An AI is only as good as the data it works with. But which data sources do professional trading systems actually use? A look behind the scenes.

1. Price Data

The foundation of every trading AI: historical and real-time price data. This includes Open, High, Low, Close (OHLC), volume, and tick data. Quality is crucial — professional data feeds from Bloomberg, Refinitiv, or Quandl cost tens of thousands of euros per year but deliver cleaned, gap-free data.

2. Order Book Data (Level 2)

The order book shows where buy and sell orders sit in the market. Large buy walls at certain prices indicate support zones. The AI detects imbalances between supply and demand before they become visible in price.

3. Fundamental Data

Quarterly earnings, revenue growth, profit margins, P/E ratio, debt-to-equity — the AI analyzes hundreds of fundamental metrics and compares them with industry averages and historical values. Particularly valuable: detecting trend reversals in earnings development.

4. Macroeconomic Data

Interest rate decisions from the ECB and Fed, inflation rates, employment data, purchasing manager indices — macroeconomic indicators affect entire sectors simultaneously. The AI integrates the economic calendar and reacts to deviations from expectations.

As described in the sentiment analysis article, the AI analyzes news agencies, financial blogs, Twitter/X, and Reddit. Particularly important: the speed of processing. When a profit warning is published, the signal must be generated within seconds.

6. Alternative Data

The new gold standard for institutional investors:

Satellite imagery: Parking lot occupancy of retailers as a proxy for sales
Web traffic data: Visitor numbers of e-commerce sites before quarterly reports
Supply chain data: Ship traffic, container bookings as leading indicators for trade data
Patent filings: Innovation activity of companies

7. Options Data

The options market reveals what the professionals expect. Put/call ratios, implied volatility, and unusual options activity are valuable signals. When large investors buy put options en masse, it indicates expected price declines.

The Challenge: Data Integration

The biggest technical challenge is not any individual data source but the intelligent integration of all sources into a consistent model. Different data arrives at different frequencies (tick data every second, fundamental data quarterly), formats, and quality levels. The art lies in harmonization.

Bottom Line: The more high-quality data sources a trading AI uses, the more robust its signals become. But more data alone is not enough — intelligent processing and weighting make the difference.