It's simple statistics. The author was up 4k/day over 120 days. He doesn't say what his daily volatility was, but let's assume 2k (which squares pretty well wit

It's simple statistics. The author was up 4k/day over 120 days. He doesn't say what his daily volatility was, but let's assume 2k (which squares pretty well with his claim that his worst day was a 2k loss). With a quick bit of R code, we can simulate his PnL over 120 days multiple times, assuming he has no skill, and see what the probability of him being up 4k/day is. I'll use a t-distribution with 3 degrees of freedom, which allows big up and down swings (again, accentuating the effect of luck). > pnl <- c() > for (i in 1:1000) pnl[i] <- mean(2000 * rt(120, df=3)) > mean(pnl > 4000) 0.0 That is, there's a zero percent chance that he would have made those returns if he had no skill. And remember that this simulation is overestimating the effect of luck.