Looking for peer-review on a rules-based long-term equity strategy (20-year backtest outperforming S&P 500)

Hey everyone,

I’ve spent the last several months building, refining, and codifying an investment thesis focused on long-term public equity holdings. The goal was to build a rigorous, rules-based strategy that focuses on structural advantages rather than short-term macro speculation or high-frequency trading.

**The Data So Far:**

* **The Backtest:** I’ve run a clean historical backtest spanning the last 20 years. The results show consistent, significant outperformance against the S&P 500
* **The Live Sandbox:** For the past few months, I’ve been running the exact strategy rules live inside an Investopedia simulation to monitor daily execution, slippage, and behavioral consistency. The simulation is working incredibly well and tracking perfectly alongside the historical model metrics.

**Where I'm At & Next Steps:** My ultimate goal is to transition this framework out of the sandbox and into a live, verifiable track record (likely using an institutional brokerage setup like Interactive Brokers or a turnkey SMA platform to keep it compliant).

Before I allocate significant capital or look at managing outside money down the road, I want to connect with people who can ruthlessly tear my methodology apart. I want to ensure my 20-year data has absolutely zero survivorship bias (e.g., handling delisted/bankrupt companies properly), look-ahead bias, or hidden factor correlations that I might have missed.

**Who I’d Love to Chat With:** If you are a quant researcher, software engineer working with financial data, portfolio analyst, or just an experienced developer who loves portfolio architecture, I’d value your critique.

I’m happy to share the high-level framework, performance metrics (drawdowns, Sharpe ratio expectations), and discuss the logic behind the selection rules. Drop a comment or send over a DM if you're open to looking at some data or jumping on a quick Discord call to whiteboard it.

Cheers!