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Why treating financial markets as natural language problems — not physics problems — yields better predictive models. The theoretical framework behind our AI trading approach, inspired by Renaissance Technologies and modern NLP.
How to use on-chain perpetual futures data — funding rates, open interest, and liquidation heatmaps — as quantitative trading signals. Python implementations and backtest results on crypto markets.
How we built an autonomous LLM-powered trading agent that runs on Hyperliquid for $1.30/day in API costs. Complete architecture, cost breakdown, prompt engineering, and live results.