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Comment by dperfect | Hacker News Reader

I ran some tests of my own with simulated automated trading strategies over the past few years of historical Bitcoin price data. I used a genetic algorithm to encode a range of time parameters for MACD, along with different trading triggers and amounts. After a day or two of running it against a huge number of permutations and generations optimized for highest overall return, I came to a similar conclusion: the fittest configurations could do fairly well in automated trading (the more active ones were highly sensitive to trading fees [which were simulated] and market depth [was not simulated]), but over the span of more than a few months, a simple buy and hold strategy produced better returns in almost any time window.

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Depending on the automated strategy, they can dramatically reduce risk though. That has value well beyond the simple return of buy and hold, where if you don't time the sell perfectly, it's all useless. I think that's the big advantage automated trading strategies can have if designed well.

Yes - I found that to be true in my testing as well. The configurations optimized for highest absolute returns did very poorly in unfavorable market conditions (and max drawdown became significant), while there were some that performed moderately well and were much less affected by downturns in the market. Like most investments, it really comes down to the same thing: risk vs return.

Reduce risk but also involve more time and energy.

Not really for an automatic trading algorithm. Write once, use for quite a while. Say a week of work (40 hours) for returns say 1/10th of holding is still quite worth it. Both of those numbers are quite conservative.

Not to be rude but I stopped reading at "MACD". TA is not the go-to strategy in stock trading for a reason. Indicators like MACD, RSI, Bollinger Bands etc re-paint a lot. So the perfect signals everyone sees on the charts are, for the most part, are just "profitable system" mirage.

Thanks. The conclusions may be obvious to experts in algo trading (and I heard similar things about the problems of TA for stock trading prior to doing this), but it was educational for me to go through the exercise because (1) it confirmed to me that the crypto markets behave similar to other markets, and (2) it helped me understand exactly where and how a technical indicator like MACD falls short for making trade decisions.

Before trying it myself, I was somewhat skeptical when reading about how these indicators don't work well. The overlays on charts always looked like good buy/sell indicators, so I didn't really understand why they couldn't make for a good algorithm. Only after trying it myself did I gain insight as to why they don't actually work so well.

I guess it's better to learn the hard way than to learn the hard-and-painful way with real money ;)

It isn't clear to me why technical indicators aren't viable.

> Only after trying it myself did I gain insight as to why they don't actually work so well.

Would you care to elaborate on which indicators you tried, and what happened?

From my experience with MACD specifically, it basically came down to the fact that with longer periods (or less sensitive thresholds), you lose out on most of a price swing by the time your trigger fires, and with shorter periods (or more sensitive thresholds), you end up making a lot of trades that don't move the needle (can also rack up significant fees if you aren't careful). You can find something reasonable in the middle, but by doing so, you're decreasing both risk and potential returns.

There are at least two other important considerations apart from the indicator itself: how to actually execute effectively with limited funds (when your trigger fires, do you trade everything you can, only a certain percentage, or some variable amount based on technical factors? - that's an entirely separate algorithmic rabbit hole); and even if you do find a good algorithm and it performs great in backtesting historical data, it's almost always harder to achieve the same results in real market conditions (it's difficult to simulate the spread and dynamics of limited market depth).

What is an example of a "go-to strategy in stock trading"?

1. Buy undervalued groups of stocks 2. Hold on to them for a few decades 3. Profit.

TA is "Technical Analysis", according to Google. You're telling me those indicators are NOT used by rules-based traders in the stock market?

The reason TA is a bunch of malarkey is because if somebody could simply look at a chart to predict future price movements than everybody else would do it to, making such prediction worthless. Same is true of anything else that is public knowledge. Once everybody has that knowledge, you lose the ability to profit off it.

It is one of the core foundations that underpin up modern finance (something that Bitcoin advocates would be keen to learn about...) - see also [Efficient Market Hypothesis](https://en.wikipedia.org/wiki/Efficient-market_hypothesis), [Modigliani and Miller Capital Structure Theory](https://en.wikipedia.org/wiki/Modigliani%E2%80%93Miller_theo...), or the [Modern Portfolio Theory](https://en.wikipedia.org/wiki/Modern_portfolio_theory).

In short, TA has refuted time and time again. It's complete garbage used only by amatures.

I can't argue on merit, and I agree that if everyone used it, it would be worthless. The insiders and professional market researchers would do best, then the group who uses TA, then the buy/hold crowd, then the shoot-the-rabbit sell-low-buy-high group.

I don't think TA is complete garbage, but I agree it's not a fundamental.

Is that another way of saying "the bubble hasn't burst yet"?

Well, the data did include Bitcoin's first rise above $1000 and subsequent crash (which most people called "a bubble"). If you had bought right before that one "popped", yes - you would have waited a while to be back in positive territory, so that's one time window where you might have lost money compared to the algorithm, but only if you were impatient.

I find this an impressive fit, did you come up with it on the spot or did you read about strategy simulation somewhere else ?

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