/install auto-trading-winner
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:
- Calls
auto_redeem(). - Discovers markets for the configured venue.
- Filters markets to a configurable YES price band, default
30%to70%. - Sorts the remaining markets by trading volume.
- Builds a ranked candidate pool and highlights the top
5by default. - Prints the shortlist for review.
- In
RUN_MODE=manual, lets you manually select one candidate. - In
RUN_MODE=auto, starts from rank1automatically unlessSELECT_CANDIDATEis provided. - If the chosen candidate fails indexing or safeguard checks, falls through to the next ranked candidate in the full ranked pool automatically.
- Checks context safeguards before trading.
- Sizes the trade with
simmer_sdk.sizing.size_position(). - Defaults to dry-run unless you explicitly pass
--live.
Required Files
This skill follows Simmer's manual ClawHub pattern:
SKILL.mdclawhub.jsontrade_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, orpolymarket. Default:simRUN_MODE:manualorauto. Default:manualMARKET_QUERY: Optional query term used during discovery. Default: empty string.MIN_PRICE: Minimum YES price allowed. Default:0.30MAX_PRICE: Maximum YES price allowed. Default:0.70MAX_MARKETS: Maximum number of discovered markets to inspect before ranking. Default:50CANDIDATE_LIMIT: Number of ranked candidates to show. Default:5FAIR_PROBABILITY: Fair YES probability used for sizing and side selection. Default:0.55MIN_EDGE: Minimum edge required before trading. Default:0.03MAX_SLIPPAGE_PCT: Skip trades if estimated slippage exceeds this threshold. Default:0.15SIMMER_ENABLE_LIVE: Set totrueto allow live order placement. Default:falseSELECT_CANDIDATE: Optional 1-based index of the candidate to trade in non-interactive runs.AUTO_CONFIRM_LIVE: Optional explicit override required if you wantRUN_MODE=autotogether with live execution onkalshiorpolymarket. Default:false
Safety Model
- Dry-run is the default.
- Every trade is tagged with
sourceandskill_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=manualis the default for all venues.RUN_MODE=automakes the skill non-interactive and starts from the top-ranked candidate.- Automatic live execution on
kalshiandpolymarketrequires an explicitAUTO_CONFIRM_LIVE=trueoverride. - 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
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install auto-trading-winner - After installation, invoke the skill by name or use
/auto-trading-winner - Provide required inputs per the skill's parameter spec and get structured output
What is Auto Trading Winner?
Cross-venue trading skill for ClawHub that supports both manual candidate selection and unattended auto mode, while filtering markets by price band and tradi... It is an AI Agent Skill for Claude Code / OpenClaw, with 51 downloads so far.
How do I install Auto Trading Winner?
Run "/install auto-trading-winner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Auto Trading Winner free?
Yes, Auto Trading Winner is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Auto Trading Winner support?
Auto Trading Winner is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Auto Trading Winner?
It is built and maintained by Sky (@skybinjf); the current version is v1.0.0.