validate-predictions

Validate Predictions

Review all pending predictions and check them against current market data.

Workflow

Step 0: Auto-Find Expired Predictions

Delegate using the Task tool with subagent_type: general-purpose and model: opus:

"You are the learning-agent. Read agents/learning-agent.md for your analysis framework. First, use get_crypto_prices() from crypto-data MCP to get current prices for major coins (bitcoin, ethereum, solana, etc.). Then call find_expired_predictions(current_prices='{"BTC/USDT": ..., "ETH/USDT": ...}') from crypto-learning-db to discover predictions whose timeframe has passed. For each expired prediction, reason about how close it was and validate with an NL evaluation using validate_prediction(). Do NOT use the Edit tool."

Step 1: Check Remaining Pending Predictions

Delegate using the Task tool with subagent_type: general-purpose and model: opus:

"You are the learning-agent. Call query_predictions(status='pending') from crypto-learning-db for predictions still within their timeframe. For each prediction:

1. Use get_exchange_prices(symbol=...) from crypto-exchange MCP to check current price
2. Compare current price against the prediction's target_value
3. Report current progress toward or away from target Do NOT use the Edit tool."

Step 2: Present Results

Show a summary table:

## Prediction Validation Report

### Resolved This Check
| ID | Agent | Prediction | Target | Actual | Result |
|----|-------|-----------|--------|--------|--------|

### Still Pending
| ID | Agent | Prediction | Target | Current | Progress | Expires |
|----|-------|-----------|--------|---------|----------|---------|

### Overall Accuracy
- Total predictions: X
- Correct: X (X%)
- Incorrect: X (X%)
- Pending: X

### Track Record by Setup Type
| Setup Type | Total | Correct | Accuracy | Trend |
|-----------|-------|---------|----------|-------|