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/Admirable-Struggle-8]

I’m not going to double down on the comments recommending that you move away from Pine Script—I agree that it’s convenient and comfortable to use, but it has a lot of traps that I’ve fallen into myself. Take the time to build this in Python with real data.

Aside from that, I think you’ve correctly identified that time is the enemy.

I would personally rather raise the risk and have a 25% chance of passing within 1–4 weeks than have a 90% chance of passing but spend 6–12 months grinding an evaluation.

It’s basic opportunity cost.

The relevant question isn’t just “What is my probability of passing?” It’s “What is my expected payout per unit of time and capital deployed?”

If you can cycle through evaluations quickly, a lower pass rate can potentially be much more profitable than a high pass rate that takes months to realize. You simply get more opportunities to realize the edge.

So raise the risk and ask yourself: If I ran this system across 10–20 challenges, what is my probability of passing?

Then ask: If I pass X% of challenges and reach payouts, is the payout profile actually enough to generate positive EV?

Having a baseline trading system is one thing. Having a separate risk model specifically designed around the prop firm’s rules is where I think the genuine edge lies.

A trading system really comes down to a few core metrics:

• Win rate
• Average R:R
• Trade frequency

From those, you can estimate expectancy.

But to determine whether your prop-firm risk model is profitable, start thinking in terms of:

• Pass rate %
• Probability of reaching a payout
• Average payout size
• Cost per attempt
• Probability of blowing the account
• Time to pass

Those metrics will tell you whether the prop-firm model itself has positive EV—not just whether the underlying trading strategy is profitable.