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Programmatic trendline detection: how do you handle the line-selection problem? Programmatic trendline detection: how do you handle the line-selection problem? I'm building a system that detects support/resistance trendlines from OHLCV data (crypto daily/hourly, \~6 years of history, a few hundred symbols). Levels are straightforward — cluster swing highs/lows by price and count members. Breakouts and retests fall out of that easily. Trendlines are where I'm stuck. With N swing lows there are N(N−1)/2 candidate lines. A human draws one by eye. A program has to pick, and I don't want a hand-tuned rule that only works on the charts I looked at. The approach I'm considering: draw every pairwise line, extend it forward, and score it by touches (price approached within X% and reversed) minus violations (price closed through it). Keep the top-scoring lines. The idea is to let price history tell me which lines the market actually respected, instead of me choosing.