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:

When to Use This Skill

Prerequisites

Configuration

Set these environment variables or edit defaults in your environment:

| Variable | Default | Purpose | |----------|---------|---------| | KALSHI_KEY_ID | (required) | Your Kalshi API key ID from dev.kalshi.com | | KALSHI_KEY_PATH | (required) | Path to your Kalshi private key file (ES256 PEM) | | PORTFOLIO_DRIFT_THRESHOLD | 5.0 | Percentage change to trigger alert (e.g., 5 = 5%) | | PORTFOLIO_DRIFT_INTERVAL | 60 | Minutes between checks (rate limiting, prevents API spam) |

How to Trigger

Use any of these phrases in conversation:

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:

If any metric ≥ threshold, flags that position.

Step 4: Format and Output

Outputs drift alerts with:

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

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

| Profile | Threshold | Interval | Use Case | |---------|-----------|----------|----------| | Tight | 3% | 30 min | Active day trading, many positions | | Standard | 5% | 60 min | Default — balanced signal-to-noise | | Loose | 10% | 120 min | Long-term positions, fewer alerts | | News-reactive | 2% | 15 min | Event-driven trading (FOMC, elections) |

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

| Connected Skill | How It Connects | |----------------|-----------------| | Market Morning Brief | Morning/evening briefs bookend your day; drift alerts fill the gaps | | Kalshi Command Center | Monitors positions opened through the command center | | Kalshalyst | Tracks exposure on positions flagged by contrarian analysis |

Install the complete stack:

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

Performance & Cost

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

Additional Resources

Feedback & Issues

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

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

Set these environment variables or edit defaults in your environment:

| Variable | Default | Purpose | |----------|---------|---------| | KALSHI_KEY_ID | (required) | Your Kalshi API key ID from dev.kalshi.com | | KALSHI_KEY_PATH | (required) | Path to your Kalshi private key file (ES256 PEM) | | PORTFOLIO_DRIFT_THRESHOLD | 5.0 | Percentage change to trigger alert (e.g., 5 = 5%) | | PORTFOLIO_DRIFT_INTERVAL | 60 | Minutes between checks (rate limiting, prevents API spam) |