Historical Stock Trend Analytics

Budget: ₹12500 - ₹37500

I have a large archive of end-of-day price data from our stock market institution and I want a clear, statistically sound picture of how those prices have behaved over time. Your task XXXX XXXX take the raw files, clean and normalize them, then carry out a full historical trends analysis.

I need more than just a set of charts—I’m looking for a concise narrative backed by numbers. Please compute daily, weekly, and monthly returns, identify significant breakouts or regime shifts, and highlight periods of abnormal volatility. Standard indicators such as moving averages, Bollinger Bands, and RSI should be part of the study, but feel free to propose additional metrics if they reveal meaningful patterns.

Preferred tools are Python (pandas, NumPy, matplotlib, seaborn, SciPy) or R (tidyverse, quantmod), delivered as well-commented notebooks plus an executive-level PDF summary. All code must be reproducible on a fresh environment and reference the exact data transformations you apply.

Acceptance criteria • Cleaned data files and documented preprocessing steps • Annotated notebook that runs end-to-end without manual tweaks • Visualizations and tables that clearly illustrate major historical trends • A short written report (2-3 pages) translating technical findings into plain-language insights ready for our internal stakeholders

If you’ve tackled similar historical price studies before, I’d be happy to see a brief sample or repo link when you respond.