Developer Builds AI-Powered Japan Stock Pipeline That Disproved Three Strategies

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A freelance web developer in Japan built an automated stock research pipeline using Claude Code, the J-Quants API, and a macOS cron job to backtest trading strategies on Japanese equities. To avoid bias, he imposed a strict rule in the AI's instructions: no predictions allowed, limiting Claude Code to data structuring, metric computation, rule-checking, and sourced explanations. Over several weeks, the pipeline systematically invalidated three strategies — a classic breakout/RSI approach that returned just 15.4% against the Nikkei's 138.8% benchmark gain, a large-cap post-earnings drift strategy that showed no exploitable edge, and a promising small-cap earnings drift pattern that collapsed once a minimum liquidity filter was applied. The small-cap strategy showed strong backtested returns until stocks with under ¥100 million in daily trading volume were excluded, leaving only 33 illiquid, untradeable positions with negative average returns. The developer highlights the project as a case for using AI as a disciplined engineering partner rather than a forecasting tool, with negative results framed as the pipeline's most valuable output.

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