Gapless Crypto Data
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
Links
1 maintainer
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