When working on algorithmic trading strategies, backtesting, or quantitative research, reliable historical market data is critical. I’ve used Binance’s public API extensively for pulling historical klines (candlestick data), and one recurring pain point is incomplete datasets—especially for high‑frequency (1m/5m) or long‑range periods. Gaps, truncated bars, and hidden missing rows often lead to biased backtests and unreliable models. In this post, I’ll share a practical, battle‑tested workflow that ensures zero missing klines and can be reused across projects. Why Klines Go Missing Binance enforces a strict API limit: Max 1,000 klines per request, regardless of timeframe (1m, 5m, 1h, 1d). Common causes for missing data: Large time ranges → data truncation at the end Fast consecutive requests → HTTP 429 rate limiting No timestamp validation → hidden gaps remain undetected It’s rarely the API itself—it’s how you query it.…