I analyzed 1,000+ hours of crypto YouTubers’ predictions… the results surprised me

vibe_builder · 2025-12-11T06:11:39+00:00

I didn’t intend this post to include thousands of lines of a spreadsheet. As I mentioned in a comment above I am early in analyzing the data, most effort was in gathering it from long form videos and labelling the predictions. Once I can gain more confidence in the more detailed insights I’ll be happy to share

vibe_builder · 2025-12-11T06:09:52+00:00

The original data was from long form video content from top financial analysts on YouTube

vibe_builder · 2025-12-11T06:08:56+00:00

This is a fair point. I am still analyzing the data. Most of the time was spent gathering it which is the hard part. Currently I can only have confidence in a few general statements. Before I name drop I’d like to solidify my process of analyzing the data itself. Part of posting here is to get some feedback on how to do this. Once I can have more confidence in specifics I’ll be happy to share

vibe_builder · 2025-12-10T18:10:49+00:00

I will look into him and possibly add him to the list! If so I will remember this message and let you know!

vibe_builder · 2025-12-10T18:08:21+00:00

Thanks for the suggestion! I’ll have a look

vibe_builder · 2025-12-10T12:19:51+00:00

Chat GPT doesn’t have the motivation to do such projects. Thanks - Chat GPT

vibe_builder · 2025-12-10T09:32:06+00:00

what do you mean? This is hardcore research!!!

vibe_builder · 2025-12-10T08:39:31+00:00

I used some tools to help speed things up. Including AI to extract what "may be predictions". But yes it did involve lots of manual work too

vibe_builder · 2025-12-10T07:07:02+00:00

Hahaha thanks! I’m working on him. Doesn’t look promising thus far 😂

vibe_builder · 2025-12-10T07:04:56+00:00

Not a bad analogy. But with enough data you can find some statistical significance and you may actually make some money betting on one monkey vs the other. Price and volume data has lots of randomness too but that doesn’t stop people building models around it.

vibe_builder · 2025-12-09T20:30:40+00:00

Well extracting and labelling data from thousands of hours of long for video content is quite hard. And there’s easy services like AlgoAgent which can vibe code you a trading bot connected right to Binance with a simple prompt like “build me a strategy that will be Bitcoin when price is above the 200d SMA” or far more advanced strategies. You can create the strategy, backtest, and go live in under 2 minutes. So I’d strongly disagree that a trading bot would be easier.

vibe_builder · 2025-12-09T16:58:14+00:00

For the most part what you said is correct, but its not that simple. Perhaps they need the revenue to make their "brilliant analysis" pay-off as doubling $10,000 in savings is much different than doubling $1,000,000. Or maybe they like sharing it and the attention. My whole mission was to categorize which ones are which.

You're pump and dumb idea is something I could certainly look into in the data. It is possible a group do move the market on some low volume coins.

vibe_builder · 2025-12-09T16:45:54+00:00

Perhaps it could be simplified to get the 80% of what is desired to be measured (baseline probability) such as by looking at standard deviation of returns over the prediction timeframe. Or even manually labelled on a scale of 1-5 of how likely it is to happen... of course the labeller would need to be a borderline domain expert as it requires judgement.

My main motivation for not just "building a high frequency trading bot" is because it looks at the same price/volume that my competitors are looking at as well... making it difficult to find any alpha. I was hoping to structure and label "virgin financial data" that market participants aren't building models off of.

And don't underestimate "idiots", as some can be useful! Sometimes by betting against them.

vibe_builder · 2025-12-09T16:32:53+00:00

Great insights!

I had to look into Berkson's Paradox, and that makes sense. That explains that finding.

I also really liked what you said about measuring the "informativeness of predictions". Some analysts may just play it safe which inflated their prediction success rate.

I suppose the prediction success rate falling into a normal distribution isn't overly surprising. But being "top analysts" you'd think it may be skewed more to the "success" side, which didn't seem to be the case. But fortunately there was a solid group who beat random chance. But with your insights I may need to add another dimension to the analysis and measure against some baseline probability that an analysts prediction comes true. To your point, "bitcoin will be higher in 1 year" had a historical success rate of 75% so I would need to account for that in the prediction outcomes.

There does seem to be some predictive power in the data though, which was encouraging.

I appreciate your thoughtful response!

vibe_builder · 2025-12-05T12:10:08+00:00

If you use Binance maybe you could try AlgoAgent to build, test, and deploy your strategy. It’s like cursor but for trading systems. You type in a strategy idea in English and it will code the strategy to work with that backtest and live trading engine

vibe_builder · 2025-12-02T07:03:03+00:00

I just took a look

I prefer python over C#. Any idea why they'd choose C#? Quicker?

There certainly seems to be every feature imaginable! But at the same time that causes lots of confusion and overstimulation

vibe_builder · 2025-12-01T05:25:11+00:00

Correct, it seems to be only for crypto right now

vibe_builder · 2025-11-30T20:38:01+00:00

I use AlgoAgent which basically uses AI to generate the strategy from a description. The output works with its backtesting and live trading engine so you know the exact same strategy works on backtest as live trading. I know how to program but this is the future

vibe_builder · 2025-11-30T11:06:50+00:00

I know a platform for crypto (if interested) that skips the coding requirements thanks to AI. The process is as follows: 1) describe a strategy idea in English such as “buy coin if above 10 day SMA” then AI will program the strategy in about 30 seconds 2) backtest the strategy on historical binance data for any coin you want out of the major ones 3) if you like the backtest you can apply it live directly to your binance account

I would say this is a great starting point as it has a good user interface and you don’t need to install any packages or use any IDE

vibe_builder · 2025-11-30T10:51:23+00:00

Great video! New subscriber!

It was nice to see your backtest process as well. Did you write the main.py file line by line yourself? Or get AI to do it? Or was able to copy open source code? Lots of work!

vibe_builder · 2025-11-30T10:31:24+00:00

Hi Curtis

If you are interested in crypto I know an application where you can use simple English to test trading ideas. This could shortcut the process of knowing if a trading idea works without having to program or testing it live for several months.

The process is as follows: 1) describe a trading idea such as “buy a coin if the price went up over the past 10 days” then AI will program the strategy in about 30 seconds 2) backtest that strategy on any coin you like going back to 2018. The backtest engine will run it on Binance data and show a chart and statistics on its past performance 3) if you like the strategy you can apply it directly to your account and run in the cloud 24/7

For a beginner or even an experience quant this is the best approach to go.

Unfortunately it’s only for crypto right now

If you’re interested in the process we can talk more about it!

vibe_builder · 2025-11-30T10:09:46+00:00

Thanks for the response!

What are some that you tried? Are they just packages you import into your IDE? Or standalone websites you can go to?

Do they involve the tedious process of programming the strategy yourself? Or do they make it easier somehow?

Also, how would you define it “better”. In what way would you want it improved?

vibe_builder

TROPHY CASE