Binance Historical Data: Download Crypto Price History with Python

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Accessing accurate and comprehensive historical cryptocurrency data is essential for traders, developers, and analysts building strategies, backtesting models, or conducting market research. The binance_historical_data Python package simplifies this process by enabling users to download complete price and volume records directly from Binance servers — all with minimal code and no requirement for a Binance account.

Whether you're analyzing spot markets or futures trading activity, this lightweight yet powerful tool supports multiple asset classes and data types, delivering structured .csv files ready for immediate use in data analysis pipelines.

What Is binance_historical_data?

binance_historical_data is a Python library (compatible with Python 3.8+) designed to streamline the retrieval of historical crypto market data from Binance. It eliminates complex API handling by wrapping Binance's data endpoints into a clean, intuitive interface.

With just three lines of code, you can:

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Key Features

Installation

Getting started is straightforward using pip:

pip install binance_historical_data

Ensure your environment meets the minimum Python version requirement (3.8 or higher). Once installed, import the package and initialize the data dumper.

How to Use the Data Dumper

Initialize the Main Object

Start by creating an instance of BinanceDataDumper with your preferred configuration:

from binance_historical_data import BinanceDataDumper

data_dumper = BinanceDataDumper(
    path_dir_where_to_dump=".",
    asset_class="spot",           # Options: spot, um, cm
    data_type="klines",           # e.g., aggTrades, klines, trades
    data_frequency="1m"           # e.g., 1m, 5m, 1h, 1d
)

Configuration Parameters

Downloading Historical Data

Core Method: dump_data()

The primary method to fetch data is simple and flexible:

data_dumper.dump_data(
    tickers=None,
    date_start=None,
    date_end=None,
    is_to_update_existing=False,
    tickers_to_exclude=["UST"]
)

Parameter Breakdown

Example: Full Historical Dump

To download all kline data for every USDT trading pair since inception:

data_dumper.dump_data()
⚠️ Note: The initial full download may take up to 40 minutes depending on your connection and system performance.

Example: Update Existing Dataset

To refresh your local dataset with newly available data:

data_dumper.dump_data(is_to_update_existing=True)

This intelligent update avoids re-downloading existing files and only fetches new entries.

Example: Targeted Date Range Reload

Re-fetch data for a specific period to correct gaps or refresh corrupted files:

import datetime

data_dumper.dump_data(
    date_start=datetime.date(2021, 1, 1),
    date_end=datetime.date(2022, 1, 1),
    is_to_update_existing=True
)

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Managing Local Data Files

After downloading, data is stored as .zip archives and automatically unzipped into structured directories.

Delete Outdated Daily Files

Once monthly aggregates are downloaded, daily files become redundant. Clean them up efficiently:

data_dumper.delete_outdated_daily_results()

This reduces storage usage without losing data integrity.

Kline Data Format (.csv Columns)

Each kline (candlestick) record contains the following fields:

This structure aligns with standard OHLCV formats used in financial analysis libraries like Pandas and TA-Lib.

Additional Utility Methods

Enhance your workflow with built-in helper functions.

Get All Available Trading Pairs

all_pairs = data_dumper.get_list_all_trading_pairs()
print(all_pairs)

Returns a list of all active tickers on Binance matching the selected asset class.

Find Earliest Available Data Date

min_date = data_dumper.get_min_start_date_for_ticker("BTCUSDT")
print(min_date)

Useful when setting up backtests or validating data availability.

List Locally Stored Tickers

saved_tickers = data_dumper.get_all_tickers_with_data(timeperiod_per_file="daily")
print(saved_tickers)

Helps manage large datasets across different time granularities.

Retrieve Dates with Saved Data

dates = data_dumper.get_all_dates_with_data_for_ticker("ETHUSDT", "monthly")
print(dates)

Ideal for auditing or incremental processing.

Access File Paths and Names

Determine where specific files are stored:

local_dir = data_dumper.get_local_dir_to_data("BTCUSDT", "monthly")
filename = data_dumper.create_filename("BTCUSDT", datetime.date.today(), "daily")

Facilitates integration with external scripts or backup systems.

Frequently Asked Questions (FAQ)

Q: Do I need a Binance account or API key to use this package?
A: No. This tool accesses public Binance endpoints, so no registration or authentication is required.

Q: How frequently is new data available?
A: Daily updates appear on Binance servers shortly after 00:00 UTC. There may be a brief delay before they’re accessible via the API.

Q: Can I download minute-level data for multiple years?
A: Yes. The package supports granular intervals (down to 1-minute) and long time ranges starting from 2017.

Q: Is futures market data supported?
A: Absolutely. You can download mark price klines, index prices, and metrics for both USD-margined (um) and coin-margined (cm) futures.

Q: What happens if my download gets interrupted?
A: Use is_to_update_existing=True to resume. The tool detects missing dates and completes the download seamlessly.

Q: How much disk space will I need?
A: A full USDT spot dataset at 1-minute resolution may require several gigabytes. Monthly aggregation helps reduce footprint significantly.

Final Thoughts

The binance_historical_data package empowers developers and analysts with frictionless access to high-quality crypto market history. Its simplicity, flexibility, and reliability make it ideal for research, strategy development, and long-term trend analysis.

Whether you're running quantitative models or visualizing price movements, having a complete local copy of Binance data puts you in full control of your analytical pipeline.

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