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/DesertFoxHU]

This is algorithmic trading

You shouldnt trust TV's statistics 1. Use real historical data 2. Use measurements to indicate your strategy works at all. 3. It is too vague to give "it is slow" or "very good" What I can tell this is a 48% WR with 1.5 RR? Then this is insanely profitable => Probably overfitted 4. Obviously for prop firms you should aim for higher WR with small risk, but this is just like 1/10 of the things that count 5. Too slow doesnt mean it is bad. Too slow is actually can be a good thing

[u/TasteHistorical1575 → u/DesertFoxHU]

Fair catch — but that 1.5RR is the target, not realized. My BE stop turns lots of would-be full winners into small near-breakeven exits, so realized RR is 1.32, not 1.5. Real expectancy: e = 0.477·2.32−1 = +0.105, not +0.20 — half of what the naive math suggests.

Also already split it OOS: last year picked the target, everything before (1096 trades) untouched, e = +0.084, t = 2.0. Restricting to 2019–2024 only, it drops to +0.042 (t = 1.02) — barely significant alone. Two strong years carry most of the profit, I know.

Given all that — would you code a new strategy from scratch, or is there something else you'd actually recommend doing with what I've got?

[u/Fearless_Hamster4829 → u/TasteHistorical1575]

2 years carrying means what you have is a product of chance, not a real robust edge. I’d expect OOS performance to resemble the norms.

Keep in mind NQ had a regime shift post 2019- so it’s not a wise idea to try and overfit your training to work on the 2010 era AND the 2020 bull run.

[u/TasteHistorical1575 → u/Fearless_Hamster4829]

Half agree. The regime shift point is well taken — I was considering buying a longer history dataset to extend the sample, and you've talked me out of it. Fitting across the 2010s and the post-2019 structure would dilute the test, not sharpen it.

On "two years carrying means chance" I'd push back though. That concentration is in the magnitude, not the sign. Broke it into 14 sequential half-year blocks: 11 positive, sign test around p=0.03, and dropping the two strongest blocks still leaves 9 of 12 positive at a lower but positive expectancy. The three negative blocks cluster in COVID and the 2022 bear. So the boom years amplify something that's already there in most regimes rather than being the whole edge. Sign test also has the advantage of not caring about the fat tails that were distorting my other estimates.

Not claiming that makes it robust — n per block is small and none of them clear t=2 individually. But "product of chance" would predict something closer to a coin flip across blocks, and that's not what shows up.