prediction-trade-journalAuto-log trades with context, track outcomes, generate calibration reports to improve trading.
Install via ClawdBot CLI:
clawdbot install adlai88/prediction-trade-journalTrack every trade, learn from outcomes, improve your edge.
Use this skill when the user wants to:
# Sync trades from API
python tradejournal.py --sync
# Show recent trades
python tradejournal.py --history 10
# Generate weekly report
python tradejournal.py --report weekly
# Export to CSV
python tradejournal.py --export trades.csvAPI Reference:
https://api.simmer.marketsAuthorization: Bearer $SIMMER_API_KEYGET /api/sdk/trades/api/sdk/trades to fetch trade history| Command | Description |
|---------|-------------|
| --sync | Fetch new trades from API |
| --history N | Show last N trades (default: 10) |
| --sync-outcomes | Update resolved markets |
| --report daily/weekly/monthly | Generate summary report |
| --config | Show configuration |
| --export FILE.csv | Export to CSV |
| --dry-run | Preview without making changes |
| Setting | Environment Variable | Default |
|---------|---------------------|---------|
| API Key | SIMMER_API_KEY | (required) |
Trades are stored locally in data/trades.json:
{
"trades": [{
"id": "uuid",
"market_question": "Will X happen?",
"side": "yes",
"shares": 10.5,
"cost": 6.83,
"outcome": {
"resolved": false,
"winning_side": null,
"pnl_usd": null
}
}],
"metadata": {
"last_sync": "2025-01-29T...",
"total_trades": 50
}
}Other skills can enrich trades with context:
from tradejournal import log_trade
# After executing a trade
log_trade(
trade_id=result['trade_id'],
source="copytrading",
thesis="Mirroring whale 0x123...",
confidence=0.70
)This adds thesis, confidence, and source to the trade record for better analysis.
š Weekly Report
========================================
Period: Last 7 days
Trades: 15
Total cost: $125.50
Resolved: 8 / 15
Win rate: 62.5%
P&L: +$18.30
By side: 10 YES, 5 NO"SIMMER_API_KEY environment variable not set"
export SIMMER_API_KEY=sk_live_..."No trades recorded yet"
python tradejournal.py --sync to fetch trades from APITrades not showing outcomes
python tradejournal.py --sync-outcomes to update resolved marketsGenerated Mar 1, 2026
An individual trader uses the skill to log trades from platforms like Kalshi or Polymarket, tracking win rates and P&L to refine strategies. They generate weekly reports to assess performance and adjust bets based on calibration analysis, improving decision-making over time.
A hedge fund analyst employs the skill to automatically sync trade data from API feeds, storing outcomes for backtesting and risk assessment. They use the export feature to integrate with external tools, generating reports to evaluate strategy effectiveness and optimize portfolio allocation.
An educational service uses the skill to help students log simulated trades, track outcomes, and generate calibration reports for learning purposes. Instructors analyze reports to provide feedback, enhancing students' trading skills and confidence in real-market scenarios.
A product manager integrates the skill into a trading app to auto-log user trades, providing insights like win rates and P&L dashboards. They use the data to improve user engagement, offer personalized recommendations, and drive feature development based on usage patterns.
A researcher uses the skill to log trades based on market predictions, tracking outcomes to validate hypotheses and publish reports. They leverage the skill's integration capabilities to add context like thesis and confidence, supporting data-driven publications and consulting services.
Offer a SaaS platform where users pay a monthly fee to access enhanced trade journal features, such as advanced reports, AI-driven insights, and API integrations. Revenue is generated through tiered subscriptions, with premium plans offering more data exports and customization options.
Sell enterprise licenses to banks, hedge funds, or trading firms that need scalable trade logging and compliance reporting. Revenue comes from one-time licensing fees or annual contracts, with added value through custom integrations, support, and security features tailored to institutional needs.
Provide a free basic version of the skill for individual users, with paid upgrades for advanced features like automated syncing, detailed calibration reports, and third-party tool integrations. Revenue is generated through in-app purchases or one-time fees for premium modules, targeting hobbyist traders and professionals.
š¬ Integration Tip
Integrate with other skills by using the log_trade function to add context like thesis and confidence, enhancing trade analysis and enabling cross-skill data sharing for comprehensive insights.
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