Want to make money with data? First, understand these four steps.

In the crypto market, quantitative trading relies on prediction signals as your weapon. But the truth is: most people's strategies collapse as soon as they go live, and the problem is often not the complexity of the model, but the lack of proper preparation beforehand.

Data preparation, feature engineering, machine learning modeling, and ensemble configuration—these four stages are indispensable. Many people only focus on stacking algorithms and applying the latest models, unaware that 70% of failures stem from the fundamental stages of data and features.

What exactly should you do? There’s a lot to handle in data processing: cleaning, alignment, denoising. Market data itself is full of noise, with a very low signal-to-noise ratio. Feature engineering is even more critical—how to extract predictive signals from raw data? This requires understanding both financial logic and technical details.

Different model families excel at different tasks during the modeling phase. Some are suitable for capturing linear relationships, while others excel at nonlinear patterns. Choosing the wrong one means that even the most refined parameter tuning is useless. The final ensemble configuration involves organizing multiple signals to improve overall signal purity.

A key insight: don’t just focus on total return prediction, but break down the sources of returns and model specific signals. Predictions based on this approach are more robust and interpretable.

For quantitative researchers, this methodology is worth serious study. Understanding the logic and technical details of these four stages is the foundation for building long-term, usable quantitative strategies.