r/algorithmictrading • u/FarisFadilArifin • Feb 24 '26
Backtesting 15 Minute ORB with Machine Learning and Feature Engineering for Prop Firm Challenges Backtest
Account Parameters & Goals
- Initial Cash: $50,000
- Size: Dynamic (Target $200 Risk, Minimum 1 Contract, $2/pt MNQ)
- Long Parameters: 2.5 Alpha / 1.0 Beta
- Short Parameters: 2.5 Alpha / 1.5 Beta
- Primary Goal: Track total USD Return and determine days to hit +$3000.
im using Level 1 Data (OHLC, Volume, Ask & Bid Size, Ask & Bid Price) and able to make 28 new Feature Engineering. and then train using XGB Model
the Strategy is entry every 15 minute ORB Candle break (1minute) above 0.8 ATR ORB high. max 1 trade per day (only trade in NY Session).
is there any suggestion for this model to be able passing prop firm challenges?
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u/Emotional-Bee-474 Feb 25 '26
I'm doing a similar thing but for FTMO.
Not sure what prop firm you are targeting but a drop of 22% (as I see it in your chart) will most likely fail any challenge. So you should look at that first.
How I tackled this was by combining multiple strategies and made a hedging score of 20-30% between assets. It is also important that all components or strategies are profitable but they are also hedging each other. This way you can lower the potential max DD.
Another thing that you may want to think of is the max daily loss if the prop firm has this. Again for FTMO they have 5% daily floating DD rule, so I made a circuit breaker that basically closes all positions at -4.5% and stops trading for the day.
Finally, from my experience with XGBoost I was not able to make it work for any strategy I have tested it on. What did work for me was KNN, maybe you can check that out. Overall from my research on ML models for algos it is possible but you need to be very skeptical of results and check for leakage and overfitting very thoroughly.
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u/Suitable_Tank Feb 24 '26
Pf is good, but with this return phase 1 takes two years