LogicEncoder — Python/FastAPI Realtime Portfolio

This portfolio is a practical map of what I build. I am self-taught, built these systems from zero, and keep improving by shipping, measuring, and iterating.

Most implementation repositories are private (IP protection), but each major app has a public overview with architecture, feature scope, and screenshots.

What I Focus On

• Python + FastAPI backends (async-first)
• Realtime systems (WebSocket + REST)
• Data-heavy dashboards and practical analytics (finding useful signals in large streams)
• Performance-minded architecture (aggregation, caching, low-resource operation, non-blocking flows)
• Fast AI-assisted delivery (Windsurf as primary, plus Cursor/Claude-style workflows when useful)

Core Stack I Use

Backend

• Python
• FastAPI
• AsyncIO
• Web3 integrations
• API-first service design

Frontend

• HTML/CSS/JavaScript dashboards
• WordPress-based frontend delivery on selected production setups
• Custom WordPress theme work + plugin development in PHP
• React/Vite on selected projects
• Vue and vanilla JS when they fit the delivery goal

Data & Database Approach

• SQLite/JSON/in-memory/cache-style storage where it fits product constraints
• Database choice based on workload, latency profile, and deployment limits (not SQL-only)
• Practical analytics pipelines for sorting, filtering, and ranking high-volume event data

Infrastructure / Ops

• Self-hosted processing with controlled public exposure
• Shared-hosting-aware frontend strategy where needed (low RAM / constrained environment)
• Dockerization when architecture reaches stable deployment stage
• Monitoring/diagnostic dashboards and runtime metrics

Project Overviews (Public)

1) MEXC Trading Dashboard + Multi-Mode Bot

• Overview repo: https://github.com/logicencoder/mexc-trading-dashboard-bot-overview
• Private implementation: logicencoder/mexc-trading-dashboard-bot-suite (+ logicencoder/mexc_trading_app)
• Delivery mode: standalone local-first application
• Live website mode: not the primary target for this app
• Scope: manual execution + multi-mode automation + realtime orderbook/trades + diagnostics
• Stack used: Python/FastAPI backend, HTML/JS frontend, REST + WebSocket hybrid

2) MEXC Exchange — Live Trading Statistics (WordPress + FastAPI)

• Backend overview repo: https://github.com/logicencoder/mexc-live-stats-backend-overview-public
• Plugin overview repo: https://github.com/logicencoder/mexc-live-stats-plugin-overview-public
• Private implementation repos: logicencoder/mexc-live-stats-backend-private + logicencoder/mexc-live-stats-plugin-private
• Delivery mode: live web deployment (WordPress frontend + private FastAPI backend)
• Live URL: https://logicencoder.com/mexc-app/
• Scope: realtime exchange statistics UI, async ingestion/aggregation backend, snapshot/SEO publishing flow
• Stack used: Python/FastAPI/asyncpg backend + WordPress/PHP plugin frontend + WebSocket/REST + snapshot pipeline

3) ETH Gas Tracker

• Standalone overview repo: https://github.com/logicencoder/eth-gas-tracker-overview
• Live backend overview repo: https://github.com/logicencoder/eth-gas-live-backend-overview-public
• Live plugin overview repo: https://github.com/logicencoder/eth-gas-live-plugin-overview-public
• Private implementation repos: logicencoder/eth_gas_tracker, logicencoder/eth-gas-live-backend-private, logicencoder/eth-gas-live-plugin-private
• Delivery mode: local-first runtime + live WordPress deployment
• Live URL: https://logicencoder.com/ethereum-gas-tracker/
• Scope: realtime gas intelligence, heatmaps, calculator, alerts, mission-control observability
• Stack used: Python/FastAPI backend, WordPress/PHP plugin frontend, native HTML/JS dashboard, SQLite, WebSocket + REST, Cloudflare Tunnel

4) DNX Swap WebApp / Arbitrage Engine

• Overview repo: https://github.com/logicencoder/dnx-swap-webapp-overview
• Private implementation: logicencoder/dnx-swap-webapp
• Delivery mode: standalone operator runtime
• Live website mode: not the primary target for this app
• Scope: swap/arbitrage operations with execution controls and safety tooling
• Stack used: Python/FastAPI backend, HTML/JS frontend, chain-aware tx workflow

5) ETH Chain Swaps Monitor

• Overview repo: https://github.com/logicencoder/eth-chain-swaps-monitor-overview
• Private implementation: logicencoder/eth_chain_swaps_monitor
• Delivery mode: standalone local-first monitor
• Live website mode: planned (public link will be added when released)
• Live URL: Coming soon
• Scope: swap detection + transaction decoding + alert feed + operator dashboard
• Stack used: Python/FastAPI backend, HTML/JS frontend, WebSocket + REST APIs

Requirement -> Evidence (Quick HR Match)

Typical requirement	Evidence in portfolio
Python backend development	FastAPI-based backend systems across MEXC, ETH Gas Tracker, DNX Swap, ETH Swaps Monitor
Realtime architecture	WebSocket + REST hybrid patterns documented in all overview repos
Performance optimization	Low-latency dashboards, caching/aggregation patterns, and low-resource hosting constraints handled in production workflows
Data analytics mindset	Gas heatmaps, trend analysis, ranked event feeds, and signal-focused dashboard design
Practical frontend delivery	HTML/CSS/JS operator UIs + live WordPress frontend deployments + custom PHP plugins/themes
Architecture communication	https://github.com/logicencoder/react-vite-architecture-for-dummies + structured architecture sections in overviews
Tooling flexibility	Windsurf-primary workflow with Cursor/Claude support when it improves delivery speed/quality
Learning velocity	Built stack independently from zero and continuously expand tools based on project requirements

Supporting Public Repositories

• https://github.com/logicencoder/react-vite-architecture-for-dummies (my architecture documentation template/style for readable handoff)
• https://github.com/logicencoder/hardware-monitor (runtime observability tooling)
• https://github.com/logicencoder/dynex-0xdnx-dhip-richlist-monitor (blockchain analytics dashboard)

How I Work

• Start with deep requirement/domain research before implementation
• Build from concrete problem to working version quickly
• Keep architecture readable so another developer can follow it
• Optimize continuously for speed, low resource usage, and non-blocking behavior
• Use prompt-engineering playbooks and AI workflows to accelerate repetitive/problem-structuring tasks
• Learn missing tools fast when needed
• Prefer honest capability statements over inflated claims

Experience Statement (Honest)

I built and learned this stack independently from zero by shipping real projects. I do not claim to know everything; I focus on learning fast, solving practical problems, and delivering systems that work.

Availability

Open to roles focused on Python/FastAPI backend, realtime systems, practical analytics delivery, and production-minded optimization.

Contact: https://logicencoder.com/contact/