akfamily

akfamily / akquant

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AKQuant is a high-performance quantitative research and trading framework built on Rust and Python! 开源量化回测框架

akquant.akfamily.xyz akshare backtest finance quant
300
29
100% credibility
Found Feb 10, 2026 at 42 stars 7x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

AKQuant is a user-friendly toolkit for testing trading strategies with fast backtests, performance charts, and risk checks.

How It Works

1
🔍 Discover AKQuant

You hear about a simple tool to test trading ideas without coding headaches.

2
📦 Get it set up

Download and install it quickly on your computer.

3
📊 Load market data

Pull in stock prices from free sources to start experimenting.

4
✏️ Build your strategy

Copy a ready example or tweak rules like buy on green days.

5
⚡ Run your backtest

Hit go and watch it crunch years of data in seconds.

6
📈 Review key stats

See returns, risks, and win rates in a clear summary.

7
📉 View pretty charts

Spot patterns in equity curves and drawdowns easily.

🎉 Strategy insights unlocked

You now know if your idea works and how to improve it.

Sign up to see the full architecture

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Star Growth

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AI-Generated Review

What is akquant?

AKQuant is a high-performance quantitative research and trading framework built on Rust and Python, letting you run fast backtests, parameter optimizations, and live trading sessions from simple Python strategies. It handles data loading from Parquet or DataFrames, computes indicators, manages positions with sizers and risk rules, and spits out equity curves or trade lists via Plotly or Matplotlib. You get Rust-speed execution for heavy lifts like vectorized indicators or ML retraining, wrapped in a Python API that feels like Zipline but crushes on benchmarks.

Why is it gaining traction?

It bridges Rust's raw speed for backtests on millions of bars with Python's quant ecosystem—think Polars for data, Torch/Sklearn for models, and CTP gateways for Chinese futures live trading. Parameter grids parallelize across CPU cores, auto-inferring warmup periods from your strategy code, and it supports T+1 rules, slippage, and multi-asset fees out of the box. Devs dig the bilingual docs (English/Chinese) and zero-copy array feeds that shave seconds off runs.

Who should use this?

Quant traders backtesting A-share or futures strategies who hit Pandas bottlenecks on large datasets. Python quants prototyping ML signals with rolling retrains, or teams bridging research notebooks to CTP live desks. Skip if you're deep in US equities or need broker-agnostic sims—it's China-market tuned.

Verdict

Grab it for high-performance Python quant workflows if you're okay with early-stage polish (21 stars, 1.0% credibility). Docs are solid and multi-lang, tests via pre-commit look clean, but expect tweaks for production live trading. Strong prototype pick.

(198 words)

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