Complete Kalshi trading command interface — portfolio P&L, live market scanning with edge scoring, trade execution, and risk management through your OpenClaw agent. Built-in safety: $25 max trade, 100 contract cap, $50 daily loss cutoff. Scan 600+ markets, query positions, execute trades with configurable blocklists and retry logic. Part of the OpenClaw Prediction Market Trading Stack — pairs with Kalshalyst for intelligent execution and feeds portfolio data to Market Morning Brief.
SKILL.md
Kalshi Command Center
A complete command-line interface for Kalshi prediction market trading. Provides portfolio visibility, live market scanning, trade execution, and risk management through a unified command API.
Overview
The Kalshi Command Center bridges your OpenClaw AI assistant with Kalshi's API. Core capabilities:
Portfolio Management: Real-time P&L tracking with emoji-annotated positions
Live Market Scanning: Heuristic edge scoring across 600+ open markets
Market Queries: Fetch live bid/ask data before trading
Trade Execution: Buy/sell with Kelly sizing, risk validation, and audit logging
Risk Management: Hard caps on trade size, position count, and daily loss
Research Cache: Store opportunity rankings for quick reference
Available Commands
All commands are exposed through kalshi_commands.py with argparse routing. Use the module directly or import individual handlers.
python kalshi_commands.py execute 1 # buy pick #1 with Kelly sizing
python kalshi_commands.py execute 2 qty 25 # buy pick #2 with manual 25 contracts
python kalshi_commands.py execute 3 15 contracts # buy pick #3 with manual override
The execute handler:
Looks up the pick from research cache
Fetches live market data
Calculates Kelly-sized position (if available)
Validates risk limits
Places the order with audit logging
Brier Score Calibration
python kalshi_commands.py brier # 90 day full report
python kalshi_commands.py brier claude # filter by Claude estimator
python kalshi_commands.py brier 30 # 30 day lookback
Prerequisites
System Requirements
Python 3.10+
API SDK
pip install kalshi-python
Authentication & Configuration
Set environment variables OR edit your OpenClaw config:
Sports markets are intentionally excluded from the production stack. Recent evaluation did not show durable model edge there, so the system does not route sports markets into scanning or execution.
Time Window
Default scan: 7-180 days to expiration (interactive trading sweet spot)
Includes volume floor (>10 contracts traded)
Markets with tight spreads and high OI ranked first
Risk Limits
Hard caps enforced on all trades:
Limit
Value
Enforced By
Max single trade cost
$25.00 USD
_check_risk()
Max position size
100 contracts
_check_risk()
Max daily loss
$50.00 USD
Kelly sizing + risk validator (if available)
Trade Audit Log: All trades (accepted/blocked/failed) logged to ~/.openclaw/logs/trades.jsonl.
CRITICAL: When you (the agent) discover and fix a bug in any script during a conversation, you MUST write the fix to the source file on disk using file editing tools. Showing the fix in chat is NOT sufficient — the next scheduled run starts a fresh session and will not have your conversational fix.
Checklist after finding a bug:
Edit the actual .py file on disk (e.g., ~/skills/kalshi-command-center/scripts/kalshi_commands.py)
Also update the installed copy at ~/.openclaw/skills/kalshi-command-center/scripts/kalshi_commands.py
Confirm both files are written before telling the user the fix is done
If the fix is significant, stage a git commit in ~/skills/
Never say "fixed" if you only demonstrated the fix in conversation output.
Attribution
Author: KingMadeLLC
Version: 1.0.0
Feedback & Issues
Found a bug? Have a feature request? Want to share results?
