I had a simple trading structure that I summarized from a few sources. Curious what you might think of it.
1. Filter all underdog stocks.
2. Have a catalyst detector - e.g. OpenAI announces new model. Link that to NVIDIA, MSFT, etc.
3. Among these stocks, when you see a marubozu break through the previous resistance point, buy the stock. Then sell it near peak. (Need a peak detection algorithm)
The trick is having all three work fine. But it's easier to debug and test when one doesn't work if you break it into parts. You also don't need one really good methodology, you just need a few decent ones.
Or you could find similar patterns that can be broken down into small parts and do those too.
In realiity, trading is hard because it requires 2 points where you have to get the timing right.
First you have buy the position at a time when it is favorable. Then you have to exit the position when you get profit. Sounds easy, but the hard part is that this profit also has to cover any past losses. where you failed timing the entry into the position.
Its easy when the stock goes up 800% and then falls 50% to 400% your original position, that you should have sold when it was at its "peak". But along the way to 800% you had so many times to sell for 500%, 600%, 700%, etc. and along the way the stock had fluctuations with many peaks.
If you sell to early you can't get enough profit to cover past and future losses. If you sell to late same story. So you have to nail the exit position also and that is where most models that rely on past data fail. People just walk through the parameters until the entry and exit positions on their test data line up to make a profit, but then can't replicate when going "live".
Another way to look at investing is everyday you are in the market, it is almost the same logically as selling and choosing to reinvest every day. So if you hold AAPL for 5 years, that is about 1000 days where the algo is choosing to invest (i.e. keep invested) in AAPL. Its pretty tough to have that many decisions points because even 0.1% noise would cause you to sell.
What we did was compare it to the previous peak, and then 1.1x that or so. That works well for trading breakthrough patterns.
If you're going long term, then it's quite different because there may never really be a peak. There might be corrections or something every now and then, but they whole idea behind long term is that these don't matter.
I would actually tie it to the other two - you'd have to detect when a stock is overpriced, and you'd have to detect catalysts.
Still tough. Meta sunk on their rebranding not so much because of the metaverse but because FB had been dead for a while. But the announcement was the catalyst. If Meta had been underpriced, it would have been a positive catalyst and people would have applauded the metaverse. Which would probably have triggered catalysts on RBLX, MSFT, and other metaverse players.
NVDA had been underpriced for a while, and the multiple catalysts have made it shoot up, though it's possible another one in the future could make it crash.
So IMO it's not just one point, the other detectors also help to filter the noise.
1. Filter all underdog stocks.
2. Have a catalyst detector - e.g. OpenAI announces new model. Link that to NVIDIA, MSFT, etc.
3. Among these stocks, when you see a marubozu break through the previous resistance point, buy the stock. Then sell it near peak. (Need a peak detection algorithm)
The trick is having all three work fine. But it's easier to debug and test when one doesn't work if you break it into parts. You also don't need one really good methodology, you just need a few decent ones.
Or you could find similar patterns that can be broken down into small parts and do those too.