Hey devs who venture into quant finance 👋. If you’re used to building APIs and microservices, stock market data might seem like “just another JSON”. I thought so too, until I built my first backtesting engine for US equities. Turns out, historical K-line (OHLCV) data is a swamp of edge cases. Let me walk you through how I tamed it. The Wake-Up Call: A “Perfect” Backtest That Couldn’t Trade I coded a simple breakout strategy in Python. Backtest result: +35% annually, drawdown 4%. I deployed a paper-trading version using the same data pipeline. Live results: -12% in three weeks. After days of debugging the algorithm, I found the issue: the minute bars I downloaded had timestamps in UTC but I had assumed they were US Eastern. The “breakouts” my strategy captured happened outside market hours — pure noise that couldn’t be traded. My entire evaluation was based on unexecutable signals.…