I want to put numbers behind an intuition: many NSE/BSE mid-cap counters seem to jump on roughly the same calendar dates each year. Your task is to confirm (or debunk) that seasonality with hard data. Please pull at least the last ten—ideally fifteen—years of daily price and volume data for a representative basket of Indian mid-cap stocks. Work only with the dates themselves; I’m not interested in earnings days, dividend announcements, or any other event tags. Once the data are cleaned, run a date-centric seasonality scan that shows how often each stock closed up by a meaningful margin on every trading date across the sample. Think of it as creating a probability table for “surge days.” Deliverables (Excel/CSV only): • A spreadsheet where each row contains Stock Symbol, Calendar Date (dd-mm), Avg % Move, Hit Rate (% of years the move exceeded the chosen threshold), Sample Size, plus any other metrics that sharpen the insight. • The well-commented Python/R script you used so I can rerun or extend the study. Acceptance criteria: • Data span ≥10 full years per stock. • Results reproducible with the supplied code and publicly available data (NSE Bhavcopy, BSE archives, yfinance, Quandl, etc.). • Spreadsheet opens cleanly with no macros and figures reconcile with the code output. Use whichever toolchain you prefer—Pandas, NumPy, R tidyverse, matplotlib/seaborn for quick sanity plots—so long as the final answer is a clear, filter-friendly spreadsheet that tells me the probability of a meaningful up-move on any given day of the year for the mid-caps under review.