Walkthrough of building a 24/7 AI trading agent using Claude Opus 4.7 and Cloud Code routines. The creator migrates an OpenClaw-based trading system (which outperformed the S&P in a 30-day test) into remote Cloud Code routines, integrates Alpaca for brokerage, Perplexity for research, and ClickUp for notifications. Video covers memory architecture (file-based persistence), guardrails, cron schedules (pre-market, open, midday, close, weekly review), environment secrets, GitHub deployment and testing best practices. It emphasizes iterative development, safety caps, and context-budget management for reliable long-term performance.
– Tech stack: Claude Opus 4.7 with Cloud Code routines, Alpaca (broker), Perplexity (research), ClickUp for notifications, and GitHub for remote runs and persistence.
– Memory & scheduling: use file-based memory to make stateless routines disciplined; schedule cron triggers for pre-market, open, midday, close and a Friday weekly review; choose remote vs local execution carefully.
– Safety and ops: implement guardrails (paper trading, max position size, daily loss caps), store API keys in environment variables, rotate secrets, and require test runs before live trading.
– Practical guidance: migrate existing agent context and prompts, manage token/context budgets, instrument trade logs and weekly reviews, and iterate prompts/skills after each run.
Quotes:
I was able to beat the S&P by about 8% in 30 days.
Treat tokens like money.
Files aren’t just memory; they’re the agent’s full personality and discipline.
Statistics
| Upload date: | 2026-04-17 |
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| Likes: | 10002 |
| Comments: | 693 |
| Statistics updated: | 2026-05-17 |
Specification: I Turned Claude Opus 4.7 Into a 24/7 Trader
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