berrzebb

berrzebb / zeroquant

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Rust 기반 고성능 자동화 트레이딩 시스템

github.comberrzebbzeroquant algorithmic-trading backtesting machine-learning quant quantitative-finance
11
9
100% credibility
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AI Analysis
Rust
AI Summary

A professional Rust-based open-source framework for backtesting trading strategies with analytics, machine learning integration, and support for multiple exchanges like Binance.

How It Works

1
🔍 Discover the trading strategy tester

You hear about a free tool that lets you test trading ideas on past market data without risking real money.

2
📥 Get the tester ready

Download the simple package and set it up on your computer in minutes, like installing any app.

3
⚙️ Pick your trading idea

Choose from ready-made plans like 'buy when cheap, sell when expensive' or tweak one to match your thinking.

4
▶️ Run your first test

Hit play and watch it replay years of market history, seeing exactly what would have happened with your idea.

5
📊 Review the colorful results

See easy charts of your money growing (or not), win rates, and smart scores telling you how good it is.

6
🔧 Tweak and retest

Change a few numbers like 'wait longer' and run again to find the best version.

🎉 Confident in your winner

You now know which trading plan shines, ready to use it wisely without surprises.

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AI-Generated Review

What is zeroquant?

Zeroquant is a Rust-based automated trading system for backtesting and live execution of strategies like RSI mean reversion, Bollinger bands, grid trading, and asset allocation on stocks or crypto via Binance and Korea Investment APIs. It delivers high-speed signal processing, performance analytics, and paper trading through a REST API with TypeScript types, backed by TimescaleDB for candles and Redis caching. Users run docker-compose for infra, tweak config TOML files for risks like max daily loss, and export backtests to journals for ML refinement with ONNX models.

Why is it gaining traction?

Rust's zero-cost abstractions crush Python bots on speed for multi-timeframe signals and correlation matrices, while rust github actions cache and rust github workflow streamline CI builds. Modular strategies with liquidity gates, sector RS, and 7-factor scoring via rust github crate dependencies make it extensible, plus zeroquant paper-inspired efficient quantization nods to affordable transformer inference in trading ML pipelines.

Who should use this?

Quant devs crafting Korean stock rotators or crypto grid bots needing rust github api pulls for real-time data and rust github ci for reliable deploys. Suits backtesters frustrated with slippage models in slow tools, seeking volume profiles, survival tracking, and podman-compose.sh for local sims before live rust github client execution.

Verdict

Grab it if you're a Rust trader prototyping zeroquant-fp or zeroquant-v2 workflows—10 stars and 1.0% credibility signal early alpha with Cargo tests and README setups, but expect tweaks for prod stability. Solid foundation, test heavily first.

(198 words)

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