full auditability.