DataMaxi+ Python SDK

Official Python SDK for the DataMaxi+ API — one library for both historical and real-time crypto market data.

REST — OHLCV candles, tickers, trading fees, wallet status, announcements and token updates across centralized exchanges; perpetual funding rates, liquidations, open interest, margin-borrow rates; cross-exchange price premiums for arbitrage; index prices and forex rates; Telegram channel data and Naver search trends.
WebSocket — stream tickers, forex, premiums, funding rates, open interest, liquidations and listing announcements as they happen.
Sync or async — a synchronous client, a coroutine-based async twin, and an async WebSocket client, all sharing the same resource tree and arguments.

Compatible with Python v3.10+.

Installation
Authentication
Quickstart
Clients
REST API Reference
- CEX — Candle, Ticker, Fees, Wallet Status, Announcements, Token Updates, Symbol
- Derivatives and Leverage — Funding Rate, Liquidation, Open Interest, Margin Borrow
- Pricing and Cross-Exchange — Premium, Index Price, Forex
- Alternative Data — Telegram, Naver Trend
WebSockets
Response Types
Pagination
Error Handling
Async Client
Local Development
Tests
Links
Contributing
License

Installation

pip install datamaxi

The SDK is lightweight by default (requests + pandas). Two features live behind optional extras so you only install what you use:

Extra	Installs	Enables
`pip install "datamaxi[async]"`	`httpx`	The async client, `AsyncDatamaxi`.
`pip install "datamaxi[ws]"`	`websockets`	The async WebSocket client, `AsyncDatamaxiWS`.

Combine them in one shot: pip install "datamaxi[async,ws]".

Authentication

DataMaxi+ endpoints are protected by an API key. Get one by registering at https://datamaxiplus.com/auth.

Set it once via the DATAMAXI_API_KEY environment variable (recommended, so the key stays out of source code) and every client picks it up automatically:

export DATAMAXI_API_KEY="your_api_key"

Or pass it explicitly to any client: Datamaxi(api_key="your_api_key").

Every client accepts the following options:

Option	Explanation
`api_key`	Your API key. Falls back to `DATAMAXI_API_KEY` when omitted.
`base_url`	API base URL. Defaults to `https://api.datamaxiplus.com`.
`timeout`	Seconds to wait for a server response. By default requests do not time out.
`proxies`	Proxy through which the request is routed.
`show_limit_usage`	(Deprecated) Return a dict with `"limit_usage"` and `"data"` keys. See Response Types.
`show_header`	(Deprecated) Return a dict with `"header"` and `"data"` keys. See Response Types.

Environment Variables

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

Quickstart

Set DATAMAXI_API_KEY (see Authentication), then pick the style that fits your app — synchronous, asyncio, or streaming over WebSocket. All three share the same resource tree, so an endpoint you learn in one works in the others.

Sync

from datamaxi import Datamaxi

# The client reads DATAMAXI_API_KEY from the environment automatically.
# Alternatively, pass api_key="your_api_key" explicitly.
maxi = Datamaxi()

# Telegram and Naver are mounted as `maxi.telegram` / `maxi.naver`.
channels, _ = maxi.telegram.channels()
trend = maxi.naver.trend(symbol="BTC")

# 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())

Async

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:
        df = await client.cex.candle(
            exchange="binance", symbol="BTC-USDT", interval="1d", market="spot"
        )
        print(df.head())

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

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


asyncio.run(main())

WebSocket

Requires the ws extra (pip install "datamaxi[ws]"). Streaming is async-only — see WebSockets for the full channel list.

import asyncio
from datamaxi.aio.ws import AsyncDatamaxiWS


async def main():
    # Reads DATAMAXI_API_KEY from the environment automatically.
    async with AsyncDatamaxiWS() as ws:
        # subscribe() returns an async iterator over live messages
        async for msg in await ws.ticker.subscribe("BTC-USDT@binance", market="spot"):
            print(msg["s"], msg.get("p"))  # symbol, price


asyncio.run(main())

Clients

The package ships these clients, all configured the same way (see Authentication):

Client	Import	Purpose
`Datamaxi`	`from datamaxi import Datamaxi`	Synchronous client for all REST data.
`AsyncDatamaxi`	`from datamaxi.aio import AsyncDatamaxi`	Async twin of `Datamaxi` (needs the `[async]` extra).
`AsyncDatamaxiWS`	`from datamaxi.aio.ws import AsyncDatamaxiWS`	Async WebSocket streaming (needs the `[ws]` extra).

Telegram and Naver are mounted on Datamaxi / AsyncDatamaxi (maxi.telegram, maxi.naver) so they reuse the client's shared session.

REST 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.

All examples use the sync Datamaxi client. Every endpoint works identically on the async client — just await the call. Each endpoint also has a dedicated page with a Sync/Async tab in the docs.

CEX

Data from centralized exchanges: prices, fees, wallet status, and listings.

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"
)

CEX Symbol

Per-base / per-symbol CEX metadata and aggregates: trading status, tags, cautions, delistings, volume, open interest, and liquidation.

metadata = maxi.cex.symbol.metadata(exchange="binance", base="BTC")
tags = maxi.cex.symbol.tags(exchange="binance", base="BTC")
cautions = maxi.cex.symbol.cautions(exchange="binance")
delistings = maxi.cex.symbol.delistings(exchange="binance")
volume = maxi.cex.symbol.volume(base="BTC")
oi = maxi.cex.symbol.oi(base="BTC", exchange="binance")
oi_stats = maxi.cex.symbol.oi_stats(base="BTC", exchange="binance", currency="USD")
liquidation = maxi.cex.symbol.liquidation(base="BTC", window="24h")

Derivatives and Leverage

Perpetual funding, liquidations, open interest, and margin-borrow rates.

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
)

Liquidation

CEX futures liquidation data: recent events, a firehose feed, heatmaps, maps, and bucketed history.

events = maxi.liquidation(exchange="binance", symbol="BTC-USDT", limit=100)
feed = maxi.liquidation.feed(limit=100)                       # most recent across all symbols
heatmap = maxi.liquidation.heatmap(window="1h", topN=10)      # window: 1h/4h/24h, topN 1-30
stats = maxi.liquidation.stats(window="1h")
liq_map = maxi.liquidation.map(base="BTC", exchange="binance", quote="USDT")
history = maxi.liquidation.symbol_history(
    symbol="BTC", quote="USDT", exchange="binance", interval="5m", window="24h"
)

Open Interest

CEX futures open interest: latest snapshots, reporting pairs, the token × exchange matrix, top-line aggregates, and aggregated history.

snapshot = maxi.open_interest(exchange="binance", symbol="BTC-USDT")
pairs = maxi.open_interest.list(exchange="binance")
overview = maxi.open_interest.overview(page=1, limit=20, key="binance", sort="desc")
summary = maxi.open_interest.summary(topN=10)
history = maxi.open_interest.history_aggregated(token_id="bitcoin", interval="1h")

Margin Borrow

Margin-borrow data for a single asset.

data = maxi.margin_borrow(asset="BTC")

Pricing and Cross-Exchange

Cross-exchange premiums, index prices, and forex rates.

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.

Index Price

Historical index-price time series for a single asset.

data = maxi.index_price(
    asset="BTC",
    from_="now - 1 month",   # from_ has a trailing underscore (from is a keyword);
    to="now",                # the wire-level query param is still "from"
    interval="5m",
)

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
)

Alternative Data

Off-exchange signals: Telegram channels and Naver search trends.

Telegram

Fetch Telegram channel messages and metadata.

# Fetch channels
data, next_request = maxi.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 = maxi.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).

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

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

WebSockets

Stream real-time market data over the DataMaxi+ WebSocket API. The WebSocket client is async-only and lives behind the ws extra:

pip install "datamaxi[ws]"

import asyncio
from datamaxi.aio.ws import AsyncDatamaxiWS


async def main():
    # Reads DATAMAXI_API_KEY from the environment, or pass api_key=... explicitly.
    async with AsyncDatamaxiWS() as ws:
        stream = await ws.ticker.subscribe("BTC-USDT@binance", market="spot")
        async for msg in stream:
            print(msg["s"], msg.get("p"))  # symbol, price


asyncio.run(main())

Channels

Each accessor on AsyncDatamaxiWS maps to one channel. subscribe(*params) is a coroutine returning an async iterator over live messages; stream() (for the param-less firehose feeds) does the same. Pass the raw param strings shown below — you can also read the expected shape at runtime via ws.<channel>.param_format.

Accessor	Call	Param format	Plan
`ws.ticker`	`subscribe(*p, market="spot"\|"futures")`	`SYMBOL@exchange[@currency@conversionBase]`	Basic
`ws.forex`	`subscribe(*p)`	`SYMBOL`	Basic
`ws.premium`	`subscribe(*p)`	`src:tgt:tokenId:srcQuote:tgtQuote:srcMkt:tgtMkt`	Basic
`ws.funding_rate`	`subscribe(*p)`	`SYMBOL@exchange`	Basic
`ws.open_interest`	`subscribe(*p)`	`SYMBOL@exchange`	Basic
`ws.liquidation`	`subscribe(*p)`	`SYMBOL@exchange`	Basic
`ws.liquidation_feed`	`stream()`	— (firehose, no params)	Basic
`ws.announcement`	`subscribe()`	— (no params)	Pro+

Multiplexing and filtering

One connection is opened per channel and multiplexes every param you subscribe to. Because the protocol tags messages by payload fields (not by a subscription id), subscribe() yields every message on the channel — filter client-side by symbol (msg["s"]) when you subscribe to more than one:

stream = await ws.ticker.subscribe(
    "BTC-USDT@binance", "ETH-USDT@binance", market="spot"
)
async for msg in stream:
    if msg["s"] == "BTC-USDT":
        handle_btc(msg)

Add or drop params on the fly:

await ws.ticker.subscribe("SOL-USDT@binance", market="spot")   # add
await ws.ticker.unsubscribe("SOL-USDT@binance", market="spot") # remove

Not every channel supports removing an individual param server-side — liquidation and open_interest are subscribe-only. Closing the client (see Lifecycle) always stops all streams.

Firehose feeds

ws.liquidation_feed needs no subscription — call stream() and consume:

async for evt in await ws.liquidation_feed.stream():
    print(evt["s"], evt.get("sd"), evt.get("p"))  # symbol, side, price

Reconnect and keepalive

The client is resilient by default:

Auto-reconnect — if the connection drops it reconnects and replays your active subscriptions, so your async for loop resumes without extra code. Disable with AsyncDatamaxiWS(reconnect=False).
Keepalive — an app-level PING is sent every 30s to stay under the server's idle timeout. Tune with the keepalive=<seconds> argument (0 disables it).

Lifecycle

Use AsyncDatamaxiWS as an async context manager (shown above) so all open connections close cleanly, or manage it yourself:

ws = AsyncDatamaxiWS()
try:
    async for msg in await ws.forex.subscribe("USD-KRW"):
        ...
finally:
    await ws.aclose()

Constructor options: api_key, base_url (derives the wss:// URL) or an explicit ws_url, keepalive, reconnect, and connect_kwargs (passed through to the underlying websockets.connect).

Message shapes

Each message is a plain dict. Compact channels use short wire keys (s = symbol, plus per-channel fields like p, e, r, oi, …), while others (premium, announcements) use descriptive keys. The exact typed shape of every channel is generated into datamaxi._ws_models as TypedDicts, and field meanings are documented in the API docs:

from datamaxi._ws_models import TickerMessage, PremiumMessage

Orderbook streaming is intentionally not exposed.

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'>

Response metadata (rate-limit headers, etc.) is available on the client after a call via maxi.<resource>.last_response. The older show_limit_usage / show_header options that folded metadata into the return value are deprecated and will be removed in a future major release.

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()

On the async client, next_request is itself a coroutine — await it: data2, _ = await next_request().

Error Handling

All SDK exceptions subclass datamaxi.error.Error:

Exception	Raised when
`ClientError`	Server returns a 4xx response. Has `status_code`, `error_message`, `header`, `error_data`.
`ServerError`	Server returns a 5xx response. Has `status_code`, `message`.
`ParameterRequiredError`	A required parameter was missing/empty.
`AtLeastOneParameterRequiredError`	An endpoint needs at least one of a set of parameters, none given.

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}")

Async Client

AsyncDatamaxi is the asynchronous counterpart to Datamaxi (built on httpx). It mirrors the same resource tree and arguments, 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(...)).

Every endpoint in the REST API Reference works the same under the async client — see the docs where each example has a Sync/Async tab. For real-time streaming, see WebSockets.

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/:

File	Contents
`common.txt`	Runtime dependencies (`requests`, `pandas`).
`requirements.txt`	Alias for `common.txt`.
`requirements-test.txt`	`common.txt` + test/lint tooling (pytest, responses, black, flake8).
`requirements-dev.txt`	`requirements-test.txt` + docs tooling (mkdocs).

The [async] and [ws] extras add httpx and websockets respectively — the test stack installs them so the async and WebSocket suites can run.

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

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

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docs		docs
requirements		requirements
tests		tests
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LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.md		README.md
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DataMaxi+ Python SDK

Table of Contents

Installation

Authentication

Environment Variables

Quickstart

Clients

REST API Reference

CEX

CEX Candle Data

CEX Ticker Data

CEX Trading Fees

CEX Wallet Status

CEX Announcements

CEX Token Updates

CEX Symbol

Derivatives and Leverage

Funding Rate

Liquidation

Open Interest

Margin Borrow

Pricing and Cross-Exchange

Premium

Index Price

Forex

Alternative Data

Telegram

Naver Trend

WebSockets

Channels

Multiplexing and filtering

Firehose feeds

Reconnect and keepalive

Lifecycle

Message shapes

Response Types

Pagination

Error Handling

Async Client

Local Development

Tests

Links

Contributing

License

About

Topics

Resources

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