# myhhub/stock

stock股票.获取股票数据,计算股票指标,筹码分布,识别股票形态,综合选股,选股策略,股票验证回测,股票自动交易,支持PC及移动设备。

Repository: https://github.com/myhhub/stock
Canonical: https://ross.abutalabs.com/products/stock
Language: Python
License: Apache-2.0
License Family: permissive
Topics: stock, stocks, backtest, backtesting, quantitative, quantitative-finance, strategies, strategy, broker-trading-platform, cyq, distribution-of-chips, position-cost-distribution
Last push: 2026-04-02T02:26:35+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 75, release rhythm 35, longevity 90
- inputs: {"age_days": 1262, "days_push": 154, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14142, forks 2920 (observed 2026-08-28T04:11:05.601598+00:00)

## What it is
InStock is a Python-based stock analysis system for Chinese A-shares and ETFs that captures daily market data, computes technical indicators, chip distribution (CYQ), and identifies K-line patterns. It provides comprehensive stock screening with built-in strategies, backtesting, automated trading, and a responsive web UI deployable via Docker.

## Use cases
- screen stocks by combining fundamental, technical, and sentiment filters
- calculate technical indicators like MACD, KDJ, and RSI for A-share stocks
- identify candlestick (K-line) chart patterns automatically
- backtest stock selection strategies before trading
- track chip distribution and position cost distribution
- automate stock trading through a broker
- fetch daily stock and ETF data including fund flows

## When to choose
- you trade Chinese A-shares and want an all-in-one quant analysis toolkit
- you need stock screening, indicator calculation, and backtesting in one self-hosted system
- you want a Docker-deployable stock platform accessible from PC and mobile

## When to avoid
- you need US or European market data rather than Chinese A-shares
- you require institutional-grade backtesting or low-latency execution
- you only want a lightweight library to embed in your own code rather than a full application

## Facets
- artifact type: application
- maturity: active
- function: data-science, analytics, trading, data-visualization, web-scraping, charts
- domain: fintech, data-science, analytics
- platform: python, cross-platform
- tags: stock-analysis, backtesting, quantitative-finance, stock-screening, k-line-patterns, chip-distribution, automated-trading, china-a-shares, technical-indicators, docker, web-server

## Member repositories
- myhhub/stock (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.601598+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 2026-08-29T17:12:47.313997+00:00, confidence not recorded.
  - readme: https://github.com/myhhub/stock (fetched 2026-08-28T04:11:05.601598+00:00, sha aaa2b6d9bac8)
- Data as of 2026-08-30T08:39:29.467469+00:00.