Most trading libraries begin innocently. Someone wants an EMA. Then RSI. Then ATR. Then Bollinger Bands, pivots, candlestick patterns, rolling quantiles, regime flags, entry rules, exit rules, position sizing, stop-loss templates, and one mysterious helper called generate_alpha_final_v7() that nobody dares delete because it once worked during a bull market. This is how indicator libraries become strategy junk drawers. ...
TL;DR for operators Trading platforms have spent decades giving users fixed technical indicators and then, more recently, neural models that treat those indicators as just another column in a feature table. Longfei Lu’s paper on Technical Indicator Networks, or TINs, proposes a different wiring job: make the indicator itself into the neural architecture.1 ...
TL;DR for operators Support levels are usually treated as chart objects: a line, a zone, a Fibonacci retracement, a moving average, perhaps a hand-drawn artefact with suspicious confidence. DeepSupp reframes them as latent market states: patterns in how price, volume, VWAP, and related features move together over time.1 The paper’s useful contribution is the pipeline, not the marketing-friendly phrase “AI technical analysis.” DeepSupp builds rolling Spearman correlation matrices from price-volume features, sends those matrices through a multi-head attention autoencoder, compresses them into latent embeddings, and then uses DBSCAN clustering to map dense market states back into median price levels. In plainer language: it tries to find support zones by learning how market relationships evolve, rather than by assuming that yesterday’s visual line still deserves respect. ...