⬇️ My Thoughts ⬇️

Currently, this is the most valued expert in my model factory pipeline

Not because it does anything useful, but precisely because it does not

The expert randomly emits positive(buy) or negative(sell) signals. This is a rough baseline for random behaviour. Very useful for the integration testing of the pipeline and identifying noise

My random forest looks much better after the clean-up

The feature matrix has been refactored to only include features from which the random forest will be able to learn more effectively. This should greatly reduce the noise

Now, it's time to optimize signals 😄

My ML model factory produced this amazing model that trades Bitcoin on 5-minute intervals, cut off at each 5th minute (very similar to Polymarket's 5 minute BTC markets) and it made over 200K trades in ≈7 months

Stats:
- 200K trades
- 6.69% profit
- 49% win rate

So I've pretty much discovered a coin flip 🤣

They say that AI agents will replace all white collar-jobs in the next 6-12 months 😄

So LLMs only have 5-11 months to learn about car washing.

This simple prompt breaks ChatGPT, DeepSeek, Qwen and Kimi. Works on Gemini though.

The random forest that guides my model factory needs refactoring

While the current architecture shows good prospects for generating profitable trading models, the random forest is currently a bit of a mess 😅 If you look at the image attached to the post, you can see why.

Since the random forest can’t extrapolate, features like the date and source segment will just make the forest learn on noise (it will train on association between those features, e.g. it’s not very useful to learn that volume in 2025 was higher than in 2024). But this was expected. The goal was to validate an architectural approach that could work. Now it’s time for optimizations and changes. Actually, changes before optimizations.

Another thing that I’ll get rid of is the hybrid spine for supporting data from multiple timeframes, as the model didn’t extract any meaningful alpha — and some of it has even confused the random forest. I’m sure there is way to make even with limited data, but I’ll be moving to testing it on dataset for which I have access to highly granular data (hello, Bitcoin!). So the logic of the data loader in the pipeline will be greatly simplified.

I’ll also make the labeler configurable, so that you can use the same set of features for different types of predictions. For example, you could have the “unconstrained” standard deviation labelling in the sense hat you can enter and exit the position at any time, to constrained logic scenarios such as the ones of Polymarket’s 5-minute price predictions on Bitcoin, where you can only enter a position at specific times, and the resolution happens at specific times (e.g. at the end of every 5th minute of the day, starting from 0).

The core logic of the ML pipeline remans the same.

China Banned Gold & Silver As Money In 1983

While it remained legal to hold gold as an individual in PRC, you couldn't:
- Use gold/silver as money (unit of account/pricing)
- Trade gold/silver privately (you could only sell it to PBoC)
- Lend or pledge (use as collateral) gold/silver
- Act as a dealer of gold, unless authorized by PBoC

So effectively, as an individual in PRC, you could only sell your gold & silver through the PBoC system, but it was fully lawful to own those metals. Only if you violated the rules above, authorities could force you to liquidate your gold to PBoC at a discount over the spot price.

This is a big contrast with the gold ban in the United States via Executive Order 6102 in 1933, which required all persons to surrender/liquidate their gold coins, bullion and certificates to the state. Moreover, in 1933 the U.S. government credited you $20.67 per oz gold that you surrender. About a year later, the same government that forced people to liquidate their gold at $20.67, proceeded to raise the price of gold to $35 per oz (at that time the price of the gold was fixed by the government, not determined by market price action like today). So the U.S. government made an instant ≈70% profit on the public's gold.

Gold Outperformed Bitcoin In The Last 8 Years

Let's go back in time. It's December 2017. You have $1000 to invest, and you have 2 options: gold or Bitcoin. Which one would you choose?

If you bought $1000 worth of BTC at ≈$20K/BTC in December 2017, you'd now have ≈$3500, or roughly a $2500 net profit.

If you instead bought $1000 worth of gold at ≈$1260/oz, you'd now have ≈$4000, or roughly $3000 net profit.

And throughout those 8 years of holding, gold's price never crashed by ≈50%, like it's more or less the norm with Bitcoin & rest of the crypto.

Bitcoin can be a great idea for your portfolio, but if you're so determined to HODL long-term, you may be much better off with gold.

My wife and I met the charging bull himself

He confirmed that he's heading towards commodities

China Also Banned Gold & Silver In 1983

But unlike in 1933 in the U.S., there was no global ban on the public owning gold & silver. The public was however not allowed to use it as money, collateral, lend it or trade it. Everything gold/silver related had to go through PBoC.

I'll cover this in more detail in a future post.

In 1934, the U.S. government had to legally raise the price of gold to make a ≈70% profit on it in ≈1 year

Today, gold goes up by more than 70%/year just by market forces 😄

I've been spamming you to buy gold since it was ≈$1800/oz

How USA Profited 70% On Gold In 1 Year

In 1933, the U.S. government purchased gold at $20.67/oz. In 1934, gold was valued at $35/oz, or ≈70% higher!

How did they do it? Easy:

1️⃣ Force public to liquidate gold at $20.67/oz
2️⃣ Raise the official gold price to $35/oz
3️⃣ Instant 70% profit 😄

Had the United States citizens not surrendered their gold in 1933 (which would be illegal to do!), they would be 70% richer just a year later. Instead, that wealth was forcefully transferred to the government, and the people who surrendered their gold became 40% poorer just a year later.