u/SquallLionheart ·
Reddit — r/algotrading
· June 19, 2026 at 12:46
· ⬆ 18 pts
· 💬 41 comments
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AI Summary
Summary
The post describes using a K-NN similarity search with historical return resampling to simulate 1000 plausible price paths and derive quantifiable meta-data (e.g., bull/bear probability, expected return, tail risk).
The author is seeking feedback from others more experienced in ML-driven trading, implying the methodology is exploratory and not yet a live trading system.
Quality assessment: Speculation – the post presents a conceptual framework but lacks backtest results, position sizing, or specific instrument application; it is more of a discussion starter than actionable DD.
Score18
Comments41
Upvote %70%
▶ Full Post Text
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!