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Load BingX data to DuckDB

Build a BingX to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the BingX API base URL, auth, endpoints, and incremental loading.

SourceBingXBingX API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

BingX is a global digital asset trading platform offering REST API access for spot, perpetual swap, and standard contract trading services. Everything needed to build a working BingX → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.


Build your BingX to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from BingX to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the BingX API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.


BingX API at a glance

Base URLhttps://open-api.bingx.com
Example endpointGET openApi/spot/v1/market/ticker/24hr
Records found atdata
Authenticationall requests require the X-BX-APIKEY and X-SOURCE-KEY headers, plus a signature generated via HMAC-SHA256 for private endpoints — sent in the X-BX-APIKEY header
PaginationNot paginated
API referencehttps://bingx-api.github.io/docs/

These values come from the BingX API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the BingX API?

Authenticated endpoints require the X-BX-APIKEY header containing the API key and a signature calculated via HMAC-SHA256 of the canonical parameter string passed as a query parameter. Additionally, the X-SOURCE-KEY header is required for all requests.

1. Get your credentials

  1. Log in to your BingX account at bingx.com. 2. Navigate to your User Profile/Account settings and select 'API Management' (or go directly to https://bingx.com/en/accounts/api). 3. Click the 'Create API' button. 4. Configure the required permissions (e.g., Read-only for data pipelines) and optionally add IP addresses for security (IP Whitelisting). 5. Complete the security verification (e.g., 2FA). 6. Copy and save your 'API Key' and 'Secret Key' immediately; the Secret Key will only be displayed once upon creation.

2. Add them to .dlt/secrets.toml

[sources.bingx_source] api_key = "your_api_key_here" secret_key = "your_secret_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.


What BingX data can I load into DuckDB?

These are the BingX endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
spot_ticker_24hopenApi/spot/v1/market/ticker/24hrGETdata24h price change statistics
spot_depthopenApi/spot/v1/market/depthGETdataOrder book bids & asks
swap_contractsopenApi/swap/v2/quote/contractsGETdataAll contract specifications
swap_tradesopenApi/swap/v2/quote/tradesGETdataRecent public trades
swap_tickeropenApi/swap/v2/quote/tickerGETdata24h price change statistics

How do I load only new BingX records?

The BingX API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "tickers_24h", "endpoint": { "path": "openApi/spot/v1/market/ticker/24hr", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.


What does the generated BingX pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading openApi/spot/v1/market/ticker/24hr and openApi/spot/v1/market/depth from the BingX API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def bingx_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://open-api.bingx.com", "auth": {"type": "api_key", "api_key": api_key, "name": "X-BX-APIKEY", "location": "header"}, }, "resources": [ {"name": "tickers_24h", "endpoint": {"path": "openApi/spot/v1/market/ticker/24hr", "data_selector": "data"}}, {"name": "order_book", "endpoint": {"path": "openApi/spot/v1/market/depth", "data_selector": "data"}} ], } yield from rest_api_resources(config) def load_bingx_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="bingx_pipeline", destination="duckdb", dataset_name="bingx_data", ) load_info = pipeline.run(bingx_source()) print(load_info) if __name__ == "__main__": load_bingx_to_duckdb()

Run it with python bingx_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query BingX data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

import dlt data = dlt.pipeline("bingx_pipeline").dataset() df = data.tickers_24h.df() print(df.head())

SQL:

SELECT * FROM bingx_data.tickers_24h LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.


How do I deploy the BingX to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

  • Deploy & schedule — run the pipeline as a managed job with automatic retries.
  • Monitor — observable job queues, alerting, and load metrics for every run.
  • Transform — promote raw BingX loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load BingX data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.


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