# 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。

Repository: https://github.com/fasiondog/hikyuu
Canonical: https://ross.abutalabs.com/products/hikyuu
Homepage: http://hikyuu.org/
Language: C++
License: Apache-2.0
License Family: permissive
Topics: quant, system-trading, finance, stock, trading, vestment, algorithms-trading, backtesting
Last push: 2026-08-26T08:34:01+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 5159, "days_push": 7, "days_rel": 13, "gap_med": 19.5, "n_releases_24m": 35}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3469, forks 822 (observed 2026-08-28T04:08:05.908567+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: trading, data-science, data-visualization, machine-learning
- domain: fintech, data-science, developer-tools
- platform: python, cpp, cross-platform, windows
- tags: quant, backtesting, algorithmic-trading, systematic-trading, stock-analysis, a-share, c-plus-plus-core, talib, hdf5, clickhouse, linux, macos

## Member repositories
- fasiondog/hikyuu (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:05.908567+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:36:38.676024+00:00, confidence not recorded.
  - readme: https://github.com/fasiondog/hikyuu (fetched 2026-08-28T04:08:05.908567+00:00, sha b934f48e6f36)
  - homepage: http://hikyuu.org/ (fetched 2026-08-29T09:30:35.351394+00:00, sha 23d85c722ead)
- Data as of 2026-08-30T08:39:29.467469+00:00.