Every night the bot replays every signal its strategies fired, whether it traded them or not. That's 1,681 shadow trades on file, 1,643 graded so far. One rule, buy after a sharp dip (rsi_meanrev), looks genius on some funds and terrible on others. On SMH, the chip fund, it won 81.8% of 33 shadow trades, averaging +1.96% per trade. On SPY it won 87.8% of 49. On MUB, a municipal bond fund, the same rule won 4.2% of 24 and averaged -0.52%. On IEF, a Treasury fund, it won 8.3% of 24.
The plain-English version: a rule isn't good or bad on its own. It works where the thing being bought tends to bounce back after a dip, and fails where it doesn't. Stocks and chips have bounced. Bonds dip for slow, grinding reasons, and buying them cheap is just catching a drifting rock. The bot fires these on its own, and cells that stay this bad will lose their seat.
There's a second honest note. The same dip-buying earned +0.52% per trade in uptrends versus +0.12% in downtrends. The strategies mainly get paid when the tide helps, which is why SPY comparison matters more than a pretty win rate. A scout thread on r/algotrading made a related point: normalize the rule by volatility before calling a pattern real, because a quiet year can fake a regime change. That's logged as a test idea, not a result.
One more discipline note. The day-trade desk re-test came back NO-GO: no ticker-and-strategy cell clears the luck bar plus costs. The bot wants to day-trade and keeps proving to itself it shouldn't yet.