/monitor

name: monitor
description: Autonomous monitoring loop. Checks open trades against SL/TP levels, closes trades that hit targets, evaluates expired predictions, and generates periodic summaries. Run via cron for full autonomy. Usage: /monitor
user-invocable: true

Monitor - Autonomous Learning Loop

Closes the autonomous loop: check trades, close hits, evaluate predictions, generate summaries.

**All agents use `subagent_type: general-purpose`** with explicit `model` to ensure MCP tool access. Include "Do NOT use the Edit tool" in every prompt.

Workflow

Step 1: Get Current Prices

Delegate using Task with `subagent_type: general-purpose`, `model: haiku`:

"You are the market-monitor agent. Get current prices for ALL symbols that have open trades. Call get_portfolio_state() from crypto-learning-db first to see which symbols have open positions. Then use get_exchange_prices(symbol=...) from crypto-exchange MCP to get live prices for each symbol. Return a JSON object like: {\"BTC/USDT\": 98500, \"ETH/USDT\": 3200} Do NOT use the Edit tool."

Step 2: Check Open Trades Against SL/TP

Using the prices from Step 1 and the open trades from get_portfolio_state():

For each open trade, check:

• **Long trade**: Did price drop to or below `stop_loss`? Did price rise to or above `take_profit`?
• **Short trade**: Did price rise to or above `stop_loss`? Did price drop to or below `take_profit`?

If SL or TP was hit, delegate using Task with `subagent_type: general-purpose`, `model: opus`:

"You are the portfolio-manager agent. Close trade {trade_id}. Current price is ${price}. The {SL/TP} at ${level} was hit. Call close_trade(trade_id='{trade_id}', exit_price={price}, close_reason='{SL/TP} hit at ${level}') from crypto-learning-db. Do NOT use the Edit tool."

Step 2b: Trailing Stop Adjustment

For open trades that were NOT closed (still active), check if the trade is profitable:

• **Long**: current price > entry_price
• **Short**: current price < entry_price

If profitable AND the current stop_loss hasn't been optimally trailed, delegate using Task with `subagent_type: general-purpose`, `model: sonnet`:

"You are the risk-specialist agent. Read agents/risk-specialist.md for context. Trade {trade_id} ({symbol}, {side}) is profitable. Entry: ${entry}, Current: ${price}, SL: ${stop_loss}, TP: ${take_profit}. Analyze whether to trail the stop-loss. Use calculate_volatility(symbol=...) and get_support_resistance(symbol=...) from crypto-technical MCP. If you recommend adjusting, call update_trade(trade_id='{trade_id}', stop_loss={new_sl}, notes='your reasoning') from crypto-learning-db. Rules: only trail in profitable direction, never widen the stop, leave room for normal volatility. Do NOT use the Edit tool."

Step 3: Post-Mortem for Closed Trades

For each trade closed in Step 2, delegate using Task with `subagent_type: general-purpose`, `model: opus`:

"You are the learning-agent. Read agents/learning-agent.md for your analysis framework. Trade {trade_id} was just closed ({result}, PnL: {pnl}). Do a full post-mortem: 1. Call query_predictions(trade_id='{trade_id}') from crypto-learning-db to find all predictions 2. For each pending prediction, evaluate: read the original, compare to what happened, write a detailed NL evaluation 3. Call validate_prediction() with your evaluation for each one 4. Call upsert_pattern() if you identify a named pattern 5. Write a post-mortem report to data/reports/ Do NOT use the Edit tool."

Step 4: Evaluate Expired Predictions

Delegate using Task with `subagent_type: general-purpose`, `model: opus`:

"You are the learning-agent. Call find_expired_predictions(current_prices='{prices_json}') from crypto-learning-db using the current prices. For each expired prediction: 1. Read the original prediction text 2. Compare to the current price / market state 3. Write a natural language evaluation explaining how close it was and why 4. Call validate_prediction() with your evaluation Do NOT use the Edit tool."

Step 5: Monthly Summary (conditional)

Check if today is the last day of the month (or within 2 days of month end). If yes, delegate using Task with `subagent_type: general-purpose`, `model: opus`:

"You are the learning-agent. Generate a monthly summary. Call generate_summary(summary_type='monthly') from crypto-learning-db. Also check if it's end of quarter — if so, call generate_summary(summary_type='quarterly') too. Do NOT use the Edit tool."

Step 6: Report

Present a summary of what happened:

## Monitor Report

**Open Trades:** X active
**Trades Closed This Run:** X (list with PnL)
**Predictions Evaluated:** X expired validated
**Summary Generated:** Yes/No

### Next Actions
- [Any trades approaching SL/TP levels]
- [Any concerning patterns noticed]