# How to Use AI for Quantification and Backtesting in Crypto Prediction Markets

Let's talk about some practical stuff today.

Let's use 5-minute or 15-minute up/down as examples.

For instance, many people's strategies in prediction markets are very simple and crude.

In the final few minutes before an event ends, when the price reaches 90-95, they directly buy in. This is commonly called "sweeping the tail end of the market."

First, can you make money by sweeping the tail end? Absolutely. But you can't do it this simply and crudely.

Sweeping the tail end pursues extreme win rates. And to pursue extreme win rates, you must filter. You can't use simple conditions as a blanket approach.

So how do you pursue extreme win rates? The prerequisite is that after you buy in, the price cannot reverse.

Thus, the method is to instruct AI to do data analysis.

First, fetch K-line data from the past 1-2 years and feed the data to AI.

Tell AI: I need you now to calculate and design any solution. Screen out instances where buying before the 15-minute candle closes won't result in price reversal.

(If you think "won't reverse" isn't solid enough, add another condition: the closing price must have a certain distance from the opening price. You can keep adding. It's up to you.)

Use the existing K-line data to design and combine solutions.

Then, to improve efficiency, you have AI install a few convenient open-source frameworks for backtesting.

AI will then conduct fierce calculations and analysis. Finally, it will organize the results for you. It will tell you what algorithms it used and what results it obtained.

You don't need to manage anything. Just let AI run idle and do its operation. AI will organize the results for you.

This process is the most time-consuming. You'd better prepare several AIs to run simultaneously.

Finally, write everything into rules. These become factors. You'll get numerous factors. Each factor in the backtest points to results that won't reverse or has an extremely low probability of reversal.

When a certain factor is triggered, you simply execute the trade. That's it.

That's all there is to it. Throughout the entire process, you don't need to understand anything.

The above is the conventional way to do quantification and backtesting in prediction markets using AI.

However, achieving stable profitability ultimately requires live trading to verify. For instance, issues like fees, slippage, order book prices, and overfitting problems.

These cannot be factored in. Because you can't obtain prediction market data.

The above hopefully provides some help to those who only know how to play around carelessly.