lotech-market-data 3.0.0

Python client for accessing LO:TECH market data

lotech-market-data

Python client for accessing Lotech market data from AWS Athena.

Installation

pip install lotech-market-data

For running the advanced plotting examples:

pip install lotech-market-data[examples]

Usage

Basic Example

import logging
from datetime import datetime, timezone
from lotech_market_data import LotechMarketData

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

client = LotechMarketData()

# Get time range for today
query_date = datetime.now(timezone.utc).date()
start = datetime.combine(query_date, datetime.min.time()).replace(tzinfo=timezone.utc)
end = datetime.combine(query_date, datetime.max.time()).replace(tzinfo=timezone.utc)

# Fetch top of book data
df = client.load_top_of_book(
    exchange="BINANCE",
    instrument="BTC-USDT:SPOT",
    start=start,
    end=end,
)

logger.info("Total rows: %d", df.height)

API Methods

The LotechMarketData class provides the following methods:

Market Data Methods

All market data methods accept the following parameters:

• exchange: Single exchange string or list of exchanges (e.g., "BINANCE" or ["BINANCE", "BYBIT"])
• instrument: Single instrument or list (e.g., "BTC-USDT:SPOT" or ["BTC-USDT:SPOT", "ETH-USDT:SPOT"])
• start: Start datetime (timezone-aware)
• end: End datetime (timezone-aware)
• columns (optional): List of columns to return

Available methods:

• load_top_of_book() - Top of book (bid/ask) data
• load_trades() - Trade data
• load_funding() - Funding rate data
• load_reference_price() - Reference price data
• load_open_interest() - Open interest data
• load_orderbook_incremental() - Incremental orderbook updates

Instrument Static

# Get all instruments for an exchange (latest)
df = client.load_instr_static(exchange="BINANCE")

# Get all instruments at a specific datetime
df = client.load_instr_static(
    exchange="BINANCE",
    datetime=some_datetime
)

# Filter for specific instrument(s)
df = client.load_instr_static(
    exchange="BINANCE",
    instrument="BTC-USDT:SPOT"
)

df = client.load_instr_static(
    exchange="BINANCE",
    instrument=["BTC-USDT:SPOT", "ETH-USDT:SPOT"]
)

Column Filtering

You can filter columns to reduce data transfer:

df = client.load_top_of_book(
    exchange="BINANCE",
    instrument="BTC-USDT:SPOT",
    start=start,
    end=end,
    columns=["exchange", "instrument", "ingress_ts", "bid_price", "ask_price"],
)

Multiple Exchanges

Query multiple exchanges:

df = client.load_top_of_book(
    exchange=["BINANCE", "BYBIT"],
    instrument="BTC-USDT:SPOT",
    start=start,
    end=end,
)

Multiple Instruments

Query multiple instruments:

df = client.load_top_of_book(
    exchange="BINANCE",
    instrument=["BTC-USDT:SPOT", "ETH-USDT:SPOT", "SOL-USDT:SPOT"],
    start=start,
    end=end,
)

Available Exchanges

The set of exchanges is fetched dynamically from the API and reflects exactly what your API key is permissioned for — the client does not hardcode a list:

exchanges = LotechMarketData.get_available_exchanges()
logger.info("Available exchanges: %s", exchanges)

Instrument Format

Instruments follow the format: SYMBOL:KIND[:SUBKIND]

Examples:

• BTC-USDT:SPOT - Spot market
• BTC-USDT:PERP:LINEAR - Linear perpetual
• BTC-USDT:PERP:INVERSE - Inverse perpetual
• BTC-USD:FUT:LINEAR - Linear future
• BTC-USD:FUT:INVERSE - Inverse future

Examples

Basic Examples (examples/basic/)

These examples showcase fetching each data type and display all available columns:

• top_of_book.py - Fetch bid/ask data
• trades.py - Fetch trade data
• orderbook_incremental.py - Fetch orderbook updates and build orderbook
• funding.py - Fetch funding rate data
• open_interest.py - Fetch open interest data
• reference_price.py - Fetch reference price data
• instrument_static.py - Fetch instrument metadata
• list_exchanges.py - List all available exchanges

Advanced Examples (examples/advanced/)

These examples demonstrate more complex use cases:

• plot_btc_usdt_exchanges.py - Plot BTC-USDT mid price across multiple exchanges
• plot_trades.py - Visualize trades with volume bars
• analyze_instruments.py - Analyze instrument metadata with statistics
• animate_orderbook.py - Create animated orderbook visualization
• plot_polymarket_binary.py - Compare Polymarket binary options to GBM theoretical pricing

Example Outputs

The advanced examples generate visualizations in examples/outputs/:

BTC-USDT Mid Price Across Exchanges

Comparison of BTC-USDT perpetual mid price across BINANCE, BYBIT, OKX, BITMART, and BITMEX exchanges showing price movements and spreads.

LTC-USDT Trades Visualization

Trades visualization for LTC-USDT perpetual contracts showing taker buy/sell trades colored by exchange, with weighted mid price overlay and 30-second volume bars.

DOGE-USDT Orderbook Animation

Animated visualization of DOGE orderbook across 5 exchanges (BINANCE, BYBIT, OKX, COINBASEUS, BITSTAMP) showing cumulative order book depth, mid price movement, and cross-exchange dynamics at 10ms intervals.