Code watermarking supports provenance tracking for code generated by LLMs. Modifying token selection to embed watermarks as an LLM generates code can create a trade-off between detectability and functional correctness. Other methods instead watermark completed code using predefined transformations or trained neural models. Recurring patterns can make watermark choices predictable across programs, while treating patterns common in unwatermarked code as watermark evidence can cause false detections. We therefore introduce SEW, which embeds and detects watermarks in already generated code through three components: (i) code style rules collected from style guides and transformation rules, with style choices determined by a secret key and each program's structural context; (ii) style-preference calibration, which evaluates watermark evidence using style probabilities estimated from human-written code; and (iii) context-aware style aggregation, which combines evidence from structurally matching locations assigned the same code style choice, preventing repeated applications of that choice from inflating watermark evidence. On CodeContests across three LLMs and three programming languages, SEW achieves a mean relative improvement of 12.44% in TPR@FPR5% over the baselines and is robust to four non-LLM code-editing attacks, with only a 0.94% mean relative decrease. Our code is available at https://github.com/suhanmen/SEW.

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