shbadrconsulting-source

Hello DEV Community! 👋

I wanted to share my latest open-source project: Hunter-Base-Intelligence (v17 Plus). It is a fully local-only cryptocurrency intelligence dashboard that scans DEX tokens on the Base blockchain, scores them using a multi-factor logic, and simulates a paper-trading shadow portfolio.

🛡️ Why Local-Only?

Most on-chain analytics tools require sensitive private keys, leak user data, or rely heavily on slow, paid external infrastructure. I engineered this tool to be fully local—it requires no wallets, no seed phrases, and sends your data nowhere. Pure local analysis using Python, Flask, and SQLite.

⚙️ How It Works (Core Architecture)

The ecosystem runs on a continuous ~60-second scan cycle:

  1. scanner.py: Discovers active and newly created tokens using DexScreener, BaseScan, and direct EVM RPC factory logs.
  2. scorer.py: Every token is evaluated across 6 independent dimensions (Momentum, Manual Trade Feasibility, Execution Reality, Money Flow, Multi-Timeframe Pulse, and Composite Rank).
  3. hunter_court.py: A proprietary "Court" analytics engine that runs a risk-free paper-trading shadow portfolio with realistic gas, fee, and slippage simulation. It evaluates its own past decisions to continuously calibrate scoring thresholds!

📊 System Features

  • Adaptive Exit Parameters: Automated position sizing and execution simulation (exit_engine.py).
  • System Guardian: Keeps the system running 24/7 with auto-restart on crashes and automatic local database backups (system_guardian.py).
  • Beautiful Dashboard: Clean, real-time local web interface for tracking active simulated trades and market analytics.

📂 Explore and Contribute

The project is licensed under the MIT License and is open for contributions. Whether you want to optimize the scoring algorithms, expand the web API endpoints, or improve the dashboard frontend, feel free to dive in!

👉 Check out the Repository here: https://github.com/shbadrconsulting-source/Hunter-Base-Intelligence

I would love to hear your feedback, thoughts on the scoring logic, or any feature suggestions! Drop a comment below or star the repo on GitHub if you like the concept. ⭐️