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yllvar/README.md

🚀 About Me

Quantitative developer and financial technologist specializing in:

  • Algorithmic Trading: HFT systems, market-making algorithms, and quantitative research
  • Blockchain Development: Smart contracts, DeFi protocols, and Web3 infrastructure
  • Data Science: Time-series forecasting, statistical arbitrage, and ML-driven strategies
  • System Architecture: High-performance trading systems and distributed computing
graph TD
    A[Quant Developer] --> B[Algorithmic Trading]
    A --> C[Blockchain]
    A --> D[Machine Learning]
    B --> E[High-Frequency Systems]
    B --> F[Market Making]
    C --> G[Smart Contracts]
    C --> H[DeFi Protocols]
    D --> I[Predictive Models]
    D --> J[Anomaly Detection]
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🎯 Core Skills

⌨️ Programming Languages

JavaScript Python Solidity TypeScript Go Rust Vyper Move

🎛 Frameworks and Runtime Environments

Angular Node.js PyTorch TensorFlow NestJS Next.js

⚒️ Development Tools

Hardhat Truffle DappTools Foundry Anchor

🗃 Dependency and Environment Management

Conda npm pnpm Yarn Docker

🛠 Cloud & Infrastructure

AWS Cloudflare Kubernetes Terraform

🔧 Development Tools & Libraries

Git Postman NumPy OpenCV SciPy

🌐 Web3 & Blockchain

ethers.js web3.js The Graph

🛠 Technical Arsenal

Core Competencies

pie
    title Technology Distribution
    "Blockchain" : 40
    "Quant Finance" : 35
    "Machine Learning" : 20
    "System Design" : 5
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Languages & Frameworks

Category Technologies
Trading Python Rust C++
Blockchain Solidity Move TypeScript
Data PyTorch Pandas NumPy

Blockchain Ecosystem

graph LR
    EVM[EVM] --> HD[Hardhat]
    EVM --> FD[Foundry]
    EVM --> WC[Web3.js]
    SOL[Solana] --> AN[Anchor]
    SOL --> SP[Seahorse]
    APT[Aptos] --> MS[Move SDK]
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🔭 Featured Projects

Trading & Quantitative Finance

journey
    title Trading System Development
    section Research
      Market Analysis: 5: Me
      Strategy Design: 8: Me
    section Development
      Backtesting Engine: 9: Me
      Optimization: 7: Me
    section Deployment
      Execution System: 8: Me
      Risk Management: 6: Me
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  1. Market-Making Bot: Rust-based HFT system with adaptive spread logic
  2. Quant Research Framework: Python toolkit for strategy development and backtesting
  3. Volatility Arbitrage: Statistical arbitrage strategy using options pricing models

Blockchain & Web3

  1. DeFi Yield Optimizer: Smart contract system for automated yield strategy rotation
  2. Cross-Chain Bridge: EVM-compatible bridge with zero-knowledge proofs
  3. NFT Marketplace: Gas-efficient marketplace with batch transactions

📈 Development Activity

graph BT
    contributions[Code Contributions] -->|Daily| trading[Trading Systems]
    contributions -->|Weekly| blockchain[Blockchain Projects]
    contributions -->|Monthly| tools[Developer Tools]
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Metric Status
Total Repos Repos
Active Projects Active
Languages Languages

📫 Collaboration Opportunities

graph LR
    Idea -->|Discuss| Whitepaper
    Whitepaper -->|Implement| Prototype
    Prototype -->|Deploy| Production
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Interested in:

  • Advanced trading strategies (HFT, market-making)
  • Blockchain protocol development
  • Quantitative research collaborations
  • Open-source financial infrastructure

Not interested in:

  • Job recruiter and resumé parasites from LinkedIn

Visitors

Popular repositories Loading

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    Using GMGN Smart Degen to analyze top performing wallet and rank it based on profitability and honeypot evaluation

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    Kalshi Advanced Quantitative Trading Bot is an enterprise-grade automated trading system designed for the Kalshi event-based prediction market. Built with cutting-edge quantitative algorithms and p…

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    Analyze gmgn.ai token data over multiple timeframes, groups by token address, computes the aggregated metrics (including a consistency count), filters out tokens based on volume and market cap thre…

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    Agentic trading platform integrating real-time market data, technical analysis, and large language models (LLMs) for AI-powered trading signals.

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    Using NLTK to analyze the trending Meta from recent Pump.Fun launch, applying sentiment analysis and time series analysis to capture current meme hype

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