Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Logs crypto trades with full context including technical indicators and macro conditions, analyzes win/loss patterns across dimensions like day of week, RSI ranges, and leverage, and extracts actionable trading rules. Rules are written back to an agent memory file, creating a feedback loop that refines decisions over time.
Rather than manually reviewing trade notes in a spreadsheet, it extracts statistically grounded rules from structured trade history and writes them directly into agent memory for use in future decisions.
When to use it
- Logging a BTCUSDT long trade with RSI and MACD values right after closing
- Identifying which days of the week have the highest win rate across 50+ trades
- Generating AVOID and PREFER rules once enough trade history accumulates
- Running a weekly review to compare current performance against the prior week
- Auto-updating agent memory with data-backed rules before the next session
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: I just closed a BTCUSDT long at +1.92% with RSI 28 and a bullish MACD cross. Log it and refresh my rules.
- 1Runs log_trade.py with symbol, direction, entry, exit, pnl_percent, indicators JSON, and market context JSON
- 2Runs analyze.py to compute win rates by direction, day of week, RSI range, and leverage level
- 3Runs generate_rules.py to extract AVOID, PREFER, and CAUTION rules from detected patterns
- 4Runs update_memory.py to append a Learned Rules section to MEMORY.md
MEMORY.md updated with rules such as 'AVOID: LONG when RSI > 70 (win rate: 23%, n=13)' and 'PREFER: SHORT on Mondays (win rate: 78%, n=9)'