# Barca0412/Introduction-to-Quantitative-Finance

AI+金融（量化）：1.多因子股票量化框架开源教程 2.学界和业界的经典资料收录 3.AI + 金融的相关工作，包括LLM, Agent, benchmark(evaluation), etc.

Repository: https://github.com/Barca0412/Introduction-to-Quantitative-Finance
Canonical: https://ross.abutalabs.com/products/introduction-to-quantitative-finance
Homepage: https://barca0412.github.io/Introduction-to-Quantitative-Finance/
Language: Python
License: MIT
License Family: permissive
Topics: finance, investment, quant, quantitative-finance, quantitative-research, quantitative-trading, trading, agent, llm, ai4fin, llm4fin
Last push: 2026-08-25T21:36:35+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 80
- inputs: {"age_days": 1125, "days_push": 8, "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 1696, forks 179 (observed 2026-08-28T04:05:23.896596+00:00)

## What it is
A Chinese-language open-source knowledge base for quantitative finance research, featuring a multi-factor equity investing tutorial, curated tools/courses/papers, and a daily AI+Finance arXiv Radar that fetches, tags, and summarizes papers with LLMs. It is educational material rather than a runnable trading system.

## Use cases
- learn multi-factor quantitative equity research from scratch
- find open-source backtesting frameworks like Qlib or Backtrader
- track the latest AI and LLM papers in finance daily
- discover courses, forums, and datasets for quant research
- learn factor mining including ML and LLM-based factors
- study portfolio optimization and Barra-style risk models

## When to choose
- you are a student or researcher starting in quantitative finance
- you want a curated Chinese-language learning path for quant investing
- you need to stay current on AI+Finance research papers
- you want a map of open-source quant tools before building your own

## When to avoid
- you need production-ready trading or execution software
- you want a runnable backtesting engine rather than tutorials and links
- you need English-only documentation
- you expect financial advice or guaranteed strategies

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, agent-framework, data-science, trading
- domain: fintech, machine-learning, large-language-models, education, tutorials, awesome-lists
- platform: python, cross-platform
- tags: quantitative-finance, multi-factor, backtesting, factor-mining, portfolio-optimization, arxiv-radar, ai4finance, chinese-language, open-tutorial, web-server

## Member repositories
- Barca0412/Introduction-to-Quantitative-Finance (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:23.896596+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 2026-08-30T03:37:52.802112+00:00, confidence not recorded.
  - readme: https://github.com/Barca0412/Introduction-to-Quantitative-Finance (fetched 2026-08-28T04:05:23.896596+00:00, sha 166e03efff40)
  - homepage: https://barca0412.github.io/Introduction-to-Quantitative-Finance/ (fetched 2026-08-29T11:12:40.406186+00:00, sha d9129e65c707)
- Data as of 2026-08-30T08:39:29.467469+00:00.