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From crypto-trading-desk
Closes an open trade and runs post-mortem analysis including prediction validation and pattern updates. Useful for systematic trade management.
npx claudepluginhub hugoguerrap/crypto-claude-desk --plugin crypto-trading-deskHow this skill is triggered — by the user, by Claude, or both
Slash command
/crypto-trading-desk:close-tradeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Close trade $ARGUMENTS and run a post-mortem analysis.
Closes a paper trading position by ID, booking realized P&L. Shows open positions if no ID given. Price defaults to last mark.
Autonomous monitoring loop for crypto trades: checks SL/TP levels, closes trades that hit targets, evaluates expired predictions, generates periodic summaries. Run via cron for full autonomy.
Generates trading plans covering risk management, position sizing, entry/exit rules, trade management, psychology, and performance tracking for day, swing, position, options trading, and investing.
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Close trade $ARGUMENTS and run a post-mortem analysis.
Delegate using the Task tool with subagent_type: general-purpose and model: opus:
"You are the portfolio-manager agent. Read agents/portfolio-manager.md for your decision framework. Close trade $ARGUMENTS. If a price is specified after 'at', use that as exit price. Otherwise, get the current market price using get_exchange_prices() from crypto-exchange MCP. Call close_trade(trade_id='...', exit_price=..., close_reason='...') from crypto-learning-db MCP. PnL, portfolio balance, and stats are updated automatically. Do NOT use the Edit tool."
After the trade is closed, 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. Run a post-mortem analysis on the recently closed trade $ARGUMENTS. Call query_trades(status='closed', limit=1) from crypto-learning-db to get the trade data. Read any related reports from data/reports/. Analyze what worked, what didn't, and provide specific recommendations for improvement. Do NOT use the Edit tool."
After the post-mortem, delegate using the Task tool with subagent_type: general-purpose and model: opus:
"You are the learning-agent. Validate all predictions for trade $ARGUMENTS. Call query_predictions(trade_id='...') from crypto-learning-db to find all predictions tied to this trade. Compare each prediction against the actual outcome. Call validate_prediction() for each one with a detailed NL evaluation of how close the prediction was and what we can learn. Then call upsert_pattern() to update the pattern library with the setup from this trade. Do NOT use the Edit tool."
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