index-constitution 0.1.0

Point-in-time and current constituents for major stock indices (CSI 300, S&P 500, NASDAQ-100), packaged as pandas DataFrames.

Index Constitution

中文

Purpose

This repository was created to make it easier to train quantitative models on major stock indices. Reliable historical index composition data (constituent additions and removals over time) is notoriously hard to obtain — vendors often charge for it, official sources are scattered across PDFs and announcements, and free APIs rarely expose point-in-time membership. Without this data, backtests suffer from survivorship bias and lookahead bias.

This repo collects and normalizes that information into plain CSV files so it can be consumed directly by research and modeling pipelines.

Datasets

Index	Description	Source
CSI 300	Top 300 A-share stocks listed on the Shanghai and Shenzhen exchanges	Official announcements from China Securities Index Co. (csindex.com.cn)
S&P 500	500 leading large-cap U.S. companies listed on U.S. exchanges	Wikipedia: List of S&P 500 companies
NASDAQ-100	100 largest non-financial companies listed on the Nasdaq Stock Market	Wikipedia: NASDAQ-100

Structure

src/index_constitution/_data/
    history/
        csi300.csv
        nasdaq100.csv
        sp500.csv
    latest/
        csi300.csv
        nasdaq100.csv
        sp500.csv

Python package

This repo also ships a small Python library that embeds the CSVs and exposes them as pandas DataFrames.

Install:

pip install index-constitution

Usage:

import index_constitution as ic

ic.list_indices()                    # ['csi300', 'sp500', 'nasdaq100']

ic.latest("sp500")                   # current S&P 500 members
ic.history("csi300")                 # full CSI 300 history with opt-in/opt-out
ic.constituents_at("sp500", "2015-06-30")   # point-in-time membership
ic.is_member("sp500", "AAPL", "2020-01-02") # True

Use Cases

• Check the current constituents of an index
• Reconstruct point-in-time index membership for backtesting
• Avoid survivorship bias when training quantitative models
• Keep a consistent structure for adding more indices later