I have been following a well-publicised equities strategy that shows impressive, verifiable returns, yet no one has ever published the full rule set. I have already gathered every public reference I can find—interviews, YouTube clips, podcast transcripts, even social-media threads—and I also hold a paid subscription to quality historical stock data. Your mission is to piece together the missing logic, express it cleanly in Python, then prove that the reconstructed model delivers the same performance figures the original author claims. I will share links, time-stamped notes, and benchmark numbers (CAGR, max drawdown, win rate) so you can line up the results precisely. Because the brief is highly specific, I will judge success on tangible output: • Well-commented Python code (function or class-based, ready for reuse) • A repeatable back-test that pulls my data locally, handles corporate actions, and outputs core metrics and equity curves • A short report comparing your results to the public figures, highlighting any gaps and hypotheses for variance • Optional: basic parameter sweep or walk-forward test to show robustness If you prefer pandas, NumPy, backtrader, zipline, or similar libraries, that is fine as long as the final script runs in a plain virtual environment on my side. Precision matters more than speed; clarity of assumptions is critical. Let me know how you would approach the detective work, any prior examples of strategy reconstruction you have done, and an estimated timeline for first validation. There are 7 strategies are used to make a trading decision. All strategies are relatively straightforward.