DataMaxi+ Python SDK

This is the official implementation of Python SDK for DataMaxi+ API. The package can be used to fetch both historical and latest data using DataMaxi+ API. This package is compatible with Python v3.10+.

Table of Contents

• Installation
• Configuration
• Quickstart
• API Reference
• CEX Candle Data
• CEX Ticker Data
• CEX Trading Fees
• CEX Wallet Status
• CEX Announcements
• CEX Token Updates
• DEX Data
• Funding Rate
• Premium
• Forex
• Telegram
• Naver Trend
• Response Types
• Pagination
• 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.

Option	Explanation
api_key	Your API key
base_url	If base_url is not provided, it defaults to https://api.datamaxiplus.com.
timeout	Number of seconds to wait for a server response. By default requests do not time out.
proxies	Proxy through which the request is queried
show_limit_usage	Return response as dictionary including "limit_usage" and "data" keys
show_header	Return response as dictionary including "header" and "data" keys

Environment Variables

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

Env	Description
DATAMAXI_API_KEY	Used instead of api_key if none is passed.

Quickstart

DataMaxi+ Python package includes the following clients:

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

from datamaxi import Datamaxi, Telegram, Naver

# Initialize clients
api_key = "your_api_key"
maxi = Datamaxi(api_key=api_key)
telegram = Telegram(api_key=api_key)
naver = Naver(api_key=api_key)

# 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())

API Reference

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"
    sort="desc"              # Optional: "asc" or "desc"
)

DEX Data

Fetch data from decentralized exchanges. (Experimental)

# Get supported chains
chains = maxi.dex.chains()

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

# Get supported pools
pools = maxi.dex.pools(exchange="klayswap", chain="kaia_mainnet")

# Get supported intervals
intervals = maxi.dex.intervals()

# Fetch trade data
df, next_request = maxi.dex.trade(
    chain="bsc_mainnet",                              # Required: blockchain
    exchange="pancakeswap",                           # Required: DEX name
    pool="0x6ee3eE9C3395BbD136B6076A70Cb6cFF241c0E24",  # Required: pool address
    fromDateTime=None,       # Optional: start datetime (format: "2006-01-02" or "2006-01-02 15:04:05")
    toDateTime=None,         # Optional: end datetime
    page=1,                  # Optional: page number
    limit=1000,              # Optional: items per page
    sort="desc",             # Optional: "asc" or "desc"
    pandas=True              # Optional: return DataFrame or dict
)

# Fetch candle data
df, next_request = maxi.dex.candle(
    chain="bsc_mainnet",
    exchange="pancakeswap",
    pool="0x6ee3eE9C3395BbD136B6076A70Cb6cFF241c0E24",
    interval="1d",           # Optional: candle interval (default: 1d)
    fromDateTime=None,
    toDateTime=None,
    page=1,
    limit=1000,
    sort="desc",
    pandas=True
)

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 filtering
df = maxi.premium(
    source_exchange=None,    # Optional: source exchange
    target_exchange=None,    # Optional: target exchange
    asset=None,              # Optional: asset symbol (e.g., "BTC")
    source_quote=None,       # Optional: source quote currency
    target_quote=None,       # Optional: target quote currency
    source_market=None,      # Optional: "spot" or "futures"
    target_market=None,      # Optional: "spot" or "futures"
    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")
    currency=None,           # Optional: price currency
    conversion_base=None,    # Optional: conversion base

    # Price difference filters
    min_pd=None,             # Optional: min price difference (USD)
    max_pd=None,             # Optional: max price difference (USD)
    min_pdp=None,            # Optional: min price difference percentage
    max_pdp=None,            # Optional: max price difference percentage
    min_pdp24h=None,         # Optional: min 24h price difference %
    max_pdp24h=None,         # Optional: max 24h price difference %
    # ... and more time-based filters (pdp4h, pdp1h, pdp30m, pdp15m, pdp5m)

    # Volume filters
    min_sv=None,             # Optional: min source 24h volume
    max_sv=None,             # Optional: max source 24h volume
    min_tv=None,             # Optional: min target 24h volume
    max_tv=None,             # Optional: max target 24h volume

    # Funding rate filters
    min_net_funding_rate=None,
    max_net_funding_rate=None,
    min_source_funding_rate=None,
    max_source_funding_rate=None,
    min_target_funding_rate=None,
    max_target_funding_rate=None,

    # Other filters
    only_transferable=False, # Optional: filter by transferable assets
    network=None,            # Optional: filter by network
    source_funding_rate_interval=None,
    target_funding_rate_interval=None,
    premium_type=None,       # Optional: premium type
    token_include=None,      # Optional: include specific tokens
    token_exclude=None,      # Optional: exclude specific tokens

    pandas=True              # Optional: return DataFrame or dict
)

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()

Local Development

git clone https://github.com/bisonai/datamaxi-python.git
cd datamaxi-python
pip install -r requirements/common.txt

Tests

# Install test dependencies
pip install -r requirements/requirements-test.txt

# Run unit tests (no API key required)
python -m pytest tests/test_api.py -v

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

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

# Run all tests
python -m 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