Load ApolloX Finance data in Python using dltHub

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

In this guide, we'll set up a complete ApolloX Finance 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 apollox_finance_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://fapi.apollox.finance/fapi/v1/",
            "auth": {
                "type": "hmac",
                "algorithm": "sha256",
                "key": api_secret,
            },
        },
        "resources": [
            aggTrades, depth
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='apollox_finance_pipeline',
        destination='duckdb',
        dataset_name='apollox_finance_data', 
    )
    # Load the data
    load_info = pipeline.run(apollox_finance_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 apollox_finance’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 Data: Retrieve aggregate trades, order book depth, exchange information, and ping for connectivity
  • Price & Index Data: Access funding rates, premium index, and mark price data
  • Klines/Candlesticks: Get candlestick data for regular prices, mark prices, and index prices
  • Trade History: Fetch historical trade records and aggregate trade information

You will then debug the ApolloX Finance 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:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with ApolloX Finance support.

    dlt init dlthub:apollox_finance duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for ApolloX Finance API, as specified in @apollox_finance-docs.yaml 
Start with endpoint(s) aggTrades and depth and skip incremental loading for now. 
Place the code in apollox_finance_pipeline.py and name the pipeline apollox_finance_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 apollox_finance_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    API requests must include a timestamp parameter (millisecond timestamp when the request was created) and may include a recvWindow parameter (milliseconds after timestamp the request is valid for; defaults to 5000). The server validates that the timestamp is within 1000ms of server time and the request age does not exceed recvWindow. Additionally, requests use HMAC SHA256 signature for authentication, with the signature computed over the request parameters and sent as part of the signed payload. An apiKey and secretKey are required to generate the HMAC SHA256 signature.

    To get the appropriate API keys, please visit the original source at apollox-finance.gitbook.io. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python apollox_finance_pipeline.py

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

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

  5. 📈 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 apollox_finance_pipeline show

  6. 🐍 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("apollox_finance_pipeline").dataset()
# get aggTrades table as Pandas frame
data.aggTrades.df().head()

Extra resources:

Next steps