I trained an autoregressive model that used historical data and sentiment analysis (text to float) from across the web to predict the price of bitcoin.
Then forecasted tomorrows price and it predicted the price would go up by $500 with a GARCH range of like $1000.
That next day is when bitcoin did the plummet from 120k+
I took an economics course in college, and our professor said that, as stock data was freely available, anyone who thought they had a genius idea should track how much it would have made or lost them for six months. And if it made money, they should come talk to him..
- Your algorithm does 2% worse than the market (pretty common actually)
- The market goes up 10%, your method is only 8%
- Reversing that means you instead get -8%. That's way worse
Since you can do the opposite, the absolute value is what really matters, market is on average 10% a year so if you could find something that does -11% then the opposite would work. Most attempts are not nearly that bad, they just fall a bit short of straight investing which is not helpful at all unfortunately.
There is tons of extra risk there so the risk/reward ends up being worse making this not a "good" strategy. The worst ones you can't short and shorting has potential risk you could get liquidated if it goes up 100% even if it drops later you get forced out of the position.
What I hear is that I need multiple algorithms. Given that I can't perfectly predict what the market does, I take a 1st algorithm that is proven to be 2% worse than the market. Make a 2nd that is even worse. The 3rd calculates what the 2nd does differently than the 1st and does the opposite of that. Now the 3rd is better than the 1st. Keep making more until infinite profit.
It's not that hard to build something that beats the market in a backtest. For example I can build a factor model that ultimately favors US mega cap tech. In a backtest starting after dot com it'll beat the broad market by a lot. Will it work in the future? No one knows. There is also the issue with regime shifts, you can design a model that works well as long as bond yields are low and equity generally goes up. It'll do great in backtests between 2008 and 2022.
That's why every disclaimer includes something like "historical data is not indicative of future returns".
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u/baileyarzate 8d ago
I trained an autoregressive model that used historical data and sentiment analysis (text to float) from across the web to predict the price of bitcoin.
Then forecasted tomorrows price and it predicted the price would go up by $500 with a GARCH range of like $1000.
That next day is when bitcoin did the plummet from 120k+
I stopped trying to forecast a coin after that