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Gapless Crypto Data

PyPI version Python Versions Downloads License: MIT

Cryptocurrency OHLCV data collection with gap-free guarantee. Retrieves microstructure-enriched kline data from Binance Public Data Repository with automatic gap detection and filling.

Installation

# UV (recommended)
uv add gapless-crypto-data

# pip
pip install gapless-crypto-data

Quick Start

import gapless_crypto_data as gcd

# Fetch historical data
df = gcd.download("BTCUSDT", timeframe="1h", start="2024-01-01", end="2024-06-30")

# Fetch recent data with limit
df = gcd.fetch_data("ETHUSDT", timeframe="4h", limit=1000)

# Get available symbols and timeframes
symbols = gcd.get_supported_symbols()
timeframes = gcd.get_supported_timeframes()

# Fill gaps in existing data directory
results = gcd.fill_gaps("./data")

Data Format

Returns pandas DataFrames with microstructure columns:

Column Type Description
date datetime64 Period open timestamp
open, high, low, close float64 OHLC prices
volume float64 Base asset volume
close_time datetime64 Period close timestamp
quote_asset_volume float64 Quote asset volume
number_of_trades int64 Trade count
taker_buy_base_asset_volume float64 Taker buy volume (base)
taker_buy_quote_asset_volume float64 Taker buy volume (quote)

See Data Format Specification for column semantics and constraints.

Supported Timeframes

All Binance spot kline intervals. Query dynamically:

import gapless_crypto_data as gcd
print(gcd.get_supported_timeframes())

API Reference

Function-based API

import gapless_crypto_data as gcd

# Primary collection function
df = gcd.download(symbol, timeframe, start, end)
df = gcd.fetch_data(symbol, timeframe, limit=None, start=None, end=None)

# Gap filling
results = gcd.fill_gaps(directory, symbols=None)

# Discovery
symbols = gcd.get_supported_symbols()
timeframes = gcd.get_supported_timeframes()

Class-based API

from gapless_crypto_data import BinancePublicDataCollector, UniversalGapFiller

# Data collection with full control
collector = BinancePublicDataCollector(
    symbol="BTCUSDT",
    start_date="2024-01-01",
    end_date="2024-12-31"
)
result = collector.collect_timeframe_data("1h")
df = result["dataframe"]

# Gap detection and filling
gap_filler = UniversalGapFiller()
gaps = gap_filler.detect_all_gaps(csv_file, timeframe)
result = gap_filler.process_file(csv_file, timeframe)

Full API documentation: Python API Reference

Data Sources

Source Method Use Case
Binance Public Data Repository Monthly/daily ZIP archives Historical bulk collection
Binance REST API Per-request klines Gap filling, recent data

Collection strategy: Repository archives for bulk historical data, API for gaps and recent periods. See Data Collection Guide.

AI Agent Integration

Programmatic discovery via __probe__ module:

import gapless_crypto_data
probe = gapless_crypto_data.__probe__

# API discovery
probe.discover_api()
probe.get_capabilities()
probe.get_task_graph()

See Probe Usage for AI agent integration patterns.

Development

Setup

git clone https://github.com/terrylica/gapless-crypto-data.git
cd gapless-crypto-data
uv venv && source .venv/bin/activate
uv sync --dev
uv run pre-commit install

Commands

Task Command
Run tests uv run pytest
Format uv run ruff format .
Lint uv run ruff check --fix .
Type check uv run mypy src/
Build uv build

Project Structure

src/gapless_crypto_data/
├── __init__.py          # Package exports
├── api.py               # Function-based API
├── __probe__.py         # AI agent discovery
├── collectors/          # Data collection
├── gap_filling/         # Gap detection/filling
└── validation/          # Data validation

Full development guide: Development Setup

Architecture

  • BinancePublicDataCollector: Bulk data retrieval from public repository
  • UniversalGapFiller: Gap detection and API-based filling
  • AtomicCSVOperations: Corruption-proof file operations
  • ValidationStorage: DuckDB-backed validation persistence

Architecture documentation: Overview

License

MIT License - see LICENSE

Metadata

Release files for gapless-crypto-data 5.0.3

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