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. 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 MAXPOSITION
5. Skip markets with spread > MAXSPREAD 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:

Domain	Bias	Why
Metaverse / NFT	0.70x	Media hype cycles inflate YES; most milestones miss
Humanoid robots

0.75x | YouTube demos precede real deployments by 6–18 months |
| Quantum computing | 1.30x | arXiv progress is systematic; markets lag by weeks |
| Synthetic biology | 1.25x | Regulatory filings are public; market underweights precedent |
| DeFi / TVL | 1.20x | On-chain data is real-time; market repricing lags 2–6h |
| Other | 1.00x | No 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

CODEBLOCK0

Risk Parameters

Parameter	Default	Notes
Max position size	$25 USDC	Emerging tech markets are volatile
Min market volume

$2,000 | Some niche markets start illiquid | | Max bid-ask spread | 15% | Accept wider spreads for edge markets | | Min days to resolution | 14 | Technical milestones need longer lead time | | Max open positions | 8 | Diversify across sub-categories |

Sub-Category Edge Analysis

Category	Edge Source	Typical Market Bias
Quantum Computing	Academic paper lag (arXiv 6–24h before news)	Retail underestimates IBM progress
Humanoid Robots

YouTube demo videos precede deployments | Fan hype overprices Tesla Optimus | | DeFi/TVL | On-chain data is real-time | Markets lag DeFiLlama by 2–6h | | Lab-Grown Meat | Regulatory filings public before decisions | Market underweights FDA precedent | | NFT Markets | OpenSea/Blur volume APIs | Volume 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

CODEBLOCK1

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.

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 | 14 | Min days until market resolves |
| 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 | 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.