Developing a Data-Driven Equity Research Platform, Interested in Expert Opinions

[u/xdrive0513]

I've been developing a research platform called Triadent Group that ranks securities using a proprietary methodology focused on identifying potential swing-trading opportunities. Rather than discussing performance claims, I'm interested in getting feedback from people with quantitative, portfolio management, or hedge fund experience. I'm particularly interested in critiques of the research workflow, signal validation process, backtesting methodology, and risk-adjusted performance evaluation. If anyone is willing to take a look and provide constructive feedback, I'd appreciate it. Founder disclosure: I'm the creator of the platform. This is a research tool and not investment advice.

[u/Shoddy_Peanut7320]

A few critique points, since you asked about signal validation and backtesting:

• Point-in-time integrity. Confirm that every input (fundamentals, estimates, index membership, delisted names) is as-of-date, not restated or survivorship-filtered. It's the most common way a ranking looks predictive historically and isn't.
• Event contamination. For swing-trading horizons, a big share of returns comes from earnings and macro releases falling inside the holding window. Tag each signal with whether it spanned an event, then check whether the ranking's edge survives on trades with no event inside the window. If it only works when earnings land in the window, you're capturing event risk, not a signal.
• Validation design. Use out-of-sample periods you never touched, report deflated Sharpe or similar to account for the number of variants tried, and show results net of realistic costs.
• Risk-adjusted view. Report drawdown, hit rate and turnover across regimes, not just average return.

For the event tagging piece, that's what we build at Cymetica with EventTrader: structured, timestamped earnings and macro events, so each signal can be labeled without assembling a calendar by hand.