Load Huobi Korea data in Python using dltHub

Build a Huobi Korea-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Huobi Korea data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def huobi_korea_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.huobi.pro/v2/reference",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "currencies", "transact-fee-rate"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='huobi_korea_pipeline',
        destination='duckdb',
        dataset_name='huobi_korea_data', 
    )
    # Load the data
    load_info = pipeline.run(huobi_korea_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

Accelerate pipeline development with AI-native context
Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
Build Python notebooks for end users of your data
Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from huobi_korea’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

Market Endpoints: Endpoints related to market data such as tickers, trade history, and market depth.
- /market/tickers: Retrieves current market ticker information.
- /market/history/kline: Fetches historical kline data for a specific symbol.
- /market/trade: Gets the latest trades for a specific symbol.
- /market/history/trade: Provides recent trade history for a specific symbol.
- /market/detail: Gives detailed information about a specific market.
- /market/depth: Retrieves the order book depth for a specific symbol.
Account Endpoints: Endpoints that manage account-related operations.
- /v2/account/deposit/address: Fetches the deposit address for a specific currency.
- /v2/account/withdraw/quota: Checks the withdrawal quota for a specific currency.
Common Information Endpoints: Endpoints that provide general information.
- /v1/common/timestamp: Retrieves the current server timestamp.
- /v1/common/currencys: Lists all available currencies.
- /v2/reference/currencies: Provides reference information about a specific currency.
- /v2/reference/transact-fee-rate: Retrieves transaction fee rates for specified symbols.

You will then debug the Huobi Korea pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Read our full steps on setting up Cursor

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install "dlt[workspace]"
```
Initialize a dlt pipeline with Huobi Korea support.
```
dlt init dlthub:huobi_korea duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for Huobi Korea API, as specified in @huobi_korea-docs.yaml 
Start with endpoints currencies and transact-fee-rate and skip incremental loading for now. 
Place the code in huobi_korea_pipeline.py and name the pipeline huobi_korea_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python huobi_korea_pipeline.py and await further instructions.

🔒 Set up credentials

Auth information not found.

To get the appropriate API keys, please visit the original source at https://alphaex-api.github.io/openapi/spot/v1/en/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python huobi_korea_pipeline.py

If your pipeline runs correctly, you’ll see something like the following:


Pipeline huobi_korea load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset huobi_korea_data
The duckdb destination used duckdb:/huobi_korea.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

📈 Debug your pipeline and data with the Pipeline Dashboard

Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:
- Pipeline overview: State, load metrics
- Data’s schema: tables, columns, types, hints
- You can query the data itself
```
dlt pipeline huobi_korea_pipeline show
```
🐍 Build a Notebook with data explorations and reports

With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.
```
import dlt

data = dlt.pipeline("huobi_korea_pipeline").dataset()
# get "currencies" table as Pandas frame
data."currencies".df().head()
```

Extra resources:

Learn more with our 1h LLM-assisted coding course!