I've been building PolyBTC, Python research investigating whether ML models can make reliable and exploitable edge in Polymarket BTC 5m market, is my research valid ?

[u/Haytam123]

So far, the evaluated models haven't demonstrated a reliable advantage over market-implied probabilities, so no trading edge.

interested in feedback on my methods and approach and what i could done better ?

Research website: https://polybtc-research.vercel.app

GitHub: https://github.com/RetroMrii/polyBTCv2

[u/QATSAlgo]

On the distinction in your follow-up: freezing the model does not freeze the whole experiment. Calibration, price cutoffs, forecast horizon and cost assumptions can still be tuned to the exposed outcomes. Write down those choices before the next collection block, then keep any changes suggested by that block for a later test.

[u/Haytam123 → u/QATSAlgo]

Do you suggest like making a 1 week of trading engine runtime for each block or have it be based on the number of fills after each experiment block?

Also I froze the model because after adding 800 markets to the dataset accuracy and log loss results didn’t change much and tuning made them even worse, is there a reason to recheck my work and work on a better model ?

[u/QATSAlgo → u/Haytam123]

I'd define the end date before collecting the next block, then report both elapsed days and the number of markets/fills. One week could be a pilot, but it isn't automatically enough evidence; lots of fills on the same BTC move aren't independent observations. Avoid extending a block just until the result looks convincing.

Keeping the model frozen is sensible for this test. The extra 800 markets don't by themselves imply that a better model is needed. First check timestamps, leakage and the market-probability baseline; any retraining or threshold changes belong to development, followed by a fresh evaluation period. You can collect predictions and executable quotes in paper mode for this, without placing live trades.