Portfolio Drift Monitor

Portfolio Drift Monitor

Real-time monitoring of Kalshi portfolio positions with configurable alerts when positions move significantly. Ideal for active traders tracking exposure, P&L swings, and position concentration.

Overview

The Portfolio Drift Monitor tracks your Kalshi positions and alerts when any position moves beyond your configured threshold percentage since the last check. It:

• Compares snapshots: Current positions vs. last recorded snapshot
• Detects drift: Flags positions that moved ≥ threshold (default 5%)
• Rate-limits checks: Prevents excessive API calls (configurable, default 60min interval)
• Formats alerts: Emoji indicators (📈/📉), directional arrows, price changes
• Persistent state: Stores snapshots locally at ~/.openclaw/state/portfolio_snapshot.json

When to Use This Skill

• You hold Kalshi positions overnight and want alerts if something moves sharply
• You want hourly drift checks between your morning and evening briefs
• You manage multiple positions and need to catch concentration risk early
• You want a lightweight risk monitoring layer that runs on a schedule without manual checking

Prerequisites

• kalshi_python SDK: Install via pip install kalshi-python
• Kalshi API credentials: KALSHI_KEY_ID and KALSHI_KEY_PATH environment variables
• API key file: Located at path specified by KALSHI_KEY_PATH (typically ~/.kalshi/key.pem)

Configuration

Set these environment variables or edit defaults in your environment:

How to Trigger

Use any of these phrases in conversation:

• "check portfolio drift"
• "portfolio alert"
• "position monitor"
• "drift check"
• "kalshi positions"

How It Works

Step 1: Fetch Current Portfolio

The skill calls Kalshi's API to get your current open positions:

python scripts/portfolio_drift.py

This reads KALSHI_KEY_ID and KALSHI_KEY_PATH from environment, authenticates with Kalshi, and retrieves all open positions.

Step 2: Load Previous Snapshot

On first run, creates baseline snapshot. On subsequent runs, compares current positions with the last recorded snapshot stored at ~/.openclaw/state/portfolio_snapshot.json.

Step 3: Detect Drift

For each position, calculates percent change in:

• Holdings (share count)
• Unrealized P&L (USD value)
• Price (avg entry price vs. current)

If any metric ≥ threshold, flags that position.

Step 4: Format and Output

Outputs drift alerts with:

• Directional emoji: 📈 (up) or 📉 (down)
• Position symbol and side (YES/NO contract)
• Percentage change with sign (e.g., "+12.5%")
• Time since last check

Step 5: Update Snapshot

Saves current portfolio as new baseline for next check.

Output Format

Example output for a position that drifted:

📈 YES/TRUMP.25DEC → +12.5% (↑$1,250 P&L, ↑350 shares)
   Last check: 47 minutes ago

📉 NO/ELEX.HOUSEGOV → -8.3% (↓$425 unrealized P&L)
   Last check: 42 minutes ago

No significant drift: KALSHI.USD, CRYPTO.BTC

Error Handling

• First run: Creates baseline snapshot, no alerts
• API failure: Logs error, doesn't update snapshot
• Rate limit hit: Skips check if interval not elapsed
• No positions: Outputs "Portfolio is empty"

Command Examples

Check portfolio now (bypassing rate limit)

PORTFOLIO_DRIFT_THRESHOLD=3.0 python scripts/portfolio_drift.py

Check with custom threshold

PORTFOLIO_DRIFT_THRESHOLD=10.0 python scripts/portfolio_drift.py

Check and see full state file

python scripts/portfolio_drift.py && cat ~/.openclaw/state/portfolio_snapshot.json | python -m json.tool

Threshold Tuning

OpenClaw Ecosystem Integration

Risk management layer for the Prediction Market Trading Stack. Schedule hourly checks to catch position swings between Morning Briefs.

Scheduling:

skills:
  portfolio-drift-monitor:
    enabled: true
    schedule: "0 * * * *"  # Every hour
    timeout_seconds: 30

Install the complete stack:

clawhub install kalshalyst kalshi-command-center polymarket-command-center prediction-market-arbiter xpulse portfolio-drift-monitor market-morning-brief prediction-stack-setup

Performance & Cost

• Runtime: <5 seconds per check
• API calls: 1 (Kalshi portfolio read)
• Cost: $0 (Kalshi free tier)
• State file: ~2KB (portfolio_snapshot.json)

Scaling

24 checks/day (hourly) = 24 Kalshi API calls. Well within free tier limits.

Debugging

Check logs and state:

# View current snapshot
cat ~/.openclaw/state/portfolio_snapshot.json | python -m json.tool

# Run manually with debug output
python -u scripts/portfolio_drift.py 2>&1 | head -50

# Verify credentials
echo $KALSHI_KEY_ID
ls -la $KALSHI_KEY_PATH

Agent Bug-Fix Protocol

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:

1. Edit the actual .py file on disk (e.g., ~/skills/portfolio-drift-monitor/scripts/portfolio_drift.py)
2. Also update the installed copy at ~/.openclaw/skills/portfolio-drift-monitor/scripts/portfolio_drift.py
3. Confirm both files are written before telling the user the fix is done
4. If the fix is significant, stage a git commit in ~/skills/

Never say "fixed" if you only demonstrated the fix in conversation output.

Known Limitations

• Kalshi-only: Currently monitors Kalshi positions only. Polymarket positions require separate tracking.
• Price-based drift: Drift is calculated on mid-market price changes, not on volume or open interest shifts.
• No alerting transport: Outputs to stdout. Your OpenClaw agent or a wrapper script handles delivery (iMessage, Slack, etc.).

Additional Resources

• For complete configuration reference, see drift-config.md
• For API details, consult kalshi_python SDK docs

Feedback & Issues

Found a bug? Have a feature request? Want to share results?

• GitHub Issues: github.com/kingmadellc/openclaw-prediction-stack/issues
• X/Twitter: @KingMadeLLC

Part of the OpenClaw Prediction Stack — the first prediction market skill suite on ClawHub.