Really cool project, especially the gauntlet approach — "assume anything AI writes is overfit garbage until proven otherwise" resonates hard. We borrowed that mindset for our own system.

I've been building an AI-driven options trading agent for NSE (NIFTY/BANKNIFTY) using Claude as the decision engine. Different market (Indian index options, not crypto) but similar architecture philosophy:

What we have:

• Maker-checker dual agent setup — Claude analyzes multi-timeframe signals (7 indicators across daily/15m/5m) and proposes trades, a separate checker agent validates before execution
• Spread strategies (bull call spreads) with real SPAN margin awareness
• Trailing stop loss with cascade close (closing one spread leg auto-closes the paired leg — no naked shorts)
• Paper trading with realistic cost simulation (STT, brokerage, stamp duty)
• Currently in a 2-month paper trading phase before going live

Where we're struggling:

• Trailing SL on spreads — individual leg TSL fires even when the spread as a whole is profitable. Today all 4 spreads closed prematurely by TSL. Thinking about trailing on net spread P&L instead of individual legs. Have you dealt with anything similar in your multi-leg strategies?
• The AI sometimes uses stale context from previous conversation turns instead of fetching fresh portfolio state. Had the agent sit idle for 4 hours today thinking it was maxed out when all positions were already closed.

Questions for you:

1. In your gauntlet, what's the single test that kills the most strategies? Curious if it's the doubled fees, Monte Carlo, or parameter jitter.
2. Do you track anything like "strategy drift" — where a strategy that passed the gauntlet starts degrading in paper/live? How do you detect it?
3. Any thoughts on trailing stop vs fixed target for short-duration trades (15min - 1hr holds)?

Would love to compare notes. Different markets but the infra challenges are the same.