Discussing the article: "Active Learning: An EA That Only Asks for Human Confirmation on Its Most Uncertain Trades"

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Check out the new article: Active Learning: An EA That Only Asks for Human Confirmation on Its Most Uncertain Trades.

This article builds an uncertainty-based routing layer for an EA: an online logistic regression scores P(up), Shannon entropy measures uncertainty, and a streaming P² quantile enforces a fixed review budget. Signals auto-trade, auto-skip or go to a human checkpoint, replaced by proxy modes in the Strategy Tester. It is judged on a held-out window against a trade-everything baseline, matched-frequency and seeded controls, and an exact profit split. No edge is claimed.

Every discretionary trader knows the setups that are easy to take, the ones that are easy to skip, and the handful in between that make you pause. This article builds a routing layer around that instinct. A small online classifier scores the next move, entropy turns the score into an uncertainty number, and the signal goes one of three ways: trade it automatically, discard it as noise, or put it in front of a human checkpoint. A human cannot click buttons inside the Strategy Tester, so the checkpoint is replaced there by mechanical proxies.

Building the router is the easy half. The harder half is finding out whether it does anything, and a backtest is easy to fool: pick a threshold on a run, report that same run, and compare against a weak benchmark. So the article follows a test protocol. The entropy ceiling is chosen on 2026.01.01 to 2026.06.30 by a rule fixed before any held-out run, then everything is judged on 2026.07.01 to 2026.09.14 against four controls: a trade-everything baseline, a matched-frequency control that thins the baseline at random to the same trade count, 50 seeded coin-flip runs, and an exact split of profit between auto-traded and queried trades.

Author: Olamide Daniel Adebayo