To analyze financial data, you can chart the open, high, low, close, and volume (OHLCV) information for a financial asset. Using this data, you can create candlestick charts that make it easier to analyze the price changes of financial assets over time. You can use candlestick charts to examine trends in stock, cryptocurrency, or NFT prices.
In this tutorial, you use real raw financial data provided by Twelve Data, create an aggregated candlestick view, query the aggregated data, and visualize the data in Grafana.
Before you begin, make sure you have:
- Signed up for a free Timescale account.
This tutorial covers:
Setting up your dataset: Load data from Twelve Data into your TimescaleDB database.
Querying your dataset: Create candlestick views, query the aggregated data, and visualize the data in Grafana.
Bonus: Store data efficiently: Learn how to store and query your financial tick data more efficiently using compression feature of Timescale.
This tutorial shows you how to ingest real-time time-series data into a Timescaledatabase. To create candlestick views, query theaggregated data, and visualize the data in Grafana, see the[ingest real-time websocket data section][advanced-websocket].
The financial sector regularly uses candlestick charts to visualize the price change of an asset. Each candlestick represents a time period, such as one minute or one hour, and shows how the asset's price changed during that time.
Candlestick charts are generated from the open, high, low, close, and volume data for each financial asset during the time period. This is often abbreviated as OHLCV:
- Open: opening price
- High: highest price
- Low: lowest price
- Close: closing price
- Volume: volume of transactions
TimescaleDB is well suited to storing and analyzing financial candlestick data, and many Timescale community members use it for exactly this purpose. Check out these stories from some Timescale community members:
Keywords
Found an issue on this page?Report an issue or Edit this page in GitHub.