Drawdown Circuit Breaker
Evaluate account-level drawdown circuit breaker rules from trader-memory-core state and...
Install
npx skills add tradermonty/claude-trading-skills --skill drawdown-circuit-breaker -g -a claude-code -y- Repository
- tradermonty/claude-trading-skills3.5k
- Category
- Engineering
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 2,075 tokens
- Runs scripts
- Yes, review before use
What it does
Evaluate account-level drawdown circuit breaker rules from trader-memory-core state and decide whether new trade risk is allowed today. Uses realized P&L, losing-streak cooldowns, and weekly/monthly drawdown limits without any external API.
Drawdown Circuit Breaker
Overview
Evaluate whether the trader should take new trade risk today based on account-level realized P&L and recent terminal trade outcomes. This skill reads trader-memory-core thesis YAML files only. It produces a circuit_breaker_decision artifact that complements the market-side exposure_decision from exposure-coach.
The circuit breaker is a recommendation and recordkeeping tool. It does not replace human judgment, and it does not enforce broker-side blocks or automated order rejection.
When to Use
- Before screening or sizing any new swing trade candidate
- After a losing trade or partial trim to check whether a cooldown is active
- During daily planning when trader-memory-core contains recent closed or partially closed positions
- As a workflow gate before swing-opportunity-daily proceeds to candidate generation
- When reviewing whether daily, weekly, or monthly loss limits have been breached
Prerequisites
- Python 3.9+
- Local trader-memory-core thesis YAML files, usually under
state/theses/ - Account size in dollars
- No API keys or network access required
Workflow
Step 1: Read Trader Memory State
Point the script at the thesis state directory:
python3 skills/drawdown-circuit-breaker/scripts/check_circuit_breaker.py \
--state-dir state/theses \
--account-size 100000 \
--output-dir reports/
The script scans every th_*.yaml file and reads realized P&L from each thesis status_history[] ledger entry. It does not use _index.json for P&L, because the index is a lightweight lookup file and does not contain the required realized-P&L ledger.
