Tick data, real-time data, period data and end-of-day data compared. Which data type suits trading, analysis, backtesting or long-term investments depends on your goal.
This overview shows what types of market data exist, their pros and cons and what they are suited for.
Market data comprises all the information generated in the financial markets – the foundation of all market analysis and trading decisions.
Not every market participant needs the same information. A long-term investor has different requirements from active traders – which is why market data is provided in different resolutions and levels of timeliness.
Each serves its own purpose – a brief overview here, detail below.
OHLC candles per time interval – the basis of charts.
As soon as a transaction occurs, the information is transmitted immediately. Ideal for day trading, futures, scalping, market monitoring and trading systems.
Typically 1–20 minutes of delay. Commonly used for market monitoring, education and long-term analysis.
Tick data is the highest data resolution: every single price change is stored – with timestamp, price, volume, bid and ask. Ideal for backtesting, order-flow analysis, quantitative models and futures trading.
Period data aggregates multiple market moves into fixed time units. Instead of storing every price change individually, a time period is condensed – the basis of most charts.
Many individual ticks thus form, for example, a single 5-minute candle.
End-of-day data (EOD) stores exactly one record per trading day, consisting of open, high, low, close and volume. Ideal for long-term investments, trend and fundamental analysis and portfolio management.
Order book data shows not only executed transactions but also currently open buy and sell orders – with liquidity, supply, demand and market depth. Also known as Level 2 data.
| Data type | Timeliness | Detail level | Suited for |
|---|---|---|---|
| Delayed data | 1–20 min old | Medium | Market monitoring |
| Real-time data | Current | High | Trading |
| Tick data | Current | Very high | Backtesting, order flow |
| Period data | Current / hist. | Medium | Technical analysis |
| End-of-day data | End of day | Low | Investments |
| Order book data | Current | Very high | Futures & order-flow trading |
The optimal basis depends on the strategy: tick data for scalping, futures and intraday; period data for technical analysis, swing trading and trend-following; end-of-day for long-term systems.
Automated systems usually need real-time data, tick data, historical data and an API to calculate and execute strategies.
The right data type should always match the trading style – here are the most common mistakes.
There is no single best data type – the right choice depends on your goal.
| Goal | Suitable data |
|---|---|
| Day trading | Real-time + tick data |
| Futures trading | Real-time + order book |
| Swing trading | Period data |
| Backtesting | Historical data |
| Long-term investments | End-of-day data |
| Automation | API + real-time data |
The most important types are real-time data, delayed data, tick data, period data, end-of-day data and order book data. They differ in timeliness and level of detail.
Period data aggregates multiple price moves into fixed time units (e.g. 1-, 5- or 15-minute candles). OHLC values are stored per period: open, high, low and close. They are the basis of most charts.
Tick data stores every single price change and offers maximum resolution. Period data condenses many ticks into one candle per time interval – clearer and more storage-efficient, but less detailed.
Day traders use real-time and tick data, futures traders additionally order book data, swing traders period data and long-term investors end-of-day data. Historical data is suited for backtesting.
There is no universally best data type. The right choice depends on the goal: timing-critical strategies need real-time and tick data, long-term approaches can manage with end-of-day data.
From simple end-of-day data to high-resolution tick and order book data: anyone who understands the differences chooses the right data supply and makes better decisions.
Long-term investors often get by with daily data, while active traders and quants need significantly more detailed information.