fasiondog/hikyuu
Hikyuu Quant Framework 基于C++/Python的超高速开源量化交易研究框架，同时可基于策略部件进行资产重用，快速累积策略资产。 observed · 2026-08-28
Health v2 · maintenance only
99/100
- Activity 99
- Release rhythm 98
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 19.5
- age_days: 5159
- days_rel: 13
- days_push: 7
- n_releases_24m: 35
Adoption not part of the score
3469 stars · 822 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Hikyuu is an ultra-fast open-source quantitative trading research framework built on a C++ core with Python bindings, focused on strategy analysis, backtesting, and extensible live-trading integration (deeply adapted to China's A-share market). It decomposes systematic trading into modular, freely combinable components such as signal indicators, stop-loss/profit rules, money management, and portfolio allocation, with support for HDF5, MySQL, ClickHouse, and SQLite storage.
Use cases
- backtest stock trading strategies on A-share market data
- build a reusable library of modular trading strategy components
- compute indicators like moving averages over tens of millions of K-line bars quickly
- research portfolio and multi-factor allocation strategies
- integrate quantitative research with numpy, pandas, and TensorFlow
- store and query historical market data in HDF5 or ClickHouse
- prototype trading systems interactively in Jupyter notebooks
When to choose
- you need a fast C++-backed quant research framework with Python ergonomics
- you trade or research Chinese A-share markets
- you want modular, composable systematic-trading components rather than a monolithic backtester
- you need flexible local storage backends for large historical datasets
When to avoid
- you need out-of-the-box live trading execution - the framework only provides extension interfaces, not built-in brokerage services
- your focus is non-Chinese markets with less built-in adaptation
- you want a fully managed cloud quant platform rather than a local research library
- you need guaranteed compliance guidance - the project explicitly disclaims investment advice and trading services
Facets
library · maturity active
trading data-science data-visualization machine-learning fintech data-science developer-tools python cpp cross-platform windows quant backtesting algorithmic-trading systematic-trading stock-analysis a-share c-plus-plus-core talib hdf5 clickhouse linux macos
2 sources
- readme: https://github.com/fasiondog/hikyuu · fetched 2026-08-28 · b934f48e6f36
- homepage: http://hikyuu.org/ · fetched 2026-08-29 · 23d85c722ead
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| fasiondog/hikyuu | main | 99 |
For agents
markdown · JSON · MCP: product_card(name="fasiondog/hikyuu")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem