A Random Walk Down Wall Street is a great book and one I think every day trader should read. At the heart of it is the Efficient Market Hypothesis, which basically says that asset prices reflect all known information and thus it is impossible for a trader to beat the market over the long term (on a risk-adjusted basis).
The strongest (and most controversial) form of the hypothesis is that even professional traders cannot consistently beat the market. Even if you don’t agree with that, it seems clear to me that professional traders will always be able to obtain, and analyse, new information before “hobbyist” traders, and therefore by the time a hobbyist trader gets relevant information it will already be priced in.
That book convinced me that the best way to invest is just to dump money into low-fee, highly diversified ETFs and forget about it. I’m not saying others would necessarily have the same reaction, but at the very least I think an aspiring day trader should read it and be able to justify why they don’t think the points made in the book apply to them.
Hard agree; I think academics and programmers are especially susceptible to this. It's an addiction that hooks in intellectual hubris, a condition I have some experience with.
I tried playing a bit with some small money I was ok with losing. I didn't lose much, but realized in short order that there was no way I could ever get enough info to make a rational decision. Nearly every method/recipe/chart that I saw showed analysis after the fact. Welp, I can take anything in hindsight and make a story that fits the picture. But I never felt like there was any way to be predictive. I was only ever going to be flying blind.
I never stepped even a tiny bit beyond my tiny attempt at trading. I learned my lesson. I'll just stick to my index funds/401k/retirement plan and hope for the best.
I spent a lot of time learning from traders, and learning statistics. Most folks in trading use misleading profit and loss metrics to see if something is worth trading. I used the same kind of backtests, but I layered Bayesian inferencing on top of it.
I studied machine learning with Andrew Ng's courses, studied deep learning with Ian Goodfellow's book. Most importantly I took a course run by university professor and researcher in anthropology, Richard McElreath. I did my best to faithfully apply what I learned, though I am sure I strayed from academic standards.
At that point I had been doing this for years, for countless hours. It was my only hobby, and I dive hard into hobbies.
I tried my damnedest to be predictive every which way. I kept meticulous records to avoid fooling myself. Sometimes my models fooled me, and sometimes they combined with luck for my records to fool me. Long term, it's pretty clear. No evidence of any edge, ever, for any approach taken.
At the end of all of this toil and labour, I have the skills I learned along the way: statistical skepticism, a hands-on understanding of fat tails, an appreciation for the experience of randomness and the highs and lows of gambling. I think that's worth a lot - but I also think you can learn that a lot easier some other way.
I have done very well with buy and hold, it's fantastic. There's some bullshit in how you assign your portfolio - what proportions of what exposures - but its very profitable and exceptionally low stress compared to trading. It definitely has a better Sharpe/sortino/ulcer metric.
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