atabet-data 1.0.3

Atabet-Data: A data retrieval library for financial data from multiple sources

Atabet-Data

A data retrieval library for financial data from multiple sources with token-based authentication.

Authentication

Atabet-Data uses token-based authentication. Token authentication is mandatory for all API calls.

Using the AtabetData Class

All API access is through the AtabetData class, which validates authentication tokens:

from atabet_data import AtabetData

# Create client with a valid authentication token
client = AtabetData(token=your_token)

# Use the client to access data APIs
data = client.get_data(
    tickers=['AAPL US Equity'],
    flds=['PX_LAST'],
    source='bql'
)

# All methods are available
historical = client.get_historical_data(
    tickers=['AAPL US Equity'],
    flds=['PX_LAST'],
    start='2025-01-01',
    end='2025-01-31',
    source='bql'
)

Note:

• Token authentication is mandatory and validated when creating the AtabetData instance
• If an invalid token is provided, InvalidTokenError is raised during initialization
• Once initialized, all methods can be called without additional authentication
• Contact the administrator to obtain a valid authentication token

Yahoo Finance (source='yahoo')

Install the optional extra (pip install atabet-data[yahoo] or pip install yfinance), then pass source='yahoo'. Tickers must be Yahoo symbols (for example AAPL, 700.HK, ^GSPC), not Bloomberg-style identifiers. Supported fields are PX_OPEN, PX_HIGH, PX_LOW, PX_LAST, and PX_VOLUME for snapshot and daily history. get_datasets, get_mem_weights, and run_query are not implemented for this channel.

Available Methods

The AtabetData class provides the following methods:

• get_data() - Get current snapshot data
• get_historical_data() - Get historical data
• get_datasets() - Get datasets data
• get_mem_weights() - Get index members weights
• run_query() - Run raw queries

Technical Analysis (ta accessor)

Atabet-Data ships with a built-in technical analysis module. Any OHLCV DataFrame can compute indicators directly via the .ta accessor (no external dependency required):

from atabet_data import AtabetData

client = AtabetData(token=your_token)
df = client.get_historical_data(
    tickers=["AAPL US Equity"],
    flds=["PX_OPEN", "PX_HIGH", "PX_LOW", "PX_LAST", "PX_VOLUME"],
    start="2024-01-01", end="2024-12-31", source="yahoo",
)
ohlcv = df.ext.to_ohlcv("AAPL US Equity")

# Individual indicators
ohlcv.ta.sma(length=20)        # Simple Moving Average
ohlcv.ta.rsi(length=14)        # Relative Strength Index
ohlcv.ta.macd()                # MACD (returns DataFrame with line/signal/histogram)
ohlcv.ta.bbands(length=20)     # Bollinger Bands
ohlcv.ta.atr(length=14)        # Average True Range
ohlcv.ta.obv()                 # On-Balance Volume

# Append results to the DataFrame
ohlcv.ta.sma(length=50, append=True)
ohlcv.ta.rsi(length=14, append=True)

# Batch: run a preset strategy (Common or All)
ohlcv.ta.strategy("Common")

# Or define a custom strategy
from atabet_data.ta.strategy import Strategy
my_strat = Strategy(name="Quick", ta=[
    {"kind": "ema", "length": 9},
    {"kind": "rsi", "length": 7},
    {"kind": "bbands", "length": 20, "std": 2.5},
])
ohlcv.ta.strategy(my_strat)

Indicator categories

Category	Indicators
Overlap	sma, ema, wma, rma, dema, tema, hma, vwap, hlc3
Momentum	rsi, macd, stoch, roc, cci, willr, mfi
Trend	adx, aroon, psar
Volatility	true_range, atr, bbands, kc, donchian
Volume	obv, ad, adosc, cmf
Statistics	stdev, variance, mad, zscore
Performance	log_return, percent_return, drawdown

Functional API

All indicators are also available as standalone functions:

from atabet_data.ta import sma, rsi, macd
sma(ohlcv["close"], length=20)
rsi(ohlcv["close"], length=14)

Custom indicators

Register your own indicators at runtime:

from atabet_data.ta.custom import bind, create_dir, import_dir

# Scaffold a directory tree, write custom modules, then import them
create_dir("~/my_indicators")
import_dir("~/my_indicators")

See samples/ta_example.py for a complete walkthrough.

Environment preparations

conda create -n py39_atabet_data python=3.9
conda activate py39_atabet_data
(py39_atabet_data_test) conda install conda-forge::cython
(py39_atabet_data_test) conda install anaconda::pandas
(py39_atabet_data_test) conda install anaconda::setuptools # Windows

Packaging

# Basic usage - will cythonize all .py files including __init__.py
python atabet-data/run_packaging.py

# Remove source files from the wheel
python atabet-data/run_packaging.py --remove-source

# Specify a different module name, version, or distribution directory
python run_packaging.py --module="my-module" --version="2.0.0" --dist-dir="dist"

# Combine options
python run_packaging.py --remove-source --module="my-module" --version="2.0.0"

Test

conda create -n py39_atabet_data_test python=3.9
conda activate py39_atabet_data_test
(py39_atabet_data_test) conda install anaconda::pandas
(py39_atabet_data_test) pip install jquant/atabet_data-1.0.0-cp39-cp39-macosx_10_15_x86_64.whl # (Mac OS/Linux)
(py39_atabet_data_test) pip install .\jquant\atabet_data-1.0.0-cp39-cp39-win_amd64.whl # (Windows)
(py39_atabet_data_test) pip install tushare --upgrade
(py39_atabet_data_test) python atabet-data/test.py

GitHub Actions: wheels and PyPI

Workflow: .github/workflows/build-wheels.yml. Wheels are built with cibuildwheel; options live in pyproject.toml under [tool.cibuildwheel].

When jobs run (push): the Linux / Windows / macOS matrix runs only if the head commit message includes one of:

Tag	Effect
[build ci] (or [Build CI], [BUILD CI])	Build wheels and upload per-OS artifacts; no publish.
[pub pypi] (or [Pub PyPI], [PUB PYPI])	Build wheels, then publish merged wheels to PyPI (production).
[pub test.pypi] (or [Pub test.pypi], [PUB TEST.PYPI])	Build wheels, then publish to TestPyPI.

Plain pushes (no tag above) skip the matrix so CI does not run on every commit.

Manual runs: Actions → Build wheels → Run workflow runs the build matrix only; publishing is by push with [pub pypi] or [pub test.pypi] in the commit message.

Authentication (publish): the workflow uses Trusted Publishing (OIDC) by default — no API token in GitHub secrets. Register the GitHub repo and workflow file on each index (project Manage → Publishing). See the comment block at the top of build-wheels.yml for OIDC vs API-token options.

Private repositories are supported for OIDC the same as public ones.