Media
3 items
Description
Identify candlestick patterns using Fluey algorithm A Chrome extension built for traders who want precise, no-nonsense technical analysis at their fingertips. Select any stock, define your date range, and choose your preferred chart interval from 1-minute candles for intraday scalping all the way to daily and weekly views for broader trend analysis. The extension instantly renders your chart with automated pattern recognition overlaid directly onto the price data. Support and resistance zones, trend lines, candlestick formations, volume signals, and emerging chart patterns are all identified algorithmically and annotated on-screen in real time, so the technical context you need to make informed charting decisions is surfaced visually rather than left for you to manually derive and mentally track. For active traders who need to stay current, the live refresh functionality continuously ingests the latest available price data and re-runs pattern detection on each update cycle, ensuring that newly forming trends, breakout conditions, and pattern completions are flagged on your chart as they develop rather than after the fact. The extension is extensively customizable across refresh intervals, chart timeframes, date ranges, annotation types, display density, and visual theming, giving you granular control over your analytical environment so the tool works precisely the way your trading process demands. The interface is engineered for clarity and efficiency, with a layout that keeps the chart and its annotations as the primary focus while making all controls accessible without clutter. Whether you are conducting historical backtesting across a specific date window, studying pattern behavior across different market conditions, or monitoring live price action during an active session, FLUEY provides a consistent and high-quality analytical layer that externalizes the technical work of chart reading, helping you annotate faster, identify setups more clearly, and engage with price data at a deeper level of understanding. Algorithm: Arsh Jha, University of Illinois Urbana-Champaign User Interface: Chembia Vanaathirayan, UNC Chapel Hill Promotion: Nisha Rajesh, North Carolina School of Science & Math