NexusData revolutionizes decentralized market analysis by transforming complex data workflows into a single line of code. Designed for traders and developers, our platform seamlessly aggregates high-fidelity coin spots/contracts bar data from top exchanges like Binance and OKX, delivering:
- Zero-Learning-Code: Extract, clean, and structure raw data with one intuitive function
- Turbocharged Security: Local data vaulting ensures enterprise-grade protection + sub-millisecond access
- Future-Proof Scalability: Modular architecture primed for expanding exchange integrations
- Trade smarter: build faster – where simplicity meets institutional-grade data resilience.
pip install nexusdatafrom nexusdata import auth, fetch_data- Authentication (Get credentials at https://quantweb3.ai/subscribe)
auth('your_username', 'your_token')- Fetch Data Store the data in a local specified directory (csv format)
fetch_data()Fetch data with custom parameters
fetch_data(
tickers=["BTCUSDT"],
store_dir="./tmp/data",
start_time=datetime.datetime(2021, 1, 1),
end_time=datetime.datetime(2022, 1, 2),
data_type="klines",
data_frequency="1m",
asset_class="um"
)-
tickers (
list[str]):
A list of trading pairs to fetch data for.
Example:["BTCUSDT"]. -
store_dir (
str):
The directory path where the fetched data will be stored.
Example:"./tmp/data". -
start_time (
datetime.datetime):
The starting timestamp for the data retrieval.
Example:datetime.datetime(2021, 1, 1). -
end_time (
datetime.datetime):
The ending timestamp for the data retrieval.
Example:datetime.datetime(2022, 1, 2). -
data_type (
str):
The type of data to fetch. Defaults to"klines", which represents candlestick (OHLCV) data. -
data_frequency (
str):
The frequency at which the data is sampled.
Example:"1m"for one-minute intervals. -
asset_class (
str):
The asset class identifier. For instance,"um"might indicate a specific market type.
Note: The accepted values should be defined by your application context.
The returned DataFrame contains the following columns:
| Column | Description |
|---|---|
| Open time | The moment when the candlestick period started. |
| Open | The opening price for the period. |
| High | The highest price reached during the period. |
| Low | The lowest price reached during the period. |
| Close | The closing price at the end of the period. |
| Volume | The total traded quantity during the period. |
| Close time | The moment when the candlestick period ended. |
| Quote asset volume | The traded volume in terms of the quote asset during the period. |
| Number of trades | The total number of trades executed during the period. |
| Taker buy base asset volume | The amount of the base asset bought by takers during the period. |
| Taker buy quote asset volume | The amount of the quote asset used for taker buy orders during the period. |
| Ignore | A field reserved for future use and typically disregarded. |
- Visit Quantweb3.ai Subscription Page(Note: New users get a 7-day free trial)
- Register and obtain authentication credentials
- Use the
auth()function to authenticate
Note: Open an account using one of the above links and provide a screenshot to get 1 year's of free data service(Anyone).
- You can view the demo on Google Colab by clicking here.
- You can also look at the example folder in the directory
- python-snappy >= 0.7.2
- grpcio >= 1.64.1
- pandas >= 1.5.3
- protobuf >= 4.25.3
- tqdm >= 4.65.0
Q: How to handle authentication errors?
A: Ensure your username and token are correct, and check your network connection.
Q: What is the data update frequency?
A: Hstorical data is updated daily.
Issues and Pull Requests are welcome!
This project is licensed under the MIT License - see the LICENSE file for details
- Website: quantweb3.ai
- Email: quantweb3.ai@gmail.com
- X: https://x.com/quantweb3_ai
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