This thread and system reads like a cautionary tale for overfitting backtested results.
Its def been an ENDLESS battle of SMARTY-Smart Martingale vs overfitting. Recent posts to this dev log have been devoted to stable parameter selection and NOT overfitting the parameters. My current parameter selection backtests arent near as pretty as my previous ‘fitness’ algorithms that had a touch of ‘overfit’. The current are more anti-overfit and still project returns with very low DD risk at Monte-Carlo 95% (10% skip). I’ve been slowly boosting risk since DD has only been 1.2% since I finalized SMARTY algo Mid Dec 2025.
I’ve been computing my own ‘fitness’ formula to combat overfitting and I thought I had cracked it earlier until chatting with a couple of the LLM’s. I never talk in specifics to them. Only topics and they showed me proof that my solution wich partially relied on a LARGE number of trades and the law-of-large-numbers as secondary verification could return valid and ‘overfit’ solutions. It def sparked my current anti-overfit solution and showed me how easily a genetic solution could be ‘overfit’ - even with safeguards.
So, at this point I’m past the coding challenge and almost any recent changes have been adjusting my pre-programmed algo controls. Posted are my current parameter selected backtests. Results are downside since I test with 100ms delay and VPS is often more profitable.
Currently running C2 with larger lot multiplier than backtest for higher RR with much room to expand. I’ll make sizing adjustments every 3 months. Finishing my system was well timed with the end of 2025. Finished the code and moved it to a new more powerful VPS server for execution stability. 2026 should be exciting!
I WILL NOTE: that its BETA=0.01 is holding up quite well against the STORM thats happening in Stocks, Metals and Bitcoin. Its def showing its ‘Edge as a Hedge’. You almost cant see the storm reflected in the trading activity and the algo trades 24/5 non-stop. No intervention.
