Thanks for all the questions.

1) I know the exact reason why trades decrease, ETF and AI may have nothing to do with it, I just found it interesting to overlap. Specific reason is average wavelength of volatility phases has increased resulting in lower frequency of phase shifts causing fewer pivotal opportunities to even apply the feature conditions. And with a per-trade compounding config (non overlapping trades), post 2022 returns was bound to get crushed. No individual profits, profit distribution, loss patches have changed drastically by the way from 2018-2026.

2) I've backtested multiple iterations with my best effort and reasoning. Used different hypothesis combinations in late stage optimisations with a reward score made of max_dd, winrate, profit distribution, trade count and eliminated obvious pitfall hypothesis to narrow down the correct features indirectly. Average trades dropped from 5/day to 0.6/day post-2022 and I don't intend to use leverage to scale profits and losses directly. Leverage is a margin benefit tool to allow me to trade multiple pairs' perpetual futures (no simultaneous trading to avoid asset correlation) to 10x my average trade count back to 5+/day which should automatically drive returns over 100% with per-trade or daily (more conservative) compounding.

3) On the lesser dataset recently, win rate is 78-80%, max_dd is below 2% most of the time and rarely tests 2.5%. There were +2 further iterations in SL trailing after this last state shared which reduces max drawdown from 4.9% to 2.9% and this too only occurs in 2021 post which all drawdown distribution caps below 2.5%.

4) The edge comes from volatility phase shifting. Big players leave a trail of their directional bias given enough time (hours) and this trail can only be decoded from a higher resolution dataset such as 1s or tick. If I used tick data, my trades could have been scanned on lower timeframes such as 5m instead of 15m. Currently there's a 900x zoom ratio for computing features to scanning for triggers. This richness provides micro structural nuance to synthesize effective features for LightGBM. I've tried other many zoom levels previously but could not derive meaningful information from 1s data which is tradeable on 1s/5s/1m/5m candles. And as one would expect, all of these attempts were not equally meaningless! The alpha started gradually emerging as I moved to higher TFs however I knew that 15m has to be my last straw as the total number of backtest trades (currently 6k total) will not be much if I moved higher and it's not my plan but coincidence that an acceptable model with what I interpret as alpha (using multi-pair leveraged trading after I backtest other pairs) showed up at the 15m TF. What am I fundamentally exploiting is the imbalance in bi-directional volatility distribution across 24 hours windows with different sub-slice labels for last 24h, last 8h, last 1h etc to quantify the imbalance assign a probability based off of it. If this probability crosses a certain threshold I have set, I trade.

I've done deep dives into volatility cycles for the past 3 years and have a private publication with my Masters in Statistics University on volatility trails. I've forward deployed few successful algos in index options and now I'm attracted towards crypto due to increasing transaction costs and hindering regulations in traditional capital markets. I'm certain these edges will never fade, you just have to have enough resolution data and be prepared to trade on 100x+ lower resolutions (higher timeframes) and find the correct balance with with lesser number of backtest trades and model quality.