u/parrot_chirik

I wanted to answer what I thought was a fairly simple question:

Does order-book microstructure contain enough information to beat Polymarket's 5-minute crypto markets after transaction costs?

Turns out the harder part wasn't training the model.

It was getting the data.

As far as I could tell, there wasn't any public historical dataset with tick-level quotes, trades and order-book updates for these markets, so at the end of June I started collecting everything myself.

The archive now covers BTC, ETH, SOL and XRP 5-minute markets.

For every trading day it contains best bid/ask quotes, trade prints, spot prices, and (from 20 July onward) full L2 depth. Everything is stored as daily Parquet files with manifests and measured uptime for each day.

Once I finally had enough history, I trained a regime-routed LightGBM ensemble using only information available at the decision timestamp.

The disappointing part is that the model actually behaves reasonably well.

Calibration is good.

Out-of-sample Brier scores are consistently around 0.06–0.10 depending on asset and market regime.

But the strategy still loses money.

Across roughly 4,300 simulated trades it finishes around -$277.

The hit rate is about 47%.

After spread and fees you need something closer to 49% just to break even.

That two-percentage-point gap turned out to be the entire problem.

Looking back, most of the important work wasn't machine learning at all.

I found that my simulator had been using fees that were 3.5× too low.

One validation gate wasn't actually filtering anything because of a bad threshold.

At one point the trading system quietly stopped opening new positions because several expired markets hadn't been marked as resolved yet, while every monitoring dashboard still showed the process as healthy.

Those bugs changed the conclusions far more than another week of feature engineering.

I'm curious whether anyone here has managed to build a strategy on prediction-market microstructure that survives realistic execution costs.

If you have, I'd genuinely like to hear what worked.

And if anyone wants to reproduce the experiment or try a completely different approach, I'm happy to share the dataset. It now contains a little over a month of tick-level history and keeps growing every day.

I wanted to answer what I thought was a fairly simple question:

Does order-book microstructure contain enough information to beat Polymarket's 5-minute crypto markets after transaction costs?

Turns out the harder part wasn't training the model.

It was getting the data.

As far as I could tell, there wasn't any public historical dataset with tick-level quotes, trades and order-book updates for these markets, so at the end of June I started collecting everything myself.

The archive now covers BTC, ETH, SOL and XRP 5-minute markets.

For every trading day it contains best bid/ask quotes, trade prints, spot prices, and (from 20 July onward) full L2 depth. Everything is stored as daily Parquet files with manifests and measured uptime for each day.

Once I finally had enough history, I trained a regime-routed LightGBM ensemble using only information available at the decision timestamp.

The disappointing part is that the model actually behaves reasonably well.

Calibration is good.

Out-of-sample Brier scores are consistently around 0.06–0.10 depending on asset and market regime.

But the strategy still loses money.

Across roughly 4,300 simulated trades it finishes around -$277.

The hit rate is about 47%.

After spread and fees you need something closer to 49% just to break even.

That two-percentage-point gap turned out to be the entire problem.

Looking back, most of the important work wasn't machine learning at all.

I found that my simulator had been using fees that were 3.5× too low.

One validation gate wasn't actually filtering anything because of a bad threshold.

At one point the trading system quietly stopped opening new positions because several expired markets hadn't been marked as resolved yet, while every monitoring dashboard still showed the process as healthy.

Those bugs changed the conclusions far more than another week of feature engineering.

I'm curious whether anyone here has managed to build a strategy on prediction-market microstructure that survives realistic execution costs.

If you have, I'd genuinely like to hear what worked.

And if anyone wants to reproduce the experiment or try a completely different approach, I'm happy to share the dataset. It now contains a little over a month of tick-level history and keeps growing every day.

I've recently joined this community and have seen many recipes for meals prepared days or weeks in advance. So, I wondered, how do you eat them? Because, in my opinion, if a dish is refrigerated for several days, it becomes tasteless or has a terrible texture after reheating. I can't imagine freezing an entire dish (unless it's soup or pasta sauce, etc.) and having it taste as good as freshly prepared.

I prefer to do meal prep the way it's done in restaurants (preparing and cutting chicken, beef, pork, you name it) and then cooking it when needed. You wouldn't be able to tell the difference between fresh chicken and chicken that was frozen, as long as it was thawed properly. But it's possible because only part of dish was "prepared" in a correct way separatly from other ingridients comparing to freezing whole meal.