Most people meet market data as candles: open, high, low, close and volume for some interval. Candles are convenient and compact, but they are a lossy summary. Tick data is the underlying reality they summarise — every individual trade and every quote update, timestamped to the millisecond or finer.
What a tick actually is
A trade tick records a single execution: price, size and timestamp, often with venue and conditions. A quote tick records a change to the best bid or offer. Between two one-minute candles there can be thousands of ticks — and the order they arrived in carries information that the candle has thrown away.
Why the summary lies
A one-minute bar tells you the high and the low were both touched, but not which came first. For a strategy with a stop and a target inside that minute, the ordering decides whether you were stopped out or took profit — and the bar cannot tell you. Backtesting on bars quietly assumes the answer that flatters your strategy. This is one of the most common reasons a backtest looks brilliant and live trading does not.
Ticks also expose microstructure that bars erase: spread, liquidity at the touch, and the difference between a print that swept the book and one that sat on the bid. If your edge lives anywhere near execution, that detail is the edge.
Match research to production
The fix is to backtest on the same resolution you trade on. If production consumes a live tick stream, research should replay historical ticks — not bars — through the same code. Pig Data delivers full historical tick-level trades and quotes over REST and live ticks over WebSocket with an identical schema, so the only thing that changes between backtest and production is the clock.