ML for future price distribution

Hey, I have a big interest in deriving "actionable intel" from data. I am pretty new in the area and constantly learning as I go. The image is an output of K-NN similarity search with historical return resampling. It is simulating 1000 plausible price paths and finding the median. This is a nice visual, but what is more useful is quantifiable meta-data that can be discerned from it... "features": { "bull_probability": 0.09, "bear_probability": 0.91, "expected_return": -0.025426595630122065, "median_return": -0.026664237238893884, "tail_risk": -0.04825986706065677, "volatility_forecast": 0.0033507490744171444, "drawdown_probability": 0.45, "breakout_probability": 0.215 }, I would love to hear from anyone who is further down the ML path or uses ML derived data in their algo stack!