A multi-language exploration for the Crypto Bucket HODL Trading Engine construction — a comparative study among Go, Rust, and Python.
This project explores how each language handles real-world crypto trading logic, market data ingestion, portfolio management, and engine performance — all within a unified, reproducible testbed.
- Build identical trading engines in Go, Rust, and Python
- Compare their performance, maintainability, and reliability
- Use a common strategy framework (focused on HODLing behavior)
- Simulate real-time and historical crypto market conditions
- Generate logs, visual reports, and metrics for fair comparison
Addicted-to-HODL-A-Crypto-Engine-Triathlon/
├── engines/
│ ├── go_engine/ # Go implementation
│ ├── rust_engine/ # Rust implementation
│ └── python_engine/ # Python implementation
├── data/ # Historical market data & simulation inputs
├── configs/ # Shared strategy configs
├── benchmarks/ # Performance test runners & metrics
├── reports/ # Output results, logs, and plots
└── README.md
Each engine implementation includes:
- Market Data Feed – Simulated live/historical prices
- Strategy Core – Basic HODL logic with optional DCA and rebalancing
- Execution Layer – Portfolio tracking, trade execution, logging
- Performance Monitor – Latency, memory, and throughput tracking
| Metric | Description |
|---|---|
| Latency | Reaction time to new price ticks |
| Accuracy | Execution correctness under identical inputs |
| Memory Usage | Efficiency in resource consumption |
- Clone the Repo bash
git clone https://github.com/@QuantDevJayson/Addicted-to-HODL-A-Crypto-Engine-Triathlon.git cd Addicted-to-HODL-A-Crypto-Engine-Triathlon
- Run Engines
cd engines/go_engine go run main.go Rust bash
cd engines/rust_engine cargo run --release Python bash
cd engines/python_engine pip install -r requirements.txt python engine.py
cd benchmarks python run_all.py
Benchmark results (execution speed, memory, logs, graphs) will be saved to the /reports/ directory.
Each engine uses a shared set of rules to simulate common HODL strategies:
- Buy on dips greater than X%
- Periodic rebalancing (optional)
- Dollar-Cost Averaging (optional)
- Always HODL unless major reallocation is triggered Strategy configs are language-agnostic and stored in /configs/.
- Language Comparison – Real-world side-by-side evaluation
- Multilang Application – Same logic, same data, no unfair advantages
- Educational Value – Great for devs learning trading systems
- Bot Testing Ground – Safe place to prototype trading bots
MIT License – Free to use, modify, and share. Attribution appreciated.
- GitHub: QuantDevJayson
- PyPI: jayson.ashioya
- LinkedIn: Jayson Ashioya