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Polymarket Emerging Tech Trader

Trades Polymarket prediction markets on Web3/DeFi milestones, NFT market recovery, metaverse adoption, humanoid robotics deployments, quantum computing break...
在 Polymarket预测市场交易 Web3/DeFi 里程碑、NFT 市场回暖、元宇宙采用、人形机器人部署、量子计算突破等预测。
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数据分析 clawhub v0.0.3 3 版本 99852.9 Key: 需要
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概述

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:

DomainBiasWhy
---------
Metaverse / NFT0.70xMedia hype cycles inflate YES; most milestones miss
Humanoid robots0.75xYouTube demos precede real deployments by 6–18 months
Quantum computing1.30xarXiv progress is systematic; markets lag by weeks
Synthetic biology1.25xRegulatory filings are public; market underweights precedent
DeFi / TVL1.20xOn-chain data is real-time; market repricing lags 2–6h
Other1.00xNo systematic bias detected

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

ParameterDefaultNotes
---------------------------
Max position size$25 USDCEmerging tech markets are volatile
Min market volume$2,000Some niche markets start illiquid
Max bid-ask spread15%Accept wider spreads for edge markets
Min days to resolution14Technical milestones need longer lead time
Max open positions8Diversify across sub-categories

Sub-Category Edge Analysis

CategoryEdge SourceTypical Market Bias
--------------------------------------------
Quantum ComputingAcademic paper lag (arXiv 6–24h before news)Retail underestimates IBM progress
Humanoid RobotsYouTube demo videos precede deploymentsFan hype overprices Tesla Optimus
DeFi/TVLOn-chain data is real-timeMarkets lag DeFiLlama by 2–6h
Lab-Grown MeatRegulatory filings public before decisionsMarket underweights FDA precedent
NFT MarketsOpenSea/Blur volume APIsVolume data available before price consensus

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.

ScenarioModeFinancial risk
--------------------------------
python trader.pyPaper (sim)None
Cron / automatonPaper (sim)None
python trader.py --liveLive (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

VariableRequiredNotes
---------------------------
SIMMER_API_KEYYesTrading 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.

VariableDefaultPurpose
----------------------------
SIMMER_MAX_POSITION25Max USDC per trade (reached at 100% conviction)
SIMMER_MIN_VOLUME2000Min market volume filter (USD)
SIMMER_MAX_SPREAD0.15Max bid-ask spread (0.15 = 15%)
SIMMER_MIN_DAYS14Min days until market resolves
SIMMER_MAX_POSITIONS8Max concurrent open positions
SIMMER_YES_THRESHOLD0.38Buy YES if market price ≤ this value
SIMMER_NO_THRESHOLD0.62Sell NO if market price ≥ this value
SIMMER_MIN_TRADE5Floor for any trade (min USDC regardless of conviction)

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.

版本历史

共 3 个版本

  • v0.0.3 当前
    2026-05-03 04:21 安全 安全
  • v1.0.1
    2026-03-30 02:08 安全
  • v1.0.0
    2026-03-19 20:28

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

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