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Polymarket Whale Scanner Trader

该 Skill 从 predicting.top 排行榜筛选 SmartScore 与胜率达标的高绩效钱包,抓取其近期活动并聚合鲸鱼共识,再与 conviction 信号方向一致时按风险参数下单。默认纸面交易,仅在显式传入 --live 时使用真实资金,需要 SIMMER_API_KEY。

diagnostikon
diagnostikon
热度 42.86
最近更新 2026-05-11 17:40:15
分类:自动化依赖:代码执行 / 第三方 API应用场景:财务 / 市场分析 / 自动化脚本 / API 集成 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add polymarket-whale-scanner-trader
Metadata
name
polymarket-whale-scanner-trader
description
Scans public Polymarket leaderboards to identify top-performing whale wallets by SmartScore, then trades markets where these whales have high-conviction positions. Dynamically discovers the best traders -- no manual wallet configuration needed.
metadata
{"author":"Diagnostikon","owner":"Diagnostikon","version":"1.0.0","displayName":"Whale Scanner Trader","difficulty":"advanced"}

Whale Scanner Trader

This is a template. The default signal follows top Polymarket whales ranked by SmartScore -- remix it with on-chain analysis, wallet clustering, or time-weighted activity decay. The skill handles all the plumbing (leaderboard fetching, whale scoring, consensus building, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Top Polymarket traders have an edge. The public predicting.top leaderboard ranks wallets by SmartScore -- a composite of win rate, Sharpe ratio, profit factor, and consistency. This skill dynamically discovers the best-performing traders, fetches their recent activity, and builds a consensus map of what the whales are betting on.

When multiple high-scoring whales agree on a direction AND the conviction-based signal independently confirms that direction, the skill trades. This dual-confirmation requirement filters out noise and only acts on high-confidence setups.

Signal Logic

Step 1: Fetch and Filter Leaderboard

Top traders are fetched from predicting.top/api/leaderboard and filtered by:

Filter Default Purpose
SmartScore >= 70 Only follow proven traders
Win rate >= 55% Must have positive edge

Step 2: Build Whale Consensus

For each qualifying whale, recent activity is fetched from the Polymarket data API. Net positions are extracted (buy YES vs buy NO volume per market), then aggregated across all whales:

whale_consensus[market] = {yes_votes, no_votes, total_size, whales}

Markets are ranked by total whale size -- strongest conviction first.

Step 3: Match and Confirm

For each whale consensus market:

  1. Find matching Simmer market via keyword/title matching
  2. Run compute_signal() -- standard conviction-based sizing per CLAUDE.md
  3. Only trade if whale direction (YES/NO) aligns with signal direction
  4. Check context_ok() for flip-flop and slippage safeguards

Conviction Sizing

Standard conviction-based sizing from CLAUDE.md:

p conviction size
38% 0% $5 (floor)
30% 21% $8
20% 47% $19
0% 100% $40

Remix Ideas

  • On-chain clustering: Group wallets by transaction patterns to identify whale syndicates
  • Time decay: Weight recent whale activity more heavily than older positions
  • Size-weighted votes: A whale's $50k position counts more than a $1k position
  • Contrarian mode: Fade the whales when SmartScore drops (they're losing their edge)
  • Sector filtering: Only follow whales in categories they historically perform best in

Risk Parameters

Parameter Default Notes
Max position size $40 USDC Per market, reached at max conviction
Min market volume $3,000 Liquidity filter
Max bid-ask spread 10% Avoid illiquid markets
Min days to resolution 7 Need time for the trade to work
Max open positions 8 Concentrated whale-aligned bets

Installation & Setup

clawhub install polymarket-whale-scanner-trader

Requires: SIMMER_API_KEY environment variable.

Cron Schedule

Runs on demand or via automaton. Cron is set to null -- configure it in the Simmer UI when ready.

Safety & Execution Mode

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

Scenario Mode Financial risk
python trader.py 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
SIMMER_API_KEY Yes Trading authority -- keep this credential private.

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
SIMMER_MAX_POSITION 40 Max USDC per trade (reached at 100% conviction)
SIMMER_MIN_TRADE 5 Floor for any trade (min USDC regardless of conviction)
SIMMER_MIN_VOLUME 3000 Min market volume filter (USD)
SIMMER_MAX_SPREAD 0.10 Max bid-ask spread (0.10 = 10%)
SIMMER_MIN_DAYS 7 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_SMART_SCORE 70 Min SmartScore to qualify as a whale to follow
SIMMER_MIN_WIN_RATE 0.55 Min win rate to qualify as a whale to follow
SIMMER_LEADERBOARD_LIMIT 20 How many traders to fetch from the leaderboard

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

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