I Slashed My AI Trading Agent Token Costs by 80% — Here's the Architecture

Short summary

Author optimized an autonomous AI trading agent from $240–600/month to $90–300 by inserting a statistical pre-filter layer before expensive AI calls, cutting daily AI invocations from 7,200 to 960. Key moves: multi-timeframe technical analysis filtering, removing 80+ unused skills, and aligning scan frequency with signal timeframes. Demonstrates layered architecture where each component reduces workload for the next.

• •80%+ cost reduction achieved via statistical pre-filtering before AI model calls
• •Removed 80+ unnecessary skills and system prompt overhead (25% size reduction)
• •Layered architecture: scan → trigger → filter → AI research → execute

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