As others have said, "Average return is just one statistic". When trading, losses hit harder than wins. Go up 50% then down 50% and you're not even, you're down 25%. The degree of overestimation from this mean return -> "annualized return" calculation depends on what the returns distribution looks like. Here's the calculation used in main.js line 77 applied to a very extreme unrealistic example. I simulated 253 days…
Show HN: I discovered a trading algorithm that returns ~24.85% annually
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Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#92Earlier quoted context omitted.
Interesting My ML knowledge is somewhat rusty... does overfitting occur more often on models with many input parameters (ie.. neural networks). His algorithm seems very simple, without really using ML at all, it's more of just a procedural 1-2-3 step thing, with no actual learning. Can you explain how overfitting works into his algorithm?
If you really want the answer to this question on your own, try out his algorithm and you will see what happens. Either he's right, and you make the suggested returns in a year, or he's wrong and you slowly realize why. It will be a learning experience. Don't take anyone's word for it.
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#93As others have said, "Average return is just one statistic". When trading, losses hit harder than wins. Go up 50% then down 50% and you're not even, you're down 25%. The degree of overestimation from this mean return -> "annualized return" calculation depends on what the returns distribution looks like. Here's the calculation used in main.js line 77 applied to a very extreme unrealistic example. I simulated 253 days…
Where do you take that uniform distribution from? I don’t think any ETF would conform to that.
The uniform distribution is a pedagogical choice, to explain why OP's average return calculation is misleading.
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#94Earlier quoted context omitted.
It looks like it is mean reversion. This is my first time hearing that term. The way I discovered this algorithm was initially I wanted to buy the previous day's best performing sector ETF, with a hypothesis that the momentum would continue. But I learned that it actually ended up losing money. So I decided to inverse the algorithm. There are still a few optimizations I can test out, e.g. Buying the previous day's wo…
No offence, but how can you have spent any time trading without knowing about mean reversion? It's the most basic and well known phenomenon in trading, along with momentum.
I'm sure that in the ~60 seconds I wrote this post, some brilliant future software engineer just reinvented binary search.
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#95This is a simple trading algorithm I discovered that operates on the Vanguard sector ETFs. This backdating algorithm provides on average a return of ~0.0878% for each trading day, or ~24.85% annualized return assuming 253 trading days per year. ## The Algorithm This algorithm is really simple. 1. On day `n`, determine which ETF gave the highest return 2. On day `n+1`, short sell the previous day's highest performing…
I forked your project and added a financial metrics analysis package. .067 sharpe ratio, .11 sortino, largest drawdown was ~41% Not very good numbers. Fun stuff though! [0] https://github.com/maxto/ubique
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#96Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#97You don't say...
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#98Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#99If so, it would be clear that buying anything at the opening price is not easy.
Re: Show HN: I discovered a trading algorithm that returns ~24.85% annually
#100Earlier quoted context omitted.
I forked your project and added a financial metrics analysis package. .067 sharpe ratio, .11 sortino, largest drawdown was ~41% Not very good numbers. Fun stuff though! [0] https://github.com/maxto/ubique
If there a link to your fork?