Your Backtest Is Lying to You
I evolved 1,513 strategies to snipe shitcoins off DexScreener. They printed +40% a month in simulation and made nothing live — here are the four ways a backtest manufactures an edge that was never there.
> be me. build an evolutionary bot to snipe fresh shitcoins off dexscreener.
> it breeds strategies and culls the losers — 1,513 of them across nine generations, over ~23,000 token-days of data.
> simulation prints +40% a month. strategy after strategy.
> push it live. it bleeds. every single time.
> the tempting move is to tune — better stop, better filter, blame the market.
> instead i tried to kill it, to prove the edge could never exist.
> lie #1, the stop that never fills. 9.3% of trades rug to about −90%, nowhere near the −7% the sim booked.
> lie #2, the moonshot. the top 1% of trades carry 95% of the gains. median trade: zero.
> lie #3, the winner's curse. breed thousands of variants and a few look like genius over 25 trades. pure luck.
> lie #4, the regime. the "edge" was just the weather the strategy was born into.
> so i re-screened honest: rugs eat the full loss, cap the upside, demand a median above zero, hold out half the data.
> 0 of 1,513 strategies survive. re-ran it today. still zero.
> then the part that ends the argument: the tokens that 10x are the tokens that go to zero. one population.
> every filter that trims the downside trims the upside in lockstep. safety and profit are the same dial.
> there was never an edge here. the machine's most valuable output was proving it, before it cost real size.
The Receipts · By the Numbers
Appendix · Further Signal
The Verdict
The most useful thing this ever produced wasn't a winning strategy — it was a clear, early no. A negative result you can trust is cheap; the same lesson learned live, in real drawdown, is expensive. I'd rather pay for it in compute than in capital.
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