Emerging Tech Trader

This is a template.
The default signal is keyword discovery + on-chain data signals — remix it with DeFiLlama TVL feeds, GitHub commit velocity for quantum computing projects, robotics deployment trackers, or synthetic biology investment databases.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Emerging tech markets are among the highest-edge opportunities on Polymarket because most retail participants lack domain expertise. A trader with genuine technical knowledge in robotics, quantum computing, or DeFi holds massive informational advantage.

This skill covers 5 sub-categories:

1. Web3 & DeFi

• Prediction market TVL milestones, cross-chain liquidity thresholds
• NFT market recovery volume markers
• Tokenized prediction position collateral milestones

2. Metaverse & VR/AR

• Meta Horizon DAU milestones, VR headset sales
• Virtual real estate transaction volumes

3. Robotics & Automation

• Humanoid robot factory deployments (Figure, Tesla Optimus, 1X)
• Autonomous delivery robot counts
• Warehouse automation penetration rates

4. Quantum Computing

• IBM qubit count milestones
• Commercial quantum revenue thresholds
• Quantum advantage demonstrations

5. Synthetic Biology

• Lab-grown meat regulatory approvals
• Precision fermentation market size
• Engineered bacteria commercial deployments

Signal Logic

Default Signal: Conviction-Based Sizing with Hype-Cycle Bias

1. Discover markets matching emerging tech keywords
2. Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
3. Apply domain_bias() multiplier — boost underappreciated domains, dampen hype-prone ones
4. Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAX_POSITION
5. Skip markets with spread > MAX_SPREAD or fewer than MIN_DAYS to resolution

Domain Bias (built-in, no API required)

Different emerging tech categories have systematic mispricing patterns. domain_bias() adjusts conviction based on known retail behavior in each domain:

Example: quantum market at 25% → conviction 34% × 1.3x = 44% → $11 position. Metaverse market at same price → 34% × 0.7x = 24% → $6 (conservative).

Remix Ideas

• DeFiLlama API: Replace market.current_probability with TVL-implied probability — trade the divergence between on-chain data and market price
• GitHub API: Measure commit velocity on IBM Qiskit / Google Cirq repos as quantum progress signal
• CoinGlass / OpenSea: NFT floor and volume data as leading indicator for NFT milestone markets
• The Good Food Institute: Lab-grown meat regulatory tracker for synthetic biology markets
• arXiv API: Monitor quantum/ML paper releases as leading signal before market repricing

Market Categories Tracked

KEYWORDS = [
    'Web3', 'DeFi', 'NFT', 'blockchain', 'metaverse', 'VR', 'AR',
    'robot', 'humanoid', 'autonomous delivery', 'Boston Dynamics',
    'Tesla Optimus', 'Figure robot', 'warehouse automation',
    'quantum', 'qubit', 'IBM quantum', 'Google quantum',
    'synthetic biology', 'lab-grown meat', 'cultivated meat',
    'precision fermentation', 'Solana', 'Ethereum', 'TVL',
]

Risk Parameters

Sub-Category Edge Analysis

Key Data Sources

• DeFiLlama: https://defillama.com/
• GitHub API: https://api.github.com/
• IBM Quantum Network: https://quantum.ibm.com/
• The Good Food Institute: https://gfi.org/
• CoinGlass NFT: https://www.coinglass.com/nft

Installation & Setup

clawhub install polymarket-emerging-tech-trader

Requires: SIMMER_API_KEY environment variable.

Cron Schedule

Runs every 15 minutes (*/15 * * * *). Emerging tech events are infrequent but high-impact when they occur.

Safety & Execution Mode

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

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

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.

Dependency

simmer-sdk 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.