Feature Engineering and Data Preprocessing
Before building any AI model, data processing is the most critical step. Raw market data is often messy and unusable directly; it must be converted through feature engineering into input formats the model can understand.
This process is not just about “cleaning data,” but also about structuring market information. For example, converting price sequences into returns and volatility, turning on-chain data into capital inflow and outflow indicators, or transforming sentiment data into quantitative scores.
Common data processing steps include:
- Removing outliers and noisy data
- Standardization and normalization
- Constructing time windows (such as sliding window features)
- Multi-source data integration (on-chain + order book + sentiment)
The quality of feature engineering often directly determines the upper limit of the model. Good features can significantly improve predictive ability, while low-quality data renders even the most complex models meaningless.
Supervised Learning and Time Series Prediction Models
After processing the data, the next step is for models to learn patterns from historical data. Supervised learning is currently the most common method; its core is using existing data (inputs and outcomes) to train models to predict future trends.
In the crypto market, most problems can be viewed as time series predictions, such as price movements, volatility changes, or trend continuation. Models learn patterns from historical sequences to make probabilistic judgments about the future.
Common models include linear regression, random forests, and more complex deep learning models (such as LSTM, Transformer, etc.). Each has its strengths and weaknesses, but their essential goal is the same: extract repeatable patterns from historical data.
It’s important to note that since market conditions constantly change, models don’t “predict the future” but make optimal estimates based on historical experience. Therefore, continuous updating and dynamic training are key to maintaining model effectiveness.
Reinforcement Learning in Trading Strategies
If supervised learning focuses on “prediction,” reinforcement learning is more about “decision-making.” It interacts with the market environment, continuously tries and optimizes strategies, learning which actions are optimal under different circumstances.
In trading scenarios, reinforcement learning models are usually designed as an “agent” aiming to maximize long-term returns. It decides whether to buy, sell, or hold based on market conditions and adjusts its strategy according to results.
Advantages of reinforcement learning include:
- Direct optimization of trading outcomes (rather than simply predicting prices)
- Adaptation to complex and changing market environments
- Support for dynamic strategy adjustment and self-evolution
However, it also faces high training costs and strict requirements for environment simulation. In practice, reinforcement learning is often combined with other methods rather than used alone.
Signal Generation and Strategy Evaluation Methods
After training the model, the ultimate goal is to convert its output into actionable trading signals. This step maps predictions into specific actions such as “buy,” “sell,” or “wait.”
Signal generation isn’t just simple threshold judgment; it also involves risk control and adapting to market conditions. For example, reducing position size during high volatility or lowering trading frequency when trends are unclear—these are signal-level optimizations.
For strategy evaluation, the focus isn’t solely on “profitability,” but on balancing risk and return. Common evaluation metrics include:
- Return rate and annualized yield
- Maximum drawdown
- Sharpe Ratio
- Win rate and profit-loss ratio
These indicators help assess whether a strategy has stability and sustainability—not just good short-term performance.
