Volatility Contraction Pattern Algo Development

I want a clean, well-documented Python routine that reliably spots the Volatility Contraction Pattern (VCP) in equities. The code must be able to ingest both historical price series (for back-testing) and a live market feed (for real-time screening), then return a clear signal whenever a qualifying pattern finishes forming. Key points you should factor in as you build: • Data scope – daily and intraday OHLCV from yahoo finance or a zerodha broker/API stream; your code should let me swap data sources with minimal edits. • Pattern logic – I follow Mark Minervini’s classic VCP definition (series of narrowing price/volume contractions that resolve near a pivot). Please encode that logic with tunable hyper-parameters so I can tighten or loosen the rules later. • Tech stack – pure Python, leveraging pandas, NumPy, TA-Lib (or your own study functions), and websockets/REST wrappers for the real-time side. • Output – a simple, structured signal object (ticker, time stamp, stop level, entry level) that I can pick up in another script. Optional helper functions for quick Matplotlib plots are welcome for visual validation. Deliverables 1. A modular .py package (back-test module, real-time module, shared utilities). 2. Example Jupyter notebook showing back-test results on at least one major index constituent. 3. README with setup instructions and parameter explanations. 4. Short demo clip or GIF proving the real-time alert fires as expected. Acceptance Criteria • Reproduces historical VCPs on test tickers with <2 % false-positive rate in sample period I supply. • Able to run continuously on a live feed for at least one trading session without crashing or memory leaks. • Code passes pylint 8.0+ score of 8 or higher and includes docstrings/type hints. If you have previous work detecting chart patterns or streaming market-data projects, great—send a one-liner summary with your bid. Let’s keep the focus tight: functional, transparent, and ready for me to extend once it’s in place.

Project ID: 39731695