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Auto Trading Winner

核心功能:扫描sim、polymarket、kalshi平台市场,按30%-70%价带筛选,以成交量排序生成候选池,支持手动选标或自动依次尝试候选,结合风控规则执行交易,默认回测可切换实盘。典型场景:量化交易员跨平台交易测试、自动盯盘,或手动筛选优质标的。输入为环境变量配置,输出候选列表与交易结果。

skybinjf
skybinjf
热度 30.35
最近更新 2026-05-18 11:49:57
分类:自动化依赖:代码执行 / 第三方 API / MCP 服务 / 读写文件应用场景:财务 / 自动化脚本 / 数据处理 / 市场分析

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 auto-trading-winner
Metadata
name
auto-trading-winner
description
Cross-venue trading skill for ClawHub that supports both manual candidate selection and unattended auto mode, while filtering markets by price band and trading volume.
metadata
{"author":"GitHub Copilot","version":"1.0.0","displayName":"Auto Trading Winner","difficulty":"intermediate"}

Auto Trading Winner

This skill scans markets on sim, polymarket, or kalshi, filters for markets priced in a configurable middle band, ranks them by trading volume, and supports both manual selection and unattended auto mode.

This is a template. The default signal is simple volume ranking plus a price-band filter. Remix it with your own alpha, liquidity rules, timing rules, or fair-value model. The skill handles market discovery, venue-specific indexing, context checks, sizing, and trade execution plumbing.

What It Does

On each run, the skill:

  1. Calls auto_redeem().
  2. Discovers markets for the configured venue.
  3. Filters markets to a configurable YES price band, default 30% to 70%.
  4. Sorts the remaining markets by trading volume.
  5. Builds a ranked candidate pool and highlights the top 5 by default.
  6. Prints the shortlist for review.
  7. In RUN_MODE=manual, lets you manually select one candidate.
  8. In RUN_MODE=auto, starts from rank 1 automatically unless SELECT_CANDIDATE is provided.
  9. If the chosen candidate fails indexing or safeguard checks, falls through to the next ranked candidate in the full ranked pool automatically.
  10. Checks context safeguards before trading.
  11. Sizes the trade with simmer_sdk.sizing.size_position().
  12. Defaults to dry-run unless you explicitly pass --live.

Required Files

This skill follows Simmer's manual ClawHub pattern:

  • SKILL.md
  • clawhub.json
  • trade_skill.py

Environment Variables

Credentials

  • SIMMER_API_KEY (required): Your Simmer API key.
  • SOLANA_PRIVATE_KEY (optional): Needed only for live Kalshi self-custody trading.
  • WALLET_PRIVATE_KEY (optional): Needed only if your Polymarket setup uses an external wallet flow.

Strategy Config

  • TRADING_VENUE: sim, kalshi, or polymarket. Default: sim
  • RUN_MODE: manual or auto. Default: manual
  • MARKET_QUERY: Optional query term used during discovery. Default: empty string.
  • MIN_PRICE: Minimum YES price allowed. Default: 0.30
  • MAX_PRICE: Maximum YES price allowed. Default: 0.70
  • MAX_MARKETS: Maximum number of discovered markets to inspect before ranking. Default: 50
  • CANDIDATE_LIMIT: Number of ranked candidates to show. Default: 5
  • FAIR_PROBABILITY: Fair YES probability used for sizing and side selection. Default: 0.55
  • MIN_EDGE: Minimum edge required before trading. Default: 0.03
  • MAX_SLIPPAGE_PCT: Skip trades if estimated slippage exceeds this threshold. Default: 0.15
  • SIMMER_ENABLE_LIVE: Set to true to allow live order placement. Default: false
  • SELECT_CANDIDATE: Optional 1-based index of the candidate to trade in non-interactive runs.
  • AUTO_CONFIRM_LIVE: Optional explicit override required if you want RUN_MODE=auto together with live execution on kalshi or polymarket. Default: false

Safety Model

  • Dry-run is the default.
  • Every trade is tagged with source and skill_slug.
  • Every trade includes public reasoning.
  • Market context is checked before order placement.
  • Position sizing uses bankroll and edge, not a fixed stake.
  • Kalshi markets use Simmer's check-then-import indexing path before trading.
  • RUN_MODE=manual is the default for all venues.
  • RUN_MODE=auto makes the skill non-interactive and starts from the top-ranked candidate.
  • Automatic live execution on kalshi and polymarket requires an explicit AUTO_CONFIRM_LIVE=true override.
  • If the selected candidate fails, the skill tries later ranked candidates automatically.

Local Usage

Review candidates without trading:

export SIMMER_API_KEY="sk_live_..."
export TRADING_VENUE="sim"
python trade_skill.py

Trade candidate 2 in a non-interactive run:

export SELECT_CANDIDATE="2"
python trade_skill.py --live

Unattended dry-run from the highest-ranked candidate:

export TRADING_VENUE="kalshi"
export RUN_MODE="auto"
python trade_skill.py

Fully unattended paper-trading run on sim:

export TRADING_VENUE="sim"
export RUN_MODE="auto"
export SIMMER_ENABLE_LIVE="true"
python trade_skill.py

Interactive review:

export TRADING_VENUE="kalshi"
python trade_skill.py

Remix Ideas

  • Replace the volume ranking with your own score.
  • Add time-to-resolution filters.
  • Use venue-specific volume thresholds.
  • Add per-category or per-market fair values.
  • Expand manual selection into a multi-pick workflow.

Publishing

From inside this skill folder:

npx clawhub@latest publish . --slug auto-trading-winner --version 1.0.0

Always publish with an explicit --slug.

Install Verification

After publishing, verify the install path explicitly:

npx clawhub@latest install auto-trading-winner

If you update the skill, publish a patch version:

npx clawhub@latest publish . --slug auto-trading-winner --bump patch

Recommended local smoke test before publishing:

export SIMMER_API_KEY="sk_live_..."
export TRADING_VENUE="sim"
export MARKET_QUERY="bitcoin"
export SELECT_CANDIDATE="1"
python trade_skill.py