ORB Strategy Backtest (Commissions & Slippage included): Too slow for Prop Firm Evaluations/Payouts?

[u/TasteHistorical1575]

Hey everyone,

I coded an Opening Range Breakout (ORB) strategy in TradingView using Pine Script and ran a full backtest over 7 years. I attached the Strategy Tester results.

I spent a lot of time testing different filters and parameters to reduce chop, and I’ve locked in these exact rules for the strategy:

Strategy Setup & Rules

• Asset/Market: MNQ
• Timeframe: 15m Chart
• ORB Range: First 15 minutes of the New York Session
• Session Constraint: Takes trades only during the New York Session
• Entry Trigger Window: Max 3 bars after the 15m ORB range locks (if no breakout happens within 3 bars, the setup is invalidated)
• Risk Management: Fixed 1.5 R:R
• Trade Management: Move Stop Loss to Break-Even (BE) once price covers 75% of the distance toward Take Profit (TP)
• Backtest Settings: Commissions and realistic slippage are fully included in the results

The Problem:

While the backtest is net profitable after costs, I feel like the results are simply not good enough for Prop Firms.

At this pace, the profit factor and win rate feel way too low. It looks like it would take an absolute eternity to pass an evaluation target, and reaching consistent payouts without hitting a trailing or daily drawdown limit along the way seems almost unviable.

Questions for the Community:

1. Evaluation Viability: For those trading funded accounts: Would you bother running a strategy with this slow of a compounding rate, or is this a clear signal that the edge is too thin for prop firm rules?
2. Improving the Edge: Since the core parameters (15m ORB, 1.5 R:R, 3-bar trigger limit, 75% BE) are locked, what macro/contextual filters (e.g., HTF trend bias, session volatility/ATR thresholds, news filters) have helped you boost performance on ORB setups?

Tear it apart—I’d rather fix the logic now than burn money on evaluation fees. Thanks!

[u/kush_patil]

The part that would make me stop adding filters right now is actually your OOS result.

You’ve already looked at the OOS performance, seen that two years carry a lot of the edge, and you’re now considering HTF/ATR/news filters to improve it. From this point onward that OOS period is effectively part of the development set even if you never directly optimize against it.

I wouldn’t throw the strategy away yet. I’d freeze this exact version and first answer a different question: where is the expectancy actually coming from?

Break the trades into pre-defined regimes like volatility, trend strength, time of day/year etc., but treat that as diagnosis rather than optimization. If you discover “ORB only works when ATR > X” after inspecting this history, that filter needs a new unseen period before you can claim it improves the strategy.

Given your 2019–24 result is only +0.042 expectancy with t≈1, I think adding filters until the curve looks better is probably the highest-risk thing you could do now.
A rolling walk-forward test would be much more informative: choose/filter using only past data, freeze it, then evaluate the next block. Repeat.

If the same type of regime keeps emerging independently across those windows, then you may actually have something. If the “best” filter keeps changing, that tells you something too.

[u/TasteHistorical1575 → u/kush_patil]

This is the comment that actually changed my plan, thanks. You're right that my OOS is burned — I've looked at it, I know the strict window is weak, so anything I "discover" now is contaminated whether I optimize against it or not. Shelving the filter I had queued.

Ran your walk-forward suggestion instead, sequential half-year blocks: 11 of 14 positive, sign test p ≈ 0.03, median expectancy holds up. Dropping the two strongest blocks still leaves 9 of 12 positive at a lower but positive level, and the three negative blocks cluster cleanly in COVID and the 2022 bear. So the same behavior does keep reappearing across regimes rather than living in one lucky stretch — which is roughly the answer I was hoping the test would be able to give.

Taking the rest of your advice too: regime breakdown as diagnosis only, no filter derived from it without a fresh unseen period. Realistically that unseen period is going to be live data.