Research question
What would a small, transparent research environment need in order to compare equity signals fairly across time and market conditions?
Motivation
Quantitative research needs an auditable path from raw data to interpretation. This case study is a proposed direction for that workflow; it does not claim a tested signal or investment outcome.
Methodology
Planned: define a research universe, establish point-in-time data rules, construct a small set of candidate factors or signals, and compare them through a consistent backtesting and reporting framework.
Data and assumptions
No data vendor or universe has been chosen. Future documentation will state survivorship handling, corporate actions, timing, liquidity screens, and cost assumptions before presenting results.
Implementation
Planned: modular Python and SQL components for ingesting and validating financial data, running factor-based research and backtests, and reporting results. The repository link will appear when code is available.
Results and findings
No backtest or performance statistics are available. Future charts should show both results and the uncertainty around them.
Limitations and next steps
Potential concerns include data quality, look-ahead bias, multiple testing, regime dependence, and the gap between research conditions and implementation.
Next: select a point-in-time data source, define a universe and validation protocol, then implement a small research workflow before presenting any findings.
Lessons learned
To be completed after the work is carried out and reviewed.