I want to quantify how my point-and-figure rules perform on a set of individual equities. I will supply the daily price history in a simple CSV format (or you can fetch it yourself through yfinance, Quandl, etc.). Your task is to build a clean, reproducible back-test that lets me tweak box-size and reversal parameters and immediately see updated results. At the end of each run I need the script to return: • Win/Loss ratio • Return on Investment (ROI) • Max Drawdown Python is preferred because I already work with pandas, NumPy and Jupyter, but I am open to R or a platform such as AmiBroker if full source code and step-by-step instructions come with the delivery. A modular design that separates data import, point-and-figure plotting, signal generation and performance reporting will make future tweaks easier. The back-test should: – Process roughly 100 tickers over 10-20 years of daily data without choking on memory or speed. – Produce a concise trade log and a summary CSV with the three metrics above. – Match trades to conventional point-and-figure charts on random spot checks; accuracy in the plotting routine is essential. Deliverables • Well-commented source code or notebook • Brief README explaining setup and how to change parameters • Sample output files (trade log and metrics summary) Once this baseline is working we can explore position sizing, portfolio-level analytics and additional entry filters, but the first milestone is a solid, repeatable back-test returning those three core metrics.