DataMaxi+ Python SDK

Official Python SDK for the DataMaxi+ API. Fetch both historical and latest market data across centralized exchanges (OHLCV candles, tickers, trading fees, wallet status, announcements), perpetual funding rates, cross-exchange price premiums for arbitrage, forex rates, Telegram channel data, and Naver search trends.

This package is compatible with Python v3.10+.

Table of Contents

• Installation
• Configuration
• Quickstart
• Async Client
• API Reference
  • CEX Candle Data
  • CEX Ticker Data
  • CEX Trading Fees
  • CEX Wallet Status
  • CEX Announcements
  • CEX Token Updates
  • Funding Rate
  • Premium
  • Forex
  • Telegram
  • Naver Trend
• Response Types
• Pagination
• Error Handling
• Local Development
• Tests
• Links
• Contributing
• License

Installation

pip install datamaxi

Configuration

Private API endpoints are protected by an API key. You can get the API key upon registering at https://datamaxiplus.com/auth.

Environment Variables

You may use environment variables to configure the SDK to avoid any inline boilerplate.

Quickstart

DataMaxi+ Python package includes the following clients:

• Datamaxi - Main client for crypto trading data (CEX, funding rates, premium, forex)
• Telegram - Client for Telegram channel data
• Naver - Client for Naver trend data

Set your API key via the DATAMAXI_API_KEY environment variable (recommended, so the key stays out of source code):

export DATAMAXI_API_KEY="your_api_key"

from datamaxi import Datamaxi, Telegram, Naver

# Clients read DATAMAXI_API_KEY from the environment automatically.
# Alternatively, pass api_key="your_api_key" explicitly to each client.
maxi = Datamaxi()
telegram = Telegram()
naver = Naver()

# Fetch CEX candle data (returns pandas DataFrame)
df = maxi.cex.candle(
    exchange="binance",
    symbol="BTC-USDT",
    interval="1d",
    market="spot"
)
print(df.head())

# Fetch ticker data
ticker = maxi.cex.ticker.get(
    exchange="binance",
    symbol="BTC-USDT",
    market="spot"
)
print(ticker)

# Fetch premium data
premium = maxi.premium(asset="BTC")
print(premium.head())

import asyncio
from datamaxi.aio import AsyncDatamaxi


async def main():
    # Reads DATAMAXI_API_KEY from the environment automatically.
    # Alternatively, pass api_key="your_api_key" explicitly.
    async with AsyncDatamaxi() as client:
        # Fetch CEX candle data (returns pandas DataFrame)
        df = await client.cex.candle(
            exchange="binance",
            symbol="BTC-USDT",
            interval="1d",
            market="spot",
        )
        print(df.head())

        # Fetch ticker data
        ticker = await client.cex.ticker.get(
            exchange="binance",
            symbol="BTC-USDT",
            market="spot",
        )
        print(ticker)

        # Fetch premium data
        premium = await client.premium(asset="BTC")
        print(premium.head())


asyncio.run(main())

Async Client

The SDK also ships an async client, AsyncDatamaxi (built on httpx). It mirrors the same resource tree and arguments as Datamaxi, with one rule: every method is a coroutine and must be awaited. Install the async extra:

pip install "datamaxi[async]"

import asyncio
from datamaxi.aio import AsyncDatamaxi


async def main():
    # Reads DATAMAXI_API_KEY from the environment, or pass api_key=... explicitly.
    async with AsyncDatamaxi() as client:
        df = await client.cex.candle(
            exchange="binance", symbol="BTC-USDT", interval="1d", market="spot"
        )
        print(df.head())


asyncio.run(main())

Use AsyncDatamaxi as an async context manager (shown above) or call await client.aclose() yourself. Paginated endpoints return an async next_request — await it too (data, next_request = await client.cex.announcement(...)). Telegram and Naver have standalone AsyncTelegram / AsyncNaver clients.

Every endpoint in the API Reference works the same under the async client — see the docs where each example has a Sync/Async tab.

API Reference

Discovery helpers. Most endpoints expose helpers to list valid argument values before you fetch — commonly .exchanges(), .symbols(exchange=...), and (for candles) .intervals(). Use them to discover supported exchanges, trading pairs, and intervals. The examples below show them per endpoint.

CEX Candle Data

Fetch historical candlestick (OHLCV) data from centralized exchanges.

# Get supported exchanges
exchanges = maxi.cex.candle.exchanges(market="spot")  # or "futures"

# Get supported symbols for an exchange
symbols = maxi.cex.candle.symbols(exchange="binance", market="spot")

# Get supported intervals
intervals = maxi.cex.candle.intervals()

# Fetch candle data
df = maxi.cex.candle(
    exchange="binance",      # Required: exchange name
    symbol="BTC-USDT",       # Required: trading pair
    interval="1d",           # Required: candle interval (1m, 5m, 15m, 30m, 1h, 4h, 1d)
    market="spot",           # Required: "spot" or "futures"
    currency="USD",          # Optional: price currency (default: USD)
    from_unix=None,          # Optional: start time (unix timestamp)
    to_unix=None,            # Optional: end time (unix timestamp)
    pandas=True              # Optional: return DataFrame (default) or dict
)

CEX Ticker Data

Fetch real-time ticker data from centralized exchanges.

# Get supported exchanges
exchanges = maxi.cex.ticker.exchanges(market="spot")

# Get supported symbols
symbols = maxi.cex.ticker.symbols(exchange="binance", market="spot")

# Fetch ticker data
ticker = maxi.cex.ticker.get(
    exchange="binance",      # Required: exchange name
    symbol="BTC-USDT",       # Required: trading pair
    market="spot",           # Required: "spot" or "futures"
    currency=None,           # Optional: price currency
    conversion_base=None,    # Optional: conversion base
    pandas=True              # Optional: return DataFrame or dict
)

CEX Trading Fees

Fetch trading fee information from centralized exchanges.

# Get supported exchanges
exchanges = maxi.cex.fee.exchanges()

# Get supported symbols
symbols = maxi.cex.fee.symbols(exchange="binance")

# Fetch fee data
fees = maxi.cex.fee(
    exchange="binance",      # Required: exchange name
    symbol="BTC-USDT"        # Required: trading pair
)

CEX Wallet Status

Fetch deposit/withdrawal status for assets on centralized exchanges.

# Get supported exchanges
exchanges = maxi.cex.wallet_status.exchanges()

# Get supported assets
assets = maxi.cex.wallet_status.assets(exchange="binance")

# Fetch wallet status
status = maxi.cex.wallet_status(
    exchange="binance",      # Required: exchange name
    asset="BTC",             # Required: asset symbol
    pandas=True              # Optional: return DataFrame or list
)

CEX Announcements

Fetch exchange announcements (listings, delistings, etc.).

# Fetch announcements
data, next_request = maxi.cex.announcement(
    page=1,                  # Optional: page number (default: 1)
    limit=1000,              # Optional: items per page (default: 1000)
    sort="desc",             # Optional: "asc" or "desc" (default: desc)
    key=None,                # Optional: sort key
    exchange=None,           # Optional: filter by exchange
    category=None            # Optional: filter by category
)

# Get next page
data2, next_request2 = next_request()

CEX Token Updates

Fetch token listing/delisting updates.

# Fetch token updates
data, next_request = maxi.cex.token.updates(
    page=1,                  # Optional: page number
    limit=1000,              # Optional: items per page
    type=None,               # Optional: "listed" or "delisted"
)

Funding Rate

Fetch funding rate data for perpetual futures.

# Get supported exchanges
exchanges = maxi.funding_rate.exchanges()

# Get supported symbols
symbols = maxi.funding_rate.symbols(exchange="binance")

# Fetch historical funding rates
df, next_request = maxi.funding_rate.history(
    exchange="binance",      # Required: exchange name
    symbol="BTC-USDT",       # Required: trading pair
    page=1,                  # Optional: page number
    limit=1000,              # Optional: items per page
    fromDateTime=None,       # Optional: start datetime
    toDateTime=None,         # Optional: end datetime (cannot set both from and to)
    sort="desc",             # Optional: "asc" or "desc"
    pandas=True              # Optional: return DataFrame or dict
)

# Fetch latest funding rate
df = maxi.funding_rate.latest(
    exchange="binance",      # Required: exchange name
    symbol="BTC-USDT",       # Required: trading pair
    sort=None,               # Optional: "asc" or "desc"
    limit=None,              # Optional: limit results
    pandas=True              # Optional: return DataFrame or dict
)

Premium

Fetch cross-exchange price premium data for arbitrage analysis.

# Get supported exchanges
exchanges = maxi.premium.exchanges()

# Fetch premium data with common filters
df = maxi.premium(
    source_exchange=None,    # Optional: source exchange
    target_exchange=None,    # Optional: target exchange
    asset=None,              # Optional: asset symbol (e.g., "BTC")
    source_market=None,      # Optional: "spot" or "futures"
    target_market=None,      # Optional: "spot" or "futures"
    min_pdp=None,            # Optional: min price difference percentage
    max_pdp=None,            # Optional: max price difference percentage
    token_include=None,      # Optional: include specific tokens (full name, e.g. "bitcoin")
    token_exclude=None,      # Optional: exclude specific tokens (full name, e.g. "bitcoin")
    page=1,                  # Optional: page number
    limit=100,               # Optional: items per page
    sort=None,               # Optional: "asc" or "desc"
    key=None,                # Optional: sort key (e.g., "pdp")
    pandas=True              # Optional: return DataFrame or dict
)

premium() accepts many additional filters — quote currencies, time-windowed price-difference thresholds (min/max_pd, pdp24h/pdp4h/pdp1h/pdp30m/pdp15m/pdp5m), volume bounds (min/max_sv, min/max_tv), funding-rate bounds, only_transferable, network, and more. See the premium endpoint docs for the full list.

Forex

Fetch forex exchange rate data.

# Get supported symbols
symbols = maxi.forex.symbols()

# Fetch forex data
df = maxi.forex(
    symbol="USD-KRW",        # Required: currency pair
    pandas=True              # Optional: return DataFrame or dict
)

Telegram

Fetch Telegram channel messages and metadata.

# Initialize Telegram client
from datamaxi import Telegram
telegram = Telegram(api_key=api_key)

# Fetch channels
data, next_request = telegram.channels(
    page=1,                  # Optional: page number
    limit=1000,              # Optional: items per page
    category=None,           # Optional: filter by category
    key=None,                # Optional: sort key
    sort="desc"              # Optional: "asc" or "desc"
)

# Fetch messages
data, next_request = telegram.messages(
    channel_name=None,       # Optional: filter by channel
    page=1,                  # Optional: page number
    limit=1000,              # Optional: items per page
    key=None,                # Optional: sort key
    sort="desc",             # Optional: "asc" or "desc"
    category=None            # Optional: filter by category
)

Naver Trend

Fetch Naver search trend data (South Korea).

# Initialize Naver client
from datamaxi import Naver
naver = Naver(api_key=api_key)

# Get supported symbols
symbols = naver.symbols()

# Fetch trend data
data = naver.trend(
    symbol="BTC",            # Required: symbol to search
    pandas=True              # Optional: return DataFrame or list
)

Response Types

Most methods return pandas DataFrames by default. Set pandas=False to get raw dict/list responses.

# DataFrame response (default)
df = maxi.cex.candle(exchange="binance", symbol="BTC-USDT", interval="1d", market="spot")
print(type(df))  # <class 'pandas.core.frame.DataFrame'>

# Dict response
data = maxi.cex.candle(exchange="binance", symbol="BTC-USDT", interval="1d", market="spot", pandas=False)
print(type(data))  # <class 'dict'>

Pagination

Many endpoints support pagination and return a next_request function:

# First page
data, next_request = maxi.cex.announcement(page=1, limit=100)

# Get next page
data2, next_request2 = next_request()

# And so on...
data3, next_request3 = next_request2()

Error Handling

All SDK exceptions subclass datamaxi.error.Error:

from datamaxi import Datamaxi
from datamaxi.error import ClientError, ServerError

maxi = Datamaxi()

try:
    df = maxi.cex.candle(exchange="binance", symbol="BTC-USDT", interval="1d", market="spot")
except ClientError as e:
    print(f"Client error {e.status_code}: {e.error_message}")
except ServerError as e:
    print(f"Server error {e.status_code}: {e.message}")

Local Development

This project uses uv for fast dev setup. Install uv first (see the uv docs).

git clone https://github.com/bisonai/datamaxi-python.git
cd datamaxi-python

# Create a virtual environment and install dev dependencies
# (requirements-dev.txt pulls in the test and docs stacks).
uv venv
uv pip install -r requirements/requirements-dev.txt

# For runtime dependencies only:
# uv pip install -r requirements/common.txt

Dependency files under requirements/:

Tests

# Install test dependencies (skip if you already ran the dev install above)
uv pip install -r requirements/requirements-test.txt

# Run keyless tests (no API key required) — this is the lane CI runs on every push
uv run pytest tests/ -m "not integration" -v

# Run integration tests (requires API key)
export DATAMAXI_API_KEY="your_api_key"
uv run pytest tests/test_integration.py -v

# Test specific endpoint groups using markers
uv run pytest tests/test_integration.py -m "cex" -v
uv run pytest tests/test_integration.py -m "funding" -v
uv run pytest tests/test_integration.py -m "premium" -v
uv run pytest tests/test_integration.py -m "forex" -v
uv run pytest tests/test_integration.py -m "telegram" -v
uv run pytest tests/test_integration.py -m "naver" -v
uv run pytest tests/test_integration.py -m "types" -v

# Run all tests
uv run pytest tests/ -v

Links

• Official Website
• Documentation

Contributing

We welcome contributions! If you discover a bug in this project, please feel free to open an issue to discuss the changes you would like to propose.

License

MIT License