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.

Install authentication support with pip install atabet-data[auth] (requires atabet-token >= 1.2.0 for licensing-server session support).

Token types

Token source	Format	Usage
Licensing server (`/admin/generate_license`)	JWT (three dot-separated segments)	Default — `AtabetData(token=jwt)` uses `validation_method='licensing'`
Dev HMAC (`generate_secure_token` with `DEV_MODE=1`)	Hex string	Pass `validation_method='original'`

Using the AtabetData Class

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

import os
import socket
from atabet_data import AtabetData

# Licensing JWT (default validation)
client = AtabetData(
    token=os.environ["ATABET_DATA_TOKEN"],
    licensing_server_url=os.environ.get("LICENSING_SERVER_URL", "http://39.101.65.167:5001"),
    device_name=os.environ.get("ATABET_DEVICE_NAME", os.environ.get("ATABET_CLIENT_ID", socket.gethostname())),
)

# Dev HMAC token (offline / local testing)
client = AtabetData(token=dev_token, validation_method="original")

Set environment variables in the same shell that runs Python:

export ATABET_DATA_TOKEN='eyJhbGci...'
export LICENSING_SERVER_URL='http://39.101.65.167:5001'
export ATABET_DEVICE_NAME='my-macbook'

Session limits

Licensing JWTs enforce max_sessions on the server. The client automatically persists the server-issued session_token to ~/.atabet/session.json (override with ATABET_SESSION_STORE) and reuses it on subsequent launches to heartbeat the existing session instead of registering a new one.

If you see Session limit (N) reached, close other instances or call client.close() to free your slot via POST /logout. Licenses must be issued with application_id: "com.atabet.data".

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.

1-minute intraday: pass freq='1M'. Yahoo limits 1m bars to ~7 calendar days per request and only within the last ~60 days; wider or older ranges raise ValueError before calling yfinance. For multi-exchange intraday requests (e.g. AAPL + 700.HK), atabet_meta['exchange_tz'] is a per-ticker dict when zones differ. With output_tz='UTC', each ticker is converted using its own exchange zone before columns are merged.

Futu OpenAPI (`source='futu'`)

Install the optional extra (pip install atabet-data[futu] or pip install futu-api), run Futu OpenD locally (default 127.0.0.1:11111), then pass source='futu'. Tickers must be Futu codes (HK.00700, US.AAPL, SH.600000), not Yahoo or Bloomberg identifiers. Snapshot uses get_market_snapshot; history uses paged request_history_kline (see Futu API Doc v10.9). Override OpenD with host/port kwargs or FUTU_OPEND_HOST / FUTU_OPEND_PORT. Supported fields include PX_OPEN, PX_HIGH, PX_LOW, PX_LAST, PX_VOLUME, and PX_TURNOVER. Intraday freqs map to Futu KLType (1M→K_1M, 1H→K_60M, …). See samples/futu_example.py.

Big QMT Redis bridge (`source='qmt'`)

Install the optional extra (pip install atabet-data[qmt] or pip install redis), keep the shared Big QMT bridge from atabet-trader running as a live strategy, then pass source='qmt'. This is the MiniQMT sunset path: clients talk Redis only (no xtquant). Tickers use QMT codes (600000.SH, 000001.SZ). Daily OHLCV via bridge get_market_data_ex; financials via get_datasets (Balance/Income/CashFlow/Capital/Pershareindex, or native ASHARE* tables) plus get_divid_factors / get_instrumentdetail over Redis. History and financials are local QMT cache only (数据管理 → 补充数据). Configure Redis with kwargs or QMT_REDIS_HOST / QMT_REDIS_PORT / QMT_REDIS_PASSWORD / QMT_REDIS_DB / QMT_KEY_PREFIX / QMT_RPC_TIMEOUT (defaults match trader: db 5, prefix atabet:qmt). Keep xtdata for MiniQMT/XtQuant if you still need it. See samples/qmt_example.py.

Channel capabilities (native operations)

Channels do not all implement the same method matrix. Discover what each source supports at runtime:

from atabet_data import AtabetData, list_channels, describe_channel

print(list_channels())                 # summary: sources + operation names
print(describe_channel("yahoo"))       # params, ticker convention, field map

client = AtabetData(token=token)
df = client.run_operation(
    "localcsv",
    "get_historical_data",
    {
        "tickers": ["FUT.CBOT.ZC"],
        "flds": ["PX_LAST"],
        "start": "2024-01-01",
        "end": "2024-01-31",
        "csv_path": "/path/to/K_1D",
    },
)

Source	Native operations	Notes
`yahoo`	`get_data`, `get_historical_data`	Yahoo symbols; daily + intraday
`futu`	`get_data`, `get_historical_data`	Futu codes; needs OpenD + futu-api
`localcsv`	`get_historical_data`	Requires `csv_path`
`bql`	`get_data`, `get_historical_data`, `get_datasets`, `get_mem_weights`	Daily; Bloomberg IDs
`blpapi`	above + `run_query`	Daily; yellow keys
`bqia`	`get_historical_data`	Intraday only
`xtdata`	`get_historical_data`, `get_datasets`	Daily
`qmt`	`get_historical_data`, `get_datasets`, `get_divid_factors`, `get_instrumentdetail`	Daily OHLCV + financials; Big QMT Redis bridge
`tushare`	`get_historical_data`	Daily; needs Tushare token
`s3`	`get_datasets`	No OHLCV history

Calling an unsupported convenience method (e.g. get_historical_data on s3) raises NotImplementedError with the available operations and a pointer to run_operation. The static catalog is also shipped as atabet_data/channel_catalog.json.

Each channel includes docs_urls (official / primary references) and search_hints (good web_search queries) so agents know where to look before inventing vendor API usage.

Maintaining the channel catalog

Edit here (source of truth): atabet_data/capabilities.py — CHANNEL_CAPABILITIES entries (operations, params, ticker conventions, docs_urls, search_hints, notes).

Do not hand-edit atabet_data/channel_catalog.json; it is generated.

Change type	Where to edit	Then
Vendor doc URL moved / renamed	`docs_urls` / `search_hints` on that channel in `capabilities.py`	Sync + test (below)
New operation or param on a channel	`OperationSpec` / `ParamSpec` in `capabilities.py` and implement the function under `from_<channel>/`	Sync + test
New data channel	Add `ChannelCapability` + `from_<channel>/` module + wire in `api.py` `load_api`	Sync + test
QI agent behaviour only (wording)	atabet-quant-intelligence `src/lib/skills/default-skill.ts`	No catalog sync

# from atabet-data repo root (expects sibling ../atabet-quant-intelligence by default)
python scripts/sync_channel_catalog.py
# optional: --qi-path /path/to/atabet-quant-intelligence   or   --skip-qi
pytest tests/test_capabilities.py

Commit both repos when the catalog changes:

atabet-data: capabilities.py + channel_catalog.json
atabet-quant-intelligence: src/lib/data-channels/channel-catalog.json

Do not hardcode vendor doc URLs only in the QI skill — the capability catalog is the source of truth. See also scripts/sync_channel_catalog.py.

Index and timezone contract

Historical outputs use a consistent multi-index layout across channels:

Daily data: index levels (FLDS, DATE) — naive calendar dates at midnight (no timezone).
Intraday data: index levels (FLDS, DATETIME) — exchange-local timestamps by default; pass output_tz='UTC' to convert (exchange_tz may be required for naive sources such as localcsv). Yahoo and localcsv multi-exchange intraday apply UTC conversion per ticker before columns are merged.
Parameters: get_historical_data(..., freq='1M', output_tz='UTC') — freq selects bar size ('1D' daily default; intraday e.g. '1M', '1H' for Yahoo); output_tz converts intraday timestamps when supported. For localcsv, exchange_tz may be a single IANA string or a {ticker: zone} dict.
Metadata: returned DataFrames include df.attrs['atabet_meta'] with freq, index_semantics (calendar_date or timestamp), output_tz, exchange_tz (string or per-ticker dict for multi-exchange Yahoo/localcsv intraday), and frame_schema (e.g. members_weights for BQL index weights).

Channel matrix (historical)

Source	Daily `(FLDS, DATE)`	Intraday	`freq` / `output_tz`	`atabet_meta`
`bql`	yes	no	ignored (daily only)	yes
`blpapi`	yes	no	stripped by wrapper	yes
`xtdata`	yes	no	stripped by wrapper	yes
`qmt`	yes	no	stripped by wrapper	yes
`yahoo`	yes	yes	supported	yes
`futu`	yes	yes	supported	yes
`localcsv`	yes	yes	supported	yes
`bqia`	no	yes	supported	yes
`tushare`	yes	no	intraday raises	yes

Passing freq or output_tz to daily-only channels (blpapi, xtdata, qmt) is safe — the wrapper strips unsupported kwargs before calling the channel.

Available Methods

The AtabetData class provides:

Channel-native (preferred for full coverage)

list_channels() — summarize registered channels and operations
describe_channel(source) — full params / ticker conventions / field map
run_operation(channel, operation, params) — execute a native operation

Convenience facades (best-effort OHLCV / datasets where supported)

get_data() — snapshot
get_historical_data() — historical OHLCV
get_datasets() — datasets
get_mem_weights() — index member weights
run_query() — raw vendor query (primarily blpapi)

Module-level helpers (no auth): list_channels, describe_channel, catalog_as_dict.

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

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

Unit tests run offline with mocked channels (no Bloomberg, Yahoo, or licensing server required):

conda activate atabet_data_env
pytest tests/ -v

Integration tests (real licensing server) are marked @pytest.mark.integration and excluded by default. Run them explicitly when the server is available:

pytest tests/ -m integration -v

GitHub Actions runs unit tests on every push/PR via .github/workflows/test.yml. Wheel builds remain gated by [build ci] in .github/workflows/build-wheels.yml.

Sample scripts live in samples/ — see samples/README.md for offline-friendly localcsv demos using bundled sample_data/.

GitHub Actions: tests, wheels, and PyPI

Unit tests: .github/workflows/test.yml runs pytest on Ubuntu for every push and pull request (integration tests excluded by default).

Wheel builds: .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.

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Algorithm	Hash digest
SHA256	`a6845714e52ff54dcfa2e4e95154c5b0a2d9fa5e8ad2f028e883e2c1ea3cc08d`
MD5	`b9da4762cd5ef1f08236e6d4be32d052`
BLAKE2b-256	`883670b3955ac257bf1a79c586abdfb2c9ece200e9100855d83450a5af00a41a`

Algorithm	Hash digest
SHA256	`33979ecbdd3c62e82a37ce7ee55f75046527786fbb2f8dd7cde08462073008ed`
MD5	`dc635f2320507d2464d3b9a6918c64b7`
BLAKE2b-256	`24e9ce94c6e98dfd6b61ad4bceec87fad05040858e05fadc3b7b5428bfb94793`

Algorithm	Hash digest
SHA256	`62886642bf5c7969295169e67287c931ac444ed18bb0b99b5d1d61885b206c05`
MD5	`18acf2c6915492eeb4e66be4070c5a76`
BLAKE2b-256	`0ef4e4defe1984e1b20f1136a50d31c8b8c22dc700888a31db49b40ba6d1a607`

Algorithm	Hash digest
SHA256	`052d3e7e854c55dd35065a1437661b8c1b8fc53181fb5336a701acadd2f08509`
MD5	`f301349758ce8fcd7c952fad9c6c07f0`
BLAKE2b-256	`c9cc2f40054ed128c797263b660c7d6d94a439bd26e632a9a0deeff9fbd2b26f`

Algorithm	Hash digest
SHA256	`96074eedefdc8dfc537ab77bf5d713426168590538be17d5ad551c1b467ab0b4`
MD5	`789824c768e55edcf21d91b141c225d7`
BLAKE2b-256	`9463179dfe25ef489f63322aed36f2340bca704316bed57affab31d9a6ccff2f`

Algorithm	Hash digest
SHA256	`19a15343f6ee75a189d9e56000cead19dc369b1f5142da5036fc0a517f086c61`
MD5	`d0da65cb81eb57dd565f7d0fc3bae418`
BLAKE2b-256	`2b2416401f80352023ed1cec9ce3855dc90fa652fb2b5ab11dffcaa2721736ca`

Algorithm	Hash digest
SHA256	`7ab776428ead1a07c54f6b66f87454ef75303e1231becbcf7c0b203c80736034`
MD5	`3f668c1b936dc04c6f9edef2b6c0e13c`
BLAKE2b-256	`9e827ebe39c82d3bf491d1119f512f4d08fef40c202d5cd6d8c41d79cf048c01`

Algorithm	Hash digest
SHA256	`6ff05ce52e9e791ff040c6dad2132f017fb8d03c096623de3dcb513e8f2bd64b`
MD5	`e0b0283113fb8ac6b0b11bc323608ba6`
BLAKE2b-256	`1f7585ece5fc4b26f4a84d4fd39ab91528f935ea13999c834a1d73a5631ae076`

Algorithm	Hash digest
SHA256	`3f0e312a345107feb9a286aee30f9bf1f2e3be9ab829efab2ec2b3dff3c52272`
MD5	`940a09c85f4754d19242b784f023d97b`
BLAKE2b-256	`169e6a9343fea480b0298eaed1adb7e9dc07ba1cce5745c14ce4b4d71a8984db`

Algorithm	Hash digest
SHA256	`2180fc68c937340c4132b4f5be0feed3404b3080ba2932528f2ea8e6c08b9d0b`
MD5	`807a2f9e4202061d2455b418df8f8a45`
BLAKE2b-256	`b8bc5dd0717874cd8b11d07625e9a17d08c04300d9ad27c78fe1d2ec8b5b8796`

Algorithm	Hash digest
SHA256	`5a9a197e12685910d0490f4ba1270cc4be2a1a1ce2699449870f7987565f413e`
MD5	`5a17f829e4c7a437439864b11fb645ba`
BLAKE2b-256	`6c5deeb6248611d2785e0d90e6eb14ac111b28b238511a156dfa5fdbabe9809f`

Algorithm	Hash digest
SHA256	`dc270444d335b52a261b9aa0350a984b71cc1aa62c75438e6421ee6c1bb4818e`
MD5	`3f6cf4dd3a9bbfbd9604460be5ffc21a`
BLAKE2b-256	`bb268b90630d88842667367bbdc42f017b6df5339dfb5df1bd5df29f22f59a0e`

Algorithm	Hash digest
SHA256	`0503a401fe0b5810e64f2703fce58856b4e17990f1afa67156a53b2a647daecd`
MD5	`745ff6852eac3883cf74e54b0d55fdfd`
BLAKE2b-256	`f2e82719df9282c8c887782d7004f94a55d48373aa4d361fac7d961024ec7c1a`

Algorithm	Hash digest
SHA256	`d769077838838f6c5959807a2f990e3f66eacff09642fcba8e6f291c9d9a2de5`
MD5	`8f87bde3d428b6c3930db02b32ace50e`
BLAKE2b-256	`9f979642446807806b81ddf3e7cb51c23673c612bd60418c27ba7cfaab1a4ba4`

Algorithm	Hash digest
SHA256	`7b41a4d4ca4aff651c1933a3dafbd85f35e4b7b61a439d4aebf11be8753291cc`
MD5	`88e964c824531ee8789ddc85aa1bf09b`
BLAKE2b-256	`5ef3cbbe0a730c6c97f06923263a89493c49069dc4f17c19f062388cf4386d8a`

atabet-data 1.0.6

Atabet-Data

Authentication

Token types

Using the AtabetData Class

Session limits

Yahoo Finance (source='yahoo')

Futu OpenAPI (source='futu')

Big QMT Redis bridge (source='qmt')

Channel capabilities (native operations)

Maintaining the channel catalog

Index and timezone contract

Channel matrix (historical)

Available Methods

Technical Analysis (ta accessor)

Indicator categories

Functional API

Custom indicators

Environment preparations

Packaging

Test

GitHub Actions: tests, wheels, and PyPI

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

Yahoo Finance (`source='yahoo'`)

Futu OpenAPI (`source='futu'`)

Big QMT Redis bridge (`source='qmt'`)

Technical Analysis (`ta` accessor)

Algorithm	Hash digest
SHA256	`4008a37f2acb8c22a565f99f88764dba0c0800405f710352a693bd2ed9321c02`
MD5	`2059f36da553d2092fe31c7f18a2eafd`
BLAKE2b-256	`211301a4c89e9e299a7af7950647e941c073518edb09a19f842ed8355b539f3c`

Algorithm	Hash digest
SHA256	`5f743d290150f03cef5010e55e3f2222f532bcded0b8212e9234232b16de1f7e`
MD5	`6eefd26fe007f40a8298dcba0dab212e`
BLAKE2b-256	`bf73493c08581db052ec01de01465d3207b1b5140541b5fe153af5536ccd60ea`

Algorithm	Hash digest
SHA256	`57024e40cbae8e36e9d5b3317fcfe451cb99ee0328035bb0c226bdc127fe89c7`
MD5	`54578876db0a34dfc0ed006eecdec847`
BLAKE2b-256	`2c222be32b8ec83b98f4582324e705620b7b682b73e733260ab49a5e17bdeddb`

Algorithm	Hash digest
SHA256	`d7ac0c689718e6beb6b4dc0f9f285c4cbd7b83d977f6e1d1ed41d4bfbe22d7f6`
MD5	`036446c8f823540ebed45179c8e4b310`
BLAKE2b-256	`ef7e09747f3ef4f768f3307f980c65d88230c27623b12cca53f8c6cf415a5c55`

Algorithm	Hash digest
SHA256	`0517caa558d2438b58bceeb743d32961177eb2ba65057673203ba1b3cad661af`
MD5	`732c3a55d0a3ab3b8555bc582c038d7b`
BLAKE2b-256	`6f517c1321e9221c8e30028f450826f929173f26842482812df571a9c38362a3`

Algorithm	Hash digest
SHA256	`76b05856b7fe33a1470678b5ab37f906babb7af4e877bb75e554f9487fd0387a`
MD5	`d994a52950601b054aaf6c4ebd188558`
BLAKE2b-256	`144c862de44b293ed9958cd009ccae36d9df4f70ea9cfa268e4038df1c9dc302`

Algorithm	Hash digest
SHA256	`4e55442ad4ed426163f6e3fd8983acd002a72e1af316f497a095eadcd5acd55b`
MD5	`f2dcb6aacef0f4568eadfd7094b0e63b`
BLAKE2b-256	`5b9728124b90f45ea0791f387385ba72d6e0b17c4382bf2eafad87c07d47d8fe`

Algorithm	Hash digest
SHA256	`b464a0f226f61f11064b27c5ef855e0170113b63ea64256c48f965c973f7cc02`
MD5	`c9d8b178f13ffd491329903e98d972ca`
BLAKE2b-256	`96f4283a84af90b3a042ad6d0bc6e47f543242e216fa21d74e043bd410fe4887`

Algorithm	Hash digest
SHA256	`b1e0b81b79fc62fe86aa9141aedc59a30f86299362e72c2f129c10fee5521478`
MD5	`fbec1034a5abcf3a2cbf5d6255e9b021`
BLAKE2b-256	`6362722e32c89a0f53d7c884a89f1f87fedd3d75c5c2fc336f9a1bf1b4bf3236`

Algorithm	Hash digest
SHA256	`42b53fc160b92767f0b0cad216de74a17e0a742187d50c883c69fef9f312a9f5`
MD5	`9fd111af0355cef1e696734e4367d32a`
BLAKE2b-256	`e20707dc300e5ba44c569c4c38a25bd11943c3f506b64471e18ee6418e4db907`