Built a crypto futures bot, now stuck and can not figure out a way forward

So I've been working on this for a while now. Automated strategy for crypto perp futures, 15-minute timeframe. Uses oscillator confluence for entries with a signal-reversal exit mechanism and a few filters to keep it out of bad trades. Nothing fancy, no ML, no GPT nonsense. The results are decent — 5 out of 6 walk-forward windows pass (6-month windows, 70/30 train-test split), Sharpe around 1.86, max drawdown under 1.5%, profit factor 2.29. Tested across 3.25 years covering basically every market condition you can think of — bear market, ETF rally, ATH, that brutal crash earlier this year. I also ran a brute force optimizer over 47k parameter combos and the top results all cluster around the same values, which I think is a good sign. Anyway I'm not posting to brag because honestly I'm stuck on several things and could use some input from people who've been through this. The overfitting question So I tested 47,000 configurations and picked the best one. Even though it passes walk-forward, there's obviously selection bias. I've been reading about Deflated Sharpe Ratio (the Bailey & Lopez de Prado paper) and I get the concept but I haven't implemented it yet. Has anyone here actually done this? Did you combine it with Monte Carlo bootstrapping or was DSR enough? Mostly I want to know — when you applied it, did your strategies still look significant or did everything fall apart? Doesn't work on other assets I took the exact same parameters and ran them on 4 other crypto assets. Results were pretty bad honestly. One got 4/6 windows but the overall Sharpe was like 0.4 which is basically nothing. Another one showed promise early then completely fell apart in the second half of the data. One was 1/6 which is just a fail. Is this normal? Do most of you who run systematic strategies just accept that each strategy is asset-specific and develop separate ones? Or is there some way to make things generalize better that I'm missing? Right now I'm leaning toward just accepting it and scaling through leverage and maybe adding a second timeframe on the same asset. Regime detection This one bugs me the most. Two of my six walk-forward windows fail, and they fail on literally every single one of the 47k configurations I tested. Both are choppy sideways periods where the signals fire but price just doesn't follow through. I need the bot to recognize when it's in one of these regimes and just stop trading. I've been looking at HMMs and realized volatility switches but I'm worried about overfitting the regime filter itself. Has anyone built something like this that actually held up out of sample? What worked for you? Backtester is painfully slow My backtester is loop-based Python and the optimizer took about 5 hours for 47k configs on 113k bars. I know vectorizing with NumPy would help but my exit logic is stateful (tracks reversal signals, partial exits) so it doesn't vectorize cleanly. Anyone dealt with this? Did you go NumPy, Numba, Cython, or something else? Curious what kind of speedup you actually saw in practice. \--- If anyone's dealt with any of these I'd really appreciate hearing about your experience.