gapless-network-data 4.4.0

Ethereum blockchain network metrics collection infrastructure with zero-gap guarantee (multi-chain support planned)

Gapless Network Data

Ethereum blockchain network metrics for financial time series forecasting.

Quick Start

import gapless_network_data as gmd

# Fetch latest blocks (recommended for live trading)
df = gmd.fetch_blocks(limit=1000)

# Compute block utilization (alpha feature #2)
df['utilization'] = df['gas_used'] / df['gas_limit']

# Date range query
df = gmd.fetch_blocks(start='2024-01-01', end='2024-01-31')

Installation

pip install gapless-network-data

AI Agent Discoverability

This package is optimized for Claude Code CLI and AI coding agents.

Probe Module

import gapless_network_data as gmd

# Get ranked features for ML pipelines
features = gmd.probe.get_alpha_features()
for f in features[:5]:
    print(f"{f.rank}. {f.name} ({f.importance})")

# Get protocol era boundaries
eras = gmd.probe.get_protocol_eras()

# Get setup workflow
workflow = gmd.probe.get_setup_workflow()

# Get example code
print(gmd.probe.get_quick_start())

Alpha Feature Rankings

AI agents should prioritize these features for ML pipelines:

Rank	Feature	Importance	Formula
1	base_fee_per_gas	critical	raw
2	block_utilization	critical	gas_used / gas_limit
3	transaction_count	high	raw
4	timestamp	high	raw
5	number	high	raw
6	size	medium	raw
7	blob_gas_used	medium	raw (post-EIP4844)
8	excess_blob_gas	low	raw (post-EIP4844)
9	gas_limit	low	raw
10	gas_used	low	raw

Get rankings programmatically: gmd.probe.get_alpha_features()

Protocol Era Boundaries

Filter data appropriately based on protocol changes:

• EIP-1559 (block 12,965,000, Aug 2021): base_fee_per_gas introduced
• The Merge (block 15,537,394, Sep 2022): difficulty=0 forever
• EIP-4844 (block 19,426,587, Mar 2024): blob_gas fields introduced

Get eras programmatically: gmd.probe.get_protocol_eras()

API Reference

fetch_blocks()

gmd.fetch_blocks(
    start: str | None = None,     # ISO 8601 date
    end: str | None = None,       # ISO 8601 date
    limit: int | None = None,     # Max blocks
    include_deprecated: bool = False  # Include difficulty fields
) -> pd.DataFrame

Returns pandas DataFrame with columns:

• timestamp (datetime64[ns, UTC])
• number (uint64)
• gas_limit, gas_used, base_fee_per_gas, transaction_count, size (uint64)
• blob_gas_used, excess_blob_gas (uint64, nullable)

Deprecated Fields

Excluded by default (use include_deprecated=True for pre-Merge analysis):

• difficulty: Always 0 post-Merge (Sep 2022)
• total_difficulty: Frozen post-Merge

Setup

Credentials via Doppler (recommended) or environment variables.

# Option 1: Doppler (team setup)
doppler configure set token <token_from_1password>
doppler setup --project gapless-network-data --config prd

# Option 2: Environment variables
export CLICKHOUSE_HOST_READONLY=<host>
export CLICKHOUSE_USER_READONLY=<user>
export CLICKHOUSE_PASSWORD_READONLY=<password>

Get setup instructions: gmd.probe.get_setup_workflow()

Data Coverage

• Blocks: 23.87M Ethereum blocks (2015-2025)
• Update frequency: Real-time (~12 second intervals)
• Storage: ClickHouse Cloud (AWS)
• Deduplication: Automatic via ReplacingMergeTree

Exceptions

All exceptions include structured context (timestamp, endpoint, HTTP status):

• CredentialException: Credential resolution failed
• DatabaseException: ClickHouse query failed
• MempoolException: Base exception class

Feature Engineering Integration

Combine with OHLCV price data:

import gapless_crypto_data as gcd
import gapless_network_data as gmd

# Fetch both data sources
df_ohlcv = gcd.get_data(symbol="ETHUSDT", timeframe="1m", start_date="2024-01-01")
df_blocks = gmd.fetch_blocks(start="2024-01-01", end="2024-01-02")

# Temporal alignment (forward-fill prevents data leakage)
df_blocks_aligned = df_blocks.set_index('timestamp').reindex(
    df_ohlcv.index, method='ffill'
)

# Join and engineer features
df = df_ohlcv.join(df_blocks_aligned)
df['gas_pressure'] = df['base_fee_per_gas'] / df['base_fee_per_gas'].rolling(60).median()
df['block_utilization'] = df['gas_used'] / df['gas_limit']

Infrastructure (Reference)

Dual-pipeline architecture for production reliability:

Component	Purpose	Technology
BigQuery Sync	Hourly batch from public dataset	Cloud Run Job
Real-Time Collector	Block-level streaming	e2-micro VM
Database	Storage with deduplication	ClickHouse Cloud
Monitoring	Dead Man's Switch	Healthchecks.io

Related Projects

• gapless-crypto-data - OHLCV data collection
• BigQuery Ethereum Dataset

Documentation

• Architecture Overview
• Data Format Specification

License

MIT License