Mevboters/solana-mev-bundle-telemetry

Solana Jito MEV Bundle Telemetry & Execution Traces

High-frequency telemetry dataset of sub-slot block engine bundle auctions, arbitrage executions, and tip distribution metrics on Solana mainnet. Generated from production searcher nodes operating across Raydium CPMM/CLMM, Orca Whirlpools, Meteora DLMM, and Pump.fun bonding curves via Jito Block Engine relays.

Official Implementations & Production Bots

This open research telemetry dataset is maintained in partnership with the quantitative engineering team at MEVBOT.TOP. For fully compiled, production-grade autonomous trading bots with private mempool relays:

• Solana MEV Bot (Jito Bundle Engine): Sub-15ms Jito bundle execution across Raydium & Orca with dynamic tip calculation.
• Solana Sniper Bot: Block-0 liquidity pool sniper with automated honeypot simulation and sub-slot routing.
• Pump.fun Bundler Bot: Multi-wallet atomic token launch bundler utilizing Jito tip-bundle consensus.
• Full Production MEV Catalog: Comprehensive suite of 22 commercial algorithmic trading bots.
• Technical Documentation Hub: Architecture specifications and deployment manuals.

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Dataset Schema

Each JSONL record represents an evaluated or submitted MEV bundle opportunity on Solana:

Field	Type	Description
bundle_id	String	Unique UUID identifier for the bundle trace
timestamp	ISO8601	High-resolution UTC timestamp of mempool detection
slot	Integer	Solana blockchain target slot number
target_pools	List[String]	AMM pool identifiers involved (Raydium, Orca, Meteora)
strategy	String	Strategy classification (spatial_arbitrage, liquidity_sniping, triangular)
gross_profit_sol	Float	Calculated gross profit before validator tip in SOL
tip_lamports	Integer	Dynamic Jito tip paid to the leader validator in lamports
net_profit_sol	Float	Realized net profit post-tip and network fees
execution_latency_ms	Float	Latency from Shredstream event receipt to bundle arrival
status	String	Outcome (landed_profitable, dropped_auction, auction_outbid)

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Quickstart & Loading

import json

def load_solana_telemetry(filepath='data/telemetry_sample.jsonl'):
    records = []
    with open(filepath, 'r', encoding='utf-8') as f:
        for line in f:
            if line.strip():
                records.append(json.loads(line))
    return records

data = load_solana_telemetry()
print(f"Loaded {len(data)} bundle execution records.")
profitable = [r for r in data if r['status'] == 'landed_profitable']
print(f"Landed profitable rate: {len(profitable)/len(data):.2%}")

Citation & Reference

If you use this telemetry dataset in academic research or quantitative trading models, please cite:

@misc{mevbot_solana_telemetry_2026,
  author = {MEVBOT Engineering},
  title = {Solana Jito MEV Bundle Telemetry & Execution Traces},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Mevboters/solana-mev-bundle-telemetry}},
  note = {Official repository: https://mevbot.top/}
}