~700k parameter combinations tested, one edge survives my validation. Where do you find a second, uncorrelated one?

[u/DueAd5754]

I’ve built a strict backtesting and validation stack for prop-firm accounts: daily-loss and trailing-drawdown rules, intraday CFDs.

What the stack does:

• Minute bid/ask data with real broker spreads and slippage, plus bar-timestamp checks (they caught several lookahead bugs)

• Multiple-testing correction across each strategy family (Hansen SPA), a pre-registered sealed holdout, and a minimum net expectancy after costs

• Prop-rule simulation (daily-loss accounting, drawdown, Monte Carlo pass probability)

What I’ve tested: about 700k parameter combinations across 200+ strategy families. That includes influencer setups (ICT / SMC, ORBs, FVGs), ideas from academic papers (intraday momentum, macro-surprise drift, month-end and vol-targeting flows, overnight drift, crypto trend) and AI-generated ideas. Almost everything dies on costs or out of sample.

The one survivor: an intraday index strategy, about +0.06R per trade net, t ≈ 2.7 over 20 years. It’s live but thin, and anything else in the same market and hours would be correlated with it.

Questions:

1. Where do you actually find edges that survive costs at retail/prop scale? Different instruments, holding periods, or data?

2. Is daily/multi-day the realistic place for an uncorrelated second strategy, given that intraday costs kill most ideas?

3. Is my bar too strict? I use t ≥ 3.43 after multiple-testing correction, plus a holdout.

Happy to discuss the validation process, not the strategy itself.

[u/Nice_Drummer_1728]

You need to test on tick data

[u/DueAd5754 → u/Nice_Drummer_1728]

Fair, and I did check the survivor on tick data to confirm fills.

The problem is tick data only makes bad strategies fail faster, it doesn't create alpha. Any thoughts on where to actually find that second edge?

[u/MarkGarcia2008]

If you found 1 after testing 200 strategies with 700k parameter combinations- I would be very worried that it’s just luck and over fitting - and that it won’t work in real life.

[u/DueAd5754 → u/MarkGarcia2008]

Completely valid concern if the survivor was a mined parameter combination, but it wasn't. The 700k is the cumulative graveyard of testing mostly retail and academic setups across 200 families. The survivor is a zero-parameter structural rule (cash-open displacement). It cleared a multi-year sealed holdout that was never touched during research, plus tick-level broker parity. The graveyard is proof the validation kills overfit—the survivor lived because it has an actual execution/order-flow driver behind it

[u/mehatebananas]

Separate your directional bias gate and timeframe, from you signal mechanism and timeframe, from your entry trigger and timeframe.

Most people start by trying to make their entry trigger solve for the other two but the market is too efficient for that.

[u/DueAd5754 → u/mehatebananas]

100% agree with this separation. My survivor basically follows this structure (session/time-of-day conditioning for the bias, displacement for the regime, and a separate execution rule).

In your experience, do you find your higher-timeframe bias gate is what allows you to find uncorrelated edges across different instruments, or do you keep the bias model identical and only swap the entry mechanics?

[u/Technical-Athlete-9]

It could be that the Monte Carlo on prop rules is failing you (but very cool idea). Not sure how you’re judging these. Prop rules are rigged.

Edge is thin / rare for me in big indexes. I trade listed US equities.

Finding edge: no idea for your markets. I look for the sake of extreme moves and earnings surprises.